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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Simulated Versus Measured UHF Radiated Power at LaRC Street Level

The Airspace Operations and Safety Program, System-Wide Safety Project /Technical Challenge 2 – Emerging Operations at NASA Langley aims to increase the flight safety of unmanned aerial vehicles (UAV’s) by informing UAV operators of regions of airspace where the radio frequency (RF) power at designated frequencies exceeds threshold values, indicating possible damage or interference with on-board systems. WinProp electromagnetic simulation software, a commercially available ray tracing program, has been implemented to predict radiated power from known RF sources. As a test case, the NextNav geolocation system, with nine transmitters operating at 926.227 MHz, was selected and modeled together with other existing structures at Langley. The radiated power at street level was calculated and compared with measurements taken at street level by a Signal Hound receiver and an Electro-Metrics antenna mounted on a van. The two sets of results generally agreed to within about 5 dB.

electromagnetic simulation↗
NASA NTRS1988

Neptune radio emission - Predictions based on planetary scaling laws

In this paper a prediction is advanced concerning Neptune's low-frequency radio emission based on the radiometric Bode's law for radio planets in combination with the magnetostrophic scaling law for magnetized planets. The total emitted radio power is predicted to be about 1.6 x 10 to the 7th W, very nearly the same as that predicted and observed for Uranus. Possible emission spectral shapes, based on Saturn and earth-like models, are shown. Using these models, the radio emission frequency range is predicted to extend from approximately 100 to just over 1000 kHz, with a spectral peak between 350 and 500 kHz. If radiation is beamed approximately in the sunward direction, Neptune should be detectable by the planetary radio astronomy experiment onboard the Voyager spacecraft sometime between 45 and 90 days before closest approach. This detection is likely to represent the first direct evidence of a Neptune magnetic field. Possible implications for Neptune's magnetosphere with regard to the time of first detection are discussed.

Desch, Michael D.↗
DOE OSTI2025

Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models

Accurate solar Photovoltaic (PV) power forecasting is important for enhancing both the performance and economic feasibility of PV systems. This study evaluates several deep learning models, including Dense Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid LSTMCNN model, for predicting PV power production one day in advance. Prior to optimization, the models exhibited relatively high errors, with the best model (DNN) achieving a Root Mean Square Error (RMSE) of 31.13 kW and a coefficient of determination (R 2 ) of 62.15 %. After employing Bayesian optimization, the LSTM-CNN model demonstrated the best performance, with the RMSE reduced to 9.79 kW and R 2 improved to 97.62 %, showcasing significant enhancement in predictive accuracy. Here, the economic evaluation considered three cases: rewards for underestimation (0.08 USD/kWh), no rewards, and penalties for both over-and underestimation (120 % of the utility tariff). In the rewards scenario, the LSTM-CNN model reduced the Levelized Cost of Electricity (LCOE) by 4 %, while in the penalty scenario, a backup diesel generator would have increased the LCOE by 49 %. Additionally, the LSTM-CNN model minimized financial losses, achieving the lowest penalties and maximizing net cash flow compared to other models, demonstrating its overall technical and economic superiority.

Deep learning↗
DOE OSTI2026

Observable CMB B -modes from cosmological phase transitions

A B -mode polarization signal in the cosmic microwave background (CMB) is widely regarded as smoking gun evidence for gravitational waves produced during inflation. Here, we demonstrate that tensor perturbations sourced during noninflationary epochs can yield non-negligible B -mode signals, which can in principle complicate the interpretation of future observational data. As a case study, we consider tensor perturbations sourced in the bubble collision stage of a first-order cosmological phase transition occurring in a secluded dark sector. Although phase transitions arise from causal subhorizon physics, they nevertheless exhibit a white noise power spectrum on superhorizon scales. Power is suppressed on the large scales relevant for CMB B -mode polarization, but it is not necessarily negligible. We show that for appropriately chosen phase transition parameters, the maximal B -mode amplitude can compete with inflationary predictions that can be tested with current and future experiments. These scenarios can be differentiated by performing measurements on multiple angular scales, since the phase transition signal predicts peak power on smaller scales.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1989

