Integrated Functional Safety and Safety of the Intended Functionality Analysis using ANSYS medini analyze.

1. Introduction

Functional Safety (FuSa) standards such as ISO 26262 have proved their immense importance to make the electronic components more reliable in today’s cars by delivering consistent performance and reducing critical system failures. However, with the rise of ADAS features, the autonomous driving capabilities in vehicles come with an even higher engineering challenge regarding safety and reliability. Sensors, and other components that are working as a designed part, are falling short of capabilities when running in real-time scenarios resulting in dangerous conditions. To address these types of challenges, a new standard safety of the Intended Functionality (SOTIF), ISO 21448 safety standard, will be soon introduced to identify shortfalls in the performance that can occur even when the system is in failure-free condition. It raises the expectation of every component works as designed, and the design is adequate to work to fulfill its goal with the required performance. On the other side, the new SOTIF standard requires a full range of safety analysis & engineering simulation solutions to enable autonomous vehicle development teams to build flawless performance into their designs right from the early stages of product development. The development team can validate the performance before the vehicle launch in the market.

  1. What is ISO 21448: Road Vehicles – SOTIF?

SOTIF is abbreviated Safety of The Intended Functionality and, in short for ISO/ PAS 21448, applies to functionalities that need a proper awareness of the situation to be safe. This standard concerns how to ensure the safety of the functionality even in the absence of a fault/failure. This is quite in contrast with the traditional Functional Safety (FuSa), which is majorly concerned with the risk associated with system failure.

  1. How is ISO 21448 related to ISO 26262?

ISO 26262 covers the functional safety of the system in the event of failures and has no coverage of safety hazards that result in the absence of system failures. That is the reason ISO 21448 is

mandatory in analyzing the situations where ensuring safety without system failure is so complex and complicated.

  1. Why is SOTIF (Safety of the Intended Functionality) important?

In today’s world, vehicle electronics provides features like comfort, communication, and navigation assistance, mission-critical functionality such as steering and braking & more. The global automotive standard helps engineering teams to uncover and address FuSa hazards such as software bugs and hardware failures. Safety stakes have grown even higher, and if a crucial component, let’s say the sensor is not fulfilling its needed functionality or it fails to deliver the performance needed to handle a situation – for example, failing to recognize a pedestrian in the road ahead; the application of ISO 21448 helps us to ensure that the perception algorithm systems (a combination of sensors and software algorithms) will recognize pedestrians in all situations that are part of the Operational Design Domain (ODD). This enables the systems to trigger a safe response in consideration of performance under various ODDs. SOTIF ensures robust design against any disturbances and hazards due to flawed Human-Machine Interactions.

Fig: Limited contrast resolution images in the presence of blinding sun

2. A Model-Based Workflow Integrating FuSa and SOTIF:

To successfully conduct autonomous vehicle development in compliance with both ISO 21448 and ISO 26262 there is a unique model that combines a linear process, V-shaped progression with feedback loops of evaluation and improvement to incorporate the learning and as well as comply with the standard. This model-integrated safety workbench offers all required analysis options for

Functional Safety (FuSa), Safety of The Intended Functionality (SOTIF).

Fig: Integrated V-Model workflow for FuSa and SOTIF Analysis

The following is a step-by-step look at the workflow:

  • Features of the Automated Driving (AD) functionality and the Operational Design Domain (ODD) are defined. From the above-portrayed functionalities, the requirements are derived or transferred from the Original Equipment Manufacturer (OEM) to the supplier. The initial architecture developed on a functional level, and this will begin the integrated FuSa and SOTIF process.
  • Performing the hazard analysis and investigating the causes of potential hazards strengthens the feedback loop to identify the issues during the analysis stage and rectifies them straight from the initial level to the architecture level. Ultimately, this will enhance the requirements and architecture.
  • Engineers execute the refinement and technical concretization of hardware, software, and sensor requirements and solutions, again handled in a model-based way, with a corresponding feedback loop.
  • Performing model-based control software generation will help the engineer to generate safety compliant code, Automotive Safety Integrity Level (ASIL). Moreover, level D. Camera and radar sensor technologies and perception algorithms are validated, sent for evaluation, and improved in a cyclical process until an acceptable performance level for all foreseeable situations has reached.
  • The integrated AD functionality is validated under realistic road conditions to prove that its behavior is appropriate in every situation. This step includes closed-loop simulation, supported by optimized scenario variation and parameter assignment, as well as automatic identification of “edge cases.” All insights are imported back into the safety tool, closing the validation loop.
  • Hardware, software, and the Electronic Control Unit (ECU) that support AD functionality undergo thorough integration testing on Hardware-In-Loop (HIL) benches.
  • All the insights will get mentioned in a convincing safety case that includes a graphical view of requirements refinement and traceability of all artifacts in the model-based process to demonstrate safety.

To maximize efficiency and financial returns, hardware, software, models, requirements, test cases, and other artifacts are available for re-use in future development efforts, typically with extended ODDs or extended functional capabilities.

3. Medini Analyze as a Single Source for meeting SOTIF and FuSa Standards:

ANSYS medini analyze is a software tool, which has been recognized by a different industrial standard for analyzing varied aspects of functional safety, technical safety, and compliance with the standards. Performing SOTIF analysis individually, as a stand-alone activity, will empower the product operational safety analysis and make use of architecture models, vehicle-level malfunctioning behavior analysis, and hazardous event assessments. This can eliminate redundancies and ensure consistency among all the results.

Fig: Scenario Factors according to ISO 21448 in Medini Analyze

ANSYS medini analyze has enhanced the model-integrated safety approach with new modeling elements for limitations, weaknesses, and triggering conditions, as specified in ISO 21448.

The integrated FuSa and SOTIF workflow start with an initial hazard analysis and an investigation for potential hazards – caused by failures or limitations of the nominal performance – across the system architecture. For example, fog, snow, rain, and other weather conditions can confuse the sensor’s perception capabilities into “viewing” a physical object where there is none. It can trigger risky behavior such as strong braking, which results in a rear collision with another vehicle. Even more disastrous, a sensor might interpret an actual physical object on the road as an illusion, which results in the crash of a vehicle with the physical object. Medini analyse focus at every identified hazard and utilizes key parameters like “incident severity” to classify the risk level. Additionally, it distinguishes critical safety hazards and addresses them accordingly.

ANSYS medini analyze can also address causal analysis, looking at the example, “Why is this critical performance flaw occurring?” This analysis is similar to the functional safety analysis that automotive engineering teams have been conducting for a decade and includes well-known techniques from functional safety analysis, such as fault trees and guideword analysis.

Fig: Effects of the SOTIF-caused malfunctions are added by the safety analyst in medini analyze.

