Wednesday, July 16, 2025

Podcast Spotlights Simulation’s Impact on Advancing Clean Hydrogen Solutions

With increasing concerns about climate change, carbon dioxide (CO2) emissions, and other environmental pollutants, more industries around the world are looking toward hydrogen as a cleaner alternative to fossil fuels. 

Hydrogen fuel offers zero CO2 emissions when produced using renewable energy sources to power the electrolysis of water to release hydrogen. Hydrogen production, however, also poses challenges and concerns, including safety, cost, and scalability.

Engineering simulation has gained attention as being one of the optimal ways to improve clean hydrogen production by developing new and existing technologies. In fact, the International Energy Agency (IEA) states that net-zero emissions are not even achievable without robust research and development (R&D) efforts in key technologies.

Recently, a U.K.-based media platform invited Ansys to discuss how simulation is driving innovation in this space on its podcast Hydrogen Industry Leaders (HIL). The platform aims to provide insight into how hydrogen solutions can be developed as we move toward a net-zero world. In an episode dedicated to simulation, Pepi Maksimovic, an Ansys Customer Excellence distinguished engineer, discussed how physics-based simulations deliver critical insights during R&D in hydrogen production, helping to ensure safety and build confidence in technology, drive down costs, and increase scalability.

Pepi Maksimovic 

Pepi Maksimovic, distinguished engineer, Ansys Customer Excellence

Listen to the "Physics-Based Simulations and the Role They Play in Growing the Hydrogen Economy" episode of the Hydrogen Industry Leaders podcast below.

Simulating Safer Energy Transformation

The HIL podcast series welcomes experts from around the world to share their views. With each episode focused on hydrogen production, safety, storage, and related topics, the podcast presents an opportunity to discover how different industries and sectors are advocating for green technologies to transform the energy sector.

Ansys’ Maksimovic joined HIL host Floyd March to discuss the role of simulation in episode 23: Physics-Based Simulations and the Role They Play in Growing the Hydrogen Economy. Backed by data and science, physics-based simulation helps engineers analyze and test hydrogen production processes with specific consideration to engineering dynamics such as thermal, fluid, chemical, and structural behavior. In this way, simulation helps R&D teams safely test and analyze phenomena that they otherwise couldn’t examine while also leveraging virtual testing on computers to reduce the cost and time required for physical testing and prototyping.

Green hydrogen 

Green hydrogen is sustainably produced through the electrolysis of water, excluding the use of fossil fuels.

Current energy transformation goals are rooted in the need to transition from fossil fuel-based energy solutions to zero-to-low-carbon solutions such as renewable energy, nuclear energy, and hydrogen. To achieve this transformation, the IEA has emphasized the importance in technology development, citing that almost 35% of the CO2 emissions reductions seen in its projected Sustainable Development Scenario by 2070 come from technologies that are either at the prototype or demonstration level, unable to advance at scale without further R&D. The IEA also asserts that another 40% of these targeted emissions reductions rely on technologies that are not yet commercially available in mass‑market applications.

Maksimovic explained how simulation is being used to address these two main objectives: to improve the existing technology, as well as to develop and accelerate development of brand new technologies through the R&D innovation.

Beyond production, simulation optimizes the remainder of hydrogen’s value chain, including storage, distribution, and utilization, through in-depth analyses that ensure optimal performance, safety, and environmental outcomes. 

em modeling 

Learn how Ansys simulation software is powering the hydrogen value chain.

Overcoming Hydrogen Hurdles with Simulation

The production cost of green hydrogen — which eliminates the use of fossil fuel altogether — has remained prohibitive for large-scale adoption. As engineers and R&D teams explore ways to improve production capacity, efficiency, and cost competitiveness of carbon-free, green hydrogen, other  low-emission hydrogen alternatives are being pursued in parallel.

An example of this is the application of carbon capture technology to capture and remove carbon emissions created during production of so-called blue hydrogen from fossil fuels.

Other companies are exploring alternatives such as turquoise hydrogen. This form of hydrogen production converts natural gas into hydrogen gas and solid carbon, the latter of which could be used for electric vehicle (EV) batteries instead of creating CO2 emissions.

Simulation offers significant R&D support, including predictive accuracy and limitless analysis capabilities. For example, advanced software packages such as Ansys Fluent computational fluid dynamics (CFD) helps R&D teams  accurately and efficiently analyze a variety of complex fluids phenomena at the level of detail that often exceeds the capabilities of physical testing. Such phenomena includes heat and mass transfer, chemical reactions and combustion, and others.

Fluent simulation 

Ansys Fluent unlocks new potential for research and development (R&D) in hydrogen production with computational fluid dynamics (CFD) analysis and capabilities.

Maksimovic notes that the widespread industry transition to clean hydrogen production requires time and technology. A wider adoption of clean hydrogen requires significant build up in production capacity and driving costs down to acceptable levels, again underscoring the importance of R&D.

Equipment design is another area in which simulation provides critical insight. Tools like Ansys Granta Selector help engineers and designers improve material selection for technical and environmental performance as well as cost, while Ansys Mechanical enables them to ensure structural integrity of the equipment.

Mechanical on computer 

Engineers and designers can explore thermal characteristics during hydrogen production and assess structural integrity throughout equipment design using Ansys Mechanical.

Granta Selector homepage 

Ansys Granta Selector enables R&D teams to discover more sustainable materials to improve design while upholding environmental standards.

“There’s a lot of simulation opportunities [just] for the electrolyzers themselves … how do you design the optimal cell, how do you stack the cells … understanding the polarization curve … the thermal, the heating?” she said. “So, all those challenges that engineers and designers have to grapple on an everyday basis, essentially … it’s really the crux of simulation ...”

Another hurdle in the broader adoption of hydrogen is fear for safety. While some of this stems from a general unfamiliarity with hydrogen as an energy source, other concerns are more tangible. Due to its high flammability coupled with its lower ignition energy, hydrogen can ignite faster than gasoline or natural gas, which raises safety concerns around potential fire hazards if mishandled. To mitigate these risks, government agencies and regulatory associations administer safety standards regarding its handling, including outlined safe distances from flammable gas storage, sources of ignition, and fuel gas vent pipes. There are additional guidelines in place for safe distances from employee offices or congregations of people.

According to Maksimovic, simulation helps de-risk hydrogen production processes while also easing concerns on a broader scale.

