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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DEVAP-EDDR-TES (Simulation framework for a desiccant assisted air conditioning system with heat pump regeneration and energy storage) [SWR-24-66]

This software is a simulation framework that models a load flexible air conditioner system. The system consists of an evaporatively cooled liquid desiccant air conditioner (eLD-AC) subsystem, an electrically driven desiccant regenerator (EDDR) subsystem, and a stratified liquid desiccant storage (SLDS) subsystem. The software can be used to 1) predict the steady-state performance of the system given user-specified convergence criteria; 2) predict the dynamic performance of the entire system over a typical drive cycle operation subjected to user-specified building thermal loads and desired electrical load profile; 3) evaluate the synergy of all three subsystems operating altogether and improve the energy storage control strategy.

Huang, Ransisi↗
DOE OSTI2026

Solar Panel Installation at Goucher College

As part of our multi-phased sustainability initiative, Goucher College is implementing a series of necessary changes to become a model “green” campus. This comprehensive approach includes implementing systems designed to reduce energy consumption, water usage, and waste production by 2030. The first phase of this holistic endeavor is to develop two independent solar projects. One will be on the Facility Management Services building (FMS) and the other will be on the Central Utility Plan (CUP). The anticipated duration of this project is expected to be 24 months. When completed, the new photovoltaic panels (PVPs) and resulting solar array system will help offset campus energy consumption. The projects will also help the College achieve its goal of working towards operating primarily on renewable energy. During initial review of the project, it was revealed replacing the roofs prior to installing new PVPs would not only provide a sound structural foundation for the projects, but the new roofing systems will also aid de-carbonization. The project’s scope is divided between two buildings. Building A: FMS is the first building and Building B: CUP is the second building.

14 SOLAR ENERGY↗
DOE OSTI2026

Simulation-Based Validation of An Open-Source, Scalable Framework for Building Energy Management in Small and Medium-Sized Commercial Buildings

Abstract: Small and medium-sized commercial buildings (SMCBs) represent 94% of U.S. commercial buildings but encounter substantial obstacles in adopting Building Energy Management (BEM) systems. Current approaches exhibit fundamental limitations: vendor-specific API platforms restrict interoperability through proprietary ecosystems; commercial automation software demands extensive technical expertise and licensing costs; open-source IoT solutions lack native support for building automation protocols and semantic models. This paper introduces a configuration-driven web interface framework addressing the gap between smart device advancements and accessible BEM software infrastructure for SMCBs. The framework leverages VOLTTRON middleware integrated with an automated converter that processes unified YAML configurations into heterogeneous system files, reducing required configuration artifacts from six separate files to a single unified specification. The system architecture enables vendor-agnostic operation through BACnet and Modbus protocols while supporting semantic building model integration via automated Brick Schema parsing. Configuration-driven interfaces automatically adapt to diverse HVAC types without custom development. Simulation-based validation using BOPTEST demonstrates automatic interface generation between fan coil and hydronic systems, with the automated converter successfully generating all platform-specific outputs from the single YAML input. The result demonstrates the framework's capability to streamline BEM system deployment through reduced configuration complexity. This work bridges simulation capabilities with operational deployment, demonstrating how virtual testbeds validate generalizable software frameworks for real-world building automation.

Chung, Jihoon [ORNL] (ORCID:0000000184880815)↗
NASA NTRS2022

2022 Power Global Community (Gloco) Summit Summary Report

The Prediction Of Worldwide Energy Resources (POWER) Project 25th year of providing global solar and meteorology data products to the world. The goals of this event were to inform users of new, enhanced features, gather feedback on services, identify new requests and requirements, and capture more thorough user stories while identifying new user communities and partnership opportunities. The event was “virtual” and had over 580 registrants. Over the two-day event, the Summit was attended by 162 unique attendees representing 24 countries.

