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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Transport and mechanical behavior in PEO-LLZO composite electrolytes

Abstract Composite solid electrolytes (CEs), wherein ion-conducting polymer and ceramic/glass is mixed, are promising candidates for all-solid-state batteries due to their promise of acceptable ionic conductivity and mechanical properties compared to their individual constituents. While numerous studies have focused on improving the performance of CEs, it is still unclear what the material targets are that can result in improved macroscopic performance especially in light of the coupled needs for high transport and high mechanical strength in these materials. In this study, a two-dimensional (2D) mathematical model is developed to investigate electrochemical and mechanical characteristics of CEs. The model is compared to CEs consisting of poly-ethylene-oxide (PEO) polymer and lithium lanthanum zirconium oxide (LLZO) ceramic material with examination of the impact of varying LLZO volume fractions. The potential drop at the PEO-LLZO interface is evaluated using the junction potential theory. Using experimental data from the literature, the model estimates the ionic conductivity, effective transference number, and mechanical stiffness of the CEs. While the mechanical stiffness improves with increasing volume fraction of LLZO, the impact on conductivity and transference number depends on interfacial resistance at the interface. Finally, the study reports CE’s potential to enhance Li-ion transport and mechanical properties to inhibit lithium (Li) dendrite growth.

25 ENERGY STORAGE↗
DOE OSTI2022

Numerical modeling and parametric study of a dual purpose underground thermal battery

We report integrating thermal energy storage with building energy systems can enable flexible building electric demands at buildings to help mitigate the mismatch between electricity supply and demand. A novel building heating and cooling system that integrates a dual-source heat pump with hybrid thermal storage named dual-purpose underground thermal battery (DPUTB) has been developed for reshaping building electric demands. The proposed DPUTB integrated geothermal heat pump system is an original innovation that enables Grid-interactive Energy Efficient buildings. This paper focuses on the study of the novel DPUTB. The DPUTB works as both a thermal storage tank (an inner tank) and a ground heat exchanger (an outer tank separated from the inner tank by the insulation material). High fidelity and computationally effective models are needed to predict the performance of the novel DPUTB. This study has developed a simplified dynamic model for the DPUTB according to heat transfer and energy conservation principles and validated it by using experimental data obtained from testing a small-scale DPUTB apparatus. A parametric study was conducted to identify a design that can achieve the target thermal storage performance of load shift and energy efficiency. The parametric study results show that the inner tank shell thermal conductivity and the phase change material's melting point are the two most influencing factors on the performance of the DPUTB. One single full-size DPUTB with the identified design could provide 1-ton cooling (3.51 kW) with the supply temperature lower than 11 °C for 4 hours in summer after being fully charged in 8 hours. The inner tank filled with phase change material is for cooling thermal storage as a latent tank in the design. However, its capacity can be as high as 60 MJ as a sensible water tank for heating storage in winter. In the future study, the DPUTB model will be incorporated into the dual-source heat pump system for evaluating the overall system performance of demand side management in the long term.

25 ENERGY STORAGE↗
DOE OSTI2026

Field testing and validation of a low-cost MPC for demand flexibility for grid-interactive K-12 schools

K-12 school buildings account for the highest energy consumption within the public sector. Implementing advanced HVAC controls in grid-interactive K-12 schools could bring substantial economic advantages and grid flexibility. Our previous study demonstrated that a low-cost model predictive control (MPC) solution, which coordinates multiple packaged units, can enable demand flexibility without major hardware upgrades. However, a significant gap remains between academic pilots and market-ready scalable solutions. This paper extends the previous single-site pilot to a multi-site demonstration involving three school campuses (95 total units) through a commercial technology transfer process. Addressing the challenge of verifying performance with sparse field data, we present a new statistical approach using Bayesian methods to estimate the MPC’s effect on peak demand. Unlike traditional methods, this approach robustly quantifies uncertainty in non-normal, limited datasets. The results confirm the solution’s replicability, achieving a 21.6–38.9% reduction in HVAC peak demand (10.8–22.1% at the site-level) with > 98% probability across diverse locations. Finally, we document critical barriers to scaling software-as-a-service (SaaS) solutions–such as API instability and diverse legacy systems–and offer practical strategies to accelerate the commercial adoption of grid-interactive efficient buildings.