ANSYS medini analyze also allows traceability linkage between safety analysis and complete system architecture.  It automates the allocation of the malfunctioning behavior to a specific functional block or multiple blocks. Whatever the cause, whether it is performance shortfall or a software bug, or a sensor performance limitation – medini analyze defines the areas where sensors functionality is not delivered. Because medini analyse model limitations and triggering conditions can be used in causal nets or fault tree analysis. Over this period, engineers can accumulate knowledge and lessons learned. Integrating all these findings with the previous validation activities, simulations, or virtual road tests could trigger conditions that may express in one or two words, like “sun glare” or “snow.” Others are much more complex, such as “metal object on the pavement causing a reflection from the headlights in night-time conditions” or “driving out of a tunnel at high speed.” These more complex triggering conditions can be modeled by medini analyze as scenarios. These scenarios are modeled in medini using SysML diagrams, where scenes and events are represented through pictograms.

Fig: Integrated Fault Tree Analysis of FuSa and SOTIF in medini analyze.

Triggering conditions and scenarios will also be exported from medini analyze into different formats, and then it can be imported into scenario generators for simulation. Scenarios that have been identified as potential triggers for risky behavior provide valuable inputs to product developers, simulation experts, and physical testing team members. It will enable them to investigate and address every causal effect and provides the outcomes to safety analysts, and determine parameters (e.g., critical position, speed, and distance, weather conditions).

The new SOTIF standard will also cover Human-Machine Interaction (HMI) and hazards arising from misunderstandings and even intentional misuse of the HMIs. Medini analyze can also address these concerns, general cybersecurity issues that fall outside the scope of ISO 21448 but may still be important in the horizon of autonomous vehicle development.

4. Conclusion:

The work of safety engineers from the past has been very much isolated and non-collaborative and used manual analysis and reporting techniques to communicate the findings in a significant amount of time with cost under consideration. But the race to commercialize Autonomous Vehicle designs and standards to regularize those where increasing, the delays and inefficiencies are no longer acceptable. Unifying the development and verification and a shared platform to introduce the upcoming SOTIF standard can guarantee the functionality of every component to the real-time driving challenges. It enables all functions involved in Autonomous vehicle development to share data and work to collaborate. From Electrical Engineers designing Perception Modules to Software Engineers developing Critical Software to safety, experts should come together to deliver complete FuSa and SOTIF compliance in the ANSYS Medini analyze.

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Thermal Simulation of Automotive Lamps Using ANSYS Icepak

Lighting Systems play an important role in human factors of safe driving. It is an essential part of any vehicle and has undergone significant changes and advances in lighting technology over the years. Thermal aspects play a crucial role when it comes to the designing of automotive lights. Automotive lighting systems mainly consist of outer lens, inner lens, housing, reflectors, bulb, bezel, Led, PCB and light guide, etc.

Figure 1: Automotive headlamp

Out of the parts mentioned above, bulb and led are the two primary sources of lighting that generate a lot of heat energy. Hence it is essential to design the automobile lamps such that even at an extreme ambient temperature, the temperature on each part is maintained well below the critical limit. The critical limit is usually the heat deflection temperature & the maximum temperature on the parts of the lighting system should be well below their respective material HDT values.

The role of CFD simulation in Automotive lamp designing?

Coming to the main question – “What is the role of Computational Fluid Dynamics and software tools such as ANSYS in designing the automotive lighting system”? 

CFD simulations can play a crucial role in optimizing various design parameters such as lamp size, the distance between bulb and lens, number of vents, vent location, and selection of materials according to the design requirements. The thermal simulation of automotive lamps comes under conjugate heat transfer type of analysis in which all the modes of heat transfer are essential to model. Radiation is the key source of heat transfer in lamps. Radiation affects the heat wattage from the filament or led source chip and increases the following – temperature of the bulb, reflector, housing, lens, etc. Hence, proper selection of the radiation model is important to get accurate results. Since many parts are interlinked, thermal conduction plays a crucial role in heat distribution especially when automotive lighting systems contain Led chips and PCB. 

As all three modes of heat transfer are involved in this simulation, various parameters are needed to benchmark to get the correct results. 

There are mostly three kinds of simulation done for Automotive lamps as follows: 

Simulation of Headlamps:

The bulb of the headlamp consists of two filaments called High beam and Low beam filament. The Low beam filament is situated closer towards the lens and the High beam filament is placed closer towards the bulb holder. Generally, analysis of the former is more preferred than high beam one because when the Low beam filament is switched ON, the lamp parts get more heated.  However, some companies also tend to perform analysis by turning ON both high beam and low beam filaments to predict the maximum temperature in the worst-case scenario.

Simulation of Taillamp:

Tail lamps are generally smaller in size as compared to headlamps, so to avoid high temperatures, they should be carefully designed. Tail lamps consist of tail function filament and stop function filament. Tail lamp simulation is done by turning ON both the tail function and stop function filament.

Simulation of Front turning lamp:

Headlamp consists of a signal turning bulb. Sometimes companies prefer to simulate the headlamps along with the front turning lamp. Often, two turning signal bulbs will be at the sides of headlamps. These two signal lamps may contain separate reflector parts and lens parts. The wattage of these bulbs is generally small, but as these signal bulbs are cramped to a smaller area, it may end up heating the lens and reflector way above HDT values. That is why engineers very often perform simulations for these lamps as well.

Table 1: Lamp Main parts and material description:

PartsMost Preferred MaterialHeat deflection temperature range
Outer LensPlastics100°C -140°C
BulbGlassN. A
BezelPlastics/PET+PBT90°C -140°C
Inner lensPlastics100°C -140°C

The main aim of the simulation is to predict the temperature distribution in various lamp parts and to find out if the maximum temperature is greater or lesser than the Heat deflection temperature. This can help the design team to select the best material according to the design requirement. The simulation can also help the design team to decide the proper locations of air vents by predicting the air-flow path and location of maximum temperature.

Advantages of using ANSYS Icepak in Automotive light thermal simulation:

ANSYS Icepak is the most popular tool in the market when it comes to electronics cooling simulation. It uses Fluent as a solver which is one of the most reliable and popular solvers when it comes to CFD.

The top advantage of using ANSYS Icepak is that it saves us from the tedious task of generating fluid domain. It can automatically generate fluid domain using a cabinet or enclosure approach and creates hexahedral mesh easily. Using Icepak we can save a lot of time which we spend in generating fluid domain and creating a high-quality mesh. Moreover, ANSYS Icepak has various radiation models, such as S2S, DO, Ray, tracing models which can be used both for participating and non-participating mediums accordingly.

To show the capability of ANSYS Icepak in simulating automotive lighting systems, a quite simple model of an Automotive headlamp is developed using Spaceclaim. Please note that this cad design is in no way sponsored by or affiliated with any organization.

Outer Lens

Figure 2: Lamp Parts

Icepak Simplification:

The Spaceclaim objects will be converted into icepack objects using the Icepak simplification feature available in Spaceclaim. Conversion to icepack objects is necessary and every geometry part must be converted to icepack objects through icepack simplification in space claim or design modeler.

