A sudden rise in the demand for N95 masks as PPE (Personal Protective Equipment), has been widely recognized among the general public during this COVID-19 pandemic. This unexpected demand has resulted in a limited supply of equipment. The need of the hour suggests disinfecting and reusing disposable N95 Filtering Facepiece Respirators (FFRs).
In this context, Ultraviolet Germicidal Irradiation (UVGI) is considered an effective adjunct. Though it is not a stand-alone technology, this could be a method for respiratory disinfection as it has corroborated effectiveness in inactivating an extensive group of pathogens including coronavirus.
Germicidal UV typically engages mercury-based lamps operating at 254nm, the energy at which strongly absorbed by nucleic acids, resulting in damaging RNA and DNA molecules in pathogens preventing their further growth and function.
Moreover, the irradiation level of UVGI inactivating those pathogens does not hamper the fit and filtration characteristics of N95 FFRs. As per the literature, in the range of 0.5-950J/cm2, FFR fit performance is at 90-100% passing rate after 3 cycles depending on model whereas exposures as low as 2-5mJ/cm2 are capable of inactivating coronaviruses on surfaces. Thus, though proven to be an efficient method, UVGI could be used to effectively disinfect disposable respirators for reuse but the maximum number of disinfection cycles will depend on the respirator model and the UVGI dose required to inactivate the pathogen.
Simulating the performance of your UV System:
The design method of UV systems needs some questions to be answered before starting the design:
How much irradiance do we need to kill bacteria?
How many UV sources do we need?
What power should they have?
Where should we place them?
Simulation tools like ANSYS SPEOS help designers to efficiently answer these questions.
Besides, the ray-tracing capabilities of the tool also help in calculating accurately radiometric distribution in UVGI Devices with different surface reflectivities and lamp configurations.
ANSYS SPEOS Simulation to calculate Irradiance over the surface of N95 Mask
Apart from Radiometric studies, Structural Integrity of the respirators is one of the major concerns & studies show a noticeable decrease in structural integrity at lower doses. In conclusion, there are so many works of literature which suggests UVGI can be used for respiratory disinfection, though the maximum number of disinfection cycle will be limited by the respirator model and UVGI doses.
We often come across the dynamics or vibration problems in most of the product development across all industries. We have gained enough knowledge through the degree of Bachelors or Masters, with an example of the famous engineer disastrous design of “Tacoma Narrows Bridge-1940”, that considering the dynamics effects in design is quite critical. Generally, a dynamics problem is categorized into linear or nonlinear dynamics. This blog is related to the linear dynamics and nonlinear dynamics will be discussed in length in another blog. The most commonly heard terminology which we stumble upon in the design of such vibrating products is “natural frequencies“, “resonance“, “eigenmodes” etc. We have many linear dynamics simulation techniques such as “Harmonic Analysis“, “Random Vibration“, “Response Spectrum“, “Transient Analysis“, “Acoustic Analysis“, etc based on the loading it undergoes and also on the output we are interested in. But all these dynamics simulations start with the first step called “Modal Analysis“.
Modal Analysis
What makes something unique is an identity of its own, right? What if I say natural frequency is the identity of a body? Let’s see why. Assume you are imparting energy (striking) to a body. What will happen is obvious, the body will start to vibrate. But the vibrational frequency will be in its natural frequencies. It doesn’t matter how much or where you give the energy it will always vibrate in its natural frequencies, period. This is why the base of any dynamic analysis is said to be modal analysis as it shows its identity or behavior.
The modal analysis calculates natural frequencies and mode shapes of the designed model. It’s the only analysis that doesn’t require any input excitation or loads, which also makes sense, as mentioned before natural frequencies are independent of the excitation loads. Natural frequency depends only on two things mass and stiffness.
Why do we need to do the modal analysis?
Yes, we can find natural frequencies, and mode shapes through modal analysis but why to do it? Let me explain that through an example, when you ride a motorcycle, sometimes you might have experienced vibration in your handle. Some bikes have too much vibration that, you can’t even use your rear mirror. This is because the handle’s natural frequency is matching with engines RPM.
