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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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The Energy 101: Wind presentation, developed for the Energy Technology Innovation Partnership Project (ETIPP), provides an overview of wind energy. It covers fundamental concepts, technologies, considerations, case studies, and additional resources.
In high-level radioactive waste geological repositories, compacted bentonite undergoes coupled thermo-hydraulic processes due to heat released from a central waste canister and groundwater imbibition from the surrounding host rock. An understanding of these processes is essential for long-term simulations of radionuclide migration and canister corrosion, which requires an understanding of temperature effects on the coupled thermo-hydraulic properties governing these processes. In this study, a tank-scale radial infiltration test was used to investigate water imbibition processes in compacted bentonite under a central heater temperature of 200 °C that simulates high thermal gradients in a repository. Interpretation of this test focuses on evaluation of the liquid water wetting front during hydration and the interpretation of the temperature-dependent transient soil water retention curve (SWRC), thermal conductivity function (TCF), and hydraulic conductivity function (HCF). The SWRC during imbibition follows a temperature-dependent wetting path. Temperature effects on the hydraulic conductivity of bentonite in saturated conditions had the greatest effect on the shape of the HCF, with minimal temperature effects at higher suctions. The transient thermal conductivity data matched well with a new TCF linked with the SWRC shape. Although the bentonite layer was restrained, local deformations during hydration may have affected the shapes of the TCF and HCF.
This manuscript contributes a Viewpoint article to ACS Sustainable Resource Management and discusses the graphite supply chain, growing mismatch between graphite demand and global manufacturing capacity, current graphite manufacturing technologies, and different feedstocks.
The lunar surface presents unfavorable constraints and harsh living conditions. To address these challenges, autonomous habitats will require complex integrated systems that combine advanced software, high-performance hardware, and cutting-edge sensors to ensure sustainability, safety, and operational efficiency. Consequently, maintaining a sustainable presence on the Moon requires reliable infrastructure and efficient development, precise monitoring, and utilization of resources within a lunar installation. These elements are essential not only to ensure that lunar settlement can be long-term, self-sustaining, and resource-efficient, but also to serve as a foundation for future missions and eventual human habitation on Mars. Humans are not native to the Moon; therefore, our survival and ability to thrive will depend on autonomous systems that can foster safety and resilience through high-availability architectures, graceful degradation, and highly fault-tolerant spaceflight hardware capable of continuing operation during failures. This requires advanced human-rated distributed systems architectures with specialized electronics, scalable capabilities, and an integrated design approach. Unlike current practices focused on short-term missions and regularly maintained components, permanent lunar compute systems must be designed for extended operations beyond mission durations. This paper explores the necessity of transitioning toward fault- tolerant, highly autonomous hardware systems designed for multi-year missions. It also identifies critical subsystems that require high levels of autonomy, supported by radiation-hardened processors and extreme thermal loads, which are essential to mitigate long-term degradation and ensure sustainable lunar habitation. Finally, the paper aligns with NASA’s identified Civil Space Shortfalls, particularly in high-performance onboard computing, advanced data acquisition, extreme-environment avionics, radiation monitoring and countermeasures, and autonomous health management. It proposes NASA’s new High-Performance Spaceflight Computing (HPSC) processor as a turnkey solution, delivering 100 times the performance-per-watt of legacy rad-hard CPUs and enabling onboard AI, edge computing, and fault-tolerant features essential for sustained lunar autonomy and beyond.
All spacecraft generate and carry contaminants, i.e., unwanted and potentially harmful material. When a spacecraft lands and operates in vacuum, as onto Earth’s Moon, it introduces contaminants into its environment that may compromise mission science objectives and engineering performance. Contamination may degrade sites of unique value to planetary science or in situ resource utilization. This presentation will identify and compare source terms and transport vectors for contaminants – in particular, organic material – generated by landed spacecraft. An integrated modeling framework for the organic contamination footprint of spacecraft missions will be described and presented.