Figure 3: Conversion of Spaceclaim parts to Icepak objects

Effort less meshing using ANSYS Icepak:

ANSYS Icepaks’ HD Mesher generates high-quality mesh even for complex geometries. The process of generating the mesh is extremely easy and less time-consuming. ANSYS Icepak generates the fluid domain automatically using the cabinet approach and saves a lot of time spent on pre-processing. The overall time required to perform the simulation reduces drastically. Referring to the current case, the overall time spent on meshing and generating high-quality mesh was ~ 15 mins and within 15 mins, 3 mesh trials were performed to identify and optimize assembly size and slack settings. Icepak automatically finds and generates the fluid domain based on empty spaces inside the cabinet/enclosure (with no solid bodies/hollow bodies). Figure 4 shows the mesh created in ANSYS Icepak.

Figure 4 – Mesh created in Icepak

Simulation and post-processing:

Simulation of a headlamp is done after giving necessary inputs/ boundary conditions required for running the simulation, such as bulb filament wattage, ambient temperature, radiation parameters, and material properties description, etc. Post-processing of simulation is done to generate temperature contours at various lamp parts. Figure 5 and Figure 6 show the temperature distribution in the bulb, lens, and housing. The temperature in the bulb is very high because the filament is enclosed in a glass bulb. As glass is a semi-transparent medium so radiation coming out from the heat source filament. Passes through the bulb and reaches the outer lens directly at the center of the lens.

Figure 5: Temperature distribution in the bulb

Figure 6: Temperature distribution in the housing and lens


The present work was an attempt to demonstrate ANSYS Icepak’s capabilities in solving a wide range of conjugate heat transfer problems across various domains and its ability to handle any complex modeling project. ANSYS Icepak is the most trusted software tool when it comes to electronics cooling simulation, but it can also be used in performing different types of conjugate heat transfer simulation which may not be necessarily related to electronics cooling. ANSYS Icepak not only saves us from the tedious work of creating fluid domain but its HD mesh algorithm generates high-quality mesh effortlessly. Icepak allows us for a great deal of control on meshing. One can mesh assemblies and subassemblies with different mesh sizes while maintaining an overall coarse mesh for the entire system. Moreover, ANSYS Icepak has almost all the popular turbulence models / radiations models which can be used according to the simulation requirement. The combination of these features makes ANSYS Icepak a great tool.

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Fast-Tracking 5G, Massive Machine-Machine Communication (mIOT) & Advanced Driver Assistance Systems (ADAS)

This post discusses CADFEM expertise to maintain pace in Futuristic 5G, mIOT, and ADAS systems. 


Roadmap from 1G to 5G-ADAS

From the past few decades, the world has witnessed many versions of a cellular network, the 1G version was a basic voice communication system that supported only Analog modulation. The flavor of data connectivity was present in the second version that used in digital technology. 3G version flavored highly improved data connectivity using technologies such as Wideband Code Division Multiple Access (WCDMA) and HighSpeed Packet Access (HSPA). Currently, 3G is the largely sold version of cellular networks around the globe although the next 4G network version is closing the gap quickly. The third and fourth generations (3G and 4G) of mobile communication technologies are widely deployed, providing voice and mobile broadband as their main services. Presently the world is enjoying the pleasure of 4G that uses orthogonal frequency division multiplexing (OFDM) technology to provide bandwidth of 20 MHz with Multiple Input and Multiple Output (MIMO) antenna transmission Technology.

The world is progressing rapidly towards next-generation cellular communication and is on the verge of entering into the 5G era, with completely new infrastructure and technology. A challenge of today’s 5G research is in the waveform frequencies that are being used around the world as signals which will suffer from more noise at these frequencies. And these noise levels can be minimized to some extent by performing proper filtering at waveform level but at the same instance, it can be reduced majorly by applying proper signal processing technique at the transmit antenna side & receive antenna side. FBMC is an upgraded version of OFDM, which offers benefits such spectral efficiency and resistance to multipath with zero inter-carrier interference and this is expected to come with 5G. There is a rapid increase in demand for high definition multimedia streaming around the world. The currently utilized microwave frequencies won’t be sufficient to meet this demand due to a shortage of bandwidth.

We need up-gradation to mm-Wave frequency bands that provide a larger bandwidth to meet this demand. Several GHz of the spectrum at mm-Wave frequencies provide an abundance of bandwidth to support GBPS data rates. This abundance in bandwidth helps to incorporate large array that provides high directivity to combat path loss and reduced interference. We can successfully transmit a huge amount of data known as BIG DATA by utilizing this spectrum. The signal at these higher frequency band suffers higher path loss and rain attenuation due to which it is not suitable for outdoor communication. The wavelength of the mm-Wave signal is very small due to which it becomes practicable to embed the multiple numbers of antennas that will direct the signal into highly concentrated beams with sufficient gain to master propagation loss. This process of sharpening of beams is called beam formation, where signals will be added constructively at some point in space. Upcoming 5G systems are predicted to introduce these profound technologies.

Why 5G?

The urge for data usage is increasing day by day globally and the existing LTE network needs to be improved with LTE-Advanced that provides a bandwidth of maximum 100MHz. Even though it is continuously updated through new releases, and with LTE Advanced Pro Release being the latest one, the development of the fifth generation has been initiated. After a few more year’s LTE-Advanced technologies won’t be sufficient to satisfy the increasing data urge around the world and there will be a need for the new version of a cellular network that can satisfy the data requirements in coming years.

5G network is visualized to simplify the burden on current cellular infrastructure by offering significantly higher data rates through increased channel bandwidth. 5G communication system is expected to exploit the spectrum band at millimeter-wave (mm-Wave) frequencies. But the mobile communication at these mm-Wave spectrum band is far more complex than the current frequencies that are being used around the world as signal suffers higher propagation loss. Antennas for next-generation 5G will make use of shorter element size at high frequencies to incorporate beam formation capabilities. This helps to increase the capacity of the cellular network by improving the signal to noise ratio (SNR) and maintain an optimal BER (Bit Error Rate) at mm-Wave frequencies. 5G mobile network offers a vision of “everything everywhere and always connected” which will make use of microwave and Millimetre-wave frequencies ahead of 24 GHz. 5G mobile network is surmised of providing minimum data throughput of 1 Gigabit per second. However, due to the increasing demand for higher data rates and larger system capacity, in addition to the emergence of new Internet of Things, ADAS, and safety-oriented mobility use cases, the fifth-generation (5G) is currently being discussed and developed.

Different Dimensions of 5G

Three Dimensions of 5G are:

  • Massive Machine-Machine Communication (mIOT)
  •  Ultra-reliability-ADAS systems and
  • Enhanced Mobile Broadband (eMBB).

A key scenario for 5G, IoT, and ADAS System has connected mobility as shown in the above image, which utilizes vehicular communication for such things as infotainment, safety, and efficiency. While these requirements are already in the scope of 5G standardization, the ability to meet the requirements in practice is more important than ever because of the criticality of the safety-oriented connected mobility use cases. These cases rely on vehicular communication for such capabilities as platooning, cooperative awareness, and self-driving cars.