Ham, Sang Woo↗
DOE OSTI2021

Comparative study with two reduced test facilities for air-cooled RCCS scaling law

Korea Atomic Energy Research Institute (KAERI) and Argonne National Laboratory (ANL) conducted steady-state tests at constant heat flux conditions to investigate the scale effect on heat removal behavior of the air-cooled Reactor Cavity Cooling System (RCCS). Two differently scaled-down test facilities were used in this study, KAERI’s ¼ scale with 4.0-m cavity height, and ANL’s ½ scale with 6.8-m cavity height. Two scaling laws were proposed to simulate the radiation across the cavity and the buoyancy-driven duct flow in the riser, respectively. The test matrix for KAERI and ANL test facilities focused on the scaling effect of the heated riser length with constant heat flux at the PMR200 RCCS design. The test results showed that mixed convection in the riser duct is an important factor for accurately extrapolating the thermo-fluid behavior in the prototype from the test results in the scale-down facilities. The system analysis code, GAMMA+, with improved heat transfer models predicted fairly well the air-cooled RCCS test data from two facilities. GAMMA+ analysis results showed that the predicted radiation fractions on the heated plate in the scale-down test conditions were larger than those on the reactor vessel in the prototype. The scaling law for air-cooled RCCS was improved by considering the mixed convection in the riser duct and the same radiation fraction on the heated plate. The mixed convection effect was calculated by the height ratio. The same radiation fraction provided a conservative extrapolation from the scale-down test results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

First measurement of the |t|-dependence of coherent J/ψ photonuclear production

The first measurement of the cross section for coherent J/ψ photoproduction as a function of |t|, the square of the momentum transferred between the incoming and outgoing target nucleus, is presented. The data were measured with the ALICE detector in ultra-peripheral Pb–Pb collisions at a centre-of-mass energy per nucleon pair $\sqrt{s_{NN}}$=5.02TeV with the J/ψ produced in the central rapidity region |y|<0.8, which corresponds to the small Bjorken-x range (0.3-1.4)×10 -3 . The measured |t|-dependence is not described by computations based only on the Pb nuclear form factor, while the photonuclear cross section is better reproduced by models including shadowing according to the leading-twist approximation, or gluon-saturation effects from the impact-parameter dependent Balitsky–Kovchegov equation. These new results are therefore a valid tool to constrain the relevant model parameters and to investigate the transverse gluonic structure at very low Bjorken-x.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Measurement of three-dimensional inclusive muon-neutrino charged-current cross sections on argon with the MicroBooNE detector

We report the measurement of the triple-differential cross section d 3 σ/dE vis d cos(θ μ )dP μ for inclusive muon-neutrino charged-current scattering on argon. This measurement utilizes data from 6.4 x 10 20 protons on target of exposure collected using the MicroBooNE liquid argon time projection chamber located along the Fermilab Booster Neutrino Beam with a mean neutrino energy of approximately 0.8 GeV. The mapping from reconstructed kinematics to truth quantities is validated within uncertainties by comparing the distribution of reconstructed hadronic energy in data to that of the model prediction in different muon scattering angle bins after applying a conditional constraint from the muon momentum distribution in data. The success of this validation provides confidence that the energy transfer in the MicroBooNE detector is well-modeled within simulation uncertainties, enabling a reliable unfolding to a triple-differential cross section defined at the nominal neutrino flux over muon momentum, muon scattering angle, and visible neutrino energy. This validation not only supports accurate cross-section extraction, but also establishes a critical foundation for tuning interaction models used in future neutrino oscillation measurements. The unfolded measurement covers an extensive phase space, providing a wealth of information useful for future liquid argon time projection chamber experiments measuring neutrino oscillations. Comparisons against a number of commonly used model predictions are included and their performance in different parts of the available phase-space is discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances

The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a major challenge to the forensic and toxicological laboratories tasked with detecting and identifying them. Tandem mass spectrometry (MS/MS) is the primary method used to screen for NPS within seized materials or biological samples. The most contemporary workflows necessitate labor-intensive and expensive MS/MS reference standards, which may not be available for recently emerged NPS on the illicit market. Here, we present NPS-MS, a deep learning method capable of accurately predicting the MS/MS spectra of known and hypothesized NPS from their chemical structures alone. NPS-MS is trained by transfer learning from a generic MS/MS prediction model on a large data set of MS/MS spectra. We show that this approach enables a more accurate identification of NPS from experimentally acquired MS/MS spectra than any existing method. We demonstrate the application of NPS-MS to identify a novel derivative of phencyclidine (PCP) within an unknown powder seized in Denmark without the use of any reference standards. We anticipate that NPS-MS will allow forensic laboratories to identify more rapidly both known and newly emerging NPS. NPS-MS is available as a web server at https://nps-ms.ca/, which provides MS/MS spectra prediction capabilities for given NPS compounds. Additionally, it offers MS/MS spectra identification against a vast database comprising approximately 8.7 million predicted NPS compounds from DarkNPS and 24.5 million predicted ESI-QToF-MS/MS spectra for these compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Ab initio calculations in atoms, molecules, and solids, treating spin–orbit coupling and electron interaction on an equal footing