Structure borne noise: A structure makes peak noise when it’s vibrating in its natural frequency.
Whirling of shafts: To find the critical RPM of a shaft so that, crossing the critical speed will be done with precautions to avoid resonance.
Avoiding resonance is always preferred, thus shifting or modifying natural frequency can help to control, above mentioned issue. Natural frequency depends on only two parameters, modifying them will help to design a dynamic body part.
As an engineer, I always go in search of the physical meaning rather than proving by an equation. But at times equations are unavoidable.
Now let us see how we can get the natural frequency of a system.
Excitation force Equation of any dynamic system can be represented as
Every natural frequency is associated with each mode shape and mode shape represents the displacement behavior. There is enough material on the internet which explains mode shape. Right now, let’s concentrate on the “Mass Participation Factor” other important information from modal analysis, which would assist us in the simulation of other linear dynamic simulations. What is mass participation in general? Consider a 3 massed system as shown below.
The figure shown above is a mode of the system. Here masses participating in the X direction are M1 and M2 and thus total mass participation is M1 +M2. The only difference in FEA is that instead of lumped masses, the system will be discretized by elements.
Now let’s see ANSYS modal Analysis. Here I’m taking a cantilever beam, with 0.8478 kg. Having a cross-section of 3*60*600.
As you can see from the participation factor calculation. I have solved the first 12 natural frequencies. In this table, ANSYS solver calculates only frequency and Participation factor ie 1st and 4th column. Every other column is calculated from these two values. Let’s look at the most important column one by one.
As you can see this ratio is in direct comparison with total mass, which gives a better clarity on how much of the total mass participation is extracted. In other words, the sum of the effective masses in each direction should equal the total mass of the structure. But clearly, this depends on the number of extracted modes.
The same logic is for rotational mass participation. But here you might get ratios bigger than 1. Then, there can be confusion as mass participating is more than the total mass of the system? The answer is, participation factors for rotational DOFs are calculated about the global origin (0,0,0). The calculated effective mass essentially contains a moment arm (effective mass multiplied by the distance from centroid). Thus, the effective mass for the rotational DOFs can be greater than the actual mass and the participation factors can be greater than 1.0.
Ideally, the ratio of effective mass to total mass can be useful for determining whether or not a sufficient number of modes have been extracted for the “Mode Superposition” for further analysis. Modal superposition is a solution technique for all linear dynamic analyses. Any dynamic response can be calculated as the sum of the mode shapes, that are weighted with some scaling factor (modal coefficient). Also, the time step of the transient simulation is calculated based on the most influential mode in the modal analysis.
Modal analysis is usually done without considering any force or working environment. This assumption is taken by considering mass and stiffness to be constant. But at times this doesn’t work, like a pressurized vessel or pipe. As compressive or tensile force changes the stiffness. In such cases, we have to do a prestressed Modal Analysis.
In some cases, like rotating shafts with motors, turbines, the natural frequency will not match with the working condition. This is because, for different RPM, stiffness also changes,(Centrifugal force changes). In these cases, we need to consider the rotational speed of the shaft. In ANSYS Modal Analysis (Rotodynamic Analysis) rotational speed with Coriolis effect can be directly given to find the critical speed of the shaft.
Modal analysis is a prerequisite for all linear dynamics analyses like Harmonic, Random vibration, and Response spectrum analysis. Hence Modal Analysis plays a huge role wherever you are trying to find out vibration characteristics of a design. Thereby it is relevant for most industries like Rotating machines, construction, automobile, Machine tool, agricultural equipment, and Mining.
Engineering Simulation opens up a huge range of possibilities. Since CAE–simulation requires more than just software, we at CADFEM help our customers to adopt simulation for success in every phase of product development, thereby enabling them to realize their product promise.
Please feel free to connect with us at marketing@cadfem.in or +91-9849998435, for a quick Demo on this Product.