Simulation enables innovative ideas, that can push products beyond their traditional limits, to be tested and realized without the burden of prototype costs and time. When engineering simulation software made its debut nearly 50 years ago, early adopters quickly distinguished themselves from those companies who were slower to recognize and embrace its potential. Tomorrow, it will be part of the toolbox for every engineer. As we push for ever-smarter and more efficient product designs like 5G, we can no longer afford to only look at a single aspect of performance or alone part in isolation. In the past, engineering simulation teams were likely to isolate just one critical physics. Today, thanks to improvements in simulation software, hardware, and processing speeds, it has become much easier for engineers to study multiple physics and assess overall product performance. This is critical for the 5G ADAS Smart System, where engineers can simulate and analyze thousands of possible designs, early in the ideation process, to identify the optimal one.

Traditional workflows don’t work in the high di/dt 5G ADAS smart system era because they are blind to the spike voltages induced across layout parasitic; V_spike = L_parasitic * di/dt. In the high di/dt era, it is necessary to add a post-layout analysis step to the workflow between the pre-layout circuit simulation and physical prototyping steps. Measure predetermined Power integrity, Signal integrity, EMI, Thermal, and Structural reliability/stability competencies using simulations and practice these competencies in a risk-free environment and manage to have high knowledge retention. Ease the goal and predict with confidence that products will thrive in the real world with good expertise and a wide range of simulation solutions/ prototype inherited by CADFEM as shown in the below table and figure.

Are you working in mmWave 5G Smart Mobility Communication System and worried about the complexity?

CADFEM can help & bring down your headache considerably whether you are involved with the design of Systems, Base-band, RF, or Antenna systems. CADFEM will explain how and why to do post-layout analysis, specifically how to use the ANSYS SI wave field solver to extract layout parasitic into an EM-based model that you can add to the pre-layout circuit simulation. In this way, the spike voltages can be determined, and (using “What if…” design space exploration) reduced to an acceptable level before sending the layout for fabrication. Don’t smoke those precious power devices with expensive, time-consuming, non-deterministic board spins: use this “virtual prototype” method instead as shown in the below figure.

As 5G radio frequency (RF) and wireless communication components are integrated into compact packages to meet smaller footprint requirements while improving power efficiency, electromagnetic field simulation is the only way to make these trade-offs.

mm Wave 5G Smart Mobility Communication System requires more functionality in smaller multivariant packages. As the global power budget is reduced and the operating frequencies required to deliver rich features increase, engineers are confronting the issue of power supply noise. The chips, packages, and printed circuit board all contribute to power supply noise, so the complete system must be optimized to limit noise across the voltage and ground terminals of the transistors for error-free performance. SI Wave is a dedicated tool for electrical analysis of full PCB and complex electronic packages. SI Wave solves interrelated PI, SI, EMI challenges to deliver predictive analysis for your design. It provides solutions in both the Time & Frequency domains. HFSS 3D Signal Integrity Electronic Package Design access a streamlined 3D design flow that enables complete package system analysis with Seamless integration with EDA layout tools to create customized signal integrity, power integrity and EMI design flows. Begin the simulation process by importing the electrical model of the integrated 5G Chip (PHY model and patterns), package and board, and various memory chip models provided by manufacturers into Siwave. Then solve the imported structures and perform multiple simulations to compute resonances, trace characteristics, discontinuity reflections, and inter-trace coupling. Engineers can extract S parameters, an IBIS interconnect model, and a full-wave SPICE model. These can be imported into ANSYS NexximSIwave’s circuit simulator, for time- and frequency-domain analysis.  Nexxim can be used to generate time-domain eye diagrams and to check the data timing and voltage for overshoot and jitter of the 5G-High Speed Board. The port excitations can be set by drivers in IBIS formatpseudo-random bit sequence (PRBS) used can be used to reproduce real use cases. Eye diagrams can be used to indicate the allowable window for distinguishing bits from each other at the receiver end. The required height of the window is given by the noise margin of the receivers. 

5G Antenna System for ADAS application

5G System will be crucial to the success of autonomous vehicles by aiding in the detection and localization of pedestrians, vehicles changing lanes, and parking and braking events in complex traffic scenarios. The successful development of such systems requires a highly accurate, full-wave electromagnetic simulation tool to accurately model all system components, from inside the IC to the PCB and antennas. ANSYS HFSS is useful for electromagnetic full-wave simulation and circuit design analysis. The ANSYS solution allows us to achieve fast and highly accurate results of physical models/components used in the mm-wave 5G IC system. Moreover, ANSYS provides solutions for many issues involving radar systems on a chip that are unique to ANSYS. 5G Smart Mobility Antenna design starts with selecting and optimizing a single antenna element, but that’s the easy part. No radar system for anything as complex as autonomous driving can operate with a single antenna; an array of antennas is needed. An array can transmit radio waves in a pattern that emulates a spotlight: a bright focus point in the center. ANSYS HFSS electromagnetic field solver can be used to simulate such antennas at the very high frequency needed in automotive applications.

Problems in any part in the mm-wave 5G Smart Mobility Antenna system can ruin the functionality of the whole system, potentially costing hundreds of thousands of dollars and months of delays. Several sensors are needed to cover all short-range to long-range tasks, adding costs in a low-margin industry. ANSYS HFSS solvers and high-performance computing can be used for the analysis of components like planar inductors, baluns, power dividers, and transmission lines. Parametric sweeps and goal-driven optimization is done inside ANSYS Optimetrics. The efficient hybrid technology FE-BI is used in particular for antenna Design. For larger scenarios, HFSS SBR+ is used to simulate in-the-field antenna performance. For efficient overall workflow, ALinks interacts with the ECAD System for fast design transfer. Parasitic modeling is very important and can be easily achieved by either adding RLC Components directly to the 3D electromagnetic (EM) model or adding lumped components to the exported EM model inside the circuit environment of ANSYS Electronics Desktop. Once the complete circuit model delivers the desired result for parameters such as Q factor, inductance, and gain, we combine all components into a system simulation using the ANSYS RF option.

Software and Algorithm Modelling and Development

Just as in hardware development, simulation has a key role to play in software development. Developing and testing signal processing routines, sensor fusion algorithms, object recognition functions, control algorithms, and human-machine interface (HMI) software, with model-based software development techniques, makes the software robust, less error-prone, and safe. ADAS and autonomous driving technologies greatly multiply the complexity of vehicle systems. Not only do they create more possible causes of failure, but also many more failure cascade paths. Since ADAS and autonomous driving systems inherently have safety implications, any failure can easily be catastrophic, even fatal. Conducting functional safety analyses of such complex systems is tedious, error-prone, and vulnerable to gaps and flaws. Automated functional safety analysis tools are therefore essential to ensure the safety of ADAS and autonomous driving systems. Model-based embedded software development along with a qualified code generator greatly expedites embedded software development. Once the software models are validated, the generated code is guaranteed to be error-free thus eliminating unit testing of the code, and reducing overall software development efforts nearly in half.


This Blog presented CADFEM expertise on 5G Smart Mobility era 

I hope you found this blog useful and believe that the contents showcased in this article will help in the evolution of upcoming 5G smart technology. Please feel free to share it with friends and colleagues. If you haven’t subscribed to this blog yet, please do so. 

I would like to know if there are any questions regarding the topics above. Maybe I can help? Hence please do use the comments section below to reach out to me. I’ll be glad to be of help.                                        

Happy 5G Designing…!!!


