Simulating the “Impossible”? Automation Meets Rotating Detonation Engines

Enabling Rotating Detonation Engine (RDE) Innovation Through Simulation and Automation

The Rotating Detonation Engine (RDE) stands out as a leading technology that can advance performance and efficiency for future propulsion systems. Simulation software plays a critical role in accelerating development and tackling complex design challenges while engineers and researchers strive to unlock the full capabilities of this innovative technology. This blog explores the application of GT-SUITE together with automation for developing detailed and precise RDE models that generate 10,000 flow volumes within hours. This blog will cover RDE fundamentals alongside modeling methods and illustrate how automation transforms daunting tasks into achievable engineering achievements.

What is a Rotating Detonation Engine?

Some of you may read the title and wonder “What is a Rotating Detonation Engine?” (RDE). An RDE is a groundbreaking advancement in propulsion technology that is being actively researched by NASA, universities and research labs, established jet engine companies and start-ups. Unlike traditional engines that rely on a flame spreading through the combustion chamber at subsonic speeds, RDE’s use controlled detonation waves that travel at supersonic speeds to burn the air-fuel mixture. This innovative approach allows for the rapid generation of high-pressure and high-temperature gases, leading to significant improvements in thermal efficiency that will result in lower fuel consumption. Another key advantage of this technology is the significant reduction in moving parts, thus simplifying maintenance. However, RDE’s face challenges such as maintaining stable detonation waves, managing extreme temperatures and pressures, and optimizing components like nozzles and injectors for better performance that require study and design. The images below help illustrate the ideas behind the system.

We at Gamma Technologies have received several inquiries on whether GT-SUITE can be used to simulate such engines. We considered developing a detailed combustion model specifically for this combustion device, but it was estimated to take as much as a thousand hours to develop, and we were not sure whether the long-term demand was large enough to justify such an investment of time. However, with some creative thinking — leveraging existing capabilities in the GT-SUITE solver and features available in GT-Automation — we were able to develop a working solution in less than a hundred hours.

Simulation Methodology for Rotating Detonation Engines (RDE)

Simulation of the RDE involves complex interactions of high-speed fluid dynamics with fast chemical kinetics. The fully compressible and transient flow solver plus the integrated chemical kinetic solver in GT-SUITE allows the capture of this intricate interplay, simulating the dynamics of the entire combustion process and the movement of the shock wave through the combustor. Using GT-SUITE’s modular architecture we were able to build a comprehensive model for this combustor from scratch using existing capabilities in the software. Such a model involved discretizing the annular volume of the RDE using discrete flow volumes, both in the circumferential and axial directions, as shown in Figure 3. This resulted in a total of about ten thousand flow volumes, all interconnected with each other and with parts simulating chemical kinetics.

Automation-Driven Modeling for RDEs: Building 10,000 Flow Volumes in Hours

You may have read the part about ten-thousand flow volumes being built in the model and wondered, “how long would it take to build that model?” or “does your hand hurt after clicking the mouse that many times?”.  These are good questions to ask, and you may be relieved to learn that no carpal tunnel syndrome was triggered while building this model.  The possibility to build and modify models in GT-ISE through Python scripting and API’s by using GT-Automation was remembered and used to move forward quickly.

If you are not already familiar with it, GT-Automation is a time-saving enterprise package in GT-SUITE that enables Python scripting of GT-ISE and GT-POST operations, as well as process integration of modeling and simulation tasks. With GT-Automation, users can save time and eliminate errors that often come from repeated, tedious operations. In this instance, a Python script was written that automated the entire model building process, allowing us to quickly adapt to changes in discretization, geometry and operating conditions while significantly reducing the time and potential errors associated with manual modeling. This led to the development of an innovative quasi-3D modeling methodology using GT-SUITE, which has the potential for rapid simulations of RDEs at both the component and system levels. Also, by creative use of the existing capabilities of GT-SUITE and GT-Automation, this model was developed with no changes or additions to the physics-based solvers and completed in less than one hundred hours, providing a lot of cost-savings compared to a specialized development.

Here is a video showing the building of the model in GT-ISE that results from running the Python script in GT-Automation:

To help you understand the model in relation to the device, Figure 2 is shown again with some flow parts overlaying the image.

What is predicted?

Some results of these simulations are shown in Figure 4, below.

These results demonstrate the model’s capability to capture realistic RDE behavior, including:

• Detonation Wave Motion: Both single and multiple waves (co- and counter-rotating) can be simulated effectively.
• Performance Influences: The impact of injection parameters on engine performance has been demonstrated.
• Limit Cycle Operation: The system can achieve a stable, periodic state, which is crucial for reliable engine operation.

As a bonus, the 3D animation capabilities of GT-SUITE were used to create this video for your viewing pleasure:

Accelerating RDE Model Development Using GT-SUITE and GT- Automation

This project turned out to be a great demonstration of the capabilities and flexibility of GT-SUITE as a simulation platform and multi-physics solver. A project that was intimidating in size, scope and effort at the beginning turned into a manageable task in the end, yielding realistic results and exciting animations. GT-Automation was a critical component in empowering the team to build this model with a relatively low effort. If you are interested in learning more about how GT-Automation can support your projects, please visit our web page on the topic (GT-Automation) or contact us! You can also watch our webinar on GT-Automation, and check out our blogs on Hydrogen-Powered Rocket simulation and how engine manufacturers leverage simulation to stay ahead of increasing regulations.

Combining Measurements and Simulation to Streamline Combustion/Controls Development

How to use Simulation to Improve the Engine Development Process for Carbon Neutral Fuels 

The need for clean, renewable energy sources requires exploring carbon neutral fuels and their combustion behaviors. This is typically done using single-cylinder (SC) engines. The advantages of this process are to make quick hardware changes such as replacing the head or piston, or to change the fuel composition, which provide fuel cost savings compared to a multi-cylinder engine. Combustion control strategies, various air fuel ratios, and the impact on emissions are studied using the simulation platform, GT-SUITE and its real time engine plant model solution, GT-POWER-xRT.  