Understanding the fundamental behavior of colloidal fluids in microgravity is critical for advancing material science, manufacturing, and biological processes. In this study we investigate the dynamics of a droplet containing colloidal suspension when ejected in short term microgravity. These conditions are generated using a 2.2 second drop tower. A custom droplet generator was designed to produce consistent size-controlled droplets containing passive colloidal particles. The particles are suspended in water and subsequently ejected out of a hydrophobic wedge under microgravity conditions, where the droplet ejection time can be predicted [Torres & Weislogel, 2021]. The ejection times will be compared with de-ionized water ejected from the same system to investigate the effects of change in density and surface tension caused by the colloids. Successful passive colloidal ejection will lay the foundation for active colloid research in the future.
Energy system projections from analytic models inform actions ranging from short-term and local decisions, such as technology and infrastructure deployment, to global and long-term negotiations and targets. Computational limits require the designers of these models to trade off between coverage and resolution. Some models, such as the Global Change Analysis Model (GCAM), represent all energy sources and uses but at a relatively coarse level of resolution. GCAM balances global supply and demand of all energy carriers by endogenously projecting prices for energy sources and costs of greenhouse gas mitigation while capturing interlinkages between the energy system, water, agriculture and land use, the economy, and the climate. This global model was used to frame the Long-Term Strategy released by the White House in 2021 and has been used to inform national and global economy-wide decarbonization discussions and strategy development for decades. Other models instead focus on a portion of the energy sector with greater detail and resolution. The Regional Energy Deployment System (ReEDS) electricity-sector model, for example, projects capacity expansion with an emphasis on integration of variable renewable energy into the grid of the future. The Transportation Energy and Mobility Pathway Options (TEMPO) transportation-sector model enables analysis of household choices in adoption, charging, and use of electric vehicles. The Scout buildings-sector model supports detailed consideration of the policies and markets that can accelerate the adoption of energy conservation measures in buildings. Such sector-specific models are instrumental in informing technology research, sectoral planning strategies, and sector-specific aspects of greenhouse gas (GHG) mitigation strategies in the United States. These global and sector-specific modeling approaches can complement each other. The global approach ensures consistent, endogenous energy pricing and resource allocation, which can substantially diverge from current conditions in transformative scenarios, while the sector-specific approach facilitates representation of granular details across spatial, temporal, technological, and market dimensions that enable exploration of particular interactions and trade-offs. This report presents the results of recent work to explore the differences and tradeoffs between these approaches by comparing GCAM with the sector-specific ReEDS, TEMPO, and Scout models. The report compares both model structures and results, and discusses their potential relevance and applications.
Thermochemical energy storage (TCES) offers a transformative approach to address grid instability by harnessing reversible chemical reactions for efficient heat storage and release. Here, we introduce pharmaceutical organic salt hydrates as a class of materials with exceptional performance for low-grade waste heat recovery. We demonstrate ibuprofen sodium dihydrate (ISD) as an example organic hydrate exhibiting a dehydration temperature range of 60-110 °C and a remarkable dehydration enthalpy of up to 59.5 kJ/mol of water, ideally suited for capturing industrial and residential waste heat. Using rigorous multimodal characterization, including thermogravimetric analysis, differential scanning calorimetry, in-situ FTIR, in-situ PXRD, and NMR, we demonstrate ISD's superior thermal, chemical, and structural stability over 150 hydration-dehydration cycles, achieving an unprecedented cycling efficiency of ~99.9%. Compared to conventional inorganic salt hydrates like strontium chloride hexahydrate and calcium oxalate monohydrate, ISD showcases enhanced durability without deliquescence or pulverization, even under high-humidity conditions. In-situ analyses confirm the transition from ISD to ibuprofen sodium anhydrous (ISA) proceeds with structural reorganization, thereby combining the dehydration mechanism with phase transitions, resulting in higher energy storage capacity. Microstructural analyses reveal that repeated water intercalation and structural transitions aid in creating significant porosity that enhances water transport kinetics, further improving the hydration/dehydration performance. By combining the phase change and chemical dehydration mechanisms, ISD paves the way for designing a new class of organic salt hydrates, offering tunable properties to meet diverse thermal energy storage demands and supporting sustainable grid resilience.
NASA’s long-term goal is to deploy humans to the Moon and, from there, advance human exploration to Mars, with Artemis missions as pivotal milestones. Raman spectroscopy can uniquely identify minerals, compounds, water states, and other materials, providing distinctive fingerprints for classification. A Raman instrument has been successfully deployed and utilized on the Mars surface via the Perseverance rover, but has not yet been utilized at the lunar surface The SUCR DALI project is working towards developing a Raman spectroscopy instrument to be applied in various lunar mission concepts, including within the Artemis program. The objective of my research is to assist in the maturation of the proposed SUCR DALI lunar Raman instrument through the development of an autonomous detection and classification model capable of identifying minerals and water states on the Moon’s surface.
