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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

The global policy landscape of ISO 50001 energy management systems

While many options exist to improve industrial demand-side energy efficiency, energy management systems (EnMSs)—particularly those aligned with ISO 50001—are proven to drive continuous and meaningful energy performance improvements. Governments leverage these EnMSs in their policies to advance national objectives including enhancing industrial competitiveness and achieving environmental goals. Existing research has focused on the impact of EnMSs at the company level, while comprehensive work on EnMSs in a global policy context is lacking. We seek to close this gap by investigating the extent to which current national policies incorporate the utilization of EnMSs, particularly the ISO 50001 standard. Our paper employs a hybrid approach, combining a literature review and expert interviews across 28 governments representing > 86% of global primary energy consumption. We dissect policy mechanisms, governance levels, underlying motivations, and trends in present EnMS policies. We find that > 96% of the investigated countries include EnMSs within their policy scope; 90% of policies including EnMSs utilize the ISO 50001 standard in some capacity. Primary policy motivations include decarbonization, energy savings for industrial competitiveness, and energy system resilience. We highlight that in the EnMS context, policy mixes—combining economic incentives, regulatory instruments, and information-based approaches—are more effective than standalone measures. Our work provides a novel global overview of governmental EnMS policies, moving beyond whether EnMS should be adopted to focus on how they can be implemented most effectively.

Moreno, Francisco Luis↗
DOE OSTI2025

A Novel Approach for Microencapsulating Salt Hydrate-Based Phase Change Materials

Energy storage technologies, particularly those utilizing phase change materials (PCMs), have gained attention for their high energy density and efficient thermal management. PCMs, which store energy through solid-liquid phase transitions, can efficiently capture and release thermal energy, but face the challenge of leakage during the phase change process. Inorganic PCMs, such as salt hydrates, offer high energy storage capacity, but are difficult to encapsulate due to their corrosive nature. Conventional encapsulation techniques for inorganic PCMs are limited, particularly for scalable applications. In this work, we present an innovative method for the encapsulation of salt hydrate-based inorganic PCMs (CaCl 2 ·6H 2 O) using co-axial electrospinning. The process involves the creation of co-axial fibers, with salt hydrate as the core and polymer (e.g., PVP) as the outer shell, effectively preventing leakage and improving the stability of the PCM. This approach demonstrates the potential for scalable microencapsulation of inorganic PCMs, marking the first report of using co-axial electrospinning for this purpose. This novel technique could contribute to enhancing the performance and applicability of PCMs in thermal energy storage systems and other energy efficiency applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Defining Power Efficient Residential Electric Appliances and Equipment

While the long-established concept of "energy efficiency" has successfully reduced energy consumption and bills in homes, it is no longer sufficient to address the modern challenges of electrical load growth, infrastructure upgrades and associated affordability challenges. We introduce the concept of “power efficiency” for household appliances and equipment, providing a framework to support affordable home modernization and grid management. Energy efficiency is the measure of energy used (kWh) to provide comfort/conveniences, power efficiency is the measure of power used (kW) to provide those same comforts/conveniences. Energy efficiency was needed because energy is a limited resource and expensive. Now power efficiency is needed because power delivery into a building is a limited resource and expensive. We propose a new set of metrics for defining and measuring power efficiency by investigating how current appliance nameplate ratings are established, and assessing the availability and use of existing data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Modeling of multi-component precipitation and crystallization for zero-liquid-discharge desalination

This study proposes novel ZLD treatment trains that integrate multi-component chemical precipitation and multi-effect evaporative crystallization for efficient brine management. The methodology emphasizes sustainability by integrating CO₂ for chemical precipitation and reducing environmental impact, exemplified with two case studies: produced water that includes industrial waste heat utilization by adopting the emerging vacuum air-gapped membrane distillation (VAGMD) technology, and brackish groundwater that is abundant in sulphate which undergoes treatment by low-salt-rejection reverse osmosis (LSRRO) with interstage chemical precipitation. Here, this study is the first of its kind to simultaneously account for reducing risk of mineral scaling and effective recovery of valuable solids when incorporating VAGMD and LSRRO in ZLD treatment trains. Using Reaktoro and WaterTAP, both Python-based, open-source platforms, we model the recovery of high-purity magnesium, calcium, and sodium salts while optimizing energy consumption and operational efficiency in proposed ZLD pathways for case studies of produced water and brackish desalination brine management. Validation with experimental and reference data confirms the reliability of the models used. In both case studies, the optimized ZLD process achieves recovery rates of 97%, 99%, and 90% for Mg, Ca, and Na, with purities exceeding 99%, and brine volume reduced to less than 4% of the initial feed flow.

