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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Grid-Forming Inverter-Based Resource Research Landscape: Understanding the Key Assets for Renewable-Rich Power Systems

The shift to net zero energy systems has changed the face of our power grid. Traditional large-scale synchronous generators found inside coal and natural gas plants are being replaced with inverter-based resource (IBR) technologies. This transition to an IBR-dominant power grid introduces new characteristics, altering how our grid operates. Therefore, the role of IBRs has expanded, requiring them to provide a range of essential services to keep our grid reliable, resilient, and secure.

energy management↗
NASA NTRS2014

Controllable Multi-Functional Heat Exchanger for Space Applications

Team Spacering will design SpaceRing, a cooling and waste heat recycling system that addresses NASA’s interest for a cooling and power-generating unit in its prototype deep space habitat[1]. The merits of this system are as follows: - Controllable cooling is critical in maintaining a habitable environment and in the functionality of life support systems, powering electronics and other devices in a spacecraft. - Power is expensive to produce in a space environment. Converting wasted thermal energy into electricity will save energy and money for the space station. Space habitation is a highly complex endeavor. Consequently, the SpaceRing must adhere to additional constraints, which include: - Lack of natural fluid or gas convection due to micro-gravity environment - Limited range for heat source and cold sink temperature - Subject to high fluctuations of incoming thermal energy - Minimal weight due to the high cost of transporting items into space After consulting with NASA and experts in thermal studies, several design objectives were identified and are listed below in order of importance: - High cooling capacity - Reliable safety control and alert system - Maximization of energy generation from wasted heat - Optimization of cooling capability and power generation - Long operational life and minimization of maintenance In space, conventional cooling systems utilize passive heat pipe systems for small-scale heat rejection and pumped pipes with heat exchangers for large-scale heat rejection. Thermal power generation in space is mainly achieved by using thermoelectric material that produces electric energy from temperature differentials. We propose a design that combines cooling and thermoelectric power generation. Using a hot pipe connected to a heat source, a cold pipe connected to a radiator, a heat exchanger in contact with both pipes, and a pump in each pipe to move the fluid, we make an active cooling system. The thermoelectric material is inserted in the heat exchanger between the hot and cold pipes to extract energy from the maximum temperature difference coming from the two pipes. It also provides controllable cooling capacity through the use of the thermoelectric material as cooling modules. We have tested our system under target heat source and sink conditions and measured the temperatures and flow rates. Our preliminary results are promising, demonstrating the output of 25 Watts of power on average. We are in the process of running control tests to compare the heat transfer performance without the thermoelectric generators, which is vital in determining whether or not the generators introduce an unacceptable amount of thermal resistance. While some control tests have been run already, the results have been noisy. We have modified our testing environment and data acquisition methods to allow for simple comparison between the control test results and thermoelectric performance results. Lastly, we have modified the heat exchanger design slightly to include acrylic baffles. This will allow the heat to flow through the heat exchanger more efficiently. We have presented our preliminary results to NASA and will deliver the final product by the end of May 2014.

Lee Xiong↗
NASA NTRS2017

Evaluation of a Variable Thickness Hybrid Composite Bull Gear

For several years, NASA Glenn Research Center and the U.S. Army Research Laboratory have been investigating hybrid (composite/steel) gear technology for use in vertical lift drive systems. The hybrid gear concept replaces the structural portion of a gear between the shaft and the gear rim with a lightweight carbon fiber composite, in an effort to reduce the overall weight of a gear and increase the drive system power density. Past research includes both small-scale and large-scale hybrid gear concepts, all of which have a constant composite thickness throughout. The design described in this paper is of a variable thickness, such that the composite is thickest at the inner diameter and this thickness is gradually reduced toward the outer diameter. The resulting "stair stepped" design stems from dropping plies of the braided carbon fiber prepreg composite fabric gradually with increased radius. Additionally, the interlock pattern at the inner metallic adapter was adjusted slightly from previous designs to obtain a better stress distribution on the inner metallic adapter. The manufactured variable thickness web was tested both in static torsion tests and operationally in a relevant gearbox environment. The results of these experiments will be presented and compared to a baseline steel configuration.

