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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 use of radioisotope thermoelectric generators (RTG) as the primary source of energy in unmanned spacecraft is discussed. RTG output control, power conditioning system requirements, the electrical design, and circuit performance are also discussed.
Energy releases from extragalactic radio sources
Energy yield measurements of catabolic and anabolic activity in autotrophically growing Hydrogenomonas eutropha
Microgrid connected building communities are projected to play an integral part in the clean energy transition. These types of systems, when integrated with distributed energy resources (DERs) such as combined heat and power (CHP), district heating and cooling, renewable generation, and energy storage, can provide clean, reliable power to critical facilities and vulnerable communities. The intermittent nature of renewable generation is a challenge when integrating renewables into any grid system, but particularly in islanded microgrids. The University of Texas at Austin (UT) operates an islanded microgrid powered by a CHP plant, while also utilizing district heating and cooling systems and thermal energy storage (TES). High fidelity operating data was used to develop a validated reduced order model of UT’s integrated campus energy systems to serve as a testbed for use in a case study. Hypothetical renewable energy installations on land owned by UT in west Texas were modeled and integrated into the validated campus models along with battery energy storage (BES). Simulations showed that a combination of renewable energy from wind, and optimally controlled 24-hour thermal and battery storage systems could reduce carbon dioxide emissions on campus by 45.4%. The additional retrofit of burner systems to utilize hydrogen natural gas blends resulted in an overall annual emissions reduction of 54.7%. Carbon capture and storage eliminated the majority of the remaining emissions with increased plant energy expenditure. The presented simulations display the practical limitations of a CHP system complemented by renewable generation and short-term storage in eliminating emissions. Furthermore, results highlight the need for further research and development in long duration storage technologies and hydrogen fueled turbines to increase penetration of renewable energy and reduce emissions.
Solar light, solar wind, differences between temperatures of the lunar surface, solar photosphere, and deep space, as well as noticeable changes of surface temperature in the absence of atmosphere provide opportunities for energy production and storage on the Moon using generators ranging from solar panels and thermocouples to devices using phase transitions or magnetism. There are multiple and diverse possibilities for energy generation and storage. Compositions of the relevant materials can be compared to abundances of chemical elements in lunar regolith.
Report of Activities developing and deploying training designed for the next generation work force for energy efficient and high performance construction.
This study presents wind and solar power generation estimates derived from the US Department of Energy’s Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) Regionally Refined Models (RRM) over California at 3.25 km and 800 m horizontal resolutions, using the Python wrapper for the System Advisor Model (PySAM). The resulting wind and solar generation estimates are compared to monthly capacity factors reported to the Energy Information Administration (EIA), High-Resolution Rapid Refresh (HRRR, 3 km resolution) forecast model, and E3SM North American regionally refined model (NARRM, 25 km resolution). We systematically assess the impacts of generation modeling assumptions, meteorological models, and horizontal resolution. Results show that resolution plays a dominant role for wind energy: increasing from 25 to 3.25 km brings qualitative and quantitative improvements, most notably by resolving the phase error in the seasonal cycle found in coarser simulations. However, further refinement to 800 m offers minimal gains. SCREAM performs better than HRRR for solar power generation in single- and dual-axis tracking systems, likely due to more accurate surface radiation. The sensitivity of PySAM to system configuration, particularly for axis-tracking modeling in photovoltaics, is also highlighted. Overall, SCREAM-RRM shows strong potential for high-resolution energy assessments, with future progress depending on more in situ observations and clearer quantification of uncertainties in generation modeling.
Through the U.S. Department of Energy's Energy to Communities (E2C) program, NLR, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional energy expertise to inform key upcoming decisions. This Expert Match request supported Crow Creek in Girdwood, AK with a feasibility analysis for a microgrid to provide year-round power across the community.
A two dimensional multi-block grid generator was developed for a new design and analysis system for studying multi-blade row turbomachinery problems with an axisymmetric viscous/inviscid 'average passage' through flow code. TIGGERC is a mouse driven, fully interactive grid generation program which can be used to modify boundary coordinates and grid packing. TIGGERC generates grids using a hyperbolic tangent or algebraic distribution of grid points on the block boundaries and the interior points of each block grid are distributed using a transfinite interpolation approach. TIGGERC generates a blocked axisymmetric H grid, C grid, I grid, or O grid for studying turbomachinery flow problems. TIGGERC was developed for operation on small high speed graphic workstations.
The interplay between energy, climate, and weather is becoming more complex due to increasing contributions of renewable energy generation, energy storage, electrified end uses, and the increasing frequency of extreme weather events. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and subjective process. In this work, we assess datasets from various global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We present evaluations of their skills with respect to the historical climate and comparisons of their future projections of climate change for two climate change scenarios. We present the results for different climatic and energy system regions and include interactive figures in the accompanying software repository. Previous work has presented similar GCM evaluations, but none have presented variables and metrics specifically intended for comprehensive energy systems analysis including impacts on energy demand, thermal cooling, hydropower, water availability, solar energy generation, and wind energy generation. We focus on GCM output meteorological variables that directly affect these energy system components including the representation of extreme values that can drive grid resilience events. The objective of this work is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and scenarios in subsequent work.
Precise timing and data transmission within stringent time constraints are critical for numerous applications, such as robotics, virtual and augmented reality, industrial automation, energy and medical business, and various other sectors. Time-sensitive networks (TSN) and fifth-generation wireless communications (5G) are crucial for industrial communications, enabling convergent communication for various services using a common network core. Applications that are time-sensitive and require deterministic communications with low latency fall into this category, such as generation plant systems. This paper presents a simulated model of 5G-TSN for a fossil power plant based on the wired network parameters implemented in situ. Metrics analysis, comparison and future work are presented. © 2024 IEEE.