Author: Mr. Safal Sharma

Author Bio: Mr. Safal Sharma has in-depth hands-on expertise in RF/Microwave test Instruments like Vector Network Analyzer, Signal Generator, Spectrum/Signal Analyzer, DSO, EMI/EMC Pre-compliance Tester. Deep insight knowledge on Wireless technologies/systems such as GSM, CDMA (WCDMA), LTE, LTE-A, 5G, RADAR, MIMO Phased Array Beam-forming & Other communication systems.

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Discrete Element Method for better Mining Operations

The mining and mineral processing industry operate in a very high tonnage range which makes the equipment more prone to damage and wear. This also means that testing new designs at plant scale would lead to material wastage and time loss.

Another aspect that differentiates the mining and mineral industry from others is the fact that the raw material keeps changing from season to season. In the rainy season, the material will be wet and sticky and in summer it will be dry. This requires the design engineers and researchers to come up with a design that can work in both these conditions.

Discrete Element Method is a proven tool for designing and troubleshooting equipment. Mining and Mineral companies across the globe have included DEM in their development process and routinely use them for troubleshooting. In this blog,

What value will a DEM tool add?

Any simulation technology will help the user to get a deeper insight into their equipment/process. Testing at the plant scale also becomes an easy task as there will not be any material loss and downtime of the plant required.

Let’s take a very simple example of a chute. In case of a decrease in throughput of the chute, it becomes difficult to understand the underlying reason sometimes. Identifying the right area where material clogged will be really helpful in increasing the throughput. A DEM simulation can show the velocity profiles of all the particles inside the chute which help us identify the faulty region. Getting this type of insight from the real chute will be difficult as that would require us to have a chute made of transparent material which is impractical at plant scale.

What processes will I be able to design/troubleshoot?

Almost all mining-related processes can be handled using DEM. Here is a non-exhaustive list:

  • Excavation at the mining site
  • Wagon loading and unloading
  • Primary crushing
  • Stockpile
  • Conveying operations
  • Screening/sorting
  • Blast furnace
  • Fluidized beds
  • Packed beds

Is DEM capable of handling real-life material loading conditions? RockyDEM uses the power of multi-GPU processing to make plant scale simulations reasonable. Figure X shows a comparison of CPU vs GPU vs multi-GPU. Using multi-GPU will help us make these simulations way faster when compared to CPU. But that’s not it, with the new modules being introduced inside RockyDEM, the data saving time has decreased which would lead to total time-saving. Also, the advanced contact detection method helps reduce overall simulation time. We can further reduce the simulation time by using the Coarse Grain Model.

Figure x- Scalability using CPUs, GPUs and multi- GPU`s

How can I be sure that the simulation results are accurate?

All the models are implemented in Rocky are well-validated which means that we do not have to worry about the models predicting the behavior if supplied with accurate inputs. The most important input required will be material properties. To extract the material properties and correlate them to simulation parameters, calibration is required. For example, an angle of repose test which can be performed in the lab should also be performed with RockyDEM virtually and the same behavior should be replicated. Properly calibrated material properties will provide us with a highly accurate result.

I would also like to understand the effect of particle flow on my equipment, how can RockyDEM help me with that?

RockyDEM has some in-built post-processing capabilities which can be used to plot contours of stress and force on the equipment. RockyDEM is also integrated with ANSYS products like ANSYS Mechanical. So, the force maps from RockyDEM can be transferred to ANSYS Mechanical and a structural simulation can be performed to evaluate deformation, etc.., We can also perform a transient coupled analysis with ANSYS Mechanical.

We answered a few questions which any mining company might have while thinking about adopting simulations. Keep an eye on this space.

Author Name: Mr. Ishan Vyas (Application Engineer at CADFEM India)

Profile Bio: Mr. Ishan has completed his bachelor’s in Chemical Engineering with minors in Material Science. He is currently associated with CADFEM as an Application Engineer in the CFD and DEM domain. He holds an experience primarily in the areas of applications related to Chemical, pharma, oil, and gas industry involving both CFD and DEM codes. The Simulation of Chemical processes implementing DEM for New product development deeply interests him. He is an avid reader and loves Adventure sports.

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Wield Enabling Tool to Master CFD Meshing

The quality of essential simulation output depends absolutely on getting the mesh right. The kind of refinement and the type of mesh used should go by physics in place. For instance, a flow with dominant turbulence generation from the boundary layer separation needs a better focus on the boundary layer refinement to keep the Y+ = 1. A premixed combustion simulation in a SI engine will require an LES turbulence model and therefore a mesh refinement in the bulk to capture about 80% of turbulence kinetic energy there. Cases with moving parts in a fluid flow have to mesh with a priority to avoid negative volumes through dynamic meshing. Narrow gaps, a long list of parts in a big automobile assembly, etc.., are some overhead complexities demanding diligent craftsmanship.

This blog article shall apprise on the befitting advantages of “Ansys Fluent meshing” to thrive through such challenging meshing tasks

Endorsing motivators to adopt and benefit from Ansys Fluent Meshing

  • Generates polyhedral meshes, polyhedral prisms can easily uphold mesh quality for refined boundary layer regions.
  • Offers wrapping advantage to mesh large assemblies.
  • Parallel mode execution without using any HPC licenses, consistent speed scale-up.
  • Can run with both Solver and Pre-post license.

I choose to narrate some of my recent personal experiences as a CFD user to shed more light on these features of flair, which is worth a deep dive. Going by its craft, an electric motor meshing pursuit is the best fit to kick off this section.

While performing a Conjugate heat transfer analysis for an electric motor I faced few challenges with mesh generation. Fluent meshing with its guided task-based workflows and best in place algorithm helped in meshing complex geometry with good quality within less time & economical mesh count. In this post, I will be discussing the fluent meshing approach & how it helped with the pre-processing for motor thermal analysis.

For the electric motor analysis, I was able to achieve conformal mesh with good mesh quality but the mesh count was higher initially. Higher mesh count will consume more solving time & hence I was looking up for options that can help me reduce the mesh count & still preserve the mesh quality. One of the reasons for high mesh count was the proximity settings where the solids were also meshed with a fine sizing to maintain conformal mesh. So, a non-conformal mesh approach was utilized with solids having a bit coarse mesh which provided an advantage to generate a fine mesh to the fluid regions. With fine-mesh confined to the fluid regions, mesh count with a non-conformal approach was reduced to ~60%. But with non-conformal mesh, I had to invest time in assigning the mesh interfaces, which eventually consumed more time as multiple mesh interfaces are involved.

The same model was tried in fluent meshing under the conformal polyhexcore mesh approach, this time with prism boundary layers included. 8 cores were used for parallel meshing which exponentially reduced the meshing time. The mesh count was reduced to 40% compared to the tetrahedral mesh & the mesh quality was within the acceptable limits. For the boundary layer resolution as well, the polyhedral prisms helped in maintaining the quality compared to tetrahedral prisms within narrow air gap regions. Results for the 3 cases were compared & there was a good agreement. So, from this experience, I observed that fluent meshing can help in reducing the pre-processing time to a greater extent & I would highly recommend this approach. In the next part, I will be discussing more regarding various capabilities provided in fluent meshing which will highlight the extent to which fluent meshing can simplify pre-processing work.