Experimental assessment of helicopter rotor turbulence ingestion noise in hover

An experiment was conducted to assess the accuracy of a theory for non-isotropic turbulence ingestion. In order to generate non-isotropic turbulence in a controlled environment, a scale model rotor in a closed chamber was used so that the turbulence generated by the rotor was reingested by the recirculating flow. Simultaneous measurements of turbulence inflow properties and far field acoustics were acquired. Measurements confirmed that the inflow turbulence was highly non-isotropic. The measured aerodynamic properties were used as inputs for the noise prediction procedure. The general agreement between the non-isotropic noise prediction procedure and the experiment was good, although the procedure generally overpredicts the quasi-tonal low to mid range frequencies and underpredicts the higher broadband signals. The predicted sound power level as a function of polar angle was in close agreement with measurements, except near the rotor plane, which is not modeled by the present analysis. It is determined that the most sensitive parameter influencing the predicted noise was the turbulence intensity.

Simonich, J. C.↗
NASA NTRS2003

Navier-Stokes and Comprehensive Analysis Performance Predictions of the NREL Phase VI Experiment

A vortex lattice code, CAMRAD II, and a Reynolds-Averaged Navier-Stoke code, OVERFLOW-D2, were used to predict the aerodynamic performance of a two-bladed horizontal axis wind turbine. All computations were compared with experimental data that was collected at the NASA Ames Research Center 80- by 120-Foot Wind Tunnel. Computations were performed for both axial as well as yawed operating conditions. Various stall delay models and dynamics stall models were used by the CAMRAD II code. Comparisons between the experimental data and computed aerodynamic loads show that the OVERFLOW-D2 code can accurately predict the power and spanwise loading of a wind turbine rotor.

NREL (NATIONAL RENEWABLE ENERGY LABORATORY)↗
NASA NTRS1992

Design analysis of bipolar Li-TiS2 batteries

The present study uses an empirical model to assess the feasibility of using the Li-TiS2 bipolar battery for high power applications. Predicted performance outputs at a variety of conditions were calculated. The effects of the design parameters on the performance of bipolar Li-TiS2 batteries are presented. Specific energies greater than 150 Wh/kg can be achieved at low rates. Specific power levels in excess of 100 W/kg can be reached at high rates but with a reduction of the specific energy to less than 70 Wh/kg.

Shen, D. H.↗
DOE OSTI2025

Using a Large Language Model for Accurate Technical Language Generation in the Predictive Maintenance of Circulating Water Systems in Nuclear Power Plants

Machine learning (ML) methods for predictive maintenance (PdM) are emerging as effective proactive strategies for diagnosing equipment degradation and enabling effective decision-making. However, explainability and trustworthiness of artificial intelligence are two salient challenges that need to be addressed for wider deployment of these technologies in nuclear power plants (NPPs). Large language models (LLMs) offer a unique approach to tackle these challenges by explaining PdM, work orders, diagnosis results, and ML algorithms to users, who may not be familiar with ML and PdM in general. Moreover, by dynamically retrieving relevant information from technical documents and evaluating factuality of LLM generation, the accuracy and relevance of LLM generations can be improved. This work demonstrates using LLMs to explain the causes and consequences of circulating water system failures based on multiyear NPP work orders. This work tests the capability of multimodal LLM approaches in explaining the differences in the circulating water system from both the Salem and Hope Creek NPPs using both text and image resources. This work also demonstrates the use of multimodal LLMs in describing the diagnosis tab of a predictive maintenance software named VIsualization for PrEdictive maintenance Recommendation (VIPER) to users.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1980

Design and flight performance of the Pioneer Venus Multiprobe and Orbiter solar arrays

The designs of the solar arrays for the Pioneer Venus Orbiter and Multiprobe spacecraft are described, and the power output predicted for these arrays is compared with the in-space performance. The Orbiter solar array was designed to produce a minimum of 329 W at 28 V after 243 days in Venus orbit, except during eclipses and periapsis phases, when battery power was to be used. After 492 days in orbit, this solar array was producing 365.3 W at 29.6 V, exceeding its design objectives. The Multiprobe solar array produced sufficient power at low sun angles to effect the release of the large probe and the three small probes and to power the scientific instruments onboard the spacecraft during its approach and destructive entry into the Venusian atmosphere.