3D CT scans↗
DOE OSTI2024

Synergizing superwetting and architected electrodes for high-rate water splitting

Water splitting is one of the most promising technologies for generating green hydrogen. To meet industrial demand, it is essential to boost the operation current density to industrial levels, typically in the hundreds of mA cm -2 . However, operating at these high current densities presents significant challenges, with bubble formation being one of the most critical issues. Efficient bubble management is crucial as it directly impacts the performance and stability of the water splitting process. Superwetting electrodes, which can enhance aerophobicity, are particularly favorable for facilitating bubble detachment and transport. By reducing bubble contact time and minimizing the size of detached bubbles, these electrodes help prevent blockage and maintain high catalytic efficiency. Here, in this review, we aim to provide an overview of recent advancements in tackling bubble-related issues through the design and implementation of superwetting electrodes, including surface modification techniques and structural optimizations. We will also share our insights into the principles and mechanisms behind the design of superwetting electrodes, highlighting the key factors that influence their performance. Our review aims to guide future research directions and provides a solid foundation for developing more efficient and durable superwetting electrodes for high-rate water splitting.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Hybrid Fuel Cell and Battery Storage Power Management for Grid-Interactive EV Charging Station

With the increasing adoption of renewable energy sources in grid-interactive Electric Vehicle (EV) charging stations, the role of energy storage systems has become critical. While large energy storage systems have mitigated the intermittency of renewable energy, integrating multi-source energy management with prioritized charging can further enhance the reliability of charging stations (CS). This paper presents a decentralized energy management (DEM) approach combining battery energy storage (BES) and fuel cell (FC) systems using a rule-based line resistance correction droop (LRCD) control technique. The proposed droop control dynamically adjusts the gain to balance the state-of-charge (SoC) of the BES, enhancing power support longevity and improving battery life under varying capacity conditions by reducing current stress. Additionally, the paper addresses the challenges of using fuel cells in linear regions to optimize efficiency and manage various charging scenarios. The CS integrates unity power factor grid interaction, and power support for auxiliary loads, maintaining harmonic distortion within 5% during grid islanding. The approach evaluates DC bus voltage regulation under various scenarios of PV array power fluctuations and dynamic load variations, in both grid-connected and standalone operations. In conclusion, the proposed control strategy is validated on a laboratory prototype through various dynamic load variation and grid islanding scenarios.

Khalid, Mohd [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2025

Direct ink writing of shear exfoliated two-dimensional nanomaterial- elastomeric multifunctional nanocomposite

Direct ink writing (DIW) of polymer nanocomposites with high loadings of two-dimensional (2D) nanofillers (graphene and hexagonal boron nitride (hBN)) is challenging because of potential clogging, use of hazardous solvents, and agglomeration. Here, in this work, a shear exfoliation and sieving method to prepare DIW ink with high loading of nanofillers produced from low-cost bulk layered materials such as graphite and bulk hBN powder for successful DIW printing without the use of any solvents, binders, or plasticizers. The single-step exfoliation technique resulted in a composite with substantial layer reduction along the c-axis, as confirmed by SEM, TEM, XRD, and Raman analysis. Incorporating exfoliated graphene (40 wt%) increased viscosity by ∼6 orders of magnitude due to enhanced particle–matrix interactions, leading to pronounced yield stress behavior and a yield stress of approximately 1598 Pa, which enabled excellent shape retention during extrusion. Using the DIW technique, porous structures such as desalination membranes, self-sensing bone scaffolds, thermal management coating, and serpentine strain sensors were fabricated. When tested in a direct contact membrane distillation setup, the fabricated membrane demonstrated a promising permeate flux of 21.85 Lm −2 h −1 and a salt rejection of 74.3 %. The fabricated serpentine sensor exhibited stable signal variations under cyclic tensile loading, with a working range of 0–200 % strain and a maximum gauge factor of 43,735. A cell culture test using the printed bone scaffold demonstrated promising cell attachment and proliferation. The DIW printed hBN nanocomposite exhibited reversible shape change under heat, demonstrating potential 4D printing capability and efficient thermal management when exposed to high heat or flame.