Fluent meshing is developed with the capabilities to provide native polyhedral mesh which helps in reducing the mesh count while preserving the mesh quality. It is integrated with fluent to form a single-window workflow for CFD simulations. So, one can switch directly from fluent meshing into fluent setup, solution & post-processing module. Additional advantages are parallel meshing where one can utilize parallelization over multiple cores not just for accelerating solving but also meshing which can reduce the pre-processing time drastically. Polyhedral prisms can fit in narrow gaps without suffering distortion compared to triangular prisms which are good for boundary layer resolution. The task-based workflows provide a guided stepwise meshing approach using which one can setup meshing parameters & can edit them later if the mesh resolution is not as expected. Fluent meshing can perform conformal mesh for Watertight geometry by capturing all detail features within the geometry. In case of poor quality with surface or volume mesh, one can add a “improve mesh” option which activates the auto node movement option to improve the quality of mesh to the desired value. Apart from tetrahedral, hex-core & polyhedral meshing, Fluent meshing offers a unique option of polyhexcore or Mosiac meshing technology.

For More Information on task based Meshing watch the webinar

For dirty geometries with leakage and overlapping parts, an analyst has to spend hours preparing a watertight geometry for simulation. Fluent meshing is equipped with a Wrapping technology to capture the complex or dirty geometry. Developing a watertight geometry for components like engine or complex assemblies can be a time-consuming activity. Under hood analysis with complex & interfering geometries like engine, radiator & chassis can easily mesh with wrapping technology. Native cad file formats like STL & step or can be directly imported & part management can be performed to select the simulation model. While meshing a dirty geometry some complex features which have less impact on the simulation results can be overlooked using wrapping technology. In case the wrapper has missed any features of interest than using the edge feature extraction method one can fine-tune the wrapping function. Another option of Leakage detection allows the wrapper to patch the leakage in geometry below the provided threshold value thus eliminating the tedious need to work at the geometry level. Fault tolerant YouTube video.

For More Information on Wrapping Technology watch the Webinar

In a nutshell, fluent meshing can incorporate any type of geometry, and using the appropriate mesh strategy one can develop high-quality meshes with appreciable ease. Considering the type of complex products being deployed into the market, fluent meshing with wrapping technology and guided workflows is definitely the promising technology to reduce the overall pre-processing effort.

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Open the Door to Material Optimization


The world is evolving at a quantum speed with transforming technology. The human life cycle has enriched to tend towards advancement in every sector. Businesses have adopted the transformation & obtained the benefits of technology involved in their mainframe process to become multi-billion companies. Making the product a hero in every story, Engineering the product or designing a physical product demands focus on four major factors; Design, Analysis, Manufacturing, and Materials. Though the first three factors are digitally transformed, the materials field is still lagging. This article exclusively focuses on optimizing your right material choices.

Let’s start with an example of a company facing a similar conundrum.


Referring to the case of an OEM, that manufactures an industry renowned product.

Due to the stringent industry standards and to stay ahead of the competition, the company decided to optimize their existing product and its performance. Following the advancement, they relied on simulation results for quick feedback from different design iterations. Furthermore, parametric optimization yielded them even better results compared to the manual design iterations. Overall, they were able to achieve a 9% Reduction in Weight and a 5 % Increase in Efficiency through Design Optimization.

Much to the Team’s surprise, the product manager wasn’t satisfied with this result. Hence, he assembled his team to initiate an experiment with different material types. 

The broader idea of this activity was to understand how he could improve his product and cut down costs to the company, thereby naming this activity as Material optimization.

Optimized Product = Design Optimization + Material optimization

Following the superior’s directive, the team started dedicating their efforts to bring the best out of current results, in terms of cutting costs, reducing development time & raising standards of performance. Four of his team members heading the R & D were investigating the case study with different ideas as below. 

The first member tried to use the existing available material data with him to see if he can get the best possible combination;

The second member tried to use the materials preferred by his company to avoid supply chain issues;

The third member tried to reach out to suppliers/consultants for a piece of advice on material choices; and

The fourth member tried to browse on the internet for material data.

However, none of the above approaches answered the below questions

  1. Which material to choose?
  2. Is there a better material choice available?
  3. Is there a cheaper solution? 
  4. Is the chosen data, reliable?

This is where companies need a tool like, Granta Selector which does answer the above questions.

Granta Selector is a tool that can help optimize your material choices, which not only has material properties of metals, plastics, polymers, ceramics and various other classes of materials but also has features like search, plot and compare your choice of materials as shown in the pic.

Apart from the features mentioned above, Granta selector has:

  • FE export tool to export simulation ready material data to most of the FE platforms
  • synthesizer tool to estimate material and process cost 
  • Eco Audit tool to estimate the environmental impact of the selected material at the early stage of the design.

To use how to use above-mentioned tools, click here to understand how Tecumseh, a global leader of commercial refrigeration compressors used Granta Selector to reduce development time by three-fold and saved millions of euros of cost savings from making the right materials decisions.

please click here for more information on Material optimization.

Please feel free to connect with us at or +91-9849998435, for a quick Demo on this Product.

Author Bio:

Mr. Gokul Pulikallu, Technical Lead-South


Mr. Gokul Pulikallu has done his Bachelor of Technology  & he is carrying 9 years of experience in the field of structural Mechanics simulation and optimization. His main focus is Design Optimization & Material Optimization and helps customers adopt these technologies efficiently.

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ANSYS Structures R19 – Release Update

This post discusses latest developments and enhancements in ANSYS Structures R19 applications. Maximize your RoI and productivity with the latest ANSYS release.

Today’s ever-changing and increasingly-competitive world makes life complicated for product developers such as you. Hence, you are perpetually in a race to launch better products and increase profitability.

In order to help you realize your product promise, we are glad to introduce you to ANSYS Structures R19 with various improvements and additions. As a result of the new release, you’ll find exciting and innovative technologies which make the development of complex products effortless with help of improved solver capabilities, better usability, integration of complex physical phenomenon and solver scale-up using HPC.

Enhanced Utility and Scale-Up

This year ANSYS brings in some radical changes to help you capitalize on your current and future ANSYS investments. Starting off, the following will enhance the utility of ANSYS for many applications and help in speeding up run time.

  • The inclusion of small sliding algorithms helps significantly reduce the time involved in contact detection by performing contact search only at the beginning of the analysis. So, this leads to faster solutions.

ANSYS Structures R19 Update

ANSYS Structures R19 Update

  • Additions to the user interface such as Selection Clipboard helps you save selection information intermittently. Hence you can retrieve it whenever necessary to define BCs, Named Selection, etc.
  • Material Plots help in visualizing material assignments to the components and also to have a holistic understanding of materials in the assembly.
  • Improvements in meshing and contact algorithm is another development. Therefore, this will lead you to a faster problem definition in the interactive environment of ANSYS Mechanical.