Goldhammer, L. J.↗
DOE OSTI2025

Data efficiency assessment of generative adversarial networks in energy applications

This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. Generative AI, though seen as a solution to data limitations, requires substantial data to learn meaningful distributions—a challenge often overlooked. This study addresses the challenge through synthetic data generation for critical heat flux (CHF) and power grid demand, focusing on renewable and nuclear energy. Two variants of GAN employed are conditional GAN (cGAN) and Wasserstein GAN (wGAN). Our findings include the strong dependency of GAN on data size, with performance declining on smaller datasets and varying performance when generalizing to unseen experiments. Mass flux and heated length significantly influence CHF predictions. wGAN is more robust to feature exclusion, making it suitable for constrained synthetic data generation. In energy demand forecasting, wGAN performed well for solar, wind, and load predictions. Longer lookback hours and larger datasets improved predictions, especially for load power. Seasonal variations posed challenges, with wGAN achieving a relatively high error of Root Mean Squared Error (RMSE) of 0.32 for load power prediction, compared to RMSE of 0.07 under same-season conditions. Feature exclusions impacted cGAN the most, while wGAN showed greater robustness. This study concludes that, while generative AI is effective for data augmentation, it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Fourth Edition

As the world increasingly seeks low-carbon energy solutions, solar power emerges as the most abundant resource on our planet. However, the challenge of effectively harnessing this energy is crucial in the coming years. Solar energy applications such as photovoltaics, solar heating and cooling, and concentrating solar power use different technologies to capitalize on sunlight. Each system has unique capabilities and requirements, underscoring the need for reliable information about solar resources across diverse installations, from residential rooftops to large-scale power plants. This is especially important for substantial projects, often exceeding $1 billion in construction costs. Before embarking on such ventures, it is imperative to obtain accurate data concerning solar resource quality and reliability at specific sites. Developers require detailed historical information, including seasonal, daily, hourly, and, ideally, subhourly variability to effectively predict a power plant's annual performance. Without these vital data, financial analyses fall short. Moreover, with the growing adoption of distributed photovoltaics, integrating these generation sources becomes critical to maintaining grid reliability and stability. By accurately forecasting generation patterns, utilities and system operators can facilitate greater integration of solar energy, thus ensuring the operational stability of the grid. The complexity and importance of these issues have prompted the foremost experts in the field to collaborate under the auspices of the International Energy Agency's (IEA's) Photovoltaic Power Systems Programme (PVPS) Task 16 to publish this handbook, which summarizes state-of-the-art information about all these topics. The efforts focus on providing reliable data and insights that can help shape our investments in solar energy and drive a sustainable future.

14 SOLAR ENERGY↗
DOE OSTI2025

Wide-ranging predictions of new stable compounds powered by recommendation engines

The computational search for new stable inorganic compounds is faster than ever, thanks to high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive because of the enormous search space and the cost of DFT calculations. To aid these searches, recommendation engines have been developed. We conduct a systematic comparison of the performance of previously developed recommendation engines, specifically ones based on elemental substitution, data mining, and neural network prediction of formation enthalpy. After identifying ways to improve the recommendation engines, we find the neural network to be superior at recommending stable Heusler compounds. Armed with improved recommendation engines, we identify tens of thousands of compounds that are stable at zero temperature and pressure, now available in the Open Quantum Materials Database. We summarize this diverse pool of compounds, including the elusive mixed anion compounds, and two of their many applications: thermoelectricity and solar thermochemical fuel production.

Science & Technology - Other Topics↗
NASA NTRS1989

Results from baseline tests of the SPRE 1 and comparison with code model predictions

The space power research engine (SPRE), a free piston Stirling engine with linear alternator, is being tested at NASA-Lewis as a candidate for high capacity space power. Results are presented of baseline engine tests at design and off-design operating conditions. The test results are compared with code model prediction.

Cairelli, James E.↗
NASA NTRS1989

Results from baseline tests of the SPRE I and comparison with code model predictions

The space power research engine (SPRE), a free-piston Stirling engine with linear alternator, is being tested at NASA-Lewis as a candidate for high capacity space power. Results are presented of baseline engine tests at design and off-design operating conditions. The test results are compared with code model prediction.

Cairelli, James E.↗
NASA NTRS1973

Effect of crossflow velocity on the generation of lift fan jet noise in VTOL aircraft

Analytical studies based on a turbulent mixing noise prediction technique indicate that jet noise power levels are increased when a jet is situated in a crossflow. V/STOL model transport acoustic test data obtained in the NASA Ames 40 ft. x 80 ft. wind tunnel confirmed this jet noise power level increase due to crossflow. Increases up to 6 db at a Strouhal number of 2.5 and crossflow velocity to jet velocity ratio of 0.58 were observed. The power level increases observed in the experimental data confirm the predicted power level increases.

