Simulating Series Hybrid Tilt-Rotor Aircraft in GT-SUITE

The rise in demand for electric and hybrid-electric aircraft, particularly in the Urban Air Mobility (UAM) sector, has fueled innovation in aircraft powertrain design. One promising configuration is the series hybrid tilt-rotor system, which combines the power and efficiency of hybrid-electric propulsion with the versatile flight dynamics of tilt-rotor technology. In this blog, we’ll dive into the basics of series hybrid tilt-rotor systems, why they’re gaining popularity, and how GT-SUITE, a multi-physics simulation platform from Gamma Technologies, can simulate and optimize them early in the design and development process.

A tilt-rotor aircraft features rotors that can transition between vertical and horizontal orientations, enabling both vertical takeoff and landing (VTOL) capabilities and efficient forward flight. By integrating a series hybrid system (where an internal combustion engine (ICE) drives a generator to supply electrical power to motors that turn the rotors) we can achieve the best of both worlds: the extended range of traditional fuel-powered engines and the flexibility and control of electric propulsion.

Components of a Series Hybrid Tilt-Rotor System:

1. Internal Combustion Engine (ICE): Drives a generator to produce electrical energy, offering high energy density.
2. Generator: Converts mechanical energy from the ICE into electrical energy.
3. Battery Pack: Stores energy to supplement the generator output.
4. Electric Motors: Drive the rotors and facilitate the tilt-rotor’s unique ability to transition between vertical and horizontal flight.
5. Flight Control System: Generates Control Outputs based on the deviation between the target trajectory and the actual flight state.

System Workflow of a Series Hybrid Tilt-Rotor in GT-SUITE

The development and simulation of a series hybrid tilt-rotor aircraft involves tightly integrated subsystems working under closed-loop control to ensure stable and efficient operation throughout the flight envelope. The system architecture and workflow, as shown in the figure, can be described in the following sequence:

1. Target Mission Definition

The simulation begins with defining the flight mission profile, which includes:

• Target altitude
• Target velocity
• Desired rate of climb
• Flight states over time

These mission parameters serve as reference inputs for the flight controller to guide the aircraft through various phases like hover, transition, cruise, and descent.

2. Flight Controller

The flight controller continuously compares the current aircraft state with the mission-defined targets. Based on the error between actual and target parameters, it generates control commands such as:

• Elevator deflection
• Nacelle tilt angle (specific to tilt-rotors)
• Throttle setting
• Propeller blade pitch

These outputs ensure the aircraft maintains its trajectory and stability across dynamic flight conditions.

3. Aircraft Body and Motion Calculation

The aircraft body module receives control input and evaluates the dynamic response. This involves:

• Force calculation: Derived from aerodynamics and thrust contributions (including vertical lift in VTOL modes and forward thrust in cruise)
• Equations of Motion (EoM): Motion quantities like velocity, angular rates, and position are updated based on net external forces and moments

This module represents the 3DOF rigid body physics of the aircraft.

4. Electric Propulsion Subsystem

In a series hybrid configuration:

• The electric motor receives commands (e.g., torque or speed setpoints) from the flight controller.
• The motor drives the propeller, generating thrust needed for vertical lift or forward motion.
• The propeller model converts shaft power into aerodynamic thrust using blade element momentum or similar methods.

This closed-loop feedback ensures the thrust output aligns with what is needed for stable flight.

5. System Integration Loop

All subsystems interact in a closed-loop fashion:

• Mission target → Controller → Actuator/motor response → Aircraft body dynamics → Updated flight state.
• The updated state feeds back to the controller, ensuring continuous correction using PID controllers and mission adherence.

Simulating a Series Hybrid Tilt-Rotor Model in GT-SUITE

Let’s look at a system-level example model of a series hybrid tilt-rotor aircraft developed in GT-SUITE. This model integrates electric propulsion components, 3DOF flight dynamics, nacelle actuation, and energy management systems into a unified simulation environment.