Desalination↗
DOE OSTI2024

Lossless Phonon Transition Through GaN‐Diamond and Si‐Diamond Interfaces

Abstract Advancing Silicon (Si) technology beyond Moore's law through 3D architectures requires highly efficient heat management methods compatible with foundry processes. While continued increases in transistor density can be achieved through 3D architectures, self‐heating in the upper tiers degrades the performance. Self‐heating is a critical problem for high‐power, high‐frequency, wide bandgap, and ultra‐wide bandgap devices as well. Diamond, known for its exceptional thermal conductivity, offers a viable solution in both these cases. Since thermal boundary resistance (between the channel/junction and diamond plays a crucial role in overall thermal resistance, this study investigates various dielectrics for interface engineering, such as Silicon dioxide (SiO 2 ), amorphous‐ Silicon Carbide (a‐SiC), and Silicon Nitride (SiN x ), to make a phonon bridge at gallium nitride (GaN)‐diamond and Si‐diamond interfaces. The a‐SiC interlayer reduces diamond/GaN (<5 m 2 K per GW) and diamond/Si (<2 m 2 K per GW) thermal boundary resistances by linking low‐ and high‐frequency phonons, boosting phonon transport through the interface. Engineered interfaces enhance heat spreading from the channel/junction and rule out premature failure.

Malakoutian, Mohamadali↗
DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗
DOE OSTI2026

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Extending Rucio with modern cloud storage support

Rucio is a software framework designed to facilitate scientific collaborations in efficiently organising, managing, and accessing extensive volumes of data through customizable policies. The framework enables data distribution across globally distributed locations and heterogeneous data centres, integrating various storage and network technologies into a unified federated entity. Rucio offers advanced features like distributed data recovery and adaptive replication, and it exhibits high scalability, modularity, and extensibility. Originally developed to meet the requirements of the high-energy physics experiment ATLAS, Rucio has been continuously expanded to support LHC experiments and diverse scientific communities. Recent R&D projects within these communities have evaluated the integration of both private and commercially-provided cloud storage systems, leading to the development of additional functionalities for seamless integration within Rucio. Furthermore, the underlying systems, FTS and GFAL/Davix, have been extended to cater to specific use cases. This contribution focuses on the technical aspects of this work, particularly the challenges encountered in building a generic interface for self-hosted cloud storage, such as MinIO or CEPH S3 Gateway, and established providers like Google Cloud Storage and Amazon Simple Storage Service. Additionally, the integration of decentralised clouds like SEAL is explored. Key aspects, including authentication and authorisation, direct and remote access, throughput and cost estimation, are highlighted, along with shared experiences in daily operations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Vapor phase deposition of alumina on interior surfaces of tritium storage containers

Efficient tritium management and containment are critical challenges for nuclear fusion. To mitigate issues related to permeation into structural materials and isotopic exchange with protium, permeation barriers must be applied to the nonplanar internal surfaces of tritium handling components. In this study, we developed an atomic layer deposition (ALD) system to deposit films on canisters used to store and transport tritium. Growth conditions for deposition of Al 2 O 3 were mapped spatially along the length of a canister using silicon witness samples characterized by ellipsometry, atomic force microscopy, and x-ray reflectivity (XRR). We identified ALD process windows for growth at temperatures between 100 and 140 °C. Furthermore, we compared films grown on our Si witness samples to those grown on polished stainless steel 316 (SS316). X-ray photoelectron spectroscopy indicates two distinct chemical states for the alumina, while XRR confirms that the overall growth rate is similar to the 1.1 Å/cycle that we observed on the Si witness samples.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Extending SEER for Extreme Heterogeneity

Heterogeneous and multi-device nodes are increasingly common in high-performance computing and data centers, yet existing programming models often lack simple, transparent, and portable support for these diverse architectures. The main contribution of this work is the development of novel SEER capabilities to address this challenge by providing a descriptive programming model that allows applications to seamlessly leverage heterogeneous nodes across various device types. SEER uses efficient memory management and can select the proper device[s] depending on the computational cost of the applications. This is completely transparent to the programmer, thereby providing a highly productive programming environment. Integrating extreme heterogeneity into the SEER library as shown with the use of NVIDIA and AMD GPUs simultaneously allows it to expand and exploit the performance possibilities. Our analysis based on the well-known Conjugate Gradient algorithm reports accelerations above 1.5 × on computationally demanding steps of such an algorithm by using both architectures simultaneously.

Teranishi, Keita [ORNL] (ORCID:0000000166472690)↗
DOE OSTI2026

Wavelength contention in an entanglement network

We investigate spectral resource contention between two entangled photon sources sharing the same wavelength, demonstrating measurable fidelity impact and providing insights that enable more efficient spectral management in flex-grid quantum networks.