Based on this research, multi-cylinder (MC) engines are designed, simulated and manufactured. The main problems that can result from this methodology are the differences in behavior between a single-and multi-cylinder engine due to the cylinder-to-cylinder and turbocharger interaction. These interactions are not represented in the SC engine. Therefore, control strategies applied to the SC cannot be applied one-to-one for the MC engine. To mitigate this problem, engine simulations of a MC engine using combustion data from a SC engine are carried out to test and develop control strategies in the time before the multi-cylinder engine is built and available on a test bench. The other drawback is the time between taking SC measurements and applying it to the MC model, which can be weeks or even months. It is not uncommon to find issues or at least determine some data are questionable after analyzing MC simulation results. If possible, SC measurements are taken again, or the project is continued based on assumptions that might or might not be good.   

Using Simulation to Model Varied Engine Configurations  

A solution to both problems is running the MC model in parallel and in real-time when measuring SC data. The real SC engine provides the combustion data, which then can be applied to all cylinders in the MC model. The differences in gas exchange for each cylinder, such as varying trapped gas and residual fractions, are captured, and the same is true for the interaction with the turbo. The MC provides engine speed, crank-angle resolved intake/exhaust pressures, and average intake temperature. The SC needs to be equipped with fast acting valves (e.g. 10kHz) on the intake and exhaust side to impose the conditions that come from the MC. Similarly, changes in fuel composition and air fuel ratio (AFR) can easily be studied and control strategies for the MC can be developed. 

Why are Carbon Neutral Fuels Different? 

Combining measurements and simulation for combustion/controls developments is especially interesting for hydrogen/natural gas or methane blends. Combustion characteristics like the laminar flame speed strongly depend on the actual concentration, especially for hydrogen. Hydrogen’s ability to burn at very lean conditions combined with the fact that nitrogen oxide (NOx) formation reaches its peak at relative air-fuel ratios (‘lambda’) of ~2.0 make it useful to run at quite lean conditions. Current trends in engine development are finding that operation at lambda 3 is not uncommon and some research indicates that this could even go higher. This requires a different approach determining the charging system requirements compared to conventional fuels like gasoline, diesel, or natural gas. The charging system must be able to deliver high boost pressure levels with low exhaust energy due to low combustion temperatures caused by excess air. Therefore, optimized turbos, electric turbo (eTurbos) and/or electric compressors (eCompressors) are considered, especially for on-highway applications.   

For power generation applications, the time-to-torque is essential. Coupling SC and MC enables control strategy development accounting for transient effects like turbo lag or fueling for non-direct injection (DI) applications. 

Do I need Hardware in the Loop (HiL) Systems to Run the Real Time Model? 

Depending on test cell infrastructure, the MC model can be executed on the test bench machine. There is no need for a HiL system. The MC model can be linked directly to ETAS INCA, Vector CANape or any system simulation tool that supports FMUs, like Synopsis Silver.  

In Summary: No Combustion Analysis Test Bench Tool Available? No Problem! 

If combustion data are not already available from the SC test cell software, three pressure analysis (TPA) in GT-SUITE can be integrated in the process. A TPA model typically consists of a single cylinder representing the test cell hardware. Dynamic intake, exhaust, and cylinder pressures are used as model inputs. For this application, intake and exhaust pressures plus intake temperatures are extracted from the MC model. The output of the TPA model is a burn rate that describes how fuel and air burns. This combustion profile can be directly imposed in the MC model cylinders. 

The whole process is described in the figure below:

Learn More About our Combustion and Controls Simulation Capabilities 

If you are interested in applying this technique to your development process or have questions on the process, please contact us for specific comments or questions. Learn more about GT-SUITE and our propulsion systems applications. 

Gamma Technologies and GT-SUITE: Pioneering the Future of Simulation

Unveiling the Power of GT-SUITE

This year, Gamma Technologies celebrated a significant milestone: its 30th anniversary. Since its inception in 1994, Gamma Technologies has been at the forefront of engineering simulation, revolutionizing how industries approach design and innovation. At the heart of this transformation is GT-SUITE, the company’s flagship systems simulation software that has become a cornerstone in various fields, from automotive to aerospace, HVACR, energy, and beyond. 

Gamma Technologies grew its prowess in the automotive industry with GT-POWER, the industry standard engine performance simulation tool used by most engine manufacturers and vehicle original equipment manufacturers (OEMs). GT has continuously expanded its simulation capabilities to meet consumer demands with extensive developments in batteries, electric motors, and more with products such as GT-AutoLion, GT-PowerForge, GT-FEMAG, and others. GT continues to accelerate in agnostic powertrain and systems development worldwide. 

GT-SUITE is more than just a simulation tool. It’s a comprehensive, multi-domain platform that empowers engineers to model, simulate, and analyze complex systems. With capabilities spanning mechanical, electrical, fluid, and thermal domains, GT-SUITE offers a holistic approach to understanding how different systems interact. This versatility is essential in today’s engineering landscape, where the integration of various technologies and systems is more crucial than ever. 

In the transportation industries (automotive, on-and-off highway vehicles) GT-SUITE has made a substantial impact. The software allows for the creation of detailed simulations of vehicle systems, from powertrains to suspension systems.  

Almost every vehicle on the road has components simulated and designed with one of Gamma Technologies’ simulations. Most major automotive original equipment manufacturers (OEMs) have used GT-SUITE for engine and vehicle development. 

By providing a virtual environment to simulate and optimize designs, GT-SUITE helps manufacturers improve performance, reduce costs, and shorten turnaround times. To accelerate development time, GT’s XiL (X-in-the-Loop) modeling capabilities (method that combines virtual testing with real-world elements to validate components of an Electronic Control Unit or ECU) integrate seamlessly with industry tools, ensuring a streamlined and efficient product design cycle. 

The ability to simulate real-world scenarios and interactions is particularly valuable for developing advanced technologies such as electric and hybrid vehicles, where precise predictions and optimization are critical. 

Expanding Horizons: Aerospace, HVACR, Marine, Energy, and Beyond

The influence of GT-SUITE extends beyond automotive engineering. 