The tilt-rotor is modeled to perform a complete VTOL mission, from vertical takeoff through cruise and back to landing, while enabling detailed analysis of powertrain behavior and flight control responses under varying operational modes.

To explore the impact of different electrification strategies on flight performance and energy consumption, two distinct simulation cases were studied using a representative mission profile:
Flight Mission Profile
The simulated mission captures a complete VTOL flight cycle, including the following phases:
• Vertical Takeoff
• Transition to Climb
• Cruise Flight
• Descent
• Hover and Landing

A flight control system governs the nacelle angle throughout the mission. The nacelle starts at 90° (vertical) for takeoff and gradually transitions toward the horizontal (fixed-wing) position during the climb phase. This fixed-wing configuration is maintained during cruise. The nacelle then transitions back to vertical for the descent and hover/landing phases.

Case 1: Series Hybrid Mode
• Power Configuration: The electric motor is supported by a 64 kW hybrid assist during the Climb/Cruise Phase and Descent/Hover Phase, which can be modified accordingly in the ECU Generator controller
This configuration demonstrates how hybrid support can enhance performance and extend operational endurance during peak power demands.

Case 2: Pure Electric Mode
• Power Configuration: Fully powered by a battery-electric system, with no engine assistance.

This case showcases the aircraft’s behavior and energy consumption under pure electric propulsion for the full mission cycle.

Key Metrics and Results in Hybrid Tilt-Rotor Simulation

The simulation results include:
• Battery State of Charge (SOC): Tracks energy consumption and efficiency across flight phases.
• Battery Power Demand: Highlights real-time power draw during different maneuvers.
• Motor Power: Reflects electric propulsion load throughout the mission.
• Flight States: Includes velocity, altitude, and nacelle angle transitions to correlate system behavior with flight dynamics.

These two simulation cases provide insight into how powertrain configuration and flight phase control impact performance, range, and energy usage for a hybrid tilt-rotor aircraft. The results lay a foundation for further optimization of energy management strategies in hybrid-electric rotorcraft systems.

Learn More about Our Aerospace Simulation Capabilities  

To learn more about how GT-SUITE can help you design, simulate, and optimize advanced aerospace systems, visit our Aerospace Industry Page for detailed insights, case studies, and technical resources. For any specific questions, project inquiries, or personalized guidance, don’t hesitate to contact our team of aerospace simulation experts.

How Multi-scale Models Are Enhancing Battery Performance and Design

Beyond Experimentation: Predicting Battery Performance with Multi-Scale Models

In today’s electrified world, designing better batteries goes far beyond trial-and-error testing. Engineers and researchers are increasingly turning to simulation to accelerate innovation and reduce development costs. Lithium-ion batteries power modern energy storage systems, from electric vehicles to grid storage. As demand grows for higher performance, longer lifespan, and improved safety, accurate battery modeling becomes increasingly important. A promising path forward involves uniting atomic-scale simulations with continuum models (multi-scale modeling) to enhance the fidelity of performance predictions.

Enhancing Battery Design Through Atomic and Continuum Model Integration

Multi-scale modeling approach accelerates the design of new battery materials, reducing reliance on time-consuming and expensive experimental testing. It enables:

• Faster material development: Atomic-scale simulations allow rapid exploration of new electrolytes and additives.
• Better battery optimization: Engineers can fine-tune battery performance for specific applications, such as high-power EV batteries or high-energy grid storage solutions.
• Improved safety and longevity: Accurate predictions help optimize electrolyte formulations, reducing risks associated with lithium plating and thermal runaway.

The Role of the Electrolyte in Battery Simulation

Achieving high predictability in battery modeling requires a deep understanding of each cell component, particularly the electrolyte, which is essential for lithium-ion transport between the electrodes. The electrolyte significantly impacts overall battery performance by influencing internal resistance, voltage behavior, and degradation over time. However, key electrolyte properties, such as ionic conductivity, lithium transference number, and diffusivity, are difficult to measure and highly sensitive to variables like temperature, concentration, and interactions with electrode materials.

