Tag: machine learning

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Model Predictive Control with NARX Metamodels: Smarter Torque Requests for Fuel Cell Vehicles

As electrification expands across commercial fleets, engineers are rethinking power management to deliver efficiency, drivability, and robustness under real‑world conditions. By anticipating what the vehicle will need moments ahead, Model Predictive Control (MPC) reduces energy waste, smooths transients, and keeps the power source operating in its most efficient window. MPC paired with a dynamic, data-driven […]

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Modeling the Google Deschutes CDU in GT-SUITE: A Blueprint for Liquid Cooling Success

A Difficult Balance for Data Centers As data centers push toward higher rack power densities and rapidly scaling AI workloads, liquid cooling has become essential for managing extreme thermal loads efficiently. Designing these next generation cooling systems is challenging – engineers must balance reliability, energy use, water consumption, and safety, all while navigating tight deployment […]

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Transforming Data Center Cooling: From Physics-Based Simulation to AI-Powered Control

Data centers face an unprecedented thermal challenge that demands revolutionary approaches to cooling system design and control. As computational demands surge with AI workloads and high-performance computing applications, heat generation has increased dramatically with some next-generation systems producing heat fluxes many times higher than traditional data centers. This exponential growth in thermal loads is pushing […]

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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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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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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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Enhancing Model Accuracy by Replacing Lookup Maps with Machine Learning Models (Machine Learning Blog Part 1)

Machine Learning and Modeling Simulation Machine learning [ML] models, such as neural networks and other types of metamodels, are fast-executing mathematical representations of data that serve a variety of modeling and simulation purposes, including: Replacing computationally expensive physics-based sub-systems in integrated simulation models (for instance, we are using GT-SUITE simulation models for the HVACR industry) […]

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