In the HVACR (heating, ventilation, air conditioning, and refrigeration) industry, Gamma Technologies’ comprehensive set of validated 0D/1D/3D multi-physics component libraries have enabled HVACR engineers to tackle challenges in development such as sustainability and efficiency, decarbonization, new refrigerants, and system complexity and controls. GT-SUITE’s combined with GT-TAITherm can model human comfort which allows the user to have an additional target besides traditional temperature and humidity. The human comfort model has localized comfort zones that can be used to determine if cabin insulation or HVAC settings need to be modified. Well-known brands such as Carrier, Copeland, Daikin, Sanden, Trane, Tecumseh, Rheem, and others have found tremendous benefits utilizing GT-SUITE. Learn more about these case studies here.  

In aerospace, the software supports the design and analysis of complex systems like propulsion and avionics. Engineers from organizations such as NASA, Roush, SAFRAN, and others have used GT-SUITE to ensure that aircraft systems are both efficient and reliable, contributing to advancements in performance and safety. Some of the applications that Gamma Technologies’ simulation solutions have assisted in include cryogenic systems, propulsion system modeling, environmental controls systems (ECS), fuel cell simulation, thermal management, e-propulsion batteries, flight dynamics and controls, multi-body dynamics, landing gear development, and fuel tank modeling.  

GT is proud to say that our solutions have already been used to support the future of urban air mobility by providing simulations for electric aircraft and electric vertical take-off and landing (eVTOL) vehicles (including air taxis) development. 

In the energy and oil & gas sectors, GT-SUITE aids in the development of innovative solutions for power generation and renewable energy. Our customers are already choosing GT for upstream, midstream, and downstream applications to optimize production. The ability to simulate energy systems helps companies enhance efficiency and sustainability, addressing some of the most pressing challenges in energy production and consumption. 

It might be surprising that the marine industry is aggressively moving towards a sustainable future as well. GT is proud to have partnered and work with organizations such as the Maritime Battery Forum, WIN GD, Yanmar R&D, and Toshiba. These firms have leveraged GT-SUITE’s solutions to simulate engine and drivetrain development for ship modeling and create digital twins for electrified motors.  

Gamma Technologies has been on the forefront of implementing AI (artificial intelligence) & ML (machine learning) technologies. These tools elevate simulation capabilities by allowing thousands of variables to be considered and help designers best engineer superior products. AI and ML enhance simulations by creating accurate and dynamic metamodels (mathematical models) that can adapt to complex, real-world scenarios in real-time. These technologies also streamline the analysis of vast data sets, leading to more precise predictions and informed decision-making. 

To learn more about our machine learning capabilities, read this two-part blog series on enhancing model accuracy by replacing GT’s lookup maps and optimizing neural networks.  

A Legacy of Innovation

As Gamma Technologies celebrated its 30-year milestone, it’s clear that its impact on the engineering world is profound. GT-SUITE’s ability to provide detailed, multi-domain simulations has empowered engineers across industries to tackle complex problems and push the boundaries of what’s possible. This dedication has kept GT-SUITE at the cutting edge of simulation technology, ensuring that it meets the ever-changing demands of its diverse user base. 

Looking Ahead

As we look to the future, Gamma Technologies is well-positioned to continue its legacy of pioneering simulation technology. With GT-SUITE leading the way, the company is set to drive further advancements in engineering and design, helping industries navigate the complexities of modern technology and innovate for a better tomorrow. 

Learn More About Gamma Technologies’ Simulation Solutions

To learn more about our simulation capabilities, visit our website. Learn more about GT-SUITE here. Contact us here to speak to a GT expert! 

How Will Electric and Hybrid Vehicle Development Be Impacted by the Softening of US Rules

Governmental Regulations Impacting Automotive OEMs

In recent news, new vehicle tailpipe governmental regulations in the United States have softened for original equipment manufacturers (OEMs) development of electric vehicles (EVs) and hybrids (HEVs). 

The Department of Energy has significantly slowed the phase-out of existing rules that give automakers extra fuel-economy credit for electric and hybrid vehicles they currently sell. The real-world impact of the complex regulations has helped U.S. automakers meet new federal standards for fleetwide fuel efficiency continuing to sell traditional, internal combustion engine (ICE) vehicles. 

The Role Simulation Plays in New Vehicle Development

With these changes, it’s now imperative for the engineering community to leverage simulation platforms such as GT-SUITE in today’s automotive development for several reasons: 

1. Cost Reduction: Developing new automotive technologies, especially in the context of EVs and hybrids, can be expensive. Simulation allows OEMs to test various designs and configurations virtually, reducing the need for physical prototypes and costly trial-and-error processes.
2. Time Efficiency: With simulation, OEMs can accelerate the development process. They can quickly assess the performance of different components and systems, identify potential issues, and iterate on designs much faster than with traditional methods. This agility is crucial in a competitive market where time-to-market can make a significant difference.
3. Regulatory Compliance: Although regulations may slow down, they are unlikely to disappear. OEMs still need to meet stringent emissions standards and fuel efficiency requirements. Simulation enables them to explore different powertrain configurations, optimize efficiency, and ensure compliance with current and future regulations.
4. Technology Exploration: Even as regulations ease, the demand for cleaner and more efficient vehicles continues to grow due to environmental concerns and consumer preferences. Simulation allows OEMs to experiment with emerging technologies, such as advanced battery chemistries or fuel cell systems, and stay ahead of the curve in the evolving automotive landscape.
5. Risk Mitigation: Investing in new technologies carries inherent risks. Simulation helps OEMs mitigate these risks by providing insights into potential challenges and performance limitations before committing to large-scale production. This allows them to make informed decisions and allocate resources more effectively.
6. Optimization and Innovation: Simulation enables OEMs to optimize the performance of electric powertrains, hybrid systems, and fuel cell technologies. By fine-tuning parameters such as energy efficiency, range, and power output, they can deliver vehicles that meet or exceed customer expectations while staying competitive in the market.

Learn More About Our Simulation Solutions

While phased-in regulations may temporarily ease the pressure on OEMs, simulation remains a crucial tool for innovation, efficiency, and competitiveness in the automotive industry. Especially in the context of evolving technologies such as electric powertrains and fuel cells. 

To learn more about GT-SUITE, visit our website here. Speak to GT expert today as well here and see how to incorporate simulation for your vehicle development needs.  