Alshowkan, Muneer [ORNL] (ORCID:0000000264293450)↗
DOE OSTI2024

Connected Thermostat Alternatives for Room Air Conditioners and Minisplit Heat Pumps

The availability of smart, connected thermostats has improved climate control, energy efficiency, and grid demand-response programs for central HVAC systems. However, a significant gap exists in addressing integrated control systems for point-source heating and cooling systems such as window air-conditioners (window ACs) and mini-split heat pumps (MSHPs). This report examines the emerging market of third-party connected thermostats tailored for these systems, focusing on their effectiveness, reliability, and potential barriers to adoption.This study evaluates several commercially available products designed for room ACs and MSHPs through a series of laboratory tests. While these infrared-based (IR-based) thermostats offer remote temperature control and scheduling via mobile apps, our findings reveal that none are seamless, with reliability of basic functions being a critical factor. Promising features include integration of indoor air quality metrics and time-of-use pricing, but the latter are not yet available in the U.S. Barriers to broad user acceptance include non-seamless setup processes, challenges in thermostat placement, and unclear product differentiation. There is a pressing need for research and development in enabling MSHPs and central thermostats to coordinate, enhancing energy savings and comfort in retrofit applications. This study underscores the importance of further innovation in connected thermostat technology to address the diverse needs of single-zone HVAC systems and promote efficient energy management in households.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Evaluating the Accuracy of Machine Learning Forecasts

To improve the accuracy of forecasting in machine learning, we must investigate multiple machine learning models and see how accurately they can predict values after training. We used seven machine learning models to try and get more accurate predictions. The models that were used were ARIMA, SES, MLP, CART, LightGBM, and XGBoost. We used a processed dataset from a Terminal at LAX that had the number of people traveling through terminal X every hour in March from 2015-2019. We trained our models with the dates March 6 - March 19 to predict the value for March 20th and the hours 6:00 am to 6:00 pm since those are the most popular traveling hours. By using the different models, we had varying results of accuracy when estimating the amount of people traveling through terminal X on March 20th. We know that machine learning models are helpful for forecasting and by seeing how accurately these models can predict, we can see how forecasting can be helpful for other issues. Using these methods, airports can use forecasting to predict the amount of people coming in and out and can use these predictions to prepare their resource management, operational efficiency, and overall passenger experience.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Brief Survey on High Performance Computing Systems Power Management

This paper provides a survey of software-based power management techniques in High Performance Computing (HPC) systems. Seven existing power management and monitoring tools and frameworks are discussed. These are: Variorum, dynamic energy-performance optimizer (DEPO), Powersched, Bull Dynamic Power Optimizer (BDPO), Energy Aware Runtime (EAR), Global Extensible Open Power Manager (GEOPM), and PoLiMEr. Each of these tools is evaluated based on hardware abstraction, optimization methods, usability, and experimental validation. This survey highlights the diversity of approaches in managing energy efficiency, from vendor-neutral APIs to algorithm-driven power capping, and dynamic frequency adjustments. Given that energy requirements for large computational systems is increasing quickly, the importance of integrating these tools into existing HPC environments and the need for further research in this rapidly evolving field is also discussed.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Smart Planning for Radioactive Source Transport Advanced Tools for Increased Safety and Efficiency

End-of-life (EOL) management of high-activity radioactive sources is made uniquely challenging by the inherent risks associated with storage and transportation of these sources, the complex logistics involved, and the strict requirements for regulatory compliance. Traditional methods lack comprehensive tools for accurate site assessments and precision planning for the transportation of radioactive sources. They also frequently fail to provide the adaptability required to consider diverse operational environments, resulting in inefficiencies and potential safety concerns. This paper introduces a novel software solution developed to address these issues by integrating advanced technologies such as light detection and ranging (LiDAR)-based 3D environment modeling, smart dynamic route planning, and customizable measurement functionalities. This software enables detailed terrain visualizations, facilitating thorough environmental assessments and enabling users to virtually navigate, analyze, and plan site-specific operations. Among the key features are a user-centric interface for virtual navigation, precise site measurement tools for site evaluations, interactive visualizations that highlight potential operational hazards, dynamic route planning capabilities, and real-time collision detection to promote safe workflows. By demonstrating the effectiveness of this tool through real-world application, the present work underscores the tool’s potential to revolutionize radioactive source EOL management by improving operational efficiencies, minimizing risk, and advancing the state of practice to achieve suitable and secure radioactive material handling.

99 - GENERAL AND MISCELLANEOUS↗