The third iteration of a magnetic resonance velocimetry (MRV) challenge comparison activity consisting of 3D flow measurements for a turbulent flow in a water channel moving past centrally positioned cubic obstacles is reported. In this challenge iteration, MRV measurements are coupled with either temperature or concentration measurements to extend the diagnostic utility. Five research teams from around the world conducted the measurements on a single shared apparatus. The water channel included partial elements along the channel sidewalls that varied in height and precluded easy optical accessibility. In addition to a turbulent mainstream flow, a secondary flow entered the channel between the second and third cubic elements from a square injector hole at the channel bottom wall. The injector jet interacts with the mainstream flow and mixes turbulently as it advects downstream. For the selected flow regime and water solutions used as working fluids, the mixing of the higher temperature or concentration through the secondary flow with the mainstream flow satisfies the same dynamics so that both temperature and concentration fields behave as passive scalars and can be directly compared. The measurements are explained in detail by each participating team, and the results are interpolated onto a common grid and compared using line profiles, contour plots, and isosurfaces.
Data-driven techniques for energy demand forecasting continue to emerge with promising impacts on distribution grid planning. However, the development of robust and generalizable machine learning models requires that representative high quality training data are available. Distributed energy resources have begun to embed intelligence, gathering large amounts of data on customer demand, behavior, and household devices that are connected to the grid. Though utilities aggregate meter-level demand data for load shaping, demand response, outage management, reliability planning, and billing applications, there lies an inherent privacy concern in sharing consumption data that may identify individual consumer behavioral patterns. Hence, while sharing the data is crucial, the private sensitive customer data must be safeguarded from being exposed or manipulated. In this study, we propose a roadmap for implementing a based privacy preserving framework to support the advancement of data-driven analytics in data-sensitive distributed energy resources environments. The roadmap incorporates federated learning–a distributed training framework, differential privacy–a statistical framework that provides guarantees to safeguard the leakage of sensitive data, secure multiparty computation and homomorphic encryption– techniques for encrypting model gradients and applying secure aggregation on the server. Moreover, we perform baseline experiments on the federated short-term load forecasting (STLF) task using open-source residential load profile datasets, offering insights into the challenges of integrating differential privacy into federated learning.
In response to the needs of a rapidly growing private fusion industry, the U.S. Fusion Materials Coordinating Committee (FMCC) and the broader U.S. fusion materials research community undertook an extensive effort to create a comprehensive roadmap for fusion materials development. The result of this effort was the U.S. Fusion Materials Community Roadmap (US-FMCR), which describes the steps needed to advance the technical maturity of leading candidates for plasma-facing materials and structural materials for fusion power plants from laboratory-scale experiments to a point of sufficient technological readiness for industrial adoption and implementation. However, researchers face significant resource constraints as well as very aggressive pilot plant development timelines. Thus, the research strategies detailed in the US-FMCR require further assessment to downselect the specific tasks that must be prioritized within the next two to three years, in order to make the most efficient use of funding, human resources, and experimental facilities. This paper presents an overview of the US-FMCR and its development process. We also present the subset of research objectives that the FMCC identified as the most urgent research priorities for the U.S. fusion materials research community. The state-of-the-art of materials research is also highlighted for each class of materials considered in the US-FMCR. The recommendations presented here integrate an extensive evaluation of the current status of fusion materials research with a broad cross-section of opinion from the wider U.S. fusion community.
The Mu2e experiment requires a production target that is capable of operating under extreme thermal conditions caused by an 8 GeV proton beam. This project’s objective supports the development of the Mu2e Pro- duction Target by testing the Stickman model’s thermal behavior. Angel Flores Luviano has assisted Jonathan Williams in progressing this project by contributing to the development of a Radiative Cooling Test Fixture (RCTF) that will be used to evaluate the thermal behavior of the new production target model. Engineering calculations were performed to an- alyze thermal performance and pressure drop within the cold well and wa- ter cooling circuit. These calculations also determined the optimal sizing for key components of the water system. An engineering note was made to document these calculations. CAD models of the water circuit piping, thermocouple mounting bars, and radiator chimney were developed, and prototype test rig components were fabricated using 3D printing. Future work will focus on continued RCTF development which includes control system integration, heater hardware design, and interfaces that can be scaled up to increase thermal capacity.