How to Model Fuel Reformers with Simulation

Addressing the Evolving Needs of Powertrain Engineering Through Simulation

As the powertrain market begins to pivot from traditional diesel and gasoline engines towards hydrogen engines and fuel cells, there is the open question of how to provide hydrogen to these new powertrains. In the short term, it seems that converting an available hydrocarbon fuel to hydrogen will be needed. For mobile applications, methanol and ethanol as well as compressed natural gas are logical options.  For stationary applications, using the natural gas supply infrastructure makes sense.

Gamma Technologies has created three example models of fuel reformers for methanol, ethanol, and methane in GT-SUITE activated with the GT-xCHEM product license.  These example models help support research and development of H₂ combustion engines and fuel cells, as well as expansion into general chemical processing. The results of each of these new fuel reformer example models are summarized in this blog.

Note that in the methanol and methane reformer sections, the X axis of the figures is the catalyst material load divided by the molar flow rate of the key reactant, W/Fm, which is sometimes referred to as the contact time.  A low W/Fm value represents high flow (short residence time), and a high W/Fm represents low flow (long residence time).

 

Methanol Reformer Model

The first model we’re simulating demonstrates a methanol steam reformer reactor.  Methanol (CH₃OH) and water (H₂O) react over a CuO/ZnO/Al₂O₃ catalyst to form H₂ and CO₂. The reaction mechanism, input data, and measurement data for the reformer are from the reference Purnama et al¹.

In this specific reaction mechanism, the methanol steam reforming reaction (reaction 1) is modeled in the forward direction only. The water gas shift (WGS) and reversible WGS are modeled as two separate reactions (reactions 2 and 3).  At high temperature, the H₂ and CO₂ can react through the reverse WGS reaction to form CO and H₂O.

Reaction 1: CH₃OH + H₂O → CO₂ + 3H₂

Reaction 2: H₂ + CO₂ → CO + H₂O

Reaction 3: CO + H₂O → H₂ + CO₂

This example model is designed to recreate several figures (Figures 1 and 2) from Purnama et al¹.  Methanol and water are supplied to a packed bed reactor at a 1:1 molar ratio. Four temperatures: 230, 250, 270, and 300°C are simulated, and the catalyst load to molar flow ratio W/Fm is varied from 0.0001 to 0.03 kg_cat-s/mmol_CH3OH. The result is a good correlation for both the overall methanol conversion efficiency and the prediction of the products including hydrogen as shown in the figures below.

Ethanol Reformer Model

The next model to simulate is the ethanol reformer.   Three reactions were used to model the ethanol steam reforming process to produce H₂ over an Rh-Pd/CeO₂ catalyst.  Reactions 2 and 3 are modeled as reversible reactions.

Reaction 1: C₂H₅OH → CH₄ + H₂ + CO

Reaction 2: CO + H₂O ↔ CO₂ + H₂

Reaction 3: CH₄ + H₂O ↔ 3H₂ + CO₂

 

Simulations were run with an operating pressure of 4.5 bar, a steam-to-carbon ratio of 3, and an operating temperature of 500 to 1000 K. The results are shown in the figures below along with the measured data from Lopez et al².

In Figure 3 above shows that between 500 and 700 K the ethanol conversion rises steadily from near zero to 100%.  However, not all ethanol is converted directly into H₂.  As a result, the H₂ yield does not follow the same pattern as the ethanol.  Reaction 3 is the steam methane reforming (SMR) reaction, which begins after 700 K and causes a distinct slope shift in H₂ production as more H₂ is produced from methane.

Regarding the species molar flow rate, the bottom figure shows that the CH₄ exhibits a unimodal-shaped curve with regard to operating temperature, culminating at 700 K, when ethanol breakdown reaches 100%.  Increased temperature maintains CH₄ generation via ethanol decomposition and activates SMR to produce additional H₂.  This results in a drop in CH₄ molar flow rate.  The SMR reaction also accounts for increased CO generation after 700 K, resulting in a bimodal-shaped CO curve, with the first mode being from the ethanol breakdown process combined with the water gas shift reaction (WGS).  WGS causes an increase in the molar flow rate of CO₂ following the first CO peak.  All of these patterns are well captured by the model.

Methane Reformer Model

The methane reformer uses a chemical process called steam methane reforming (SMR) to convert methane into hydrogen gas.  The methane reformer model can be used to study the effect of temperature and pressure on the efficiency of the reformer.  The reaction mechanism used in the model is shown below.  All three reactions are modeled as reversible in this reaction mechanism.

Reaction 1: CH₄ + H₂O ↔ CO + 3H₂

Reaction 2: CO + H₂O ↔ CO₂ + H₂

Reaction 3: CH₄ + 2H₂O ↔ CO₂ + 4H₂

At higher temperatures (700-1100 K), these reactions proceed in the forward direction resulting in conversion of methane (CH₄) and water (H₂O) into carbon dioxide (CO₂) and hydrogen (H₂).  If the operating temperature is reduced (450 – 650 K), the mechanism runs in the backward direction producing CH₄ and H₂O from the reaction of CO₂ and H₂, also known as the methanation process.

The SMR model is made from information found in the reference Xu and Froment³ for a Ni/MgAl₂O­_4­ catalyst in a packed bed reactor.  In the model, the inlet feed contains H₂O, CH₄, and H₂ in the molar ratio 3:1.25:1, and the temperature is varied from 773 K to 848 K. For each temperature case, the methane contact time (W/F_CH4) is varied from 0.01 to 0.425 g_cat-hr/mol_CH4.

In Figure 4, shown below, the GT-xCHEM simulation results of the conversion of CH₄ and production of CO₂ and H₂ are plotted along with the experimental results reported by Xu and Froment³.  The GT-xCHEM simulation results correlate well with the experimental data as the conversion efficiency of the reformer increases with increasing contact time and increasing operating temperature.

Learn More About Our Chemical Systems Modeling Solutions

In this blog we presented three fuel reforming example models available in GT-xCHEM.  These example models help support research and development in H₂ combustion engines and fuel cells, as well as expansion into general chemical processing. This study has also been published in SAE’s technical papers publication in April 2024. Access this paper here.  in Gamma Technologies will continue to add to the library of ready-to-use catalyst and reactor models available in the installation directory of GT-SUITE activated with the GT-xCHEM product license.  You may need to get the newest build update to see them, or if you have an older version or build then you can request the models from [email protected].  If you have any questions and would like more information about fuel reforming modeling with GT-SUITE please contact us here.