We incorporate explicit, non-perturbative treatment of spin–orbit coupling into ab initio auxiliary-field quantum Monte Carlo (AFQMC) calculations. The approach allows a general computational framework for molecular and bulk systems in which material specificity, electron correlation, and spin–orbit coupling effects can be captured accurately and on an equal footing, with favorable computational scaling vs system size. We adopt relativistic effective-core potentials that have been obtained by fitting to fully relativistic data and that have demonstrated a high degree of reliability and transferability in molecular systems. This results in a two-component spin-coupled Hamiltonian, which is then treated by generalizing the ab initio AFQMC approach. We demonstrate the method by computing the electron affinity in Pb, the bond dissociation energy in Br 2 and I 2 , and solid Bi.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Methods of improving brain dose estimates for internally deposited radionuclides *

The US National Council on Radiation Protection and Measurements (NCRP) convened Scientific Committee 6–12 (SC 6–12) to examine methods for improving dose estimates for brain tissue for internally deposited radionuclides, with emphasis on alpha emitters. This Memorandum summarises the main findings of SC 6–12 described in the recently published NCRP Commentary No. 31, ‘Development of Kinetic and Anatomical Models for Brain Dosimetry for Internally Deposited Radionuclides’. The Commentary examines the extent to which dose estimates for the brain could be improved through increased realism in the biokinetic and dosimetric models currently used in radiation protection and epidemiology. A limitation of most of the current element-specific systemic biokinetic models is the absence of brain as an explicitly identified source region with its unique rate(s) of exchange of the element with blood. The brain is usually included in a large source region called Other that contains all tissues not considered major repositories for the element. In effect, all tissues in Other are assigned a common set of exchange rates with blood. A limitation of current dosimetric models for internal emitters is that activity in the brain is treated as a well-mixed pool, although more sophisticated models allowing consideration of different activity concentrations in different regions of the brain have been proposed. Here case studies for 18 internal emitters indicate that brain dose estimates using current dosimetric models may change substantially (by a factor of 5 or more), or may change only modestly, by addition of a sub-model of the brain in the biokinetic model, with transfer rates based on results of published biokinetic studies and autopsy data for the element of interest. As a starting place for improving brain dose estimates, development of biokinetic models with explicit sub-models of the brain (when sufficient biokinetic data are available) is underway for radionuclides frequently encountered in radiation epidemiology. A longer-term goal is development of coordinated biokinetic and dosimetric models that address the distribution of major radioelements among radiosensitive brain tissues.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Thermal Conductivity Measurement of Extrusion-Printed Silver Using Modulated Photothermal Radiometry

Flexible printed electronics is a rapidly growing field with applications in conformal and flexible devices. However, the physical properties of the films created by many state-of-the-art printing methods become highly dependent on printing parameters, resulting in varying thermal properties often differing significantly from their bulk ink components. To understand the influence of the printing process, we build upon our previous work, where a noncontact optical technique, known as modulated photothermal radiometry (MPTR), was used to measure the thermal conductivity of aerosol-jet-printed thin films. In this work, we use the method to study the thermal properties of extrusion-printed silver on glass and alumina substrates. A noise-resistant data analysis fitting technique is applied using a 2-D heat transfer model. Here, the thermal conductivity measurement is validated using the Weidemann-Franz (WF) relationship from measured electrical conductivity values.

