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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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Under the MuSiKAL project, we developed a framework for a coastal digital twin (DT) platform capable of integrating diverse data resources, configuring multiscale model simulations, performing SciML‐accelerated predictions, with applications primarily driven by storm surge and heavily rainfall events impacting the Gulf Coast of the U.S.
Under the MuSiKAL project, we developed a framework for a coastal digital twin (DT) platform capable of integrating diverse data resources, configuring multiscale model simulations, performing SciML‐accelerated predictions, with applications primarily driven by storm surge and heavily rainfall events impacting the Gulf Coast of the U.S.
The primary aim of this proposal is to improve the accurate prediction of hydrodynamic performance and dynamic load responses of the AQ10 floating axial-flow tidal turbine with a tri-cat mooring configuration. The validation of reduced-order modeling approaches with high-fidelity model will be implemented. Additionally, the frequency response domain, Response Amplitude Floating Wind (RAFT) toolbox plus an optimizer expanded for marine hydrokinetic turbines under the Submarine Hydrokinetic And Riverine Kilo-megawatt. Systems (SHARKS) program will be used for designing and exploring different key design parameters (platform dimension, mooring layout and its parameters) of next marine hydrokinetic (MHK) turbine generation.
Under the MuSiKAL project, we developed a framework for a coastal digital twin (DT) platform capable of integrating diverse data resources, configuring multiscale model simulations, performing SciML‐accelerated predictions, with applications primarily driven by storm surge and heavily rainfall events impacting the Gulf Coast of the U.S.
Enhanced Geothermal Systems (EGS) produce clean energy by circulating fluid through hot rock deep underground and bringing that heat to the surface to generate electricity. For this process to work reliably, fluids must be able to move efficiently through networks of natural or engineered fractures in the rock. Enhancing and maintaining subsurface permeability over time is essential for sustainable energy production. However, fluid injection changes the underground temperature, pressure, rock stress, and chemistry, which can alter permeability and sometimes trigger earthquakes. Predicting these interconnected processes remains a key challenge. To address this, we combined high-temperature laboratory experiments with high-fidelity simulations to better understand how fractures in geothermal reservoirs evolve over time. Our experiments measured how fractures respond to stress, slip, slip rate, and chemical reactions under geothermal conditions. These data were integrated into coupled thermal-hydrological-mechanical-chemical and earthquake (THMC+E) models tailored to the Utah FORGE site. The validated modeling framework improves predictions of reservoir performance and seismic response and helps guide operational decisions. This work reduces technical risk and strengthens the scientific foundation needed to make geothermal energy a reliable and scalable clean energy resource.
In this work we investigate the behavior of stable marine boundary layers located near the coast of the Northeastern United States. Using the ExaWind large eddy simulation (LES) codes, three stable atmospheric conditions were chosen to match the Cape Wind measurements of Archer et al. with wind speeds of 5 m/s, 10 m/s, and 15 m/s at the 20 m measurement height. The behavior of the stable boundary layers, including mean flow quantities and turbulent statistics, are examined and compared to previous computations of the neutral and unstable offshore boundary layer at the same location. This study also examines the domain and mesh requirements necessary to capture the turbulent scales for the stable offshore boundary layers. Finally, we compare solutions computed using both Nalu-Wind and AMR-Wind solvers, and compare their predicted solutions and performance in this study.
This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.
As part of the PV Magazine Webinar 'View-factor vs. ray tracing - which bifacial modelling techniques should you use?,' the second part lead by NREL will explore the different rear-irradiance calculation software, which fall into two categories: view-factor and ray-tracing models. View factor models assume isotropic scattering of reflected rays, allowing for calculation of irradiance by integration. Due-diligence software such as PVSyst or SAM use the view-factor model. Ray-tracing models simulate multipath reflection and absorption of individual rays entering a scene. Raytracing software such as bifacial_radiance, which is the only available open-source toolkit, offers the possibility of reproducing complex scenes, including shading or finite-system edge effects. This can be useful to evaluate specific system geometries, shading and obstructions, areas of different albedo and electrical mismatch, all topics of interest to the prediction of bifacial performance. Model agreement for view factors and bifacial_radiance software is improving and has been shown to be between 2% (absolute) when compared with measured results.