“By having that insight, how things behave, perform … understanding the processes themselves and understanding how the physical equipment will behave … as you’re designing, validating, verifying —doing all that before you bring product to the market — that’s de-risking for product manufacturers,” she said. “But also … there’s de-risking just in general. If people understand how much went on in designing and how much safety is put into it … that also helps the general population, I think, to embrace the use of hydrogen in everyday because everybody wants to use something that is proved to be safe and reliable.”

Looking at Worldwide Hydrogen Adoption

Simulation is being integrated into hydrogen R&D across global markets and industries, including energy, automotive, aerospace, and others.

Maksimovic believes that cost reduction in scaling hydrogen is the key to wider adoption. To this end, some energy government agencies have unveiled target programs to bring down the cost. In the U.S., for example,  the Department of Energy’s Energy Earthshots Initiative, also known as the 111 program, aims to reduce the cost of hydrogen to $1 per 1 kilogram in one decade.

To learn more about Ansys’ solutions in this area, visit Empowering the Hydrogen Value Chain Through Simulation.

Win the Race for Intelligent ADB Headlights with Ansys AVxcelerate Headlamp

The software supports NHTSA FMVSS 108 ADB Regulation Virtual Certification

Headlamp technology has come a long way. Additional enhancements, such as the continuous engagement of high beams in low-visibility night conditions, are a great example of how manufacturers are making the drive safer. Specifically, adaptive driving beam (ADB) headlamps can bathe the road with a steady stream of bright light without blinding oncoming traffic.

It's only recently, though, that U.S. safety regulators have green-lighted this technology. However, there are still obstacles to adoption. That's because the level of automatic headlamp perception and detection testing needed for validation presents numerous challenges for original equipment manufacturers (OEMs) in the U.S.

Now for some good news. Recent improvements in Ansys AVxcelerate Headlamp 2024 R1 enable OEMs to rise to a new level of regulatory requirements. This enhancement is part of a software solution that enables lighting software engineers to test-drive their control software — combined with their headlamp designs — reliably in a virtual environment, thanks to optical properties and real-time, physically-accurate optical simulation. 

New Lighting Rules Demand New Optical Solutions

With the FMVSS 108 regulation, there have been some recent changes in the National Highway Traffic Safety Administration’s (NHTSA's) lighting standard to enable ADB headlamp certification. It's defined within the context of any technology that actively modifies a vehicle's headlamp beams to deliver greater illumination without glaring at other vehicles — specifically lamps, reflective devices, and associated equipment.

"For OEMs, it's also one of the most complex regulations known in the headlamp regulatory space," says Lionel Bennes, lead product manager for AVxcelerate products at Ansys. "Passing the FMVSS 108 ADB regulation virtually requires new test tracks and a new test protocol. It also involves very specific traffic targets and is quite expensive to go through because it requires a lot of trial-and-error cycles." 

Another layer of complexity is added when the ADB system is considered as a whole, taking into account the system’s perception performance and latency, as well as optical performances against real-world headlights targets (versus lab-controlled stimuli for other regulations).

Authorizing intelligent headlamps in the U.S. based on this new regulation presents challenges for OEMs and tier one suppliers facing this constantly evolving technology. A strategic improvement in AVxcelerate Headlamp 2024 R1 enables OEMs to accurately assess cutting-edge developments like ADB in headlamp design, according to these regulatory standards.

To give additional context, the FMVSS 108 ADB regulation thoroughly assesses the performance of intelligent headlamps for glare reduction in different kinds of scenarios involving traffic in both directions. Consequently, passing the FVMSS 108 ADB regulation is quite challenging and expensive, as achieving compliance requires testing around a lot of trial-and-error cycles — which, realistically, most tier ones and OEMs cannot afford on a test track. 

avsimulation-avxcelerate-headlamp-r1-2024-fmvss-adb-virtual-regulation.jpg 

AVxcelerate Headlamp testing for FMVSS 108 Regulation

Ansys Sets the Standard for Regulatory Compliance

With AVxcelerate Headlamp, the FMVSS 108 ADB regulation can now be passed virtually. The tool considers optical design performance to make sure that the illuminance thresholds are not crossed. It also assesses ADB perception and intelligence, including software performance and latency. By providing a virtual version of this regulation, AVxcelerate Headlamp can save both time and cost for OEMs and improve efficiency during development to speed up new ADB technologies to market.

AVxcelerate Headlamp FMVSS 108 ADB regulation-specific improvements include:

  • Built-in regulation-defined trajectories that run automatically.
  • Regulation-defined targets (for trucks, cars, and motorbikes) and lighting systems.
  • Simulation runtimes at 100 Hz simulation steps, as per the regulation requirements.
  • Final report generation with detailed illuminance plots for each test section, as well as a summary highlighting pass or fail for each test scenario.

Additionally, the software uses targets equipped with lighting systems on the market that are very well specified in the regulation text. Reproducing the exact trajectories, scenarios, and vehicle targets specified by the FMVSS 108 ADB regulation in a simulation environment results in fully accurate testing.

The capability to pre-validate the proper behavior of an ADB system against the FMVSS 108 regulation in a simulation environment is a strong advantage in a very competitive market. Users can perform a high number of trial-and-error simulations, then analyze and correct issues with the help of the detailed measurement report generated at almost no cost against real testing.

The Latest Release is Available Now

Of course, any opportunity to sort through and streamline the automotive regulatory approval process is a big win for OEMs and tier one suppliers. If you're faced with regulatory pressures like these during headlamp development and design, consider the latest release of Ansys AVxcelerate Headlamp simulation software in 2024.

Ansys Technology Partner Award Winners: A Spotlight on Success

We are delighted to announce the Ansys Technology Partner Award recipients for 2023. Each winner has played a pivotal role in our mission to enable the design and delivery of transformational and innovative products that drive human advancement. We are excited to grant them this well-deserved recognition, and we look forward to continued collaboration in 2024 and beyond.

Better Together: A Year of Collaborative Achievements

Technology Partners are instrumental in delivering top-notch simulation solutions, with Ansys' partner ecosystem doubling since 2017. This open approach allows Ansys to fill capability gaps, expand its computer-aided engineering (CAE) ecosystem, and deliver tangible business value to customers. With its over 350 Technology Partners worldwide offering specialized software, high-performance computing (HPC), and cloud services, Ansys continues to empower transformative product design.

However, significant progress isn't achieved through incremental steps, but rather through bold leaps forward. Visionary companies partner with Ansys to accelerate innovation and confidently help our joint customers to reach their ambitious engineering objectives. Together, we drive advancements through the superpower of simulation, fulfilling our collective mission.