Gears; Drive Systems; Rotorcraft Propulsion;↗
DOE OSTI2026

Characterizing core and edge turbulence regimes with fluctuation imaging diagnostics in Wendelstein 7-X

Two density fluctuation imaging systems, phase contrast imaging (PCI) and gas puff imaging (GPI) measure spatially resolved density fluctuations with high time resolution throughout the core plasma (PCI) and in the scrape-off layer (GPI) of the Wendelstein 7-X (W7-X) stellarator. Both systems combined give a comprehensive overview of overall fluctuation levels, spectral properties such as their distribution in frequency and wavenumber space as well as their spatial distribution. These tools are used to assess changes in density turbulence in three representative discharges that transition into stable divertor detachment by different strategies (impurity seeding, density ramping and power starvation). Several general trends are identified when the radiated power fraction is systematically increased: In the plasma edge, the line emission observed by GPI shifts radially inward with a drop in electron temperature, and normalized intensity fluctuation profiles follow this inward shift. Skewness and kurtosis of these edge fluctuations are reduced, indicating a reduction of large intermittent transport events, and poloidal phase velocities decrease in magnitude. These observations are consistent with a reduced power input into the plasma edge and a general reduction of turbulent activity. Core density fluctuation levels remain nearly constant in the impurity seeding scenario, indicating that detachment does not significantly impact turbulence there. However, a strong reduction in the dominant outboard fluctuation phase velocity is observed that deviates from the previous interpretation of neoclassical radial electric field changes, showing that the core plasma is not completely unaffected. In the density ramp and power starvation scenarios, undesirable and irregular large-scale events arise clearly in both diagnostic systems as the radiative fraction is increased. Impurity seeding therefore seems to be a promising strategy on W7-X to achieve detachment without significantly altering core turbulence, especially when targeting a specific operating point in core density and heating power.

Wendelstein 7-X↗
DOE OSTI2022

2021 NIRT Mini Drill (After Action Report)

During the summer and fall of 2021, several functional area drills were held that focused on exercising Consequence Management’s (CM) ability to extract and use data from RadResponder for the purpose of answering intermediate-phase questions presented as technical inject requests for information (RFI) in Sandia National Laboratories (SNL) Consequence Management Operational System (COSMOS) software. The scenario chosen was that of Northern Lights 2016 (NL16) which was a large-scale nuclear power plant (NPP) release exercise in the state of Minnesota. The NL16 data was extracted from the Radiological Assessment and Monitoring System (RAMS) event where it was created and was reformatted for implanting to a new RadResponder event. Next, the beta-version of a laboratory sample data simulator was used to generate more sample data that was injected to the event. Five “mini-drills” were devised with each prompt defined by a data-based need. For each drill, a team of assessment and NARAC scientists worked the problem using the drill prompt and the available data in RadResponder. The teams held a kickoff meeting, had several days to work the problem, and then reported their results as well as observations in a hotwash. Several areas for improvement in both the software and process were identified during the course of these drills. This report will document the process of addressing each RFI and the discovered gaps in both software capability and methodology so that they can be considered for future development and investment by the CM and NIRT programs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1980

An assessment of inductive coupling roadway powered vehicles

The technical concept underlying the roadway powered vehicle system is the combination of an electrical power source embedded in the roadway and a vehicle-mounted power pickup that is inductively coupled to the roadway power source. The feasibility of such a system, implemented on a large scale was investigated. Factors considered included current and potential transportation modes and requirements, economics, energy, technology, social and institutional issues. These factors interrelate in highly complex ways, and a firm understanding of each of them does not yet exist. The study therefore was structured to manipulate known data in equally complex ways to produce a schema of options and useful questions that can form a basis for further, harder research. A dialectical inquiry technique was used in which two adversary teams, mediated by a third-party team, debated each factor and its interrelationship with the whole of the known information on the topic.

Leschly, K. O.↗
NASA NTRSDate not supplied

Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS)

To meet the needs of future deep space exploration, NASA is interested in large-scale hardware systems in the agency’s thrust areas of solar power, communications, habitats and science interests. Scalable in-space assembly of physical systems is critical to massless exploration and in-space reliance goals. The ARMADAS project demonstrates the autonomous assembly of digital materials and structures. This provides automation technologies with potential for meeting long duration and deep space infrastructure needs, such as construction and maintenance of long duration spaceport, surface infrastructure, and habitat scale systems. Project demonstrations to date include a system that can fit into a small satellite-sized payload, which automatically assembles into primary structures, such as a small habitat module or array/antenna, using onboard robotic assemblers.