Understanding Atomic-Scale Simulations in Battery Modeling

Continuum models—mathematical approaches that simulate battery behavior at the cell or system level by treating materials as continuous substances—such as the pseudo-two-dimensional (P2D) model, rely heavily on these properties to simulate ion transport and electrochemical behavior. Inaccurate values may lead to significant errors in predicting concentration gradients, voltage losses, and overall battery performance. Atomic-scale simulations—methods that simulate battery behavior at the molecular (or atomic) level by modeling individual particles or molecules—help estimate electrolyte properties across a wide range of conditions, improving the accuracy of continuum models and reducing reliance on experimental measurements.

Combining Atomic-Scale and Continuum Models for Better Insight into Battery Performance

To generate accurate electrolyte property data across various conditions, Gamma Technologies has collaborated with Compular, a company specializing in atomic-scale simulations. By integrating Compular’s molecular dynamics (MD) simulations with our GT-AutoLion continuum model, it is possible to directly calculate fundamental electrolyte properties from atomic-level simulations.

To integrate Compular’s simulation with Gamma Technologies’ (GT) GT-AutoLion, an electrolyte with an additive (LiPF6 in EC:PC:EMC, 1:3:8 by volume, with 2% FEC) is simulated across varying salt concentrations and temperatures to study the performance of both high energy and high power-dense cells.

GT-AutoLion utilizes a physics-based P2D model to simulate battery charging and discharging. It divides the battery into three main regions—anode, cathode, and separator—discretizing them to capture critical electrochemical interactions (Figure 1). However, to ensure the accuracy of these models, precise electrolyte data is necessary. This is where MD simulations come into play.

Compular Lab models electrolytes at the molecular level using MD simulations, simulating systems with about 5000 atoms. These atoms move according to the laws of physics, and we track their motion over time to observe how they interact. The simulations run at specific temperatures and salt concentrations, and for each condition, we record around 15 nanoseconds of ion movement.

To extract useful data from these simulations, Compular’s CHAMPION, a software tool, analyzes how ions move together. It calculates what’s called Onsager coefficients, which describes how different ions affect each other’s motion (see Figure 2). From these, we derive four key transport properties:

• Ionic conductivity: how efficiently ions carry electric charge through the electrolyte
• Salt diffusivity: how fast ions spread out in the electrolyte
• Transference number: the fraction of the current carried by the cation
• Thermodynamic factor: how ion–ion interactions influence diffusion and concentration behavior

Typically, calculating these properties requires long simulations because random motion (or “noise”) from non-interacting ions makes it harder to get accurate results. But CHAMPION improves efficiency by focusing only on the meaningful interactions between nearby ions, reducing the required simulation time by about 90% to approximately 10 nanoseconds.

The Workflow: From Atomic Interactions to Battery Performance

Here’s how the combined modeling approach works:

1. Molecular Dynamics Simulations: Compular’s lab tool runs MD simulations to extract crucial electrolyte properties like conductivity and diffusivity. These simulations provide insights into how electrolyte composition affects battery behavior, particularly under different temperatures and salt concentrations.
2. Integrating Data into GT-AutoLion: A Python script transfers the extracted electrolyte data into the GT-AutoLion simulation framework. The data is structured into a reference object (XYZMap) that incorporates temperature/concentration-dependent electrolyte properties.
3. Simulating Battery Performance: Using these electrolyte properties, GT-AutoLion predicts voltage vs. capacity curves for both energy-dense and power-dense cells. This allows for comparative analysis under varying temperatures and charging rates.

Simulation Findings: Electrolyte Properties and Their Impact on Battery Performance

When we tested these models, we found some interesting trends.