NASA↗
DOE OSTI2026

Prime Time for Model-Predictive Control? Assessing the Technical and Market Readiness of Advanced Controls in Buildings

Despite three decades of extensive research and field testing that have consistently validated the benefits of Model Predictive Control (MPC) in building applications, the technology has seen limited market adoption. This paper evaluates the readiness of MPC for widespread deployment, showcases recent demonstrations and field tests across diverse building types, including residential, small commercial, large commercial, and campus settings. Our results demonstrate that MPC can optimize system operations to achieve load shifting, minimize curtailment of on-site generation, and reduce energy costs by up to 80 %, while maintaining or improving occupant comfort. We also show that MPC can effectively control large assets, such as MW-sized thermal storage systems, and respond to dynamic pricing signals. However, achieving scale remains difficult due to labor-intensive workflows, reliance on a “PhD-in-the-loop” for MPC design and maintenance, susceptibility to fragile data infrastructure, and persistent workforce education and acceptance barriers. To bridge this gap, we outline a transition from bespoke, labor intensive prototypes toward streamlined, segment-targeted deployment strategies that leverage model templates, semantic tools, and generative AI. By automating control configuration and reducing engineering effort, these recommendations provide a pathway for transforming successful research demonstrations into scalable, market ready solutions for MPC-based controls.

Pritoni, Marco↗
DOE OSTI2025

Adaptive Computing for Scale-Up Problems

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptive to some objective such as optimization or model training. The framework enables the characterization and management of uncertainties associated with predictive models of complex systems when scale-up questions lead to significant model extrapolation. A key advancement of this framework is its integration of multi-fidelity surrogate modeling, uncertainty management, and automated orchestration of various computing and experimentation resources into a single integrated software package. This enables efficient multi-fidelity modeling across multiple computing resources by incorporating real-world constraints such as relative queue times and throughput on individual machines into the multi-fidelity sampling decision. We discuss applications of this framework to problems in the renewable energy space, including biofuels production, material synthesis, perovskite crystal growth, and building electrical loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A Systematic Review and Integrated Approach to Modeling of Aging Utility Scale PV Systems

The growing deployment of utility-scale photovoltaic (PV) systems has increased the importance of techno-economic modeling operational photovoltaic (PV) systems for predicting energy yield, optimizing asset management, and informing financial decisions. Through a systematic review of literature and current industry practices, we review the different common modeling practices of a system's configuration and age, performance and degradation, operation and maintenance (O&M), while also focusing on specific considerations for repowering, revamping, and decommissioning. Building on the synthesis, we develop a structured framework for techno-economic modeling of operating PV systems that integrate performance and degradation analysis, a decommissioning and repowering cost model that estimates the system's end-of-life costs to reduce uncertainty quantifications and improve consistency across the sector. This research contributes to improved modeling methodologies and potentially to reduced financial performance requirements by providing practitioners with input resources and practical approaches to estimate performance, degradation, and costs associated with continued operation, revamping, repowering, or decommissioning decisions.

14 SOLAR ENERGY↗
DOE OSTI2024

Clarifying the formation of equiaxed grains and microstructural refinement in the additive manufacturing of Ti-Cu

Controlling microstructural evolution in metallic additive manufacturing (AM) is difficult, especially in producing refined as-built grains instead of coarse, directional grains. Traditional solutions involve adding inoculants to AM feedstocks, but titanium (Ti) alloys cannot employ this approach without producing detrimental secondary phases. Ti-Cu (Ti-copper) alloys offer a solution through constitutional supercooling and/or solid state thermal cycling under AM conditions. This work analyzes a compositionally graded directed energy deposition (DED) Ti-Cu build, single-melt laser tracks, and dilatometric heat treatments to evaluate if, when, and by what mechanism(s) microstructural refinement occurs. Refinement by inoculation of unmelted powder particles was also considered. Constitutional supercooling produced no net microstructural refinement as any equiaxed dendrites which form are remelted with new deposition. This finding agreed with solidification modeling of powder bed fusion-laser beam (PBF-LB) and DED builds. Solid state thermal cycling refined microstructures only during ex-situ dilatometric heat treatments, suggesting build parameter optimization is needed to achieve refinement in-situ. Accidental heterogeneous nucleation on unmelted Ti powder, originating from the different thermophysical properties of Ti and Cu, provided the most significant microstructural refinement. This work systematically assesses the microstructural refinement mechanisms of Ti-Cu in AM builds and offers insights into microstructural control in eutectoid alloys.

