Tag: machine learning
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 […]
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 […]
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 […]
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. […]
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 […]
How Vehicle Cabin Model Order Reduction Can Optimize Passenger Comfort and Range
Why Cabin Modelling? Efficient cabin modelling has become crucial for the new age battery electric vehicles (BEVs) as every bit of energy that can be conserved will help increase the range of a BEV. The goal of a design engineer is to achieve an optimal balance between passenger comfort and the energy consumption of the […]
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 […]
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 […]
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 […]