Author Bio:
Mr. Vineesh Vijayan, Application Engineer
CADFEM India
Mr. Vineesh Vijayan has done his Master of Technology in MachineDesign. With good expertise in Structural Simulation currently, he is contributing his efforts to CADFEMIndia as an ApplicationEngineer.
This post discusses CADFEM expertise to maintain pace in Futuristic 5G, mIOT, and ADAS systems.
INTRODUCTION
Roadmap from 1G to 5G-ADAS
From the past few decades, the world has witnessed many versions of a cellularnetwork, 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 High–Speed 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 MultipleOutput (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.
CADFEM UNIQUE 5G ADAS SYSTEM PROTOTYPING
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 ADASsmart 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 Nexxim, SIwave’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 format, pseudo-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 designanalysis. 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.
CONCLUSION
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…!!!
Cheers,
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.
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.
Towards a system level modeling approach to improve mechanical ventilation
This pandemic COVID-19 outbreak has called for all possible acceleration in medical innovation to improve immunity and to stay prepared to treat the adversity on health grounds. Besides the in vivo and in vitro approaches, the in-silico ways would complement this mission. Such advanced adoption of computational modeling offers many advantages starting with patient safety going on to personalized treatments, bringing down the invasive routes of drug administration to make it more painless. The focused work on Novel Drug Delivery Systems (NDDS), a 505 (b) (2), or a Paragraph 4 filing would benefit by better optimization when the right set of simulations are devised to pave the way on.
Ethical concerns on animal testing have also been spinning off lately to grab more attention into the in-silico adoption. Digitalization and simulation put up to the right use, there’s more data generated and that would come from more candidates analyzed by a larger sample on the Design of Experiments (DOE).
The outputs derived from simulations can be better quality inputs for the PBPK/PD models to work with. So, the modeling studies actually can be ranging from a 3D component level to a system level. System-level simulations or the Reduced Order Models (ROM) derived from a detailed 3D model simulation can present quicker ways of analyzing even biological units to for necessary responses or stimuli in a virtual environment.
Even the fields as niche as tissue culture, genetics can benefit very much from computational modeling with a focus to virtually study the sensitivity of the cells to the imposed bio-environment. What if scenarios on the impeller speed, fill level and the positioning of the scaffold, etc.., can be virtually studied to find the right answers before going to be physical. Forensic science as well seeks to use simulations to discover the root cause of the victim’s damage, such as explaining the angle of attack of a bullet based on the sort of wound it plunged in the body.
As mentioned, while 3D simulations are always there to cater to the finest details needed, developing system-level models is also equally important to be quick at attending to treatments for patients requiring personalized therapies. As of today, even surgeons with a very limited mathematical background can leverage computational modeling successfully to plan and execute critical, life-sustaining surgeries proving to be a bliss to the enrolled patients.
In the case of a life-sustaining mechanical ventilator, the focus is to use patient-specific operational conditions for the pressure applied based on the flow rate of the ventilation. This is to avoid any Ventilator Induced Lung Injury (VILI) which can be either acute possibility or unless for Severe Acute Respiratory Syndrome (SARS) such as COVID-19. The below-shown representation is a system-level network built with Ansys Twin builder by mechanical & electrical elements to represent the reaction of an artificial lung connected to an input waveform from a mechanical ventilator.
Figure 1: Simulation Model in ANSYS Twin Builder
The model input is a pressure wave supplied to the lung (cylinder with piston) through the trachea (pipe). A flow sensor is used as an en-route to measure the flow rate. Mechanical spring and damping are used to represent resistance and compliance. We can see the conversion from cmH2o to Pascal & Liter’s to m3 in the above expressions to match the medical unit system for the mechanical Spring and damping coefficients.
The below representation is the simulation result as the lung volume and flow rate as a reaction to the fed pressure waveform
Figure 2: Lung Pressure, Volume and flow rate
In addition to the system modeling, a 3D simulation is performed using the Ansys CFD tools on a lung airway model. A real-time breathing cycle is used as an input to such simulation.