Recognizing Exceptional Partnerships

We congratulate all our partner award winners for their outstanding achievements in driving customer impact, growing solutions, expanding markets, enabling innovation, delivering exceptional marketing events, and achieving superior outcomes for customers. They serve as prime examples of the power of collaboration within our ecosystem.

The 2023 Technology Partner Award Winners

Amazon Web Services (AWS) — Go-To-Market Winner

AWS is recognized with the Go-To-Market Award. In 2023, AWS supported Ansys with seven tradeshows, 13 webinars​​, seven white papers, 12 blogs, 17 videos, and several compelling customer stories. The award is a testament of their exceptional efforts in effectively bringing Ansys Gateway powered by AWS to market.

AWS

Microsoft — Partner Growth Winner

Microsoft has been a pivotal partner for Ansys in support of our overall cloud strategy and a key enabler for Ansys customers who want to scale simulation in the cloud. The Partner Growth Award showcases how Ansys and Microsoft have developed a product strategy together to help Ansys customers scale their simulations in the Azure Cloud leveraging HPC — making Ansys simulation faster, less expensive and more seamless.

em modeling

NVIDIA — Digital Transformation Winner

This recognition comes from NVIDIA’s sustained investment in providing “generational acceleration” for Ansys solvers, transforming the products our customers design and build. From the beginning, Ansys’s collaboration with NVIDIA has nurtured technology leadership, domain expertise, and complementary intellectual property (IP) to deliver transformational value to customers. Ansys and NVIDIA will further deliver sustained value to customers.

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AMD — Partner Innovation Winner

In recognizing AMD with the Partner Innovation Award, we express gratitude for their steady commitment in 2023 to accelerating our simulation software on their CPUs and GPUs. Thanks to the AMD technologies integrated into an increasing number of Ansys flagship products, more customers can reduce their time to solution and reach insights faster. The Partner Innovation Award is a testament to our collaboration with AMD at every stage of product development to deliver customers more performance and energy efficiency with CPU- and GPU-based computing.

em modeling

PTC — Marketing Events Winner

The best Marketing Events Award for 2023 goes to PTC for showcasing the PTC-Ansys partnership at LiveWorx 2023, PTC’s digital transformation event. During this event, Ansys CEO, Ajei Gopal, joined PTC’s former CEO, Jim Heppelmann, in a keynote address to announce sustainability and materials management initiatives between Ansys Granta and PTC Windchill and Creo. It also addressed management of simulation data and workflows in Ansys Minerva, and the connection to PTC Windchill, to deliver simulation insights through an open product development approach.

PTC

Electro Magnetic Applications (EMA) — Partner Acceleration Winner

EMA is recognized with the Partner Acceleration Award. EMA was instrumental in providing platform-level electromagnetic modeling and simulation tools that can be combined with other Ansys solvers for easy-to-use multiphysics simulations. The award is a testament to how EMA engages with Ansys teams to deliver to customers design-to-validation workflows for cable harnesses, lightning and radiation protection, and similar applications.

em modeling

Altium — Rising Star Winner

In 2023, Altium and Ansys delivered our first integration: a bidirectional connection between Altium ECAD and Ansys simulation software. Altium is recognized as the Rising Star Award winner for this integration that connects our complementary solutions, and we are already realizing success as our sales teams engage together with joint accounts.

Altium 

Join us in congratulating these Technology Partners for their success.

About Ansys Technology Partner Awards

The Ansys Technology Partner Awards honor partners who have demonstrated outstanding results and innovation with the use of Ansys products and solutions. These awards are an acknowledgment of exceptional partner success.

Ansys Technology Partner Awards recognize a wide range of software, HPC, and cloud service partners who deliver tangible business value to our customers.

Ansys Accelerates the New Era of Digital Engineering Using NVIDIA Technology

AI-driven and accelerated simulation will bring physics to the next era of industrial digitalization.

Hardware and software are partners in a perpetual relay race, with one dependent on the other for that boost of speed that brings innovations to market that were once thought impossible to engineer.

A significant breakthrough in accelerating simulations stems from harnessing the immense parallel computing power of graphics processing units (GPUs). With the ability to distribute tasks across thousands of cores simultaneously, GPUs excel in handling complex computations swiftly. Ansys collaborates closely with NVIDIA to leverage this parallelism, continually refining and enhancing GPU-accelerated simulation solvers and algorithms. The collaboration focuses on optimizing various aspects of simulation workflows, including model building, solution algorithms, post-processing, and visualization tasks — helping drive efficiency, performance, and scalability to new heights.

Now, Ansys has announced that we are expanding our long-standing collaboration with NVIDIA to include exciting new opportunities in artificial intelligence (AI). Ansys will leverage the full stack of NVIDIA’s accelerated computing technologies to help engineers scale up their use of AI.

We’ll use NVIDIA Modulus to further develop Ansys AI+ modules, which are powered by AI and provide some of the most advanced Ansys solver capabilities. Starting with Ansys semiconductor software products for the electronic design automation (EDA) market, NVIDIA Modulus will empower simulation users with advanced, physics-based machine-learning methods for computational fluid dynamics (CFD), thermal, electromagnetic coupling, and other multiphysics challenges.

In the data center, NVIDIA GPUs such as the NVIDIA Hopper architecture-based H100 Tensor Core GPU dramatically accelerate Ansys solvers, a dozen of which are already supported. Ansys will also utilize NVIDIA GH200 Grace Hopper Superchips that are designed for giant-scale AI and high-performance computing (HPC) applications. This support is a research and development priority across Ansys. For example, Ansys is porting our semiconductor design tools, Ansys Fluent computational fluid dynamics (CFD) software, and Ansys LS-DYNA explicit simulation software to NVIDIA processors, including NVIDIA GH200 Grace Hopper Superchips, which deliver massive performance boosts for applications running terabytes of data. NVIDIA’s latest superchips empower scientists, researchers, and engineers to discover solutions to extremely complex problems faster than ever before.

NVIDIA MGX 

Accelerated systems using powerful NVIDIA GH200 Grace Hopper Superchips will be on display at NVIDIA GTC, the global AI conference running March 18-21, ready to take computing to the next level.

Ansys SimAI, the artificial intelligence (AI)-based software-as-a-service (SaaS) application that was recently launched, combines the predictive accuracy of Ansys simulation with the speed of generative AI and GPU computing. SimAI enables you to rapidly test design alternatives, around 10x to 100x more depending on the size and complexity, without the constraints of traditional solvers across all design phases. For example, a fairly large simulation like the aerodynamics of the vehicle in the video below could be tested 20x faster with SimAI, enabling you to speed development or try 20 additional design iterations in the same amount of time.