ANSYS Structures R19 Update
Speedup with DMP Scaling

ANSYS Structures R19 Update

  • Compute with 4 cores as default across entire ANSYS Mechanical (Pro, Premium, Enterprise) product lineup. Hence more value for your investments!
  • Achieve 3X scale-up using HPC with improved Structures R19 Solvers and utilize HPC Pack across entire ANSYS product portfolio. Therefore, your problems run faster and better!
Effortless Modelling of Complex Phenomena

Increased use of simulations across various industries requires engineers to simulate complex phenomenon. ANSYS Structures R19 helps make simulation of complex physical phenomena seem effortless.

  • SMART: Separating, Morphing, Adaptive and Re-Meshing Technology (SMART) makes simulation of Fatigue Cracks easy and interactive. SMART fracture capabilities simulate crack growth without the need for crafted meshes.
  • Coupled Physics: New 22X elements help in achieving a magnetic coupling of Structural and Thermal with Magnetic DOFs.
  • Enhanced FSI coupling allow faster data transfer between CFD and Structural Solvers.

ANSYS Structures R19 Update

ANSYS Structures R19 Update

ANSYS Structures R19: Other Noteworthy Enhancements
  • Additional data import from external models
  • Element Birth and Death as Native Mechanical Feature
  • Quick and Easy RST import
  • Improved NLAD-based Simulation for physics involving Large Strains
  • Beam to Beam Contacts
  • Higher Scaling in DMP

ANSYS Structures R19 Update

ANSYS Structures R19 Update

In conclusion, this article serves as a good foundation to further understanding. There is much more to learn about ANSYS Structures R19. Join us on April 12 for the ANSYS Structures R19 Update Webinar to get the details! Register now.

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ANSYS Fluids R19 – Release Update

This post discusses latest developments and enhancements in ANSYS Fluids R19 applications. Maximize your RoI and productivity with the latest ANSYS release.

How we use simulations has changed drastically since its inception. A couple of decades back simulations were majorly used for research purpose.  But today it is used for various applications ranging from airplanes to microfluidics. Simulations have also evolved to handle more complex problems in smaller run times. ANSYS, a leading simulation software company is constantly innovating to make simulation easier to use and at the same time making them more robust. With every release, the GUI is getting better and the solver is getting smarter. Hence, without further ado, let’s take a dive into a few enhancements in ANSYS Fluids R19.

In this article, I will discuss some of these developments however, I recommend you to join the upcoming webinar that I will deliver on April 17.

Enhancements for Spray Modelling

The new feature in ANSYS Fluids R19 would significantly reduce the computational effort needed for spray nozzle designers to optimize product performance. CFD has been used for modelling sprays for a while now. Multiple approaches are available for spray modelling namely, full resolution (resolving all the length scales in the spray), semi-empirical (uses empirical correlation for droplet break up and stability analysis to generate droplet data), etc. ANSYS Fluids R19 has significantly enhanced spray modelling using VOF (volume of fluid)-to-DPM (discrete phase modelling) approach. As a result, you can directly track interface instabilities and surface tension effects that result in ligament and droplet formation. Due to this, you’ll get fast, accurate spray breakup and droplet distribution with minimal effort.

ANSYS Fluids R19 - Simulation of Fuel Injector
Simulation of high pressure fuel injector spray (Fluent R19)

ANSYS Fluids R19 - Spray Jet Simulation
Simulation of a Spray Jet in Cross Wind (Fluent R19)

Accurate Preventive Maintenance

Engineers seeking to maximize up time and optimize preventive maintenance programs need to reliably predict the location and extent of erosion in pipelines that are carrying particle-laden flows. Previously, static meshes could not account for structural changes in the pipe caused by erosion and its subsequent impact on fluid flow, thereby reducing prediction accuracy. New technologies in Fluent R19 automatically couple structural changes due to erosion with a dynamic mesh so that the simulation more fully captures the degradation arising from erosion.

ANSYS Fluids R19 - Erosion Modeling
Erosion Modeling

More Computational Power

To empower the users with more computational power, significant changes have been made to the High-Performance Computing (HPC) solution.

  • High-Performance Meshing Technologies that help in meshing the complex geometries at lightning speed. Higher Productivity.
  • All core solver technologies utilize four (4) cores without HPC License Checkout. HPC products add on top of these four cores. Hence, this gives you more value for money.
ANSYS Fluids R19: Other Noteworthy Enhancements
  • Blade flutter modelling
  • Risk assessment for Urea Solid Deposition for SCR
  • Lagrangian wall film
  • Thermolysis model
  • Local residual scaling for multiphase
  • Shar/Dispersed discretization schemes with Mixture Multiphase
  • FSI: Accurate leakage flows through narrow gaps
  • Species mass transport improvements
  • Cavitation modelling improvements
  • Native rolling ball fillets
  • Variable shroud gap
  • New Workbench templates

In conclusion, this article has only covered the tip of the iceberg. There is much more to learn about ANSYS Fluids R19. Join us on April 17 for the ANSYS Fluids R19 Update Webinar to get the details! Register now.

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ANSYS HFSS R19 – Release Update

This post discusses latest developments and enhancements in ANSYS HFSS R19 applications. Maximize your RoI and productivity with the latest ANSYS release.

With the advent of one more new year, ANSYS has released a new Electromagnetics Suite with a new, dynamic and user-friendly interface. Evidently, the new release also comes with more computation power and new license packaging which will deliver an incredible amount of value to the current and future customers. In addition, ANSYS follows the tradition of taming complexities and spurring the productivity with every new version. Consequently, there are several new features announced in ANSYS HFSS R19 for you to consider!

In this article, I will discuss some of these developments however, I recommend you to join the upcoming webinar that I will deliver on April 10.

ANSYS HFSS R19 - ED 2018
Electronics Desktop 2018

Taming Complexities

ANSYS 19 tames complexity by supporting and empowering engineers with tools that amplify your engineering effectiveness, performance, speed and ease of use.

ANSYS HFSS R19 delivers an all-new Radar Cross Section (RCS) calculations, by integrating Savant (HFSS SBR+) capabilities into ANSYS Electronics Desktop for tighter integration for large-scale problems.  More so, this capability is based on ANSYS’ industry-leading shooting-and-bouncing ray plus (SBR+) method to predict far-field radar signatures for 3-D target models. The powerful and accurate asymptotic methods of HFSS SBR+ allow our users to solve computationally large simulations very quickly and is a great asset for engineers designing military and aerospace applications, such as advanced target recognition systems and stealth technology

RCS Simulation in ANSYS HFSS R19
RCS Simulation

Spurring Productivity

With every new release, ANSYS promises deliver solutions that greatly enhance productivity and create a more seamless workflow at every stage. Therefore, ANSYS has constantly been empowering engineers to accomplish more in shorter timelines.

