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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Investigation of energy management strategies for photovoltaic systems - A predictive control algorithm

The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.

Cull, R. C.↗
DOE OSTI2023

Doping Limits of Phosphorus, Arsenic, and Antimony in CdTe

Low p-type doping is a limiting factor to increase CdTe thin-film solar-cell efficiency toward the theoretical Shockley-Queisser limit of 33%. Previous calculations predict relatively high ionization energies for group-V acceptors (P, As, and Sb) and they are plagued by self-compensation, forming AX-centers, severely limiting hole concentration. However, recent experiments on CdTe single crystals indicate a much more favorable scenario, where P, As, and Sb behave as shallow acceptors. Using hybrid functional calculations, we solve this puzzle by showing that the ionization energies significantly decrease with the supercell size. When including the effects of spin-orbit coupling and extrapolating the results to the dilute limit, we find these impurities behave as hydrogenic-like shallow acceptors, and AX-centers are unstable and do not limit ptype doping. We address the differences between our results and previous theoretical predictions and show that our ionization energies predict hole concentrations that agree with recent temperature-dependent Hall measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1973

Conclusions and recommendations

Conclusions and recommendations are presented for an analysis of the total energy situation; the effect of the energy problem on air transportation; and hydrogen fuel for aircraft. Properties and production costs of fuels, future prediction for energy and transportation, and economic aspects of hydrogen production are appended.

Source record↗
NASA NTRS1990

Comparison of the bonding between ML(+) and ML2(+) (M = metal, L = noble gas)

Ab initio calculations are reported of the spectroscopic constants for the low-lying states of the molecular ions ML2(+), where M = Li, Na, Mg, V, Fe, Co, Ni and Cu, and where L is usually Ar. Comparison with existing analogous calculations on the ML(+) ions shows how the bonding and binding energy change with the addition of a second noble gas atom. The second binding energy is predicted to be essentially the same as the first for the Li, Na, Mg, and V ions, but larger for the Fe, Co, Ni and Cu ions. The binding energies of the transition metal noble gas ions are not accurately predicted at the SCF level, because correlation is required to describe their M(0)Ln(+) character. All trends can be explained in terms of promotion and hybridization on the metal ion.

Bauschlicher, Charles W., Jr.↗
DOE OSTI2025

Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability

Snow plays a critical role in carbon cycling, vegetation dynamics, and permafrost hydrology at high latitudes by influencing surface energy exchange. Predicting snow distribution patterns is essential for understanding the evolution of Arctic ecosystems, yet scaling process-level knowledge to landscape predictions remains challenging. Here, we analyze snow depth (2019 and 2022), terrain elevation, and vegetation height from a watershed on the Seward Peninsula, Alaska, to examine how topography and shrubs shape snow redistribution across spatial scales. We find that snow depth is strongly coupled to terrain at scales below ∼60 m but becomes increasingly decoupled at larger scales. The topographic model of snow depth variation, which transforms terrain data to align with these scale-dependent snow patterns, is well correlated with local snow depth variations (linear fit R 2 > 0.5 for 85% of 100-m patches). A machine learning reconstruction of shrub canopy snow trapping reveals a simple exponential relationship between canopy structure and snow accumulation ( R 2 = 0.59), highlighting the combined influence of topography and vegetation on snow distribution. Together, these empirical relationships capture much of the observed snow variability in the watershed ( R 2 = 0.49, root mean square error (RMSE) = 30 cm), though systematic limitations persist in areas of strong scour and at coarser scales where wind-terrain interactions are more complex. These findings provide a framework for more efficient snow depth prediction and offer insights to improve snow-vegetation feedback representation in Earth System Models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Optimizing Direct Air Capture Solvents to Minimize Energy Consumption of CO 2 Release in a Carbonate Electrolyzer