 
SimAI boasts the ability to predict aerodynamic performance on a new SUV geometry in less than 1 minute – a remarkable feat! Assessing more car designs is now possible with a 20x efficiency boost compared to traditional simulation methods, enabling quicker optimization.

In the application shown in the video, high-fidelity Fluent simulations were accelerated on NVIDIA H100 Tensor Core GPUs. Approximately 50 accurate CFD results, incorporating exterior shape variations and topological changes like rear mirrors, ski racks, and spoilers, were fed into SimAI, and NVIDIA GPUs were used to train the model. The trained model was then deployed via NVIDIA GPUs, illustrating that SimAI achieved less than 0.5% drag error compared to traditional CFD.

Don’t Just Visualize Simulations, Experience Them

Ansys is also in on the next technological era: the 3D internet, which includes technologies like spatial computing, digital twins, and extended reality. You can think of the industrial metaverse as a digital ecosystem — a real-time, 3D, virtual world. The possibilities for industrial sectors, entertainment, finance, and just about everything else are mind-boggling.

The time- and cost-saving benefits of using simulation to predict what will happen before physical assets are built are well-proven. The same is true of simulating systems and processes. But imagine being able to digitally create and walk through a factory before you even break ground. You could simulate workflows and the interaction of each machine via digital twins, or even ensure you will meet sustainability targets.

Now imagine that the factory is a virtual human being and that the “machine” is a 3D digital heart, enabling a surgeon to experience how an upcoming operation will go, predict future health risks, and treat them before symptoms even arise. Or maybe the virtual ecosystem is a city where you can see 6G communication challenges via antenna simulations that take place in real time as cars autonomously navigate around smart city streets. The possibilities are endless. We stand at the precipice of a new era for simulation to solve problems that were previously impossible.

NVIDIA Omniverse Ansys Simulation 

Ansys RF Channel Modeler offers a streamlined workflow for RF systems engineers to model large-scale dynamic RF systems simulations while considering the effects and impacts of complex urban environments.

We may be getting a bit ahead of ourselves, but the building blocks of the next era of digitalization, such as accelerated computing, artificial intelligence, and multiphysics simulation, are advancing at incredible speeds. Like the 2D internet, which uses hypertext markup language (HTML) as a standard, an open language and platform for the 3D internet is critical to helping the indistrial metaverse flourish.

Speaking the Language of Collaboration

Ansys has joined the Alliance for OpenUSD (AOUSD) — comprising members such as NVIDIA, Pixar, Apple, Autodesk, Adobe, and others — to help build an open, extensible ecosystem for simulating in 3D. Universal Scene Description (OpenUSD) describes geometric, material, physical, and behavioral representations of 3D worlds. AOUSD is an open, non-profit organization dedicated to promoting the interoperability of 3D content through OpenUSD.

Ansys is bringing its AI-enabled simulation expertise to the Alliance. Developers using the NVIDIA Omniverse platform can benefit from unique Ansys solver capabilities that will run directly within the platform to significantly increase the pace of innovations — from safer autonomous vehicles to improved understanding of the human heart to 6G communications and more — that will benefit everyone.

Work has already begun. Ansys’ AVxcelerate Sensors simulation software has been integrated with NVIDIA DRIVE Sim. The integration will provide high-fidelity sensor simulation outputs generated with AVxcelerate Sensors for development of advanced driver assistance systems and autonomous vehicles.

“Perception is crucial for AV systems, and it requires validation through real-world data for the AI to make smart, safe decisions,” said Walt Hearn, senior vice president of worldwide sales and customer excellence at Ansys in a recent press release. “Combining Ansys AVxcelerate Sensors with NVIDIA DRIVE Sim provides a rich playground for developers to test and validate critical environmental interactions,  to accelerate AV development.” 

 
Multiple teams at Ansys are working with Omniverse — from LS-DYNA to Ansys STK for digital mission engineering, Ansys RF Channel Modeler for radio frequency system design, and the new Ansys Perceive EM, which was made for synthetic data generation to drive AI/ML applications. Perceive EM introduces wireless channel modeling that captures the behavior of antennas and the signals traveling through a virtual twin of the environment. It provides 4G, 5G, and 6G network architects with the means to simulate — in real time — radio network systems in bespoke urban and rural locations, accounting for materials, as well as motion within the scene.

Ansys customers will benefit from our integration with an expanding set of NVIDIA Omniverse features and technologies such as photorealistic visualization, augmented and virtual reality, generative AI, and massive data manipulation. Ansys will enrich the Omniverse developer ecosystem with unique physics-based solvers that will help unlock cross-industry simulation and digital twin use cases within the platform, including for automotive, aerospace and defense, healthcare, and communications companies.

Narrow the Gap Between Simulation and Reality with Ansys at NVIDIA GTC

Visit us in booth 830 at NVIDIA GTC, a global artifical intelligence (AI) conference running March 18-21 at the San Jose Convention Center, or join virtually to see how Ansys simulation solutions use AI and accelerated computing to provide unprecedented speed, so that you can make better engineering decisions, faster.

Simulate Autonomous Driving

Ansys at NVIDIA GTC 2024

Simulate Autonomous Driving 

Among the 800 sessions at GTC, don’t miss “Driving Autonomous Vehicles in the Omniverse,” presented by Emmanuel Follin, a senior product manager at Ansys, at 5:15 p.m. PDT on March 18. Follin will share how Ansys AVxcelerate Autonomy sensor simulation is integrated with the NVIDIA DRIVE Sim platform to evaluate sensors and perception systems in diverse scenarios. AVxcelerate Autonomy is an end-to-end safety-driven solution designed to streamline advanced driver-assistance systems and autonomous vehicle (ADAS/AV) development.

This integration provides high-fidelity sensor simulation outputs generated with Ansys AVxcelerate Sensors for AV training and validation. AVxcelerate Sensors will augment NVIDIA DRIVE Sim with Ansys’s predictively accurate physics solvers for camera, lidar, and radar sensors. Follin will explore ways Ansys is using OpenUSD and NVIDIA Omniverse, a development platform for connecting and developing OpenUSD applications, to help unlock unprecedented possibilities in simulation and visualization.

OpenUSD, or Universal Scene Description, is an extensible framework and universal interchange between 3D and simulation data tools and ecosystems. Ansys is using these cutting-edge technologies to deliver new capabilities that will enhance the accuracy, efficiency, and realism of virtual prototypes and provide immense value to customers across industries by narrowing the gap between reality and simulation.