MD simulations reveal the following (Figure 3):

• Ionic conductivity and salt diffusivity decrease at lower temperatures, affecting overall battery efficiency.
• There is an optimal salt concentration (~1 M) that maximizes conductivity.
• Transport properties vary significantly with electrolyte composition and temperature, influencing battery performance.

Electrochemical Battery Modeling using GT-AutoLion

By incorporating atomic-scale insights, GT-AutoLion enables:

• Accurate predictions of voltage vs. capacity for power-dense and energy-dense cells.
• Insights into how electrolyte behavior impacts capacity, especially under high C-rates and low temperatures.
• A better understanding of trade-offs between power and energy density in different applications.

Conclusion: The Future of Battery Simulation

The progression of battery technology will rely heavily on the integration of multiscale simulation techniques that connect molecular-level behavior with system-level performance. With enhanced prediction accuracy and faster innovation, the future of lithium-ion is brighter than ever. If you are interested to read more about battery modeling you can read blogs on using simulation for battery engineering and watch webinar on “Machine Learning for Fast, Integrated Battery Modeling” or  contact us to see how Gamma Technologies can support your battery development goals.

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.

Digital Twin Simulation: Engineering Smarter, Faster, and More Reliable Products

Why Digital Twins are Necessary and Important

Imagine being able to predict equipment failures before they happen, optimize system performance in real time, and reduce expensive physical testing. This is the power of digital twins. A digital twin is a virtual replica of a physical asset, enabling real-time monitoring, simulation, and optimization. 

With increasing system complexity and the demand for faster, more efficient product development, digital twin technology is revolutionizing engineering across industries. 

How Digital Twins Help Address Major Challenges  

Downtime and Maintenance: Unplanned machine downtime can disrupt workflows, delay production schedules, and lead to significant revenue losses. Unexpected failures not only impact profitability but also strain resources and damage customer trust. Digital twins help predict failures and optimize maintenance schedules, ensuring uninterrupted operations. 

Fault Detection and Prediction: Late fault detection can result in system failures, escalating repair costs, and potential damage to other components. At the same time, excessive false alarms can interrupt production and reduce efficiency. Digital twins enable precise fault detection, balancing accuracy with minimal disruption. 

Controls Optimization: Misconfigured control systems can lead to production stoppages and hardware degradation. Control software developed in lab conditions may not perform reliably in real-world applications. Digital twins allow engineers to test and optimize control strategies in a virtual environment before deployment, improving safety margins and efficiency. 

What-if Scenarios: Physical testing of all possible operating conditions is expensive and time-consuming. Relying solely on sensor data and human intervention can introduce inconsistencies. Digital twins enable engineers to simulate countless real-world scenarios quickly, reducing reliance on costly prototype testing. 

How Simulation is Making an Impact  

GT-SUITE provides a comprehensive platform to develop digital twins, integrating robust multi-physics simulation with cutting-edge data science. By seamlessly connecting with a customer’s data collection system in a cloud-based environment, GT-SUITE enhances asset performance, minimizes downtime, and improves decision-making. Let’s explore how digital twins solve critical challenges and how you can build one to unlock the full potential of your engineering systems. 

“How to Guide” for Building a Digital Twin  

A digital twin functions by continuously updating a virtual replica of a physical asset, system, or process with real-time data. This enables simulation, analysis, optimization, and monitoring of the physical counterpart.  

Here’s how to build one: 

Step 1: Data Collection; Data can be collected from sensors on the physical asset (e.g., an engine, machine, or compressor), measuring parameters such as temperature, pressure, vibration, speed and more. Additionally, historical test data and field data from previous operations can be leveraged.

Step 2: Data Integration; All collected data—whether from sensors, past tests, or field operations—is aggregated, cleaned, and processed to ensure accuracy and usability for simulation and analysis.