Addressing climate change by carbon management is critical to achieving the goal of net zero carbon emissions by 2050. In this work, we examined the electrochemically-driven recovery of CO 2 during alkaline solvent regeneration for solvent-based direct air capture. A mathematical model was developed by incorporating carbonate chemistry with water electrolysis to predict the energy consumption per unit of CO 2 released. The predicted results were consistent with the experimental data, in which the experimental work was achieved by characterizing alkalinity and carbon loading values of solvent collected from a flow carbonate electrolyzer. Through this study, we learned that minimizing the energy expended on CO 2 release can be achieved by using an anolyte with a lower alkalinity, increasing the electric charge input to the electrolyzer, and reducing the ohmic resistance of the electrolyzer. Furthermore, using a supporting electrolyte, e.g., Na 2 SO 4 in the present work, effectively compensates for the higher ohmic resistance from using an anolyte with a lower alkalinity.

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

Development and Validation of Algorithms That Analyze Communicating Thermostat Data to Identify Enclosure Retrofit Opportunities

Annual energy savings of up to $\$ 4$ to $\$ 5$ billion could be achieved nationwide through basic insulation and heating system retrofits of existing homes. However, current utility energy efficiency programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and online advertising that lack specificity about home-specific retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments (HEAs) that are inconvenient to homeowners, expensive, and of variable accuracy. We developed computational algorithms that automatically analyze communicating thermostat (CT) heating data that could be used to increase the customer uptake of insulation and air sealing energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. The algorithms are based on an extended second-order grey-box model that characterizes a building’s thermal response using lumped elements, coupled with an empirical model of infiltration that accounts for both wind and stack effects. The basic parameters of the model correspond to actual physical parameters of the home, i.e., the home’s overall R-value of and the building envelope ACH50. Unlike the conventional approach, which estimates model parameters based on the best fit to the observed time-dependent room temperature, our approach derives correlations between the daily heating system runtime and temperature difference (indoor-outdoor) that are more robust to data quality issues in real-world applications. We also used HEA data for algorithm development and validation. With the help of our utility partners, Eversource and National Grid, we obtained data sets for hundreds of Massachusetts homes. For each home, these data sets included three sets of information anonymized by the utility: (1) CT data (HVAC runtime, room temperature, and, for some vendors, outdoor temperature and wind speed) collected by the CT vendor (one of three) over a heating season, (2) HEA report performed by the HEA vendor (same vendor for all homes), (3) Monthly utility gas bills coincident with the CT data (3 to 24 per home, depending on availability). For some homes, we also obtained blower-door test results. Initially, we applied the algorithms developed to homes with a single CT and then extended them to homes with two CTs by using an equivalent home approach. Finally, we developed algorithms for prediction of energy savings and a methodology of comparing our predictions with those generated by HEAs. The main technical results indicate that we can reliably identify homes with insulation and/or air sealing retrofit opportunities and provide accurate savings predictions. Our hypothesis is that the algorithms could be applied to utility energy efficiency programs to identify homes that could realize significant energy savings from insulation and/or air sealing retrofits. This information could then be used to reach out to those homes with highly customized outreach, thereby delivering increased program energy savings and cost-effectiveness. This would: Significantly increase the uptake rate of on-site HEAs, and Significantly increase the fraction of HEAs resulting in ECM implementation. To test these hypotheses, we designed and conducted a randomized controlled trial (RCT). The RCT results suggest that personal messaging leads to a two- to five-fold increase in the HEA uptake rate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Development of a BISON validation case for the TRISO transient irradiations in NSRR using effective heat capacity methods