Figure 3: The applied breathing cycle and the velocity in the lung air way as a 3D Simulation output
An initial pursuit has been set up to conceive a simulator which can offer an easily accessible platform to test and monitor a pressure signal and other parameters during artificial ventilation. Besides the deployment for regular medical care, this simulator is intended for academic learning and training as well. The outlook is about simulating a 3D Computational Fluid Dynamics (CFD) model and building a Reduced Order Model (ROM) of the breathing cycle in Ansys Fluent. Such ROM shall then be integrated with Twin Builder to develop a holistic response system.
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.
a) Tetrahedral Confrormal Mesh
b) Tetrahedral Non-Conformal Mesh
c) Polyhexcore Conformal Mesh in Fluent Meshing
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.
At the zenith of such a situation when the whole world is fighting against a pandemic COVID-19, a multidirectional approach for combating requires excessive attention. The quick detection of infected patients stands as a primary challenge due to variability in the symptoms. Though the field of Optics and Photonics provides various conventional molecular analysis instruments such as multiple spectrum cameras, multispectral optical spectrometers, and they still couldn’t achieve a potential detection method for the masses. Moreover, these methods are time-consuming, prone to error, and may lead to respiratory infections.
Recently, a method involving biosensor which uses thermal and optical effect for safe and reliable detection of COVID is to be seen in a picture. The Plasmonic biosensor used for detection combines Plasmonic Photothermal (PPT) and Localized Surface Plasmon Resonance (LSPR) on a tiny gold nanoisland chip kept on a glass substrate. Artificially produced DNA receptors that match specific RNA sequences of the SARS-CoV-2 virus are grafted onto the AuNI chips. Through Nucleic Acid Hybridization, sensitive detection of specific RNA sequences of the SARS virus is done. LSPR creates plasmonic near field by exciting the metallic nanostructure. The change in the refractive index is measured using an optical sensor helping in determining if the sample contains RNA strands of SARS. The LSPR response due to plasmonic sensing determines the concentration of sequences ranging from 1 pM to 1 nM. PPT helps in boosting the ambient temperature to secure the detection of only reliable matching of RNA strand and DNA receptor.
The sensing stability, sensitivity, and reliability of the device can be significantly enhanced by measuring the same at 2 different angles under 2 different wavelengths. The thermo-plasmonic heat is generated on the AuNI chips while being illuminated at their plasmonic resonance frequency for better sensing performance. The localized PPT heat can elevate the hybridization temperature during the process and facilitate the accurate discrimination of two similar gene sequences. Though this technique is not yet efficient for a high frequented location, yet it embarks a step closer towards the modern techniques of simulations for such complicated processes.
ANSYS Lumerical Suite comes with all the tools which are required to simulate surface plasmon resonance(SPR) and photothermal heating in plasmonic nanostructures. It comes with a Finite difference time domain (FDTD) solver and heat transport solver for thermal simulation.
The picture below (fig 1) shows the simulation done for getting a source incidence angle that excites the SPR. The absorption in the Silver was measured to determine the angle with the strongest coupling.
Fig:1 Electric Field intensity vs Incident Angle Source: Lumerical Knowledge Base
Simulation is done on plasmonic nano-structures to understand the effect of varying optical intensity on the performance Source: Lumerical Knowledge Base
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.
Background:
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
Which material to choose?
Is there a better material choice available?
Is there a cheaper solution?
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 reducedevelopment time by three-fold and saved millions of euros of cost savings from making the right materials decisions.
Please feel free to connect with us at marketing@cadfem.in or +91-9849998435, for a quick Demo on this Product.
Author Bio:
Mr. Gokul Pulikallu, Technical Lead-South
CADFEM India
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.
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.
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.
Speedup with DMP Scaling
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: 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
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.
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.
Simulation of high pressure fuel injector spray (Fluent R19)
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.
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.