Step 3: Virtual Model Creation;

• • Physics-based modeling: GT-SUITE enables the creation of high-fidelity digital twins using physics-based models calibrated with real-world measured data, ensuring precise system behavior predictions.
• 

  Virtual Model Building

  • Data-driven machine learning models: GT-SUITE’s Machine Learning Assistant utilizes datasets from sensors, testing, field operations, and design of experiments to create fast-running mathematical models (metamodels). These models leverage real-time sensor data for enhanced predictive accuracy and can also integrate synthetic data from physics-based simulations, reducing reliance on physical testing while improving reliability.

Step 4: Real-Time Interaction; The virtual model continuously updates via live data streams and sensor feedback which enables real-time monitoring, fault detection, and predictive analysis. This ensures that engineering teams can proactively optimize system performance, diagnose failures, and improve reliability.

Case Studies: Real-World Applications of Digital Twins 

Case Study 1: Fuel Cell Fault Simulation and Detection for On-Board Diagnostics Using Real-Time Digital Twins 

As electrified powertrains become more complex, developing reliable On-board diagnostic (OBD) systems is crucial for ensuring compliance with evolving regulations. However, the lack of prototype hardware makes traditional validation methods impractical. To address this, real-time digital twins enable virtual testing through model-in-the-loop (MiL), software-in-the-loop (SiL), and hardware-in-the-loop (HiL) simulations.

This approach involves creating high-fidelity models to identify and analyze failure modes in key fuel cell components such as the compressor, recirculation pump, humidifier, and cooling system. The process begins with fuel cell stack calibration using measured polarization curves and predictive loss models. This is followed by balance of plant modelling that integrates the stack with sub-systems like the anode recirculation loop, the cathode system including humidifier, intercoolers, compressor, and turbine, and motor controls as well as the cooling systems. To enhance efficiency, the model is optimized using 0D and map-based approaches, ensuring fast simulation without sacrificing accuracy. Fault scenarios are then defined, specifying key variables to be monitored by the control system. In the HiL phase, the fast-running fuel cell model developed in GT-SUITE is integrated with MATLAB Simulink for real-time execution. The model’s performance is assessed by running real-time simulations, comparing speed and accuracy, and introducing faults to evaluate system response alongside sensor data. By leveraging real-time digital twins, engineers can efficiently develop and validate OBD systems, ensuring robust fault detection while reducing reliance on physical prototypes. This accelerates regulatory compliance and enhances the reliability of fuel cell powertrains. 

Click here to access the complete webinar.

 Case Study 2: Enhancing Cabin Thermal Management with Digital Twin Technology 

A major automotive company leveraged digital twin technology to optimize cabin thermal management, improving energy efficiency and driver comfort in electric trucks. The challenge was to balance the battery thermal system and cabin climate control while maintaining efficient energy use. 

To achieve this, the company calibrated high-fidelity models, dividing the cabin into flow volumes and creating surface mesh models for precise simulations. A co-simulation was then conducted by integrating GT-SUITE’s fluid solver with GT-TAITherm, leveraging key parameters. The model was rigorously validated against multiple real-world test datasets, ensuring accuracy and reliability. 

Building on this foundation, the company aims to take its digital twin implementation to the next level by developing fast-running models for SIL and HIL applications and integrating internet of things (IoT) connectivity for real-time data exchange and enhanced predictive capabilities. These advancements will enable smarter automation, deeper system insights, and more responsive thermal management strategies, bringing them closer to a fully realized digital twin ecosystem. 

Click here to access more digital twin related presentations.

Learn More About our Digital Twin Solutions  

Digital twins are transforming engineering by enabling predictive maintenance, optimizing system performance, and accelerating product development. By combining physics-based modeling with data-driven machine learning, GT-SUITE provides a powerful platform for creating and deploying digital twins across industries. 

Whether you are working on fuel cell powertrains, vehicle thermal management, or other complex systems, leveraging digital twins can drive efficiency, reduce costs, and improve reliability.  