The C2SAGES project evaluated the feasibility of a community geothermal system for the planned Windy Ridge affordable housing development in Hinesburg, Vermont. Led by GTI Energy with Vermont Gas Systems, LN Consulting, NREL, and Frontier Energy, the work assessed technical design, energy performance, costs, business models, community engagement, maintenance, workforce development, and permitting. The proposed system was designed to serve 100% of the development’s heating, cooling, and domestic hot water loads. Compared with a baseline using air-source heat pumps and natural gas water heating, the geothermal system was estimated to reduce HVAC and domestic hot water energy use by about 45% to 48%, lower operating and maintenance costs, and reduce 30-year life-cycle costs by 37% for Phase 1 and 10% for Phase 2. Technical testing and modeling indicated that the Windy Ridge site is suitable for a community-scale geothermal system. The project also developed borehole field layouts, piping concepts, pump house designs, controls, maintenance plans, and supporting engineering drawings. The business model analysis found that first cost, ownership structure, and customer affordability remain major deployment challenges. Utility-led maintenance and operation were viewed favorably, but traditional utility cost-recovery models may require subsidy or revised financing structures to be practical for affordable housing. Community engagement highlighted the need for clear public education, transparent financing, reliable long-term maintenance, trained technicians, and the potential to pair geothermal systems with weatherization. Overall, the report concludes that community geothermal is technically feasible and offers meaningful energy, emissions, and life-cycle cost benefits, but broader deployment will depend on workable financing models and workforce readiness.
On behalf of the NASA Headquarters Microgravity Research Division and the Microgravity Combustion Science Discipline Working Group, we are pleased to present these proceedings of the Fifth International Microgravity Combustion Workshop. At the time we go to press we expect to welcome to the workshop over 250 presenters and participants from U.S. academia, industry, and government; and from at least 8 international partner countries. We come together for this workshop at the beginning of the International Space Station era, where the future of microgravity combustion science programs will unfold over the next few years. As we accelerate our preparations for the ISS, this book and the growing literature base cited herein provide a summary of the accomplishments of approximately forty space-flight experiment-missions (plus nearly countless drop tests and aircraft parabolas) and a valuable resource for the new experiment ideas of the future.
Conventional tomographic reconstruction typically depends on centralized servers for both data storage and computation, leading to concerns about memory limitations and data privacy. Distributed reconstruction algorithms mitigate these issues by partitioning data across multiple nodes, reducing server load and enhancing privacy. However, these algorithms often encounter challenges related to memory constraints and communication overhead between nodes. In this paper, we introduce a decentralized Alternating Directions Method of Multipliers (ADMM) with configurable quantization. By distributing local objectives across nodes, our approach is highly scalable and can efficiently reconstruct images while adapting to available resources. To overcome communication bottlenecks, we propose two quantization techniques based on K-means clustering and JPEG compression. Numerical experiments with benchmark images illustrate the tradeoffs between communication efficiency, memory use, and reconstruction accuracy.
Thermal comfort during space flight is discussed. Heat production of man during space flight and wear loss as a mean of dissipating heat are described. Water cooled garments are also considered, along with tolerance for extreme heat and body heat storage. Models of human temperature regulation are presented in the form of documented FORTRAN programs.
As NASA prepares for Mars colonization, resource conservation will be critical for survival. Artificial Intelligence (AI) powered waste sorting technologies, already emerging on Earth, offer promising solutions for recycling and material recovery. These systems use advanced sensors and machine learning algorithms to identify and separate materials with remarkable accuracy. On Mars, where every item has significant value, efficient recycling will be essential to reduce resupply needs and support closed-loop life support systems. This paper explores how terrestrial AI-based trash sorting technologies can be adapted for Martian conditions, focusing on challenges such as the harsh surface environment, minimizing system mass, power, volume, and estimating waste composition. Addressing these issues will be key to enabling sustainable operations on the Red Planet.