NVIDIA DRIVE Sim showing two cars in a tunnel 

Run large-scale, physically accurate multi-sensor simulations with Ansys powered by NVIDIA Omniverse.

Continuing the theme of autonomous driving, Lionel Bennes, a lead product manager at Ansys, will present “Enhancing AI-Based Perception Testing with Radar Sensor Simulation.” The session, which takes place March 20 at 2 p.m. PDT, explains how Level 3 and above autonomous driving requires comprehensive testing and validation. Level 3 autonomy, also known as conditional driving automation, has proven difficult to achieve in the real world. Level 3 autonomous vehicles use sensors and AI to detect and respond to environmental conditions.

This could enable a car to accelerate past a slow-moving vehicle, park, maintain safe distances from other vehicles, exit a freeway, and perform other common driving tasks. However, the driver still needs to be ready to intervene. Bennes’ session will show how high-fidelity sensor simulation for radar is critical for autonomous vehicle perception development. He will explain how physics-based solutions such as AVxcelerate and NVIDIA DRIVE Sim help enable developers to evaluate their perception systems in diverse scenarios and generate synthetic data to tailor testing. Because sensor simulation operates in real time, this approach can be deployed for both software-in-the-loop and hardware-in-the-loop testing.

GPUs Light Up Simulation Speeds

Kenneth Weselake, a project leader at BMW, will join Mathieu Reigneau, a senior product manager at Ansys, in an on-demand presentation at GTC that will explain the benefits of simulating light using graphics processing units (GPUs). The session, titled “Advancing Innovation in Accurate Simulation of the Light With GPU Computation,” covers how Ansys adapted our current CPU solvers into scalable, general-purpose solvers, accelerated by NVIDIA GPUs. Computing the equations of optics and photonics simulation requires extreme power, which could equate to long compute times. GPUs reduce the calculation times for simulating photons’ paths and energy. The presenters will also illustrate how GPU-supported simulation transforms innovation beyond acceleration, enabling you to address new challenges within reasonable deadlines and explore more “what-if'” scenarios.

Digging deeper into ray tracing, the session on “Accelerating Ansys HFSS Ray-Tracing (SBR+) Simulations with NVIDIA Professional GPUs” research by Laila Salman, a principal application specialist at Ansys, will be available at GTC. Ansys HFSS SBR+ is a high-frequency electromagnetic solver engine for modeling installed performance of antennas and signal propagation in large environments. Applications include integrated antenna performance on large vehicles and buildings, antenna-to-antenna proximity coupling effects, radar cross section modeling for large target radar signatures, and indoor/outdoor wireless channel modeling in dynamic, bespoke environments. Salman’s work shows how HFSS SBR+ with NVIDIA accelerated computing can help deliver engineering insights in record time. 

RF Channel Modeler 

Ansys RF Channel Modeler offers a fully digital workflow to address the needs of today’s most complex RF systems designs.

Visit our booth at GTC to learn more about how to leverage HFSS SBR+ simulation via cloud computing using Ansys Gateway powered by AWS. You can also get a sneak peek at new Ansys solutions, including Ansys Perceive EM and Ansys RF Channel Modeler, which can be used with NVIDIA Omniverse. Perceive EM uses a GPU-based shooting and bouncing ray solver to provide high-volume, high-accuracy modeling of radio frequency (RF) propagation in detailed terrestrial environments.  It enables you to simulate wireless networks in Omniverse to assess motion-related environmental impacts on signal propagation. RF Channel Modeler delivers an innovative network planning and optimization solution to RF systems engineers across the telecommunications industry. It provides detailed environment and kinetic motion modeling of subscribers and access points in a time-based environment simulation. 

From the keynote by NVIDIA founder and CEO Jensen Huang to over 800 inspiring sessions, 300+ exhibits, and 20+ technical workshops covering generative AI and more, GTC delivers something for people of every technical level and interest area. Join us at NVIDIA GTC.

What is Finite-Difference Time-Domain (FDTD)?

The finite-difference time-domain (FDTD) method is a 3D full-wave electromagnetic solver commonly used for modeling nanophotonic devices, processes, and materials.

While in photonics FDTD has become the industry standard, the finite element method (FEM) and the method of moments (MoM) are the predominant gold standard computational electromagnetic solvers in high-frequency electronics, each excelling in its own right. This article specifically focuses on FDTD for photonics simulations.

First introduced in 1966 by Kane S. Yee, FDTD is an algorithmic approach to solving James Clerk Maxwell’s transformative equations, officially known as Maxwell’s equations. Conceived in the 19th century, these equations not only unified electricity and magnetism, but also laid the groundwork for technologies such as radio, television, and wireless communication. Yee’s numerical method was not widely referred to as FDTD until the 1980s.

Maxwell’s FDTD Equations

Maxwell’s equations and the laws associated with them include:

  • Gauss’ law for electricity: describes how electric charges produce electric fields
  • Gauss’ law for magnetism: describes how magnetic fields do not have isolated magnetic poles
  • Faraday’s law of induction: explains how a changing magnetic field induces and electromagnetic force (EMF) in a circuit
  • Ampere’s law with Maxwell’s addition: relates electric currents to magnetic fields, incorporating the role of changing electric fields.

FDTD method 

In FDTD, each field component is solved at a slightly different location within the grid cell (Yee cell), as shown above.

How Does FDTD Work?

In FDTD, the simulation domain is the space truncated by the simulation region and discretized by the mesh. When an FDTD simulation runs, the electromagnetic (EM) fields are calculated from Maxwell’s equations in every mesh cell and the solutions are repeatedly time-stepped. Spatial discretization allows for the representation of complex geometries and structures, while temporal discretization captures the evolution of EM fields over time.

What are the Applications for FDTD?

The FDTD method is generally suitable for design cases in which some or all dimensions of the object are comparable to the size of the wavelength of light. Its accuracy and versatility make FDTD the go-to solver for a wide range of photonic designs, including:

Although FDTD presents the most general solution to Maxwell’s equations, more efficient approaches are applied to specific applications such as for maximizing design flexibility for multi-layered and diffractive optical components. Similarly, a solver combination strategy for photonic integrated components can be employed to address different structures more efficiently, such as the eigenmode expansion (EME) method for light guiding structures. Building a strategic methodology for choosing the right solver for the right problem can impact both the design process speed and efficiency, as well as the accuracy of results. 

FDTD method nanophotonics 

The FDTD method is used in a wide range of nanophotonics designs.

What are the Benefits of FDTD?