Ready to take your engineering processes to the next level? Start building your digital twin with GT-SUITE today! If you’d like to learn more about how GT-SUITE‘s capabilities, contact us! 

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! 

Simulating a NASA Hydrogen Powered Rocket

Propelling the Orion Spacecraft to the Moon

Images of the moon from the NASA Orion spacecraft reminds us of how our technological advancements have made these wonders of the night sky reachable. The Artemis I mission marked an important milestone as it is the closest a human rated spacecraft has come to the moon since the Apollo 17 mission in 1972. This mission is planned to be followed with the Artemis II launch where the Orion spacecraft will host a crew for a lunar flyby.  

We celebrate our love for space exploration by highlighting GT-SUITE’s simulation capabilities in the aerospace field and share a model study. To demonstrate the integration of multi-physics domains, a GT-SUITE model was built to replicate the steady state operation of the RL10A-3-3A hydrogen powered rocket. The model was based on data and dimensions found in publications about the RL10A-3-3A rocket engine [1][2].

Modeling the rocket engine’s turbomachinery requires coupling of fluid, mechanical, and thermal domains, along with accurate two-phase fluid properties. The two-stage liquid hydrogen and liquid oxygen pump are powered by the expansion of hydrogen across a turbine, which is made possible through fluid-mechanical coupling. Thermal energy is also recycled from the burnt gases flowing out of the rocket nozzle to aid in powering the turbine. This is accomplished through a fluid to thermal structural connection. Energy from the gases within the rocket nozzle are transferred to the nozzle wall according to the Bartz heat transfer correlation. The nozzle wall is then cooled by hydrogen lines running through it, the turbine utilizes thermal energy added to the hydrogen to power the pumps, circulating energy from combustion back into the system. The hydrogen and oxygen properties are determined according to the NIST subroutine from the REFPROP program to ensure accurate fluid behavior. 

Combustion is modeled with an equilibrium chemistry solver to calculate the composition of burned gases in the combustion chamber so effects of dissociation at high temperatures are considered. In the model it was also found that by solving the chemical kinetics to consider oxidation of radical species as the mixture expands within the rocket nozzle’s diverging section, the accuracy of the heat transfer and thrust predictions were improved. 

Results of Simulating Steady-State Rocket Engine Pressure and Temperature

The results of the GT-SUITE model allow for the visualization of pressure and temperature throughout the rocket engine during steady operation. The thrust and the specific impulse of the RL10A-3-3A engine were also calculated based on flow conditions of the exhaust gases. The GT-SUITE results were shown to match the published RL10A-3-3A performance data well [1][2]. 

Learn More about Our Aerospace Simulation Capabilities  

To learn more about our aerospace capabilities, visit our aerospace industry page.  

Contact us for specific comments or questions. 

Citations

[1] M. Binder. A Transient Model of the RL10A-3-3A Rocket Engine. Contractor Report NASA CR-195478, NASA, July 1995. https://ntrs.nasa.gov/api/citations/19950022693/downloads/19950022693.pdf 

[2] Matteo, Francesco Di, et al. “Transient Simulation of the RL-10A-3-3A Rocket Engine.” https://arc.aiaa.org/doi/abs/10.2514/6.2011-6032

This piece was originally written on December 12, 2022 

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!   

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!   

Accelerate Electric Aircraft Design Certification with Systems Simulation

Electric Aircraft Are the Future  

The aviation industry has long imagined a future with electric aircraft. Development has started on smaller scales with aircraft such as electric vertical take-off and landing aircrafts (eVTOLs), short take-off and landing aircraft (e-STOLs), and others.  

According to a May 2022 study by Aviation Today, e-aviation has potential of being a $200 billion-plus market.  Recent industry forecasts predict the growth of the global electric and hybrid-electric aircraft propulsion system market as well as the global hydrogen aircraft market to reach $74.9 billion by 2035 and $144.5 billion by 2040 respectively. 