42 ENGINEERING↗
DOE OSTI2024

Detection of a Cobalt-Containing Interphase at the Li 6 PS 5 Cl-NMC111 Interface by In Situ μ XANES and EIS

Sulfide electrolyte all-solid-state lithium batteries (ASLBs) with uncoated Li-Ni x Mn y Co 1–x–y O 2 (NMC) cathodes suffer from a large capacity loss during initial cycling and an increase in cell impedance. Decomposition reactions are known to occur at the Li 6 PS 5 Cl-NMC111 interface due to incompatibility between the two materials. If a stabilizing coating is applied to the NMC, it delivers full capacity during initial charge. However, the loss in capacity during discharge still occurs. The interface was studied by μ XANES and through EIS analysis. A chemically-formed interphase was detected by μ XANES, evident from reduction of Co at an uncoated NMC particle surface. This interphase was produced by decomposition at rest. To study the effect of the interphase on electrochemically active surface area, piecewise in situ EIS was performed and the data was modeled using a transmission line model (TLM). The charge transfer resistance R CT was used to estimate the volume specific active surface area (a act ). The median value for a act was 296 cm –1 , a factor of 7.5 lower than the theoretical value of 2216 cm –1 . This provided evidence of a lower electrochemically active surface area in the ASLB.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ride Pingo to Transit

In this project, we developed an on-demand microtransit first- and last-mile service. To integrate the service with fixed-route transit, we developed a feature called Transit Connect that prioritized riders’ on-time arrival at the transit station over other service requirements. We first prototyped service and related algorithms in a simulated environment, and then piloted the service in the city of Kent, Washington. Our algorithm incorporates request-specific hard drop-off deadlines to ensure timely arrivals for transit transfers. In the pilot, these constraints were obtained from GTFS Realtime data to accurately determine the schedule of the transit and the location of the stations. This approach introduced the ability to accept or decline new requests based on the timing of transit connections for these new requests and connection status of onboarding customers. The pilot (called “Ride Pingo to Transit”) deployed a fleet of three 14-person vans, ran from September 2021 to March 2023, and served a total of 21,329 trips. Transit Connect was offered for drop-offs at both Kent Station and the Kent Valley hub. In total, 2,844 such trips were completed. This dataset was collected from our pilot, which includes the following: - Requests: List of all trip requests, including those that were actually served and those not materialized. - Fleet: Daily vehicle service logs. - Service details: Daily vehicle stop logs (boarding and alighting). - Trip types: First mile, last mile, or point-to-point. ![pingo to transit](pingo-to-transit.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Thermophysical Property Measurements of HTM and CM (Final Technical Report)

The goal of this project is to gather thermophysical property data – specifically, thermal conductivity, thermal diffusivity, and specific heat – of heat transfer media (HTMs) and containment materials (CMs) used in Topic Area 1 and 2A at high temperatures (>700°C). This goal was met by completing objectives such as qualifying high temperature measurement techniques, defining allowable uncertainty metrics, reporting properties in a public database, and providing ongoing support for prioritized materials. These are described in detail across three budget periods

36 MATERIALS SCIENCE↗
DOE OSTI2026

Adaptive Cybersecurity for Distributed Energy Resources (AdCyDER): Online Reinforcement Learning with Stackelberg-Optimized Defenses — Pipeline Architecture, Evaluation Methodology, and Findings from a Synthetic-Data Evaluation

This report documents the design and evaluation of an integrated online-learning pipeline developed within the AdCyDER project for Distributed Energy Resource (DER) cybersecurity. The pipeline couples a Reinforcement Learning (RL) attack classifier — which produces an attack-type probability distribution — with a Stackelberg game-theoretic (GT) defense selector that consumes those distributions alongside SME-encoded priors over (defense, attack) effectiveness pairings and perdefense costs to choose grid-health-preserving defenses. The objective is not attack classification per se but production of distributions that drive effective defense selection through the Stackelberg layer, learned from delayed grid-health feedback rather than labeled attack data. AdCyDER as a whole is broader than the work presented here; this report covers the specific RL/GT loop integration and its evaluation. We present the integrated pipeline (SCADA telemetry with Fronius inverter physics, Suricata IDS, time-windowed aggregation, per-facility LSTM classifier, Stackelberg optimizer, OpenC2 actuators), an experimental campaign of 28 eight-hour iterations across three baseline modes, and a pipeline-ordered diagnostic protocol. The protocol identifies two distinct failure modes within the loop: paired supervised ceilings on the same features establish that the deployed online RL classifier (macro F1 ≈ 0.07) sits at least 4.7× below a same-architecture supervised LSTM (≈ 0.34) and 10–11× below a linear feature-signal ceiling (≈ 0.70–0.79 depending on per-facility isolation), localizing the dominant failure to the training procedure; and the reward signal driving online updates carries weak directional coupling with classifier correctness in the methodology-expected direction (multi-lens convergent: top-decile P(true) records produce more frequent state changes and slightly larger improvements, top-vs-bot Cohen’s 𝑑 ≈ −0.19), but at effect magnitudes too small to drive gradient-based learning at the campaign sample size. The original learning hypothesis is not supported by the data. The primary contributions are the diagnostic methodology — proposed as a transferable falsification protocol for online RL/GT defense pipelines learning from delayed environmental reward — and the open, reproducible experimental infrastructure. We outline reward reformulation as the highest-priority aspirational next step given the underpowered-but-aligned Q6 reading, with hardware-in-the-loop evaluation as the broadest scope-expansion option.

