Efficiency

The University of Maryland (UMD) will create inverse design tools for the development of enhanced heat transfer surfaces at reduced computational cost. Heat transfer surfaces are used to increase the efficiency of many energy conversion systems, but they are currently designed in a slow, iterative fashion. UMD will use a direct inverse design method map from given environments and performance metrics to design variables or materials.

The United Technologies Research Center (UTRC) will work to accelerate the design of high-efficiency multi-stage compressors, via machine learning (ML), with considerations of aerodynamics, structures and additive manufacturability through their framework, MULTI-LEADER.

Northwestern University will develop a machine learning-enhanced mixed-variable conceptual design optimization framework to construct new functional materials for energy savings. The team will use natural language processing (NLP) and physics-based machine ML to more efficiently guide the autonomous search for materials. The project will deliver a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, and reinforcement learning in Bayesian optimization.

IBM Research will develop a reinforcement learning (RL)-based electrical power converter design tool. Such converters are widely used and critically important in many applications. Designing a specific converter is a lengthy and expensive process that involves multiple manual steps—selecting and configuring the correct components and topologies; evaluating the design performance via simulations; and iteratively optimizing the design while satisfying resource, technology, and cost constraints.

The Princeton University team will use machine learning-enabled methods to transform the modeling and design methods of power magnetics and catalyze disruptive improvements to power electronics design tools. They will develop a highly automated, open-source, machine learning-based magnetics design platform to greatly accelerate the design process, cut the error rate in half, and provide new insights to magnetic material and geometry design.

The University of Michigan-Dearborn will develop a machine learning-enhanced design tool for the automated architectural configuration and performance evaluation of electrical power converters. This tool will help engineers consider a wider range of innovative concepts when developing new converters than would be possible via traditional approaches.

Pacific Northwest National Laboratory (PNNL) will apply multiple machine learning tools to develop next-generation natural gas to electric power conversion system designs. The project leverages a physics-informed machine learning tool for automated reduced order model (ROM) construction. This will significantly reduce prediction errors compared to traditional approaches.

The Massachusetts Institute of Technology (MIT) will develop a machine learning (ML) approach to optimize surfaces for boiling heat transfer and improve energy efficiency for applications ranging from nuclear power plants to industrial process steam generation. Predicting and enhancing boiling heat transfer presently relies on empirical correlations and experimental observations. MIT’s technology will use supervised ML models to identify important features and designs that contribute to heat transfer enhancement autonomously.

Julia Computing, Inc. will develop a neural component machine learning tool to reduce the total energy consumption of heating, ventilation, and air conditioning (HVAC) systems in buildings. As of 2012, buildings consume 40 percent of the nation’s primary energy, with HVAC systems comprising a significant portion of this consumption. It has been demonstrated that the use of modeling and simulation tools in the design of a building can yield significant energy savings—up to 27 percent of total energy consumption. However, these simulation tools are still too slow to be practically useful.

Led by Dr. YuHuang Wang, the “Meta Cooling Textile (MCT)” project team at the University of Maryland (UMD) is developing a thermally responsive clothing fabric that extends the skin’s thermoregulation ability to maintain comfort in hotter or cooler office settings. Commercial wearable localized thermal management systems are bulky, heavy, and costly. MCT marks a potentially disruptive departure from current technologies by providing clothing with active control over the primary channels for energy exchange between the body and the environment.