Biomass derived pyrolysis oil is a promising renewable energy source. Due to the variety of biomass feedstocks, the bio-oils could contain a wide range of organic constituents which would provide unique corrosive environments for structural materials. A preliminary work exposed different classes of steels and stainless steels to selected organic constituents of biomass pyrolysis oils and found that corrosion susceptibility, assessed using electrochemical impedance spectroscopy (EIS), was prominent in the steels with Cr lower than 11 wt.%. Although this result suggests stainless steels will resist corrosion by certain organic constituents, it may not be sufficient to predict the corrosion performance in real bio-oils. Therefore, this study attempted electrochemical measurements on Cr-alloyed steels and stainless steels directly exposed to a biomass derived pyrolysis oil. To overcome the low conductivity of bio-oils, a customized Luggin capillary was designed to minimize the ohmic path in bio-oils. The results of this work will be reported.
The pool boiling process can be observed in several energy conversion processes including commercial and industrial refrigeration, industrial air-cooling operations, and power generation. The process becomes more involved when pool boiling in a tube bundle is considered. In the current study, a numerical model is developed to predict the key performance parameters of a flooded evaporator while considering a range of working fluids. A kettle reboiler configuration was considered, and a performance model was developed to account for boiling on individual tubes, merging of vapor bubbles, and movement under gravity. A volume of fluid (VOF) model was used to deal with the different phases in the simulation. Various fluids were considered in this study including HFE-7000, HFE-7300, and water. The trajectories of the bubbles were tracked, and the resulting information has been summarized in physical measurable quantities.
Synchrotron high energy X-rays and neutrons are powerful complementary tools for structure-property relationship studies by probing materials under complex external stimuli and measuring the bulk-average materials properties and response mechanism, thanks to the high brightness and advanced instrumentations. The research of using the high energy X-ray or neutron scattering instruments at large facilities in high entropy alloys has greatly increased in alloy design, synthesis, property evaluation, performance testing, and predictive model validation, and thus accelerates the next generation structural material discoveries.
The Heat Pipe-cooled Microreactor (HPM) is one of the micro nuclear reactor designs under active study at the U.S. Idaho National Laboratory. Among the major concerns of HPM research is to understand the startup behavior of the heat pipe-cooled system associated with the startup of the liquid-metal heat pipes initialing from frozen state. The startup of liquid-metal heat pipes typically involves a number of nonlinear mass and heat transport processes including the phase change from solid to liquid and vapor. Hence, it is still a huge challenge to simulate the liquid-metal heat pipe startup using conventional CFD methods and software. The major difficulties of numerical CFD modeling come from the phase-change process, multiphase interaction, microporous wick flow, and compressible gas dynamics that occur during startup of the liquid-metal heat pipes. This paper proposes a simplified conduction-based method to provide practical insights into the entire startup process of the liquid-metal heat pipes while mitigating the challenges of addressing all the complex physics. We discuss the theoretical basis and modeling assumptions to analyze the liquid-metal heat pipe startup from frozen state based solely on heat-conduction equations. Then, the proposed model is implemented into the commercial CFD software to verify the model performance. The model prediction results are discussed via the comparison with the experimental data obtained from sodium heat-pipe startup experiments.
This paper describes a thermal-hydraulic modeling method of a closed-circuit electro-hydraulic actuator (EHA). Despite the high energy efficiency of EHAs, it is always important to assess their cooling requirements, to guarantee that the operating temperature remains within an acceptable range. The method proposed in this paper is based on a lumped parameter approach and simplifies some of the complex heat transfer processes by introducing empirical correction factors. The model is validated by means of temperature measurements on an EHA architecture developed by the author. The good match between the simulation results and experiments confirms the capability of the proposed methodology to predict the temperature performance of the reference EHA under different drive cycles. The paper presents a detailed analysis of the power losses and passive heat dissipation for the reference system. Based on this analysis, the cooling requirements of the EHA are studied.