Stimpert, D. L.↗
NASA NTRS1977

Analysis and design of a high power laser adaptive phased array transmitter

The feasibility of delivering substantial quantities of optical power to a satellite in low earth orbit from a ground based high energy laser (HEL) coupled to an adaptive antenna was investigated. Diffraction effects, atmospheric transmission efficiency, adaptive compensation for atmospheric turbulence effects, including the servo bandwidth requirements for this correction, and the adaptive compensation for thermal blooming were examined. To evaluate possible HEL sources, atmospheric investigations were performed for the CO2, (C-12)(O-18)2 isotope, CO and DF wavelengths using output antenna locations of both sea level and mountain top. Results indicate that both excellent atmospheric and adaption efficiency can be obtained for mountain top operation with a micron isotope laser operating at 9.1 um, or a CO laser operating single line (P10) at about 5.0 (C-12)(O-18)2um, which was a close second in the evaluation. Four adaptive power transmitter system concepts were generated and evaluated, based on overall system efficiency, reliability, size and weight, advanced technology requirements and potential cost. A multiple source phased array was selected for detailed conceptual design. The system uses a unique adaption technique of phase locking independent laser oscillators which allows it to be both relatively inexpensive and most reliable with a predicted overall power transfer efficiency of 53%.

Mevers, G. E.↗
NASA NTRS2003

Advanced Data Acquisition Systems

Current and future requirements of the aerospace sensors and transducers field make it necessary for the design and development of new data acquisition devices and instrumentation systems. New designs are sought to incorporate self-health, self-calibrating, self-repair capabilities, allowing greater measurement reliability and extended calibration cycles. With the addition of power management schemes, state-of-the-art data acquisition systems allow data to be processed and presented to the users with increased efficiency and accuracy. The design architecture presented in this paper displays an innovative approach to data acquisition systems. The design incorporates: electronic health self-check, device/system self-calibration, electronics and function self-repair, failure detection and prediction, and power management (reduced power consumption). These requirements are driven by the aerospace industry need to reduce operations and maintenance costs, to accelerate processing time and to provide reliable hardware with minimum costs. The project's design architecture incorporates some commercially available components identified during the market research investigation like: Field Programmable Gate Arrays (FPGA) Programmable Analog Integrated Circuits (PAC IC) and Field Programmable Analog Arrays (FPAA); Digital Signal Processing (DSP) electronic/system control and investigation of specific characteristics found in technologies like: Electronic Component Mean Time Between Failure (MTBF); and Radiation Hardened Component Availability. There are three main sections discussed in the design architecture presented in this document. They are the following: (a) Analog Signal Module Section, (b) Digital Signal/Control Module Section and (c) Power Management Module Section. These sections are discussed in detail in the following pages. This approach to data acquisition systems has resulted in the assignment of patent rights to Kennedy Space Center under U.S. patent # 6,462,684. Furthermore, NASA KSC commercialization office has issued licensing rights to Circuit Avenue Netrepreneurs, LLC , a minority-owned business founded in 1999 located in Camden, NJ.

Perotti, J.↗
NASA NTRS2012

Validation of the Predicted Circumferential and Radial Mode Sound Power Levels in the Inlet and Exhaust Ducts of a Fan Ingesting Distorted Inflow

Fan inflow distortion tone noise has been studied computationally and experimentally. Data from two experiments in the NASA Glenn Advanced Noise Control Fan rig have been used to validate acoustic predictions. The inflow to the fan was distorted by cylindrical rods inserted radially into the inlet duct one rotor chord length upstream of the fan. The rods were arranged in both symmetric and asymmetric circumferential patterns. In-duct and farfield sound pressure level measurements were recorded. It was discovered that for positive circumferential modes, measured circumferential mode sound power levels in the exhaust duct were greater than those in the inlet duct and for negative circumferential modes, measured total circumferential mode sound power levels in the exhaust were less than those in the inlet. Predicted trends in overall sound power level were proven to be useful in identifying circumferentially asymmetric distortion patterns that reduce overall inlet distortion tone noise, as compared to symmetric arrangements of rods. Detailed comparisons between the measured and predicted radial mode sound power in the inlet and exhaust duct indicate limitations of the theory.

Koch, L. Danielle↗