Major trends of the development of e-aircrafts include:  

1. Higher power density batteries  
2. Increased safety standards to reduce battery thermal runaway  
3. Shorter battery re-charge cycles with battery and hydrogen fuel-cell technology  
4. Lighter, more efficient electric propulsion systems 
5. Thermal management optimization  

Integrating Simulation in E-Aircraft Development 

It’s undeniable that e-aircrafts are making their impact into the next decade. 

By integrating simulation into the design process, development and testing times can be drastically reduced. This allows engineers to arrive at optimal solutions.  

Simulation tools such as GT-SUITE offers solutions to study many aspects of e-aircraft design (read this study on eVTOL design) from the overall vehicle system to battery pack electrical and thermal performance over flight missions. 

Since safety is paramount for e-aircrafts, GT-SUITE can also predict thermal runaway of battery cells and packs. Engineers can study down to the battery cell level and use GT’s electrochemistry modeling capabilities to predict cycle life, which is crucial in understanding the economic feasibility of various e-aircraft types. 

Ensuring Aircraft Design Certification with Simulation  

Despite the technological and economic development with e-aircrafts, mass adoption still faces the pressures of regulation, Federal Aviation Administration (FAA) certification and rules governing airworthiness, traffic control technology and public concerns over noise and safety.  

Certification is estimated to cost $1 million for a primary category aircraft (three seats or less), $25 million for a general aviation aircraft, and upwards of $100 million for a commercial aircraft. 

GT-SUITE can help reduce certification costs and replace some testing requirements by being able to run large, fully detailed simulations within minutes, no matter an e-aircraft’s component sizing.  

e-Aircraft Projects Using Multi-Physics Simulation 

See how GT-SUITE ensure aircraft systems performance and safety certification here. This presentation provides a breakdown of three studies GT conducted with: Airbus A³ ,NASA, and Advanced Rotorcraft Technology (ART). 

 

eVTOL pack thermal performance and state of charge (SOC) over real flight missions with Airbus A³

At the 2019 American Institute of Aeronautics and Astronautics (AIAA) P&E conference, GT presented a joint paper with Airbus A³ to study eVTOL pack thermal performance and SOC over real flight missions, compared to physical tests. The study also looked at battery SOH over different mission cycling and thermal management strategies. 

 

Battery Pack Thermal Runaway Simulation with NASA 

GT modeled a thermal runaway simulation of a NASA Orion module battery pack presented at the 2020 Thermal & Fluids Analysis Workshop (TFAWS). This study looked at lithium batteries combined with electrochemical and thermal modeling techniques and tested assumptions within the battery pack. GT’s multi-physics simulation addressed various battery pack design challenges. These included: battery selection, development of the right pack geometry, performance, degradation, and overall safety.  

Each 1D thermal runaway design takes about 15 minutes and enables users to experiment with battery thermal runaway uncertainties. On average, these models run roughly 2-4 times faster than real-time with just a laptop PC; resulting in a 30-minute simulation taking up to 7-15 minutes to run.  Solely relying on 3D-CFD models won’t provide complete variability analysis.

 

 

Click here to watch this full presentation!  

Learn more about our battery thermal simulation capabilities here! 

 

Complete ‘rotor to battery’ simulation solution in collaboration with Advanced Rotorcraft Technology 

At the 2022 Vertical Flight Society (VFS) Forum 78, GT collaborated with FLIGHTLAB – Advanced Rotorcraft Technology (ART) to highlight a complete ‘rotor to battery cell’ solution to address strict worthiness requirement for eVTOL design and development.  

Our multi-physics simulation-led approach analyzes the combined airframe and drive system to improve drive systems for electric and hybrid-electric aircraft.  These efforts meet Urban Air Mobility (UAM) configurations. 

Read the full study here!

Learn more about e-aircraft simulation 

If you’d like to learn more about how GT-SUITE can be used to solve your e-aircraft simulation challenges, contact us here!