36 MATERIALS SCIENCE↗
NASA NTRS1996

A dynamic response model for pressure sensors in continuum and high Knudsen number flows with large temperature gradients

This paper develops a dynamic model for pressure sensors in continuum and rarefied flows with longitudinal temperature gradients. The model was developed from the unsteady Navier-Stokes momentum, energy, and continuity equations and was linearized using small perturbations. The energy equation was decoupled from momentum and continuity assuming a polytropic flow process. Rarefied flow conditions were accounted for using a slip flow boundary condition at the tubing wall. The equations were radially averaged and solved assuming gas properties remain constant along a small tubing element. This fundamental solution was used as a building block for arbitrary geometries where fluid properties may also vary longitudinally in the tube. The problem was solved recursively starting at the transducer and working upstream in the tube. Dynamic frequency response tests were performed for continuum flow conditions in the presence of temperature gradients. These tests validated the recursive formulation of the model. Model steady-state behavior was analyzed using the final value theorem. Tests were performed for rarefied flow conditions and compared to the model steady-state response to evaluate the regime of applicability. Model comparisons were excellent for Knudsen numbers up to 0.6. Beyond this point, molecular affects caused model analyses to become inaccurate.

Whitmore, Stephen A.↗
DOE OSTI2025

Introduction: Neuromorphic Materials

The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.

Circuits↗
DOE OSTI2024

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, in this study, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Sand-Based Thermal Storage for Building Heating Applications: A District Energy Case Study

Buildings account for 40% of global energy consumption and contribute to 30% of global carbon emissions. As energy from renewable sources increases in availability and building designers push for increased electrification, thermal energy storage (TES) systems will play a crucial role in extending the usable time horizon of renewable energy. While water, molten salt, and phase change materials are typically used for building TES heating applications, silica-sand has emerged as an alternative medium for concentrated solar power applications due to its low cost, wide availability, and comparable system efficiency. This paper proposes a new silica-sand particle-based TES system for building heating applications. In this work, a novel steam plant for district heating applications is first designed to utilize silica-sand TES, which can be used for different district energy systems. To demonstrate the silica-sand TES plant performance, the design is modelled in Modelica based on a case study on the University of Colorado Boulder’s campus. The simulation results show that the sand TES plant is more costly to operate than a gas-boiler based plant due to the low cost of natural gas, while the site EUI and carbon intensity can be improved. This novel system shows initial promise as a low-carbon alternative to conventional natural gas steam boilers but will require further modelling and follow-up research to improve its energy efficiency. An eventual rise in natural gas prices, and reduction of electricity prices, could improve the economic viability of this system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

Effects of Rotation on Internal Structure and Dynamics of Main-Sequence Stars

Current state-of-the-art computer simulations allow us to build 3D dynamical and radiative models of stars from physical first principles with a high degree of realism. The radiative 3D dynamical stellar models obtained with the StellarBox code take into account the effects of turbulence, stellar abundances, a realistic equation of state, and radiative energy transport. In this talk, I will discuss the effects of rotation on the turbulent dynamics and surface structure for a 1.47Msun star for rotational periods of 1 and 14 days. The simulations are performed with the computational domain at various latitudes. The models reproduce stellar granulation, the subsurface shear layer, structural changes in convection, and the tachocline, which is the interface between the inner radiative zone and the outer convection zone and plays a crucial role in stellar variability. In particular, the model results reveal the formation of differential rotation and meridional circulation.

