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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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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Hybrid Solving Methods for Effective Antenna Placement

In a previous article, I mentioned about design & analysis of antenna using electromagnetic simulation and important aspects to be considered. In this article, I explain effect of a platform on radiation characteristics and how hybrid solving methods can help towards effective antenna placement.

It has become routine for automotive OEMs to integrate different types of antennas in their vehicles. In recent years, many industry professionals have been focusing on implementing projects related to Internet of Things (IoT). There’s ever-growing demand for IoT integration for consumer electronics, vehicles and so on. Consequently, estimating actual performance of the antenna with any platform (vehicles, electronic devices and buildings) is becoming challenging!

In recent years, automotive industry is introducing Advanced Driver Assistance Systems (ADAS) for automating and enhancing the vehicle system and its safety. The growing interest for wireless connectivityHybrid Solving Methods relies more and more on integrated antenna solutions customized for optimal system performance, and any failure can cause the delay in a critical product launch. ANSYS provides the technology for the various solution techniques for simulating individual antenna to final placement for estimating various characteristics.

Hybrid Solving Methods for Antenna Placement

You can easily assess the effect of the platform on the performance of the antenna using Hybrid Solving Methods. You can apply traditional approaches such as the finite element method (FEM), Finite Difference Time Domain (FDTD) to problems of moderate electrical size.  Significant computational resources are necessary for these numerical methods. Therefore, we will need to further extend the capability of FEM to the solution of electromagnetic radiation and scattering problems. These could involve disjoint obstacles such as reflector antenna systems, antennas mounted on large platforms, and antennas in the presence of radome structures. To achieve this, several methods such as method of moments (MoM), high frequency techniques such as Physical Optics (PO) and Shooting & Bouncing Rays (SBR+) have been hybridized with FEM.

Furthermore, the below schematic will allow you to select an appropriate solution technique based on the geometric & material complexity and electrical size of the problem that you wish to solve.

Hybrid Solving Methods
Decision Criteria for Selecting Hybrid Solving Methods (Courtesy: ANSYS, Inc.)

Hybrid Solving Methods provide the solution for

  • Radiation Patterns of the Antenna after mounting it on the proposed platform
  • Coupling between Antennas placed on the platform.
  • Optimal Position for an Antenna over given platform.
  • Faster Computation Times
Finite Element Boundary Integral (FEBI) & SBR+

Among the several hybrid solving methods, I’ll focus on FEBI and SBR+ in this section. In both these methods, you simulate a part of the antenna with FEM. Then, you simulate the platform effects with either integral equations or high frequency techniques. To effectively calculate currents near the antenna, you need to analyze the antenna using the FEM and feed these results into FEBI or SBR+ methods.

In general, electrically large problems could be solved with FEBI technique & electrically larger problems can be solved with SBR+ technique. For a smaller problem scope, FEM will do the trick! Since both the hybrid methods are equally applicable for many problems, you’ll need to be aware of the subtle reasons for selecting the most appropriate method that is relevant to the platform. We can help you with this if you need any assistance!

The combined simulation with feed network analysis is also possible with the help of ANSYS Circuit Simulator. With this, you can interface field solver results with those from FEM-Hybrid Techniques.

Relevance to ADAS Applications

When we think about non-monitored drivingHybrid Solving Methods, the ADAS system can handle all the situations: partial or full scenarios. Toyota President Aikido Toyoda recently said to ensure ADAS system safety, we need 8.8 billion miles of testing of autonomous vehicle design. This is not only expensive, but also impractical. ANSYS-Powered Simulations have a crucial role in ADAS because of availability of multiple software tools for different kinds of analysis and easy integration with others.

You can simulate Radar Antennas in Autonomous Vehicles with HFSS and conduct initial placement simulation with hybrid methods (FEBI or SBR+). We can simulate different driving scenarios that accounts for other vehicles, buildings, trees etc. by including detailed physics. This is possible by using HFSS SBR+. These virtual test results can be used to test & validate control algorithms and vehicle dynamics.


ANSYS Electromagnetic Simulation Software provide the necessary requisites to validate design and placement of the antennas for different applications. In addition, Hybrid Solving techniques provide for various benefits including faster computation times, optimal position studies among others.

Going a step further, you can extend these studies to ADAS applications by integrating results from ANSYS Electronics Simulation Software.

I hope the article was useful to you. If you wish, you can download a recent Webinar on Antenna Design and Placement using ANSYS Software. Of course, please feel free to reach out to me if you have any questions.

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Electromagnetic Simulation for Antennas

In this first part of a multi-part series, I will discuss many aspects of antenna design & analysis with the underlying theme of electromagnetic simulation-driven product development. In this part, I will briefly talk about performing stand-alone Antenna Design, Analysis & Optimization using Electromagnetic Simulation.