Using FDTD, designers can thoroughly study polarization and wavelength-dependent interactions of light with different materials and structures. They can receive insight into optical phenomena such as reflectance, transmission, diffraction, interference, and absorption.

  • Time and frequency domain analysis: FDTD provides a dynamic view of EM fields’ evolution over time. A built-in automated Fourier transform of the time-domain solution easily makes frequency analysis possible. 
  • Broadband capabilities: Because it is a time-domain method, FDTD can be used to calculate broadband results much faster from a single simulation.
  • Complex geometries: FDTD thrives in modeling complex geometries and can handle any arbitrarily shaped structures.
  • Accuracy and versatility: The method is inherently free of any physical approximations, making it highly versatile and accurate.

What are Challenges of FDTD?

The high accuracy and versatility of the FDTD method introduce some challenges, including:

  • Simulation size: The maximum physical size of a device that can be accurately modeled is ultimately constrained by the available compute resources.
  • Simulation time: The speed of FDTD simulations depends on several factors from the simulation setup and volume to the hardware specs of the computing system. In 3D, the simulation time is expected to scale with the following relation ~V . (l/dx)4, where V is the simulation volume and dx is the grid size. Thus, mesh accuracy and the design size can significantly impact the simulation time.
  • Memory and computing power: The sheer number of spatial and temporal unknowns in an FDTD simulation grows exponentially with finer meshes and larger simulation volumes. This results in extremely large amounts of memory and computing power requirements.
  • Memory bandwidth: Memory access and heavy data exchange between processors, particularly when dealing with sizable simulations, pose a main challenge in accelerating FDTD simulations.

How Do You Accelerate FDTD Simulations?

Ansys Lumerical leverages multiple advanced approaches to accelerate FDTD simulations.

Finely Tuned Algorithm

The FDTD algorithm in Lumerical has been fine-tuned at a fundamental level over decades to minimize computational overhead while delivering the highest accuracy. There are several patented and advanced features and functionalities to help streamline the simulation setup, including the mesh, monitors, sources, structures, materials, analysis groups, and more. Built-in advanced optimization frameworks can additionally accelerate the generation of optimized nanophotonic devices.  

Parallel Computing

Ansys Lumerical FDTD has a highly optimized computational engine able to exploit multicore CPU computing systems and harness the parallel architecture of graphics processing units (GPUs) in high-performance computing (HPC) clusters. Both CPU and GPU architectures excel in parallel processing, addressing the need for simultaneous computation in FDTD simulations. HPC systems leverage this parallelism to distribute the workload, significantly enhancing simulation performance. Large simulation jobs can be partitioned into several independent computational threads to be executed in parallel enabling large simulations of 50-100 billion grid cells in less than a few hours.

As the complexity of simulations grows, so does the need for efficient and scalable computational resources. This is where the dynamic duo of Cloud Computing and HPC steps in, revolutionizing FDTD simulations. The Lumerical solution offers CPU and GPU-compatible simulation software that users can deploy on-premises or on the cloud.

For more information on FDTD simulation software on HPC and cloud, watch our webinar “Accelerating Photonic Design with HPC and Cloud.”

To learn more about the underlying solver physics and how to set up, run, and analyze an FDTD simulation, see the FDTD learning track on Ansys Innovation Courses.

Survey of French Companies Shows the Impact of Simulation on Sustainability

Because global climate change can be traced, in part, to greenhouse gases being emitted by products and processes that are part of our daily lives, it is up to us to develop sustainable solutions to reduce the world’s carbon footprint to safer levels, as specified by the Paris Accords. At the same time, the pace of business is requiring developers to deliver new products to market faster and at lower cost. This acceleration to market would seem to be at odds with the careful steps needed to ensure that new products are sustainable.

Fortunately, Ansys engineering simulation software can help engineers develop sustainable products at the necessary pace by enabling them to virtually build and test hundreds of digital prototypes on a computer in the same time that it once took to build and test one physical prototype. Adding high-performance computing (HPC) and digital twins into the mix can further accelerate development times and make it easy to monitor the operation of the product to ensure a high level of energy efficiency in real time.

But these benefits accrue only if engineers across industries are aware of and are taking advantage of simulation software, HPC, and digital twins. To gauge the level of adoption of these technologies, Ansys engaged Infopro Digital to survey 300 companies in France about their product development practices. Here we present some of the key findings of this study.

Wind turbines

Commitment to the Environment

The survey found that 88% of the 300 companies have a clearly defined strategy for environmental protection. Seventy-seven percent said that the environmental impact of their products is a priority for their company. These numbers are very encouraging while still leaving room for improvement. 

Environmental protection strategy

One of the ways respondents are addressing environmental concerns is through material data management through the life cycle of their products. The survey found that:

  • 40% of companies definitely consider the possibility of recycling or upcycling when choosing materials for their products.
  • Another 45% said they probably take recycling or upcycling into account.
  • 82% perform material sustainability studies for their products.

So, sustainability is clearly a priority for these companies. But 58% of respondents said that they have trouble finding reliable material property information for use in designing their products. They could help overcome this problem by using Ansys Granta MI, an intelligent materials database with over 700 materials and their properties available for ready use in computer-aided design (CAD) and simulation software. Granta MI also provides a single source of truth for material properties across the company, so all engineers can access the same materials data. And it allows you to add the properties of proprietary materials you may have developed in-house to the database.

Awareness and Use of Simulation, HPC, and Digital Twins

The majority of respondents are aware of digital simulation, HPC, and digital twins, with many using them. However, more than a third were unaware of digital twin technology (38%) and HPC (36%), as shown in the table below.

em modeling

Clearly, this suggests large opportunities for companies to expand their use of simulation, HPC, and digital twins to improve the digital footprint of their products and processes. A vast majority of respondents, including simulation users and non-users, see the value in engineering simulation:

  • 82% recognize that simulation promotes innovation in the product design process.
  • 82% say that it better prevents product failures and reduces maintenance costs.
  • 81% believe that it reduces production costs.
  • 81% say that it allows rational savings on materials.
  • 80% agree that simulation improves a product’s quality and lifespan.
  • 79% say that it optimizes production processes.
  • 79% say that it better predicts the behavior of a product.

