Category: machine learning

Machine Deep learning algorithms

Combining Physics and Machine Learning to Predict Battery Aging with Confidence

Battery technology is evolving rapidly to meet the growing demands of electric vehicles, large-scale energy storage systems, and portable electronics. A major challenge lies in reliably predicting long-term battery performance within practical development timelines. Because batteries degrade gradually during both use and storage, conventional testing methods take a long time to produce accurate lifetime estimates. […]

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Engineering Insights Blog|BMS|BMS|Tilt-Rotor Animation|Operational Window|VTOL|Simulation Result Summary Table|Flight Mission Profile|Simulation Results|Series Hybrid Tilt-Rotor Model Example in GT-SUITE|Comparison of steady and transient model response|IceFraction Animation|fuel cell bus|Schematic of water recirculation|System Workflow of a Series Hybrid Tilt-Rotor in GT-SUITE_01||Ice fraction near cathode catalyst layer||Routes Selected for System Level Simulation|2D flowfield in CAD

A Year of Engineering Insights: Our Top 7 Blogs You Shouldn’t Miss

From smarter thermal systems and next-generation batteries to digital twins, fuel cells, and advanced air mobility, we explored how engineering simulation is reshaping engineering decisions across industries. If you’re working at the intersection of innovation, performance, and efficiency, these seven blogs capture the most impactful ideas we shared in 2025, each addressing real-world engineering challenges […]

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Concept Image of a Rotating Detonation Engine|Digital Twin Simulation|Virtual Model using Digital Twin||Fault Detection on OBD_02||Engineers interacting with a virtual digital twin model on a tablet|Real time integration|Data driven ML model|Virtual model building|Data cleaning|Data collection|Cabin Comfort Model in GT-TAITherm|Digital Twin Orchestration Workflow|Digital Twin Image|||||||||||||Table: Results of preliminary study on battery sizing for the tugboat application. Daily fuel consumption as a function of battery configuration.||Figure 8. Simulation results of a single mission comparing two different battery designs. The Toshiba 20 Ah SCiBTM cells are arranged in 250S/170P (light grey) and 250S/142P (black).||Toshiba GT-SUITE blog|Toshiba GT-AutoLion blog|||RDE AUTOMATION GIF|Unwrapped view of the RDE

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 […]

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Digital Twin Simulation|Toshiba GT-AutoLion blog||Toshiba GT-SUITE blog|Table: Results of preliminary study on battery sizing for the tugboat application. Daily fuel consumption as a function of battery configuration.||Figure 8. Simulation results of a single mission comparing two different battery designs. The Toshiba 20 Ah SCiBTM cells are arranged in 250S/170P (light grey) and 250S/142P (black).|||||||||||||Digital Twin Image|Digital Twin Orchestration Workflow|Cabin Comfort Model in GT-TAITherm|Data collection|Data cleaning|Virtual model building|Data driven ML model|Real time integration|Engineers interacting with a virtual digital twin model on a tablet||Fault Detection on OBD_02||Virtual Model using Digital Twin

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 […]

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HVACR simulation machine learning|hvac machine learning simulation|EV thermal system model with physics-based solution and feedforward neural net|machine learning simulation in hvac industry

Simulation for the HVACR Industry: How to Leverage Machine Learning

The HVACR Industry is Evolving As we step into 2025, major trends in systems simulation are emerging. The heating, ventilation, air conditioning, and refrigeration (HVACR) industry is seeing accelerated growth in the adoption of simulation throughout the design and development process. This industry is looking to further modernize, appeal to consumers, and demand energy-efficient, sustainable […]

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accessory drive machine learning simulation|dynamic machine learning simulation|neural network structure|predicted vs. target stored ammonia coverage and NO outlet mass flow rate|dynamic and static neural networks|metamodel predictions of battery thermal performance simulation|battery call model simulation|Transient neural network predictions of voltage and state of charge (SOC)|neural network structure|machine learning simulation|||neural network and a linear interpolating lookup map|doe simulation|||machine learning neural networks simulation|GT-SUITE accessory drive model|Torsional and Transverse Vibration in an Accessory Drive|belt tension and global slip simulation|Main Effects Plots for Specified Attributes or Inputs Ranking|Variational Analysis for Specified Attributes|belt tension and global slip simulation

Leveraging Machine Learning for Early Design Decisions on an Accessory Belt Drive Simulation

There are various challenges faced by an automotive engineer while designing a robust and optimized accessory drive system. Most original equipment manufacturers (OEMs) rely on different suppliers for their engine belt(s) and accessories (e.g. water pumps, alternators, A/C compressors, etc.). This leads to challenges in obtaining a comprehensive set of input data to incorporate in […]

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dynamic machine learning simulation|machine learning neural networks simulation|||||neural network and a linear interpolating lookup map|doe simulation|machine learning simulation|neural network structure|Transient neural network predictions of voltage and state of charge (SOC)|battery call model simulation|metamodel predictions of battery thermal performance simulation|dynamic and static neural networks|predicted vs. target stored ammonia coverage and NO outlet mass flow rate|neural network structure

Dynamic Machine Learning for Modeling and Simulation

Incorporating Dynamic Metamodeling Simulation To save computational time, engineers are persistently trying to speed up physical models, and some situations absolutely require faster simulation speeds. These situations might include more advanced co-simulation tasks, performing model-based optimization on a slower physical model, or the need to have a surrogate model for XiL (X-in-the-Loop) applications or to […]

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|GT SUITE and SUMO traffic simulation|Co-simulation GT SUITE SUMO vs SUMO|co simulation GT-SUITE and SUMO EV example|co simulation GT-SUITE|simulate driving in traffic|NASA hydrogen rocket simulation|||

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 […]

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top 10 gt suite blogs 2023|top 10 blogs gamma technologies

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 […]

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machine learning neural networks simulation|machine learning simulation|doe simulation|neural network and a linear interpolating lookup map||||

Optimizing Neural Networks for Modeling and Simulation (Machine Learning Blog Part 2)

Why Neural Networks are Effective in Machine Learning Neural networks are powerful machine learning [ML] models that can capture highly nonlinear relationships between inputs and outputs within a dataset while being computationally inexpensive to execute. The benefits of neural networks for modeling and simulation activities, using the simulation platform GT-SUITE, were covered in part 1 […]

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