In Space Assembly↗
DOE OSTI2020

Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming (MIP), sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions, and affine subspaces where the optimal solution is likely to lie, leading to a significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that using the proposed techniques, SCUC can be solved on average 4.3 times faster with optimality guarantees and 10.2 times faster without optimality guarantees, with no observed reduction in solution quality. Out-of-distribution experiments provide evidence that the method is somewhat robust against data-set shift. Summary of Contribution. The paper describes a novel computational method, based on a combination of mixed-integer linear programming (MILP) and machine learning (ML), to solve a challenging and fundamental optimization problem in the energy sector. The method advances the state-of-the-art, not only for this particular problem, but also, more generally, in solving discrete optimization problems via ML. We expect that the techniques presented can be readily used by practitioners in the energy sector and adapted, by researchers in other fields, to other challenging operations research problems that are solved routinely.

Machine Learning↗
DOE OSTI2020

Sage: parallel semi-asymmtric graph algorithms for NVRAMs

Non-volatile main memory (NVRAM) technologies provide an attractive set of features for large-scale graph analytics, including byte-addressability, low idle power, and improved memory-density. NVRAM systems today have an order of magnitude more NVRAM than traditional memory (DRAM). NVRAM systems could therefore potentially allow very large graph problems to be solved on a single machine, at a modest cost. However, a significant challenge in achieving high performance is in accounting for the fact that NVRAM writes can be much more expensive than NVRAM reads. In this paper, we propose an approach to parallel graph analytics using the Parallel Semi-Asymmetric Model (PSAM), in which the graph is stored as a read-only data structure (in NVRAM), and the amount of mutable memory is kept proportional to the number of vertices. Similar to the popular semi-external and semi-streaming models for graph analytics, the PSAM approach assumes that the vertices of the graph fit in a fast read-write memory (DRAM), but the edges do not. In NVRAM systems, our approach eliminates writes to the NVRAM, among other benefits. To experimentally study this new setting, we develop Sage, a parallel semi-asymmetric graph engine with which we implement provably-efficient (and often work-optimal) PSAM algorithms for over a dozen fundamental graph problems. We experimentally study Sage using a 48--core machine on the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) equipped with Optane DC Persistent Memory, and show that Sage outperforms the fastest prior systems designed for NVRAM. Importantly, we also show that Sage nearly matches the fastest prior systems running solely in DRAM, by effectively hiding the costs of repeatedly accessing NVRAM versus DRAM.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Demand Response Optimization and Management System for Real-TIme (DROMS-RT)

To design and demonstrate DROMS-RT, a highly distributed Demand Response Optimization and Management System for Real-Time (DROMS-RT) power flow control to support large scale integration of distributed renewable generation into the grid. AutoGrid developed a novel control and communications platform to allow highly dispatchable demand response (DR) services in time frames suitable for providing ancillary services to the transmission grid. These services will be substantially less expensive and more efficient than other forms of ancillary services options currently available to manage the intermittency associated with large-scale renewable integration. DROMSRT successfully leveraged Automated Demand Response (ADR) by fundamentally re-thinking the architecture of the DR platform from the ground up and by developing innovative new technologies in a number of areas related to DR. DROMS-RT leveraged the low-cost, open, interoperable DR signaling technology, OpenADR, and low-cost, internet-protocol based telemetry solutions to reduce the cost of hardware. This allowed DROMS-RT to provide dynamic price signals to millions of OpenADR clients. Statistically rigorous signal processing techniques were developed to reliably detect even small load reductions in the presence of noisy baseline profiles. Novel forecasting engines based on modern online machine learning algorithms enabled accurate individualized forecasts for customer loads in the presence of dynamic pricing signals, and a real-time decision engines enabled continuous optimization and optimal dispatch of DR resources across a large portfolio of heterogeneous loads that respond at varying time-scales. Moreover, the real-time optimization conducted by the decision engine can utilize grid physics to maximize load reduction at the transmission system in addition to the distribution sites, for more efficient grid operation. Finally, the Software-as-a-Service (SaaS) availability of the DROMS-RT platform has reduced the cost of deployment and enable participation of small commercial and residential customers in DR who otherwise would not be able to do so.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Electrode Erosion and Prefire Studies Towards Fusion Scale Pulsed Power