Agree with statements

Regarding HPC and digital twins, Ansys solutions that could help these companies include:

  • Ansys HPC software uses today’s multicore computers to perform more simulations in less time. These simulations can be bigger, more complex, and more accurate than ever using HPC. The various Ansys HPC licensing options let you scale to whatever computational level of simulation you require, from single user or small user group options for entry-level parallel processing up to virtually unlimited parallel capacity.
  • Ansys Twin Builder enables you to implement complete virtual prototypes of real-world systems. These can be deployed to manage the entire life cycle of products and assets. This digital twin simulation paradigm allows you to exponentially increase efficiencies over time, scheduling maintenance around predictive methodologies that become more accurate with real-world testing and response. Access to this information allows engineers to unlock additional value from existing assets, which prevents unscheduled downtime and lowers operating costs, while working at optimal efficiency, and all with agnostic Internet of Things (IoT) platforms.

The majority of respondents whose companies could potentially use these technologies think that they have sufficient means to invest in them.

Enough resources

Future Prospects for Innovation

Hydrogen and nuclear power are two of the alternative energy technologies that the survey specifically inquired about.

Seventy percent of respondents believe that hydrogen will be adopted as an energy source in the future. When asked “In your opinion, could hydrogen be a potential source of energy for the development of your products?”, 74% of respondents said “definitely” or “probably.” 

Hydrogen potential

Of these companies, 82% said their company has all the tools needed to deploy this energy vector.

Regarding nuclear power, 74% said they can envisage the achievement of carbon neutrality using nuclear power.

Regardless of the technology — be it wind, solar, sustainable aviation fuels, geothermal, nuclear, hydrogen, biofuels, batteries, carbon capture, or others — French companies for the most part recognize the value that engineering simulation brings to their product and process design efforts. For those not currently aware of simulation, HPC, and digital twin technologies, now is a great time to educate them about the benefits they are missing. It is essential that we all use the best tools available to reduce greenhouse gas emissions and mitigate global climate change for the benefit of the planet.  

 Learn more about how Simulation 4.0 leads the path to sustainable development in the "Simulation 4.0 and the Industrial Transition – The Path to Sustainable Development" webinar which covers:

  • The challenges of industrial transition in the context of sustainable development.
  • The role played by engineering simulation and other state of the art technologies such as HPC and digital twins.
  • Future challenges for manufacturing industries.

Download the full Sustainability and Engineering Simulation survey report.

Simulation Stretches the Boundaries of Extreme Mobility

Tune in to episode five of “Driven by Simulation” and see how Polaris and Potential Motors use Ansys software to rethink recreational vehicle design.

For some riders, where the pavement ends, the adventure begins. Off-roading often stretches vehicle capabilities in new, uncharted directions. Just hop in, then set your sights on any horizon and go — up and over ruts, rocks, dips, bumps, and graded roads, or fording through streams.

Today, enthusiasm for the sport is growing, as on-road adrenaline junkies shift gears to go off the beaten path. In 2020, the off-road vehicle (ORV) market amassed more than $12.3 billion globally. Adventure tourism is also attracting a more affluent fan base to the sport, specifically overlanding (exploring the back country in well-equipped ORV), which is on a growth trajectory that began at $586 billion in 2019 and will increase to $1.63 billion by 2026.

During the fifth episode of our online docuseries “Driven by Simulation,” we visit global powersports leader Polaris and automotive technology start-up Potential Motors to see how they’re going wide-open on ORV performance with a little help from Ansys.

Polaris RZR

Simulation is Polaris’ North Star for Off-Road Innovation

Think outside, live the riding experience, and work to make it better. It’s a vision that’s fueled rider-driven innovation at Polaris since 1954 — so much so, that everyone working at the shop rides the product to understand its performance and the challenges of the trail ahead. RZR XP, Polaris’ bestselling sport side-by-side off-road vehicle exemplifies this spirit, along with the high level of rider-driven innovation you’d expect from a global industry leader in powersports. 

polaris.jpg

David Elia, Director, Product Planning - Recreation at Polaris, says his team gets a lot of inspiration from customers, which is a big reason for RZR XP’s success. Polaris considers this feedback, then acts on it in anticipation of market trends to remain competitive. A recent refresh, for example, focused on delivering more all-day comfort and capability in a more rugged design. To do this, in part, required an increase in chassis strength with an assist from Ansys.

“Our chassis is about 25% stronger, or stiffer than it was,” says Elia. “With simulation we were able to tell from the inputs what we needed out of the chassis. We were able to make a completely boltless, fully welded chassis that was much stronger.”

To arrive at the RZR XP’s new 114 horsepower, Pro Star Gen 2 parallel twin engine design, Polaris turned to Ansys computational fluid dynamics software to understand air flow in and out of the engine. Zeroing in on fluid dynamics enabled the team to optimize the head, combustion chamber, intake and exhaust tracks to build in more power. From there, the clutch and driveline components were also updated for added durability.

Polaris also used simulation to ensure the engine could consistently perform as it was intended — through rock crawl areas, dunes, mud, and deep woods for hours off-road in harsh conditions.

David Elia 

David Elia, Director of Product Planning - Recreation, Polaris

“Designing an engine cooling system that survives all of these conditions is not a trivial thing,” says Pratik Chandan, Senior Engineer Manager at Polaris. “One of the issues we have is that we need to learn physical interactions of the complex systems together in the virtual environment and that’s what simulation tools like Ansys helps us do.”

em modeling 

Engine cooling is just one of the engineering features Polaris simulated with Ansys solutions.

Potential Motors Takes on the Toughest Terrain with Simulation

Off-roading takes skill, as there is a lot to consider out on the trail. Understanding a vehicle’s approach, departure, and breakover angles; knowing when to punch it or when to let up; or choosing the right time to air down your tires can mean the difference between getting through or getting stuck. 

driven-by-sim-sam-poirier.png 

Sam Poirier, CEO of Potential Motors, explains the concept of proactive vehicle systems using cameras and sensors.

Potential Motors, a member of the Ansys Startup Program, is facilitating development of all-electric, off-road vehicles that can see, understand, and proactively prepare for the toughest terrain ahead for you, enabled by software, artificial intelligence (AI), and a suite of sensors. The ultimate goal in all of this is to enable any driver, regardless of skill level, to take on the most challenging terrain with confidence.

The automotive tech company has put in a considerable amount of work into building a platform that reflects its vision for how a software-defined vehicle (SDV) might look in an electric vehicle application, stretching beyond the limits of what is available in the market today, then testing that vision. 

“We spend a lot of time in simulations trying to figure out, you know everything from what is the lidar going to see to how is the chassis going to react — so this vehicle itself is our own platform,” says Sam Poirier, Co-founder and CEO of Potential Motors. “We built it from scratch, because we really wanted a software-defined vehicle for off-road, and surprisingly enough there isn’t one available for us to just use.”