The current tristructural isotropic (TRISO) fuel assessment and validation database in BISON primarily covers steady- state irradiation and high-temperature furnace testing. Transient assessment cases are potentially needed to support U.S. industry efforts in designing and deploying commercial reactors using TRISO fuels. Historical transient tests in- volving TRISO fuels used highly conservative conditions compared to the typical high-temperature gas-cooled reactor accident scenarios. Despite this, modeling historical transient tests is fundamental for evaluating BISON’s predictive capabilities, adapting material properties for high-temperature and high-particle-power regimes, and developing a sys- tematic validation approach for TRISO transient applications. This work developed a 1D model of transient experiments carried out at the Nuclear Safety Research Reactor using BISON. BISON’s predictions of energy deposition, UO 2 melting onset, and molten volume fractions are compared against experimental measurements. Melting was modeled using an effective specific heat capacity model for UO 2 . We found that BISON’s predictions are in reasonable agreement with experimental data for low-energy-deposition cases, and that BISON overpredicts melting at higher energy depositions. We also discuss the potential causes of discrepancies between the simulated and measured results and propose ways to further develop the model. Although these simulations used conservative conditions compared to those expected for actual TRISO-fueled reactors, they extended the range of conditions reflected in the data in the existing BISON database for TRISO fuels.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Neural network potentials with effective charge separation for non-equilibrium dynamics of ionic solids: a ZnO case study

Developing neural network potentials (NNPs) accurate under non-equilibrium dynamics is challenging, as such systems require extensive sampling beyond equilibrium phases. Here we construct high-fidelity NNPs for zinc oxide (ZnO), a polymorphic ionic solid, using density functional theory (DFT) reference data. To efficiently capture transitional configurations, we combine enhanced-sampling molecular dynamics with empirical potentials, data distillation, and pretraining on short-range atomic energies (A-Train), followed by transfer learning with DFT-relabeled datasets. This hierarchical approach improves transferability across polymorphs and stress states. We further introduce effective charge separation, treating long-range Coulombic terms analytically while short-range residual interactions are learned by the NNP. The optimal effective charges fall in the range 0.5–1.0 q e , consistent with dielectric-screened values derived from formal charges but distinct from Bader estimates. Motivated by this observation, we propose a simple data-driven protocol in which effective charges are optimized by comparing DFT reference energies with explicit Coulomb calculations, without additional NNP training. This strategy improves accuracy and transferability in DFT-level predictions of energies, forces, and stress. Together, these results provide a practical charge-selection framework for robust NNP development in ionic solids, enabling reliable simulation of polymorphic phase transformations and non-equilibrium dynamics.

Chemistry↗
DOE OSTI2023

Elevating zero dimensional global scaling predictions to self-consistent theory-based simulations

In this work, we have developed an innovative workflow, Stability, Transport, Equilibrium, and Pedestal (STEP)-zero-dimensional (0D), within the OMFIT integrated modeling framework. Through systematic validation against the International Tokamak Physics Activity global H-mode confinement database, we demonstrated that STEP-0D, on average, predicts the energy confinement time with a mean relative error of less than 19%. Moreover, this workflow showed promising potential in predicting plasmas for proposed fusion reactors such as the affordable, robust, compact (ARC) reactor, the European demonstration power plant (EU-DEMO), and the China fusion engineering test reactor (CFETR) indicating moderate H-factors between 0.9 and 1.2. STEP-0D allows theory-based prediction of tokamak scenarios, beginning with 0D quantities. The workflow initiates with the PRO-create module, generating physically consistent plasma profiles and equilibrium using the same 0D quantities as the IPB98(y,2) confinement scaling. This sets the starting point for the STEP module, which further iterates between theory-based physics models of equilibrium, core transport, and pedestal to yield a self-consistent solution. Given these attributes, STEP-0D not only improves the accuracy of predicting plasma performance but also provides a path toward a novel fusion power plant design workflow. When integrated with engineering and costing models within an optimization, this new approach could eliminate the iterative reconciliation between plasma models of varying fidelity. This potential for a more efficient design process underpins STEP-0D's significant contribution to future fusion power plant development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