This study presents a comprehensive investigation of electrode erosion and discharge behavior in spark gap switches over long switching cycle lifetimes. Brass, copper–tungsten (CuW), and stainless steel electrodes are tested under controlled conditions to quantify material degradation, debris accumulation, and changes in breakdown voltage. High-resolution imaging and statistical analysis of spark channel locations and gap breakdown voltages reveal how surface evolution influences long-term performance and reliability. These results provide essential data for lifetime modeling and inform design strategies for pulsed power systems in emerging applications such as private sector fusion energy and large-scale facilities like Sandia’s Z Machine and proposed ZX upgrades, where high repetition reliability and predictable behavior are critical.

electrical breakdown↗
DOE OSTI2020

Techno-Economic Assessment of Destabilized Li Hydride Systems for High Temperature Thermal Energy Storage

A comprehensive techno-economic analysis of destabilized Li hydrides, used as thermal energy storage systems in concentrating solar power plants, is presented and discussed. Two systems, operating at temperatures on the order of 550–650 °C, are selected as thermal energy storage units for steam power plants, namely the Si-destabilized Li hydride (LiSi) and the Al-destabilized Li hydride (LiAl). Two thermal energy storage systems, operating at temperatures on the order of 700–750 °C, are selected for integration in supercritical CO2 power plants, namely the Si-destabilized Li hydride (LiSi) and the Sn-destabilized Li hydride (LiSn). Each storage system demonstrates excellent volumetric capacity, achieving values between 100 and 250 kWhth/m3. The LiSi-based thermal energy storage systems can be integrated with steam and supercritical CO2 plants at a specific cost between 107 US$/kWhth and 109 US$/kWhth, with potential to achieve costs on the order of 74 US$/kWhth under enhanced configurations and scenarios. The LiAl-based storage system has the highest potential for large scale applications. The specific cost of the LiAl system, integrated in solar steam power plants, is equal to approximately 74 US$/kWhth, with potential to reach values on the order of 51 US$/kWhth under enhanced performance configurations and scenarios.

Corgnale, Claudio↗
DOE OSTI2022

Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

ASPEC: Solar power satellite

The solar power satellite (SPS) will provide a clean, reliable source of energy for large-scale consumption. The system will use satellites in geostationary orbits around the Earth to capture the Sun's energy. The intercepted sunlight will be converted to laser beam energy that can be transmitted to the Earth's surface. Ground systems on the Earth will convert the transmissions from space into electric power. The preliminary design for the SPS consists of one satellite in orbit around the Earth transmitting energy to a single ground station. The SPS design uses multilayer solar cell technology arranged on a 20 km squared planar array to intercept sunlight and convert it to an electric voltage. Power conditioning devices then send the electricity to a laser, which transmits the power to the surface of the Earth. A ground station will convert the beam into electricity. Typically, a single SPS will supply 5 GW of power to the ground station. Due to the large mass of the SPS, about 41 million kg, construction in space is needed in order to keep the structural mass low. The orbit configuration for this design is to operate a single satellite in geosynchronous orbit (GEO). The GEO allows the system to be positioned above a single receiving station and remain in sunlight 99 percent of the time. Construction will take place in low Earth orbit (LEO); array sections, 20 in total, will be sailed on solar wind out to the GEO location in 150 days. These individual transportation sections are referred to as solar sailing array panels (SSAP's). The primary truss elements used to support the array are composed of composite tubular members in a pentahedral arrangement. Smart segments consisting of passive and active damping devices will increase the control of dynamic SPS modes.

Source record↗
DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗
NASA NTRS1978

Large scale static tests of a tilt-nacelle V/STOL propulsion/attitude control system

The concept of a combined V/STOL propulsion and aircraft attitude control system was subjected to large scale engine tests. The tilt nacelle/attitude control vane package consisted of the T55 powered Hamilton Standard Q-Fan demonstrator. Vane forces, moments, thermal and acoustic characteristics as well as the effects on propulsion system performance were measured under conditions simulating hover in and out of ground effect.

Source record↗