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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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Predictions from GLIMPS and HFAST design codes are compared with experimental data for the RE-1000 and SPRE free piston Stirling engines. Engine performance and available power loss predictions are compared. Differences exist between GLIMPS and HFAST loss predictions. Both codes require engine specific calibration to bring predictions and experimental data into agreement.
A variety of space applications require the delivery of power into sealed structures. Since the structural integrity can be degraded by holes for cabling we present an alternative method of delivering power and information using stress waves to the internal space of a sealed structure. One particular application of this technology is in sample return missions where it is critical to preserve the sample integrity and to prevent earth contamination. Therefore, the container has to be hermetically sealed and the integrity of the seal must be monitored in order to insure to a high degree of reliability the integrity of the sample return vessel. In this study we investigated the use of piezoelectric acoustic-electric power feed-through devices to transfer electric power wirelessly through a solid wall by using elastic or acoustic waves. The technology is applicable to a range of space and terrestrial applications where power is required by electronic equipment inside sealed containers, vacuum or pressure vessels, etc., where holes in the wall are prohibitive or may result in significant structural performance degradation or unnecessarily complex designs. To meet requirements of higher power applications, the feasibility to transfer kilowatts level power was investigated. Pre-stressed longitudinal piezoelectric feed-through devices were analyzed by finite element models and an equivalent circuit model was developed to predict the power transfer characteristics to different electric loads. Based on the results of the analysis a prototype device was designed, fabricated and a demonstration of the transmission of electric power up to 1.068-kW was successfully conducted. Efficiencies in the 80-90% range were also demonstrated and methods to increase the efficiency further are currently being considered.
The 2022 Asynchronous AM-Bench challenge was designed to test the ability of simulations to accurately predict laser power absorption as well as various melt pool behaviors (width, depth, and solidification) during laser melting of solid metal during stationary and scanned laser illumination. In this challenge, participants were asked to predict a series of experimental outcomes. Experimental data were obtained from a series of experiments performed at the Advanced Photon Source at Argonne National Laboratories in 2019. These experiments combined integrating sphere radiometry with high-speed X-ray imaging, allowing for the simultaneous recording of absolute laser power absorption and two-dimensional, projected images of the melt pool. All challenge problems were based on experiments using bare aluminum solid metal. Participants were provided with pertinent experimental information like laser power, scan speed, laser spot size, and material composition. Additionally, participants were given absorptance and X-ray imaging data from stationary and scanned laser experiments on solid Ti–6Al–4V that could be used for testing their models before attempting challenge problems. In total, this challenge received 56 submissions from eight different research groups for eight individual challenge problems. The data for this challenge, and associated information, are available for download from the NIST Public Data Repository. This paper summarizes the results from the 2022 Asynchronous AM-Bench challenge as well as discusses the lessons learned to help inform future challenges.
The STEP (Stability, Transport, Equilibrium, and Pedestal) integrated-modeling tool has been developed in OMFIT to predict stable, tokamak equilibria self-consistently with core-transport and pedestal calculations. STEP couples theory-based codes to integrate a variety of physics, including magnetohydrodynamic stability, transport, equilibrium, pedestal formation, and current-drive, heating, and fueling. The input/output of each code is interfaced with a centralized ITER-Integrated Modelling & Analysis Suite data structure, allowing codes to be run in any order and enabling open-loop, feedback, and optimization workflows. This paradigm simplifies the integration of new codes, making STEP highly extensible. STEP has been verified against a published benchmark of six different integrated models. Core-pedestal calculations with STEP have been successfully validated against individual DIII-D H-mode discharges and across more than 500 discharges of the H98,y2 database, with a mean error in confinement time from experiment less than 19%. STEP has also reproduced results in less conventional DIII-D scenarios, including negative-central-shear and negative-triangularity plasmas. Predictive STEP modeling has been used to assess performance in several tokamak reactors. Simulations of a high-field, large-aspect-ratio reactor show significantly lower fusion power than predicted by a zero-dimensional study, demonstrating the limitations of scaling-law extrapolations. STEP predictions have found promising scenarios for an EXhaust and Confinement Integration Tokamak Experiment, including a high-pressure, 80%-bootstrap-fraction plasma. ITER modeling with STEP has shown that pellet fueling enhances fusion gain in both the baseline and advanced-inductive scenarios. Finally, STEP predictions for the SPARC baseline scenario are in good agreement with published results from the physics basis.
Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.
A method for calculating the 'guaranteed mean' power output of a wind turbine generator is proposed. The term 'mean power' refers to the average power generated at specified wind speeds during short-term tests. Correlation of anemometers, the method of bins for analyzing non-steady data, the PROP Code for predicting turbine power, and statistical analysis of deviations in test data from theory are discussed. Guaranteed mean power density for the Clayton Mod-OA system was found to be 8 watts per square meter less than theoretical power density at all power levels, with a confidence level of 0.999. This amounts to 4 percent of rated power.
We report turbulence intensity (TI) is often used to quantify the strength of turbulence in wind energy applications and serves as the basis of standards in wind turbine design. Thus, accurately characterizing the spatiotemporal variability in TI should lead to improved predictions of power production. Nevertheless, turbulence measurements over the ocean are far less prevalent than over land due to challenges in instrumental deployment, maintenance, and operation. Atmospheric models such as mesoscale (weather prediction) and large-eddy simulation (LES) models are commonly used in the wind energy industry to assess the spatial variability of a given site. However, the TI derivation from atmospheric models has not been well examined. An algorithm is proposed in this study to realize online calculation of TI in the Weather Research and Forecasting (WRF) model. Simulated TI is divided into two components depending on scale, including sub-grid (parameterized based on turbulence kinetic energy (TKE)) and grid resolved. The sensitivity of sea surface temperature (SST) on simulated TI is also tested. An assessment is performed by using observations collected during a field campaign conducted from February to June 2020 near the Woods Hole Oceanographic Institution Martha's Vineyard Coastal Observatory. Results show that while simulated TKE is generally smaller than the lidar-observed value, wind speed bias is usually small. Overall, this leads to a slight underestimation in sub-grid-scale estimated TI. Improved SST representation subsequently reduces model biases in atmospheric stability as well as wind speed and sub-grid TI near the hub height. Large TI events in conjunction with mesoscale weather systems observed during the studied period pose a challenge to accurately estimating TI from models. Due to notable uncertainty in accurately simulating those events, this suggests summing up sub-grid and resolved TI may not be an ideal solution. Efforts in further improving skills in simulating mesoscale flow and cloud systems are necessary as the next steps.
Distributed Electric Propulsion technology is expected to yield up to a fivefold increase in high-speed cruise efficiency for NASA’s X-57 “Maxwell” flight demonstrator when compared to a combustion-powered general aviation baseline. A portion of this increased efficiency is due to beneficial aero-propulsive interaction inherent to the distributed propulsion architecture. The measure of the relative increase in efficiency between a conventional and distributed propulsion wing will be extracted from comparisons between flight test data from the electrically powered X-57 Mod II configuration with a conventional wing, and from the electrically powered X-57 Mod III/IV configuration with a distributed propulsion wing. Flight test maneuvers that accommodate errors in instrumentation and the flight test environment are developed to establish the power-off drag characteristics for all X-57 configurations. Analysis of these maneuvers with typical errors, including pilot-in-the-loop simulation data that incorporates simulated atmospheric turbulence effects, shows that the proposed power-off flight maneuvers can generate accurate power-off drag predictions for the X-57. These predictions show that the power-off differences in aerodynamic performance between the conventional and distributed propulsion configurations can be accurately measured from flight test data in the presence of typical data error sources.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Enough power flows through U.S. rivers and oceans to meet up to 60% of the country's electricity needs. But before today's early-stage marine energy technologies can harness some of that power, they need to overcome a few challenges, like a volatile ocean and high development costs. And to do that, technology developers need reliable tools to understand and overcome the challenges they face. That's why researchers at the National Renewable Energy Laboratory (NREL) have developed several open-source, validated, and customizable numerical tools for technology developers. With these tools, the marine energy community can assess their devices' potential power output, predict how their technologies might handle extreme loads (like extreme waves), and learn how changing one component might affect their device's performance. The lab's models generate highly accurate, reliable, robust data that can help marine energy devices leap from the computer screen to the ocean and, eventually, the power grid.