We present realistic theoretical predictions of the performance of the next-generation long-wave infrared (LWIR) laser for advanced particle acceleration research at the Accelerator Test Facility (ATF) of Brookhaven National Laboratory. Two upgrades are planned for the present ATF CO2 laser, which currently produces up to 5 TW peak power in 2 ps pulses at 9.2 μm. For the first upgrade, the deployment of a ten-millijoule solid-state seed laser at 9.3 μm will reduce the pulse duration 4 times, to 500 fs, and increase the peak power to 15 TW. The second upgrade will be the implementation of the postcompression of this pulse. This will reduce the pulse duration to three optical cycles (100 fs) and increase the usable peak power to 25 TW.
A system for predicting pressure waves in a sonic boom footprint calculates alternative footprints and pressure waves along a flight path based on speed and altitude modifiers. The available, adjustable parameters are bounded within a flight envelope and along an approved flight corridor. The system receives and incorporates data from external sources, such as weather data, that impacts sonic boom pressure wave predictions and aircraft performance characteristics.
Enhanced Geothermal Systems (EGS) are located in impermeable host rock. Therefore, to achieve promising circulation, hydraulic stimulation is a key factor. Stimulation involves injecting a combination of fluids and proppant particles into existing fractures under high pressure. The effectiveness of stimulation depends on a variety of different factors, including the pressurization scheme and the optimal choice of carrying fluid and proppant particles to be used. For example, high-density particles can settle out of the injected slurry quickly, whereas lighter particles as well as a dense carrying fluid allow for a long transport into the reservoir. A numerical tool is therefore essential to take all these factors into account and to make robust predictions about the performance of the hydraulic stimulation. We present a novel computational application for coupled numerical simulation of hydraulic stimulation with concurrent injection of proppant in a fractured geothermal reservoir. This application is based on the open-source MOOSE framework that has been enriched with a set of equations to account for the circulation of proppant-laden slurries. It accounts for both the fluid and proppant components of the mixture, as well as particle-driven processes that include gravity settling, particle-particle interactions, and strong density and viscosity contrasts. A particular challenge is the mutual coupling with fracture mechanical processes in a propagating fracture using the FE code Defmod. Aperture and fluid pressure on one side and slurry density and viscosity changes on the other side interact and affect each other. Their basic understanding is therefore essential for the success of the stimulation. We compare our application to common numerical benchmark problems for both slurry flow and hydraulic stimulation.
U-10wt.% Zr (U-10Zr) based metallic fuel is the leading candidate for next-generation sodium cooled fast reactor in United States. Advanced post-irradiation characterization (from sub-nanometer to micrometer) helps to understand fuel microstructure and property change during irradiation, benefiting fuel qualification for commercial application. With high velocity image data generating method, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to categorize pores caused by fission gas release and to aid phase identification. This work presents a showcase of this approach on different irradiated U-10Zr metallic fuels. This quantitative data offers insights into the fission product migration and potentially thermal conductivity degradation. This information from machine learning will be fed into fuel design code for better prediction of fuel performance.
Deep learning models have shown promise in reservoir inflow prediction, yet their performance often deteriorates when applied to different reservoirs due to distributional differences, referred to as the domain shift problem. Domain generalization (DG) solutions aim to address this issue by extracting domain-invariant representations that mitigate errors in unseen domains. However, in hydrological settings, each reservoir exhibits unique inflow patterns, while some metadata beyond observations like spatial information exerts indirect but significant influence. This mismatch limits the applicability of conventional DG techniques to many-domain hydrological systems. To overcome these challenges, we propose HydroDCM, a scalable DG framework for cross-reservoir inflow forecasting. Spatial metadata of reservoirs is used to construct pseudo-domain labels that guide adversarial learning of invariant temporal features. During inference, HydroDCM adapts these features through light-weight conditioning layers informed by the target reservoir’s metadata, reconciling DG’s invariance with location-specific adaptation. Experiment results on 30 real-world reservoirs in the Upper Colorado River Basin demonstrate that our method substantially outperforms state-of-the-art DG baselines under many-domain conditions and remains computationally efficient.