Blakely, Benjamin [Argonne National Laboratory (AN↗
DOE OSTI2020

First Images of the Protoplanetary Disk around PDS 201

Scattered light imaging has revealed nearly a dozen circumstellar disks around young Herbig Ae/Be stars—enabling studies of structures in the upper disk layers as potential signs of ongoing planet formation. We present the first images of the disk around the variable Herbig Ae star PDS 201 (V* V351 Ori) and an analysis of the images and spectral energy distribution through 3D Monte Carlo radiative transfer simulations and forward modeling. The disk is detected in three data sets with the Large Binocular Telescope Interferometer/Large Binocular Telescope mid-infrared camera at the Large Binocular Telescope, including direct observations in the Ks and L′ filters, and an L′ observation with the 360° vector apodizing phase plate coronagraph. The scattered light disk extends to a very large radius of ∼250 au, which places it among the largest of such disks. Exterior to the disk, we establish detection limits on substellar companions down to ∼5 M {sub Jup} at ≳1.″5 (≳500 au), assuming the Baraffe et al. models. The images show a radial gap extending to ∼0.″4 (∼140 au at a distance of 340 pc) that is also evident in the spectral energy distribution. The large gap is a possible signpost of multiple high-mass giant planets at orbital distances (∼60–100 au) that are unusually massive and widely separated compared to those of planet populations previously inferred from protoplanetary disk substructures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Modeling of crystallization fouling on a horizontal-tube falling-film evaporator for thermal desalination

Scale formation within horizontal-tube falling-film evaporators is an important issue for thermal desalination, because of the deterioration of heat transfer performance and added maintenance costs. Predicting the fouling process is crucial for desalination plant design, operation, and maintenance. In this paper, a model is developed to predict the crystallization fouling process during seawater falling-film flow over a horizontal tube bundle. The scaling is estimated based on deposition theory, with couple heat transfer including the in-tube steam condensation, conduction through the tube wall and the scale layer, and falling-film evaporation. The spatial and temporal variations of temperature, heat transfer coefficient, and scale thickness are predicted, and the effects of variations in process parameters of steam, feed seawater, and tube properties on the scale thickness, evaporation rate, and heat transfer coefficient for falling-film evaporation are analyzed. Comparisons to existing experimental data show the scaling layer thickness falls in the general range of reported measurements, with a slight overestimation due to neglecting the delay of scaling onset under real conditions. The scale layer thickness increases dramatically as the in-tube steam pressure is increased or the seawater flow rate is decreased. In general, the scaling layer becomes thicker on the lower tubes in the tube bundle, due to the heat and mass transfer of the falling film. The effect of tube material on scaling appears mainly dependent on thermal conductivity; thus, using polymer rather than a stainless steel tube decreases scaling and evaporation rate 96% and 88%, respectively. Finally, this work has potential to guide thermal desalination plant design, material selection, and operating parameter optimization.

42 ENGINEERING↗
DOE OSTI2025

Bridging multimodal microscopy for advanced characterization on nuclear fuel using machine learning

Uranium dioxide (UO 2 ), widely used as driver fuel in light water reactors, experiences microstructure and property change by nuclear fission reactions. This paper bridges the characterization of fresh UO 2 fuel at different length scales, serving as a baseline for future post irradiation examination of irradiated UO 2 fuel. To characterize the microstructural change of nuclear fuel, modern approaches cover a wide range of length scales through different characterization techniques, such as mm scale for Synchrotron-based X-ray computed tomography (SXCT) and microscale for focused ion beam (FIB) and scanning electron microscopy (SEM). It is challenging to bridge the data and knowledge of the same sample in different length scales. This paper proposed a deep learning framework leveraging transfer learning to detect microstructural defects, trained from a sparse FIB, SEM, and SXCT images. The proposed model achieved superior performance in defect segmentation on multiscale microscopic data compared to four of the latest deep learning models.

36 MATERIALS SCIENCE↗