 

References

1. “CO formation/selectivity for steam reforming of methanol with a commercial CuO/ZnO/Al2O3 catalyst,” Purnama, H., Ressler, T., Jentoft, R.E., Soerijanto, H., Schlögl, R., Schomäcker, R., 2004, Applied Catalysis A: General, v259, 83-94. https://doi.org/10.1016/j.apcata.2003.09.013
2. “Ethanol steam reforming for hydrogen generation over structured catalysts,” López, E., Divins, N. J., Anzola, A., Schbib, S., Borio, D., & Llorca, J, 2013, International Journal of Hydrogen Energy, 38(11), 4418–4428. https://doi.org/10.1016/j.ijhydene.2013.01.174
3. “Methane Steam Reforming, Methanation and Water-Gas Shift: I. Intrinsic Kinetics,” J. Xu, G.F. Froment, 1989, AIChE J., 35 (1), 88-96. https://doi.org/10.1002/aic.690350109

Top 10 Gamma Technologies Blogs of 2023!

From calculating EV range to heat pump design, there is a blog for every simulation! 

As we kick off 2024, let’s look back at the best blogs of 2023! Since the inception of Gamma Technologies, GT-SUITE has optimized system simulation solutions for manufacturers! In no order, these are the top 10 blogs written in 2023 that highlight the vast application use cases and technical capabilities GT-SUITE can deliver!   

1. Decreasing Battery System Simulation Runtime using Distributed Computing
2. Calculating Electric Vehicle Range with Simulation
3. Engine Manufacturers Leverage Simulation to Engineer Ahead of Increasing Regulations
4. Enhancing Model Accuracy by Replacing Lookup Maps with Machine Learning Models (Machine Learning Blog Part 1)
5. Optimizing Neural Networks for Modeling and Simulation (Machine Learning Blog Part 2)
6. Mitigating the Domino Effect of Battery Thermal Runaway with Simulation
7. Designing Thermally Secured Electric Motors with Simulation
8. Understanding Fuel Cell Systems Simulation for Vehicle Integration
9. Addressing Heat Pump Challenges, from Home to Industry with Simulation
10. Simulating Predictive Cruise Control for a Heavy-Duty Truck: Quickly and Easily

Shout-outs to our colleagues for their contributions! 

Learn more about our simulation solutions!  

If you’d like to learn more about how Gamma Technologies can be used to solve your engineering challenges, contact us here! 

Wishing you a healthy & prosperous 2024!   

Engine Manufacturers Leverage Simulation to Engineer Ahead of Increasing Regulations

Environmental agencies, such as the EPA in the United States, play a vital role in controlling nitrogen oxide (NOx) emissions by setting limitations on airborne pollutants that harm public health and the environment. These standards have grown more stringent in recent years for heavy duty and/or road diesel trucks, particularly for engine-out NOx emissions, as shown in Figure 1. It is essential for internal combustion engine (ICE) manufacturers to accurately predict and control engine-out NOx emissions under various operating conditions during the design phase to meet these standards.

 

Why GT-SUITE Should Be Your Go-To Simulation Platform for NOx prediction  

Accurate prediction of engine-out NOx emissions of IC engines requires capturing the in-cylinder interactions among fuel injection, turbulence, chemistry, piston motion, and wall heat transfer. These interactions can lead to the creation of in-cylinder stratification, as shown in Figure 2, which significantly impacts the engine’s NOx emission. 

This is where simulation modeling comes into play. Here are some modeling methods: 

3D, computational fluid dynamic (CFD) simulations can possibly provide better accuracy in capturing these interactions. However, these simulations can become computationally time-consuming, especially when trying to evaluate hundreds (or thousands) of designs and  operating conditions.  

An alternative approach is to use reduced-dimensional models, such as zero-dimensional stochastic reactor models (0D-SRM). These models represent the engine cylinder by hundreds of notional particles, providing a high-fidelity framework to capture in-cylinder stratification using detailed chemistry and accurate mixing models. A 0D-SRM model runs much faster than 3D-CFD simulations, making it a more feasible option for evaluating large numbers of designs and operating conditions. These 0D-SRM models can provide a good trade-off between accuracy and computational cost, making them a useful tool for diesel engine designers and researchers.  

Powerful simulation software, such as Gamma Technologies’ GT-SUITE, can be used to predict engine performance and engine-out emissions. It has an implemented zero-dimensional (0D) stochastic reactor model (SRM) that can predict engine performance and engine-out NOx emissions accurately using detailed chemistry [1]. The animation in Figure 3 demonstrates how the 0D-SRM model captures the in-cylinder inhomogeneity using hundreds of notional particles. This model has been extensively validated against experimental data, making it a reliable tool for predicting engine-out NOx emissions. 

In addition, GT-SUITE offers the ability to optimize the chemical reaction rates during a simulation, which can be particularly useful for improving emission prediction under different designs and operating conditions. In a study [2], different approaches were proposed to improve the accuracy of engine-out NOx predictions by optimizing the chemical reaction rate parameters (see Figure 4).  

 

Learn More About GT-SUITE’s Combustion Modeling 

Learn more about combustion and emission simulation solutions here.  If you are interested in using GT-SUITE for engine-out emission modeling needs, we encourage you to reach out and speak to a GT expert. 

References 

[1] Paul, C., Jin, K., Fogla, N., Roggendorf, K. et al., “A Zero-Dimensional Velocity-Composition-Frequency Probability Density Function Model for Compression-Ignition Engine Simulation,” SAE Int. J. Adv. & Curr. Prac. in Mobility 2(3):1443-1459, 2020, https://doi.org/10.4271/2020-01-0659. 

[2] Paul C, Gao J, Jin K, Patel D, Roggendorf K, Fogla N, Parrish S E, Wahiduzzaman S, An indirect approach to optimize the reaction rates of thermal NO formation for diesel engines, Fuel 338 (2023) 127287, https://doi.org/10.1016/j.fuel.2022.127287.  

Top 10 Gamma Technologies Blogs of 2022!