This article describes the theory, analysis, and initial bench-top testing of a minimally invasive, rotational resonator designed to produce small amounts of electrical energy for use in oceanic observation buoys. Here, this work details the systems of equations that govern such a resonator, its potential power production, and its predicted effects on the modified motion of the buoy. Finally, a bench-top test apparatus is designed and experimented upon to identify the system and verify the system of equations empirically.
A deep molecular-level understanding of the multivalent electrolyte and its correlation with the electrochemical properties is crucial for designing optimized electrolytes for next-generation rechargeable batteries. Comprehensive knowledge of the atomic level of the solvation structure and its connection with electrochemical stability and ion transport properties is especially critical. However, the interaction of these three components coupled with clear atomistic insights is lacking in the literature. Here, our current contribution evaluates representative electrolytes with the bis(trifluoromethanesulfonyl)imide (TFSI) anions for multivalent cations of Mg, Ca, and Zn, at different ionic conditions with and without a cosolvated environment in ether-based solvent. Two critical problems are investigated: first, resolving the solvation structures in the electrolyte solutions as a function of concentrations through pair distribution function analysis and the corresponding electrochemical transport properties; second, unmasking the quantitative correlation of the atomistic environment with both electrochemical kinetics and cation dependence. We discovered that the magnesium- and calcium-based electrolytes display versatile coordination lengths but poor average anodic stability due to ion pairing with TFSI - . On the contrary, the zinc-based electrolytes show the shortest solvent coordination lengths, shielding the Zn cation from rigid solvent interactions and resulting in the highest anodic stabilities. Calcium-based electrolytes exhibit the longest and most concentration-independent coordination lengths. This work provides valuable insights into the molecular structural and electrochemical features of diverse multivalent electrolyte systems with cations in various solvation environments, emphasizing the importance of the solvation structure and construction in designing high-performance electrolytes.
Neutrino astrophysics - supermassive stars, quasars, and extragalactic radio sources
In this work, we demonstrated how the Clean Sustainable Electrochemical Treatment process can be used to process seawater while simultaneously generating molecular hydrogen. The specific goal of this project is to (1) demonstrate selective removal of chemical oxygen demand over the oxygen and chlorine evolution reactions, (2) optimize electrode composition to improve electrochemical performance, and (3) demonstrate long-term (>100 h) stability experiments.
The Low Power Microrobotics (LPM) project is a NASA NIAC-funded multi-disciplinary research effort involving the research and advancement of low power electronics, low power mobility and their integration with state of the art microbial fuel cell for power generation. The goal of this project was to advance the capabilities of each of these systems with a 10-year future target of a near one kilogram space exploration vehicle that contains each of these systems. In order to accomplish this task, each system was investigated independently in order to establish the power requirements/limitations while considering how each system affects the other systems. This was performed through numerous trade studies and experimental work, primarily focused around the power capacity of the microbial fuel cell base-lined for this project and how its strengths and limitations affected the design of the remaining onboard systems. Finally an end-to-end system test utilized a microbial fuel cell to charge a super capacitor through an energy harvesting circuit, which was discharged to activate a robotic locomotion system.
This project developed and demonstrated a predictive, data-driven vehicle control system designed to improve energy efficiency and driving performance. The team created intelligent self-driving car technology that optimizes fuel and electricity use by proactively planning vehicle actions. By combining Level 4 autonomous driving capabilities with vehicle-to-everything (V2X) connectivity, the system enables vehicles to adjust speed and change lanes in response to traffic signals, surrounding vehicles, and road conditions, reducing unnecessary stops and delays. In testing, the system improved vehicle fuel economy by more than 30% and reduced travel time by approximately 10%, compared to a conventional adaptive cruise control baseline. These results demonstrate the technical effectiveness of using predictive, V2X-enabled strategies, such as traffic light timing and surrounding traffic awareness, to inform real-time vehicle powertrain control and driving behavior. Additionally, a supporting cloud platform was developed to provide dispatch and route recommendations as well as to log vehicle data, demonstrating the economic feasibility of this approach at the fleet level. By optimizing dispatching and routing operations, this technology enables electric fleet operators to use their vehicles more efficiently and reduce reliance on diesel backups, lowering both operating costs and energy consumption. Overall, this project’s technology advances the future of clean, energy-efficient transportation, enabling vehicles and fleets to reduce energy waste, cut costs, and lower emissions through intelligent automation and connectivity.
The Ohio State University’s ARPA-E NEXTCAR project was a multi-phase, multi-year research, development, and demonstration program focused on improving the energy efficiency of connected and automated vehicles (CAVs). The team developed and validated advanced vehicle motion and powertrain control algorithms that coordinate propulsion and automation systems to optimize energy use. Key technologies included Dynamic Skip Fire engine control, predictive eco-driving functions such as Eco-Approach and Departure (Eco-AND) and Eco-Adaptive Cruise Control (Eco-ACC), and powertrain-agnostic optimization frameworks for hybrid, plug-in hybrid, and battery electric vehicles. The project successfully demonstrated up to 30% energy-efficiency improvement during real-world testing at the Transportation Research Center and the American Center for Mobility. The outcomes provide a foundation for scalable, cost-effective deployment of energy-optimized CAV technologies across the automotive industry.