SMD↗
NASA NTRSDate not supplied

Lightweight Thrust Chamber Assemblies using Multi-Alloy Additive Manufacturing and Composite Overwrap

Additive Manufacturing (AM) has brought significant design and fabrication opportunities for complex components with internal features such as liquid rocket engine thrust chambers not previously possible. This technology allows for significant cost savings and schedule reductions in addition to new performance optimization through weight reduction and increased margins. Specific to regeneratively-cooled combustion chambers and nozzles for liquid rocket engines, additive manufacturing offers the ability to form the complex internal coolant channels and the closeout of the channels to contain the high pressure liquid propellants with a single operation. Much of additive manufacturing development has focused on monolithic alloys using Laser Powder Bed Fusion (L-PBF), which do not allow for complete optimization of the structure. The National Aeronautics and Space Administration (NASA) completed feasibility of an AM bimetallic L-PBF GRCop-84 copper-alloy combustion chamber with an AM electron beam freeform Inconel 625 structural jacket under the Low Cost Upper Stage Propulsion (LCUSP) Project. A follow-on project called Rapid Analysis and Manufacturing Propulsion Technology (RAMPT) is under development to further expand large-scale multi-alloy thrust chambers while maturing composite overwrap technology for significant weight savings opportunities. The RAMPT project has three primary objectives: 1) Advancing blown powder Directed Energy Deposition (DED) to fabricate integral-channel large scale nozzles, 2) Develop composite overwrap technology to reduce weight and provide structural capability for thrust chamber assemblies, and 3) Develop bimetallic and multi-metallic additively manufactured radial and axial joints to optimize material performance. In addition to these primary manufacturing developments, analytical modeling efforts compliment the process development to simulate the AM processes to reduce build failures and distortions. The RAMPT project is also maturing the supply chain for various manufacturing processes described above in addition to L-PBF of GRCop-42. This paper will present an overview of the RAMPT project, the process development and hardware progress to date, material and hot-fire testing results, along with future developments.

Additive Manufacturing↗
DOE OSTI2024

Performance Modelling and Yearlong Outdoor Degradation Analysis of a GaAs//Si Tandem Module

Silicon-based tandem photovoltaic cells and modules are forecasted to enter mass production around the year 2026. Previous research efforts on tandem photovoltaic technology have focused heavily on increasing cell efficiencies. However, for successful large-scale deployment of tandem modules, reliability and long-term durability will be equally important. Outdoor performance data beyond the one-year mark enable sophisticated degradation analysis and help build confidence in the reliability and durability of this new technology.

degradation rate↗
DOE OSTI2026

Radiative Cooling Paints: Application on Warehouses and Integration with Phase Change Materials

Despite extensive research on radiative cooling (RC), researchers are still working to understand its best implementation strategy and potential benefits. Understanding this could be an instrumental part of reducing building energy costs and combating climate change. In this work, we introduce two concepts to achieve this goal and broaden the application of RC paints. While building energy simulations for RC are common, most studies focus on single-layer paints in residential settings. Limited research exists on warehouses, and no literature has conducted multi-climate zone analyses or included RC technologies with >90% reflectance. As RC technology advances in efficiency, versatility, and affordability, large-scale simulations across diverse climates are crucial, particularly for warehouses as a passive indoor temperature control solution. Here, a TRNSYS-modeled single-story warehouse assesses the energy impact of BaSO4-based RC paints. Another under-explored RC application is its integration with phase change materials (PCMs), which provide thermal regulation via latent heat storage. Though RC and PCMs have been combined by placing PCMs atop, beneath, or within RC layers, studies focus on either experiments or modeling, with no validation between the two. Here, we develop a simple RC+PCM experimental and modeling setup, placing RC paint atop an aluminum cell filled with a Rubitherm PCM.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