From battery thermal runaway to fleet route optimization, there is a blog for every simulation! 

Since the inception of GT-SUITE, Gamma Technologies has offered state-of-the-art simulation solutions for manufacturers. Our simulation solutions help guide customers and partners toward highly optimized products.  

In no order, these are the top 10 blogs of 2022!  

1. Simulating Your Way to HVACR Innovation 
2. How a Catastrophic Ship Fire Reminded us Why Battery Thermal Runaway Simulation is Important 
3. Reducing Costs & Increasing Efficiency in Power Converter Design  
4. Using Simulation to Model Closed-Cycle Argon Hydrogen Engines 
5. Sensitivity Analysis: How to Rank the Importance of Battery Model Parameters Using Simulation 
6. Accelerate Electric Aircraft Design Certification with Systems Simulation 
7. Vehicle Modeling and Simulation: ICEV & BEV Correlation Procedure 
8. How to Automate Real World Vehicle Route Generation Using Simulation 
9. A Look Inside Large-Scale Electrochemical Storage Systems Simulation 
10. Simulating a NASA Hydrogen Powered Rocket 

Other Gamma Technologies Blogs to check out in 2022! 

1. Using Simulation for Battery Engineering: 12 Technical Blogs to Enjoy 
2. Machine Learning Simulation: HVACR Industry 
3. Fast, Accurate Full Vehicle Thermal Management Simulation with GT-SUITE and TAITherm 
4. Using Simulation To Predict Battery Aging for Real World Applications 
5. How Simulation Can Increase Productivity in Electric Vehicle Thermal Management Design 
6. Using Simulation to Optimize Driving Routes and Vehicle Emissions 
7. How Simulation Is Used To Design ICE vs. Battery Electric Vehicle Thermal Management Systems  
8. Are Your Vehicle Passengers Comfortable? How to Validate An Accurate, Thermal Cabin Management Simulation Solution  

Shout-outs to our colleagues for their contributions! 

Learn more about our simulation solutions!  

If you’d like to learn more about how Gamma Technologies can be used to solve your engineering challenges, contact us here! 

Have a great holiday season and wishing you a healthy & prosperous 2023!   

How Simulation Is Used To Design ICE vs. Battery Electric Vehicle Thermal Management Systems

Understanding Vehicle Thermal Management 

Vehicle electrification across the transportation industry is being driven by demands for reducing emissions and increasing fuel economy. However, engineering these electrified vehicles comes with a new set of challenges for thermal management of the powertrain and cabin. In this blog I will discuss some of these new challenges for battery electric vehicle thermal management and how it compares to combustion engine vehicles.  But first, I’ll discuss some common traits between thermal management of both vehicle types.

Similarities Between ICE vs. Battery Electric Vehicles Thermal Management Systems 

The goals for thermal management system design remain the same regardless of the powertrain: to keep the powertrain components in their desired temperature range, and to provide a comfortable cabin for the occupants. The optimal design should balance energy usage, system cost, and reliability. In cold environments, the thermal management system should enable fast warmup of the vehicle. Both battery electric vehicles (BEV) and internal combustion engine (ICE) vehicles are less efficient at cold temperatures. In warm environments, excess heat from the powertrain needs to be rejected to the environment to prevent damage to the components. In addition, the cabin temperature needs to be controlled for a comfortable driving experience.

Similar types of components are used between combustion engine vehicles and battery electric vehicles. A single-phase coolant loop would likely use an ethylene glycol and water mixture for the working fluid, with a pump, liquid-to-air heat exchanger, and control valve to manage the coolant flow. A cooling fan is used to enhance the air flow through the heat exchanger at low vehicle speeds. Previously mechanically driven pumps and fans were standard, but recently electrically driven components are used for greater system control. A two-phase refrigeration system is necessary for providing additional cooling below the environment temperature.

The integration of other systems is also an important consideration for transient analysis and controls. Different thermal strategies may be needed depending on the powertrain demands, component temperatures, and environment temperatures. For both a combustion engine and battery electric vehicle, a system that performs well at steady state conditions may not be sufficient to manage temperatures for transient driving cycles. The heat produced by powertrain components at ideal operating temperatures will be different than the heat generated at warmer or colder temperatures, and de-rating of the powertrain may be necessary to prevent component damage. In both types of vehicles, the demands for heating or cooling the cabin will impact the cooling circuit temperatures.

Differences Between ICE vs. Battery Electric Vehicles Thermal Management Systems 

The most obvious difference between the combustion engine vehicle and the battery electric vehicle is the heat source. In the electric vehicle, the primary waste heat to the coolant is from the motor, power electronics, and battery. If this waste heat is not sufficient, an auxiliary heater or two-phase system can be used to add heat and bring the components up to their operating temperature. Whereas in the combustion engine, the primary heat source is from the combustion process. Additional heat is added to the coolant from the engine and transmission oil caused by friction in those components.

 

 

These differences in the heat sources lead to differences in the operating temperatures of the components. The combustion engine operates at high temperatures, which allows the coolant to be used to warm the cabin in cold environments or rejected to the environment at higher temperatures. In more complicated combustion engine cooling systems, a separate lower temperature loop maybe used to provide coolant for a charge air cooler or water-cooled condenser. This separate coolant loop also would be operating at above ambient temperatures and could reject heat to the environment using a coolant to air heat exchanger. In the battery electric vehicle, the motor and power electronics can operate at higher temperatures, but the ideal battery temperature range is between 20 °C and 40 °C.  This would require a refrigeration system to provide additional cooling for the battery because the ambient air may not be enough in warm environments.

The differences in temperature requirements and operating conditions among the components in the BEV increase the complexity of its cooling system. Additional cooling is only required for the battery, so a separate cooling loop could be utilized for the battery linked to the refrigeration system. Cooling this smaller loop below ambient rather than the full cooling loop would require less energy to run the compressor, which increases the vehicle range. The requirement to heat the battery in cold environments would require either an auxiliary heater, operating the refrigeration system in a heat pump mode, utilizing waste heat from the motor and power electronics, or some combination of these strategies. To achieve these goals using a single system, multiple pumps and valves are necessary. More complex controls to route the coolant and optimize the pump speeds are required for efficient operation. In contrast, the combustion engine cooling system can typically be satisfied with a single coolant loop unless a charge-air-cooler requires additional cooling at a lower temperature.