  1. R19 comes with a new interface. Specifically, the ribbon-based interface improved the overall flow from modeling to setup solving of the problem. As a result, users with little or no simulation experience can easily understand the simulation workflow, set up and solve high-frequency electromagnetic field simulations. Hence this would greatly increase the productivity and reduce the learning curve.
  2. To empower the users with more computational power, two significant changes have been made to out our High-Performance Computing (HPC) solution. Furthermore, some notable improvements in the solver speed with GPU acceleration. Therefore, these developments would reduce the computation time for faster time to market.
    • Now all core solver technologies utilize four (4) cores without HPC License Checkout. HPC products add on top of these four cores. Hence, greater value for your money!
    • Finally, ANSYS has unified the electronics high-performance computing (eHPC) with the other ANSYS HPC licenses. One ANSYS HPC license for across all physics!! Consequently, this development will increase the productivity of your HPC licenses.

Therefore,with all these enhancements, ANSYS HFSS R19 delivers the most comprehensive set of solvers and HPC technologies in a single package on the market. In conclusion, users can now perform more comprehensive design exploration through simulation using the accurate and reliable gold standard technology of HFSS.

ANSYS HFSS R19: Other Noteworthy Enhancements
  • New Ribbon Interface for all Desktop Products helps streamline process
  • Auto-complete of the variable name
  • Optimetrics enhancements
  • Local editing of 3D component
  • Special selection and show/hide modes
  • ANSYS EMIT: RF Link Budget Analysis
  • Improved Phi Meshing Robustness
  • Added the Capacitor Library Browser to 3D Layout
  • Added TDR analyses for LNA and Imported solution
  • Top & Bottom surface roughness supported for metal layers in 3D Layout
  • Enable Field Links for Finite Array DDM
  • HFSS Transient solver included with HFSS
  • Enhanced GPU Acceleration of Direct Solver
  • SBR+ for RCS with PTD & UTD
  • Added Iron Python IDE command window for SIwave
  • New “Special Selection” Modes
  • “Simulation Setup” export/import added for all simulation types

Lastly, you should know that ANSYS, Inc. is currently working on many more additional improvements and is looking forward to introducing more features in upcoming updates.

Get Latest Updates?

Visit Here to download the latest software & update.  Please reach out to if you facing any issue with the upgrade.

There is much more to learn about ANSYS HFSS R19. Join us on April 10 for the ANSYS HFSS R19 Update Webinar to get the details! Register now.

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Transfer Chutes Design with Rocky DEM

This article focuses on how engineering simulations help better design transfer chutes for their smoother functioning and enhanced equipment life.

Transfer chutes are used widely for handling bulk materials. They can be used to change the direction of material flow and transfer material from one conveyor to another conveyor. A chute might seem to be a petty component in a huge plant, but it plays a significant role. If you don’t design the chute properly, you’ll witness a high maintenance cost, increased downtime and particle breakage. On the other hand, a well-built transfer chute can help reduce noise, decrease damage to conveyor belts and transfer chute walls, and reduce dust formation.

Prior to getting to design, I will list down the essential requirements for a good transfer chute design:

  1. Avoid material clogging
  2. Minimal equipment wear
  3. Minimal material degradation
DEM-Based Approach for Designing Transfer Chutes

Getting the optimum chute design is a time-consuming task because transfer chutes are not designed properly most of the times. For the above-mentioned design considerations, there are many constraints that you will need to apply while designing transfer chutes.

Discrete Element Method (DEM) can help designers such as you design better transfer chutes in lesser time. DEM can help you visualize the flow of particles (material) in transfer chute and provide useful data pertaining to each particle and all the boundaries in the chute. Needless to say, it is impossible to derive such level of detail from physical trials or finite element analysis. Besides providing much needed qualitative insights, DEM simulations enable you with the freedom to explore new designs and test them without the need for physical trials.

In the following sections, I’ll explain how Rocky DEM software will help you design high quality transfer chutes.

Avoid Material Clogging

Blockages in transfer chutes restrict the flow of material through the chute. Not only are blockages a detriment to the transport efficiency, but also they exert a lot of stress on the chute walls leading to serious damage. To make transfer chutes less prone to material blockages, impact angle should be decided based on material properties and inlet speed should also be tweaked accordingly.

Rocky DEM has all the capabilities necessary to address the needs for wet and dry material handling. Different material properties such as the rolling friction, static friction for particle interactions, elastic modulus, bulk density, adhesion coefficients can be accounted for. A unique capability of Rocky DEM is the availability of adhesion models that can replicate the flow of wet materials. This would foster the simulation of materials such as wet mortar in the construction industry or some crucial unit operations with wetting ingredients in the process industry.

Rocky DEM helps to model blockage and design better transfer chutes
Rocky DEM simulation showing blockage (blue colored particles are stationary)

Minimize Equipment Wear 

Wear leads to increased maintenance cost not only for transfer chutes, but also for the conveyor belt. Abrupt changes in material flow direction is one major reason for surface wear. The smoother the flow, the lesser will be the damage to transfer chutes. The angle at which the material hits the walls of the chute largely affects the wear. This angle will depend on the material velocity and properties of the material. Modification can be done to the chute design to minimize wear, such as by adding a curved guide surface at the mouth of the chute to direct the flow and make it smoother.

As you will understand, making these changes in physical trials and capturing test data is cumbersome. With Rocky DEM, this task is just a few clicks away. You can use any CAD tool to make geometry changes and then bring it into Rocky DEM to simulate performance of modified designs. Rocky DEM will provide you wall impact force; using this, you can decide the optimum velocity and finalize the most suitable design for the transfer chute.

Rocky DEM simulations showing Instantaneous Shear Power for transfer chutes
Instantaneous Shear Power for a transfer chute

Rocky DEM simulations showing Mean Shear Power for transfer chutes
Mean Shear Power for a transfer chute.

Minimize Material Degradation

The quality of the end product decides its price. Undesired material breakage and extravagant segregation or degradation can lead to a poor quality process outcomes. Particle degradation occurs due to impact on chute and shear either from collisions with walls or with other particles. Information about the forces that a particle experiences can give an indication of how much damage will occur.

Rocky DEM not only provides forces on each particle, but also simulates breakage of particle under higher forces using models which are backed by years of research. You can visualize the particle actually breaking into fragments as it proceeds through the chute.

Rocky DEM: Helping You Engineer Better Transfer Chutes

Designing Transfer Chutes is a complex process and testing multiple designs is an expensive affair. However, Rocky DEM makes it simpler, cost-effective, lesser equipment maintenance and upkeep, and helps you to produce a better quality product. It has the ability to

  • analyze large number of particles quickly due to its multi-GPU capabilities,
  • represent particle shapes used in your industry, and
  • multiple post-processing options to help you analyze the data efficiently.

Quite clearly, Rocky DEM will help you and your organization minimize prototype costs and product development times. You’ll recognize that these benefits will further accelerate product launches into the market and fetch your organization higher profitability margins.

Still curious to know all of this actually works? I’m going to speak in a webinar called Assessing and Improving the Bulk Material Handling Practices with Rocky DEM Simulations on February 15th at 2:30 PM IST. Come, join me in an hour of learning how Rocky DEM simulations can help you save and generate more revenue for your bulk handling business.

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