Increasing Importance of Electromagnetic Simulation (EM)

While still in university, I imagined antenna design to be very simple. Based on the given frequency, we will need to calculate dimensions and then fabricate the design. That’s it. A decade ago, I found simulation to appear like dark art or black magic. If the fabricated antenna did not work well, I needed to iterate the physical design till it gave good results.

During the recent years, several EM simulation tools have emerged to evaluate the exact solution of Maxwell Equations for estimating the electromagnetic behavior of the devices. These tools used underlying methods like Finite Element Method (FEM), Method of Moments (MoM) and Finite Difference Time Domain (FDTD). Generally, we can divide the part components of the electronic design into active and passive devices. The modelling of the active devices is based on nonlinear measurement data parameters like S-parameters and X-parameters. When we come across modelling of passive devices, they are very simple because of their linear nature. However, it is important to understand the limitations of those devices.

The main role of the simulation is for to engineers to be able to accurately predict how complex products will behave in real-world environment enabling the complete virtual prototyping. ANSYS HFSS, a state-of-the-art high-frequency electromagnetic simulation, helps to estimate the radiation characteristics of the antenna and optimize the design as per requirement.

Parameters To Be Considered For Antenna Simulation

In general, engineers know that dimension can be reduced by increasing the substrate dielectric constant. Using standardized equations, we can estimate the size of the patch. However we cannot estimate radiation characteristics among a few other quantities. Using simulation tools, we can replace physical iterations with virtual iterations; we can identify the optimal design that matches the required specifications.

Why do some engineers get different results? Is there anything else that needs to be considered? Yes, engineers who focus only on model dimensions and not on boundaries and excitation will obtain inaccurate results.

Modeling and Setup

Let me consider the example of a GPS antenna that needs to have a gain of 3.5dB. For this gain, we’ll need to identify a antenna design with the smallest possible antenna dimension.

Let’s look at three substrates RT Duriod 5880, FR4 and Alumina. Using ANSYS HFSS, you can model the full antenna by using in-built modeling options or import the design from external CAD software. Initial dimensions of the patch are calculated using standard formulas available in academic literature.

Image of 3D CAD model of a patch antenna before performing electromagnetic simulation
Antenna Model

Patch antennas can be fed power by various methods such as microstrip line or coaxial/SMA. While using coaxial input, many don’t consider the dimensions of the coaxial. A good engineer initially checks for the dimensions of the coax in order to get the characteristic impedance, which directly affects the frequency of operation and voltage standing wave ratio or VSWR.

For assignment of different materials for model, HFSS has an inbuilt material library where you can select the required material for substrate, conductors, etc. If you want to use a material which is not in the library or if you want to add some frequency-dependent properties, then you can modify or create a new material.

Image of Materials available in HFSS before performing electromagnetic simulation
HFSS Material Library

Image showing addition/modification of materials before performing electromagnetic simulation
New Material Creation

For antenna design, radiation is another important boundary in order to accurately estimate the EM emission. As a good practice, the distance of at least λ/4 or λ/8 must be maintained between the antenna and the boundary. For example, λ/4 will be a good distance for radiation boundary and λ/8 for PML boundary. This is an important aspect that many engineers fail to consider. Upon completion of the initial setup, I ran the simulation to check for its performance.

Parameterization of Antenna

After simulation, check the input electric field in coaxial and the impedance of the transmission line/coax in order to verify the expected excitation. In post-processing, do check important parameters for radiation characteristics like pattern and gain. Even the near field data, which is complex to obtain from measurement, can be estimated with simulation.

Since we are not considering any fringing field and probe effects, there will be variation of results. To further improve the design, I suggest using optimization algorithms such as Optimetrics or ANSYS optiSLang. Such tools also permit sensitivity of the design due to fabrication tolerances.

The available optimetrics options in HFSS
Optimetrics in HFSS

Image describes the effect on resonance frequency due to probe position variations while performing electromagnetic simulation
Probe position effect on resonance frequency

Optimal Design of Antenna

Finally, the best design can be selected after evaluating the gain characteristics of the all variations. For the three substrates, I evaluated the optimized dimensions of the patch using Optimetrics:

  • 12.5 x 10 cm² for Duroid
  • 9.5 x 7.5 cm² for FR4
  • 7 x 5 cm² for Alumina

Image shows the estimated gain plot for different substrate materials while performing electromagnetic simulation
Gain Variation vs Substrate

Per this, antenna with FR4 substrate meets the required gain of 3.5dB with the least possible dimension. Better performance can be obtained by varying other parameters such as height of the substrate, etc.

The next time you perform electromagnetic simulation of antenna, do remember to consider all the boundaries.

This concludes the first part of a multi-part series on antenna design & analysis. In the next part, I will discuss about antenna placement analysis.

If you have any questions, please feel free to comment or fill out the contact form.

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