How Simulation Is Used For Thermal Management System Designs

With the increased interaction between the vehicle systems in a BEV, an integrated system simulation is necessary for optimal design. Over a transient driving cycle, the thermal management of the battery and cabin need to be energy efficient to maximize the vehicle range. During a fast-charging event, the battery temperature needs to be carefully managed to prevent unnecessary cell aging. For a rapid acceleration or towing event, the motor and inverters need to be properly cooled to prevent component damage. GT-SUITE is the optimal simulation platform to manage these simulation needs by providing:

• Industry leading sub-system models
  GT-SUITE simulations are recognized across the automotive industry for their accuracy and flexibility. Our publications page highlights customer use cases for every vehicle system across the electrical, mechanical, thermal, fluid, chemical, and controls domains. 
• Detailed component models and real-time capability
  GT-SUITE provides detailed simulations for individual components that will greatly enhance the model capabilities. For the battery and motor, the temperature distributions over a driving cycle or fast-charging event in a 3D finite element model can predict hot spots and the effects of different cooling strategies. Electro-chemical models of the battery can predict the cell aging over a vehicle life cycle.  In addition, the 3D cabin comfort model linked to GT-TAITherm can accurately predict occupant comfort over a wide range of vehicle conditions. These detailed models can be reduced to a real-time capable model for software or hardware in the loop simulations. 
• Robust model integration
  GT-SUITE is designed to properly model the interaction between vehicle systems in an integrated model. For example, the heat generated within the motor and battery can be added as a source term in the thermal component models, with individual component temperatures used to calculate the correct performance within the electrical and mechanical system models. By building these sub-system models in the same tool, it is easy to model the interaction between them and change the simulation parameters for different analyses.

Closing Thoughts on Thermal Management System Design 

The design of electric vehicles requires additional complexity for properly managing the battery, motor, power electronics, and cabin temperatures. The interaction between the single-phase and two-phase systems must be included to accurately predict the battery temperatures over a range of operating conditions. More complex controls are needed to create a robust and efficient system.  Because of these complexities and enhanced interactions, simulation is necessary for system design. We will be expanding on these topics to discuss the component and system models in subsequent blog posts.

If you’d like to learn more or are interested in trying GT-SUITE to understand thermal management in ICE or xEV, Contact us!

Written by Brad Holcomb
This blog was originally published on May 26, 2021

Vehicle Modeling and Simulation: ICEV & BEV Correlation Procedure

Vehicle Level Simulation is a rapidly expanding technique which most OEMs are exploring to help reduce testing cost and time. To be effective, these simulations must accurately represent the vehicle being simulated. This can be achieved in two steps, first by collecting the data required for modeling and feeding it into a simulation tool to create a virtual replica. The second step is to make sure that the model is well validated or correlated. In this blog, we are going to highlight some tips which simplifies model validation process.

Conventional Vehicle (ICEV) Correlation Procedure:

Let us consider that user-A is working on a conventional vehicle and trying to extract Fuel Economy output. User fed all the inputs required by the vehicle model which included some assumptions due to lack/absence of data. He found that mileage of the vehicle is having slight mismatch when compared to test results. To give you some background, mileage is an output from simulation which indicates how much distance a vehicle travels on an average per unit volume of fuel being consumed. Typically, in the units of mpg or kmpl. Final outcome (Mileage) from the model depends on various quantities, few of them involves BSFC/Fuel-Rate map input (for a map-based engine) and engine operating points which indirectly depends on drag coefficient, frontal area, rolling resistance, tire-rolling-radius, gear reductions, driveline efficiencies, inertias, effective mass, GVW, shift pattern, etc. to name some of them. It might be hard to identify which of these quantities is the culprit for mismatch with respect to test results. Hence, we have come up with a procedure that can help you in eliminating few parameters at a time to make your model correlation task easier with an ultimate goal of creating a virtual replica of your actual system/subsystem. Additionally, calibrated GT-Predictive Engine (GT-POWER) models can be used to generate engine maps like BMEP, BSFC, etc.

ICEV Flow Chart:

Battery Electric Vehicle (BEV) Correlation Procedure:

On the other hand, let us consider that user-B is working on an electrified vehicle and trying to extract current and voltage response of a Battery to a standard drive cycle like IDC/NEDC. He found that there is a mismatch for current and voltage results in simulation when he compares it with test data. To give you some background about typical vehicle model workflow, driver decides the power demand based on the target drive cycle and corresponding resistive forces (aerodynamic drag, rolling resistance, road grade, driveline inefficiency, etc.). Motor speed primarily depends on gear reductions, tire rolling radius and vehicle speed at that instance. Based on power demand and current operating speed, torque demand can be derived which the motor is asked to deliver to meet cycle demands. Power delivered by motor is known as brake power. Due to motor and inverter efficiency, there will be some losses and a summation of these losses, aux loads, and Brake power is what the battery needs to deliver which is also known as Electrical power. Finally based on the OCV-IR maps defined for an ECM (Equivalent Circuit Model), using fundamental equations of Electrical circuits, we arrive at current and voltage. Hence you can clearly see that current and voltage response is one of the final outcomes which depends on many other factors, hence it’s hard to predict the exact reason for mismatch of simulation results when compared to test data. Similarly, as we explained for an ICEV, we have listed a procedure which could simplify your task and help you eliminate and verify few parameters at a time.

 

BEV Flow Chart:

As described in the correlation flowcharts, at an initial stage of vehicle development, users might not have all the data required for vehicle modeling. Hence to assist them, we have many options available within our tool GT-SUITE. Some of the relevant ones are discussed below:

1. Characterization: for ECM (Equivalent Circuit Model) model creation using test data.
2. ECM Database: for ECM model creation in absence of test data created using GT-Autolion database.
3. Static Analysis: for gear shift pattern generation.