Driven by Sim Potential Motors 

Bill Lamey, CTO of Potential Motors, discusses remote sensing technology with Driven by Simulation host, Miss Emma Walsh.

Software already supports the advanced driver-assistance systems (ADAS) we are used to seeing in our vehicles. In the future, however, our vehicles will primarily be defined by software. Otherwise known as SDVs, they will have the ability to shape our driving experiences through connectivity, AI, automation, and personalization. 

In developing his SDV platform, Poirier also realized he was solving a fundamental problem related to off-road efficiency that extends beyond the recreational audience to commercial sectors like mining or logging, where conquering environmental conditions is often a prerequisite for daily productivity.

Potential Motors simulation

“Without simulation I think we’d have a very hard time building something like this,” says Poirier. “Especially, you know we’re a small team, we’re a start up here in Canada, and with limited resources you have to do stuff efficiently. Simulation means that you can do things efficiently. You can do a lot more with a small team.”

Off the Beaten Path

"Driven by Simulation," the future of mobility is definitely going places. Be sure to watch the latest installment of our online docuseries as we head off in search of adventure with Polaris and Potential Motors.

Maximizing Bioreactor Efficiency with Simulation

The process to make pharmaceuticals — whether pills, vaccines, or specialty medicines — is intense. Not only does the average drug take 10 to 12 years to develop, but it also costs billions of dollars. From research and development to manufacturing and distribution, every step must be carefully controlled to maintain patient safety. For biopharmaceuticals, bioreactors are at the heart of the process. Used to house organisms, these tanks are critical in manufacturing and safeguarding lifesaving drugs for patients. Drug manufacturers are always looking for new ways to optimize these processes, and the Ansys team recently developed a solution methodology to achieve that. 

Lab chemical bioreactor

What is a Bioreactor?

Bioreactors can broadly be defined as vessels that house a biological catalyst to achieve a chemical transformation. They are designed to optimize growth and metabolic activity of an organism through biocatalysis using enzymes or cells of animals or plants. Bioreactors are different from chemical reactors in that they support and control biological matter, while chemical reactors only support chemicals. Because organisms are more sensitive and less stable than chemicals, bioreactors are more tightly controlled to prevent process deviations and contamination.

The reaction rate, cell growth, and process stability depend on the conditions inside the bioreactor. The following are a few examples of conditions that need to be closely monitored and controlled:

  • Gas (e.g., air, oxygen, nitrogen, carbon dioxide) flow rates
  • Temperature
  • pH and dissolved oxygen levels
  • Agitation speed and circulation rate
  • Foam production

Pharma bioreactor equipment

Types of Bioreactors

There are three main types of bioreactors: batch, continuous, and semi-continuous. The prospect of tank contamination has led many companies to use batch bioreactors in a closed process to minimize the chance of viruses or bacteria entering the tank. This is because contamination would require the company to discard the entire batch of drug, which could be worth millions of dollars in lost revenue.

Type

Advantages

Disadvantages

Batch Process

  • Simple equipment
  • Suitable for small production volumes along with multiproduct flexibility
  • Downtime for loading and cleaning
  • Reaction conditions change with time

Continuous Process

  • High productivity
  • Better product quality due to constant conditions
  • Good for kinetic studies
  • Requires flow control and catalyst longevity
  • Needs organisms with high stability

Semi-continuous or Fed-batch Process

  • Control of environmental conditions
  • Most flexible for selecting optimal conditions
  • Most frequently used in biotechnological processes and the chemical industry
  • Requires feeding strategy

Batch bioreactor design 
Example of a batch bioreactor design. Image adapted from Singh, Jagriti & Kaushik, Nirmala & Biswas, Soumitra. (2014). Bioreactors – Technology & Design Analysis.  

Optimizing Bioreactor Design

Bioreactor processes are run for a period of weeks, and the amount of drug inside the tank at the end of the process represents how much is available to sell to patients. Therefore, manufacturers are continually striving to improve efficiency to maximize the value of each batch.

Biopharmaceutical manufacturers are not new to modeling the hydrodynamic environment inside their mixing tanks. They traditionally use computational fluid dynamics (CFD) to predict fluid flow patterns and how gas bubbles distribute throughout the tank and what that means for level of dissolved oxygen in the culture media. However, one major element of the manufacturing process is often ignored in modeling efforts: the relationship between the mixing patterns and the cells living inside the tank. To explore this, Ansys engineers developed a digital twin model of a bioreactor using Ansys Twin Builder and Ansys Twin Deployer. The digital twin model is composed of a CFD model coupled to a cell metabolic model.

A steady-state, 3D model of fluid flow and mass transport inside a bioreactor was developed and validated against experimental data. A design of experiments (DOE) was then conducted over the control space of the bioreactor to develop an understanding of how the rotation rate of the impeller and flow rate of oxygen impacted the mass transfer of oxygen into the liquid phase. The results of this DOE were exported as a functional mock-up unit (FMU).

A metabolic model representing the rates of cell growth, product production, tank pH, and sugar and oxygen consumption was developed using the Modelica programming library in Twin Builder. This model is composed of five ordinary differential equations — one for each parameter of interest. The metabolic model was validated against literature data. Note that the cell metabolic model runs quickly enough to not require a ROM.

The digital twin model was developed in Twin Builder, which consists of the mixing tank ROM connected to the Modelica model. A proportional-integral-derivative (PID) controller was tuned to ensure that the digital twin model would operate the mixing process to meet all performance requirements. In this case, the goal of the controller was to maintain the amount of oxygen in the liquid phase above 95% saturation.

The digital twin model was deployed using Twin Deployer. Using the resulting software development kit (SDK), a dashboard was created to facilitate visualization. This customizable dashboard provides a summary of the data streaming off process sensors and provides the current output of virtual sensors indicating other metrics of tank performance, e.g., concentration of cells in the tank and amount of drug product manufactured.

Ansys’ bioreactor digital twin model provides biopharmaceutical industry manufacturers with a more holistic understanding of bioreactor performance. Uniquely, Ansys’ solution includes an explicit relationship between the flow and mass transfer conditions in the tank and how those conditions impact the biological cells that are creating the drug. This enables drug manufacturers to continuously analyze and optimize tank performance based on current process parameters, ultimately leading to an increased tank yield.

To try it yourself, download a free trial of Ansys Twin Builder

Podcast Spotlights Simulation’s Impact on Advancing Clean Hydrogen Solutions

With increasing concerns about climate change, carbon dioxide (CO 2 ) emissions, and other environmental pollutants, more industries a...