Characterization:

GT offers a quick and easy to use tool which is capable of parameter estimation for Electrical Equivalent Circuit models including 0 to 3 RC branches. We call it Characterization tool. Glimpse shown below:

Electrical Equivalent Model aka ECM Database:

Starting with v2022B1, you can find a database of 30-coin cells comprising of different chemistries and applications. This was developed using actual test data and is quite reliable in absence of data at initial stages of development process. Database comprises of 10 different chemistry combinations and 3 variants of each based on application (power dense, energy dense or balanced). This can be easily scaled up to a cylindrical/pouch/prismatic cell and even up to a pack following instructions mentioned in the template help of our electrical equivalent battery template. This comes as a part of installation and can be located in the following directory: %GTIHOME%\v2022\resrc\BatteryLibrary\ElecEq.

Static Analysis:

GT offers two modes for vehicle level simulations which are mainly dynamic and kinematic. Kinematic or static mode is used to perform these following tasks:

1. Imposed speed analysis for drive cycle demand calculations.
2. Grade Climbing Ability: Gradeability analysis over different gears, Tractive Force Calculations (gross and net), Tractive power calculations (gross and net), N-V curve, Acceleration potential, and others.
3. Shift Strategy Generation and Optimization: Generation of shift strategy based on acceleration potential curves and drivability involving additional FE constraints.
4. Controls Optimization for HEVs: ECMS, DP and Dynamic ECMS.

 

View Testimonials from the New Energy Leadership Summit in Bengaluru, India

Gamma Technologies partnered with ET Auto at a 1 day-summit in Bengaluru, India that brought together eminent leadership from various firms (vehicle OEMs, component manufacturers, R&D and CAE leaders, simulation professionals, testing agencies, and others) who shared their thoughts on the new energy ecosystem, the challenges associated with it, and the role of simulation in tackling these challenges.

Click here to view our series of testimonials from this event on our YouTube channel!

Acronyms:

1. RPM: Revolutions Per Minute
2. MPG: Miles Per Gallon
3. KMPL: Kilometers Per Liter
4. BSFC: Brake Specific Fuel Consumption
5. GVW: Gross Vehicle Weight
6. BMEP: Brake Mean Effective Pressure
7. IDC: Indian Drive Cycle
8. NEDC: New European Driving Cycle
9. OCV: Open Circuit Voltage
10. IR: Internal Resistance
11. VKA: Vehicle Kinematic Analysis
12. ECM: Equivalent Circuit Models (Resistive/Thevenin)
13. N-V: Motor/Engine RPM vs Vehicle Speed curve
14. FE: Fuel Economy
15. HEV: Hybrid Electric Vehicles
16. ECMS: Equivalent Consumption Minimization Strategy
17. DP: Dynamic Programming
18. RC: Resistance and Capacitance
19. FDR: Final Drive Ratio or Sprocket and Chain Ratio
20. PGR: Primary Gear Reduction if present
21. GR: Gear Ratio of transmission if present

Using Simulation to Model Closed-Cycle Argon Hydrogen Engines

Evaluating Renewable Hydrogen Fuel

With the increasing demands for fuel-efficient and low-to zero-emissions technologies in the automotive industry, renewable hydrogen fuel is regarded as a promising energy storage form for vehicles. Pure hydrogen combustion emits no greenhouse gas CO2, and noble gases can eliminate environmental pollutants such as NO_x by replacing nitrogen. As a result, hydrogen combustion in a noble gas is expected to eliminate both carbon emissions and other pollutant emissions.
Additionally, the higher ratio of specific heats, can increase the theoretical thermal efficiency of an Otto cycle, k = Cp/Cv, as  can be determined from the equation below:

For which CR is the compression ratio.

Therefore, the use of a monoatomic working gas with a high specific heat ratio such as argon would ideally achieve much higher thermal efficiency than conventional internal combustion engine using air (nitrogen) as the working gas. Figure 1 shows the relationship of theoretical thermal efficiency and the specific heat ratio of working gas [1]. For a compression ratio of 10, the thermal efficiency drastically improves by about 30% on a relative basis, or 18% on an absolute basis from k = 1.4  to k = 1.67.

Simulating Hydrogen Engines with GT-SUITE

To demonstrate the possibility that GT-SUITE can model a closed-cycle argon-hydrogen engine, Gamma Technologies has built and included a model of such a system within the recently released v2022 build 1. In the example model ‘Ar-H2_ClosedCycle_Engine’, several advanced modeling concepts are applied, including condensation of combustion products, removal of water from the system and a semi-predictive condenser. Argon is recirculated in the system and oxygen is supplied via an injector object; hydrogen, as the only fuel present in this system, is injected directly into the engine cylinder. Upon the completion of combustion, the exhaust gas passes through the condenser to convert the water vapor to liquid water, which is later removed from the system.

A quite critical aspect for the closed-cycle simulation is to ensure the steady solution, which requires a strict balance of the system mass. Namely, it is required to maintain a balance between the mass entering the system and the mass exiting the system. Otherwise, the constantly changing system mass would prevent simulations from converging on a steady result. Consequently, the oxygen supply and hydrogen injection need to be carefully controlled to maintain the stable simulation.

The example model simulates at different levels of argon fractions in the working gas, namely the ratio of argon gas in the argon and oxygen mixture. The trends of thermal efficiency and specific heat ratio of the in-cylinder gas are observed to vary with the argon fraction as shown in Figure 3, which are consistent with the theory described in the Introduction section. The slopes of efficiency and specific heat ratio shown in Figure 3 are dependent on the condenser design parameters in the example model. Engine efficiency and specific heat ratio should be more sensitive to the change in argon fraction with a condenser of better performance.

Benefits of Hydrogen/Noble Gas Engines Simulation

The discussed Ar-H2 closed cycle engine simulation in this blog is demonstrated with a non-predictive combustion model in GT-SUITE. It allows the user to perform a similar proof-of-concept hydrogen/noble gas engine simulation and serves as a reference/example for closed-cycle engine simulations. The research and development work on this topic can be extended to combine with a predictive combustion model in the future, such as the SI Turbulent Flame Combustion Model which has been widely used for gasoline combustion engine simulations.

References

[1] Kuroki, R., Kato, A., Kamiyama, E., and Sawada, D., “Study of High Efficiency Zero-Emission Argon Circulated Hydrogen Engine,” SAE Technical Paper 2010-01-0581, 2010, https://doi.org/10.4271/2010-01-0581.