An Energy Calculator for Simple Commercial Buildings

According to the EIA, simple commercial buildings account for 97% of total commercial building stock. However, most simple commercial buildings for example small- to mid-sized offices, retail, schools and warehouses do not benefit from the data-driven decision-making capabilities of whole-building energy modeling. The high cost of custom modeling limits the use of energy modeling of simple buildings for new construction or retrofit measures. Lack of tools providing helpful information on interactive savings estimates creates difficulties in meeting aggressive decarbonization and energy efficiency goals for simple building designers and utility program managers. This paper reviews a beta phase Simple Building Calculator with the ability to generate relatively accurate and interactive modeling results based on a limited but robust set of inputs. It can evaluate whole-building or single measure savings in new or existing buildings, compare measure package choices, or provide simplified performance modeling for energy codes and utility incentives. The tool combines physical (annual whole building prototype simulation) and statistical modeling techniques to predict annual energy performance. It supports a variety of building characteristics for envelope, HVAC, and lighting with parameters ranging from vintage to max tech configurations, as well as support for single-zone and simple multi-zone HVAC systems. The Simple Building Calculator was designed to provide immediate feedback for otherwise computationally intensive tasks like measure comparison, development of multiple measure package combinations, or verification that measures meet efficiency targets—all with the goal of providing a tool for quick annual energy simulation of simple commercial buildings.

Hart, Reid↗
DOE OSTI2026

Building Operation Model (Morpheus) for Dallas Fort Worth Airport (CRADA CRD-19-16301 Final Report)

The primary objective of this project was to leverage digital twin technology to enhance the design and operation of DFW Airport terminals and their associated energy systems. To achieve this, Morpheus, a building digital twin, was developed to guide improvements in airport operations, specifically targeting reductions in peak power demand and overall energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Predictive model for real-time energy disaggregation using long short-term memory

To provide affordable energy-saving solutions for the small and medium-sized manufacturers (SMMs), we propose a unified framework for generating predictive models that support real-time disaggregation of power consumption from combined inputs, enabling automatic machine state identification simultaneously for joint analysis of energy usage patterns. Further, the proposed framework transforms raw power consumption into a time series with look-back and bootstrap capabilities for historical pattern detection, while a learning architecture utilizes the stacked long short-term memory (LSTM) layers as encoders for embedding generation with sequential awareness. Experimental results demonstrate 93.65% minimum accuracy in ideal case of real-time energy usage and machine state prediction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Evaluating the Potential for Tidal Phase Diversity to Produce Smoother Power Profiles

Although tidal energy conversion technologies are not yet commercially available or cost-competitive with other renewable energy technologies like wind turbines and solar panels, tides are a highly predictable resource. Tidal energy’s predictability indicates that the resource could introduce less volatility into balancing the electric grid when compared to other renewables, a fundamentally desirable attribute for the electric system. More specifically, tidal energy resources are unique in that they have the potential to produce relatively smoother power profiles over time through aggregation. In order to generate smooth power profiles from tidal resources, sufficient complexity within the timing of tides is necessary within electrical proximity. This study evaluates the concept of aggregating diverse tides for the purpose of reducing periods of no and low energy production and creating smoother power profiles in regions around Alaska and Washington by calculating cross-correlations of tidal current velocity time series. Ultimately, study results show limited potential to exploit the resources for this purpose and describe the institutional mechanisms necessary to realize the benefits in practice.

16 TIDAL AND WAVE POWER↗
NASA NTRS1985

Comparison of Forecast and Observed Energetics

An energetics analysis scheme was developed to compare the observed kinetic energy balance over North America with that derived from forecast cyclone case. It is found that: (1) the observed and predicted kinetic energy and eddy conversion are in good qualitative agreement, although the model eddy conversion tends to be 2 to 3 times stronger than the observed values. The eddy conversion which is stronger in the 12 h forecast than in observations and may be due to several factors is studied; (2) vertical profiles of kinetic energy generation and dissipation exhibit lower and upper tropospheric maxima in both the forecast and observations; and (3) a lag in the observational analysis with the maximum in the observed kinetic energy occurring at 0000 GMT 14 January over the same region as the maximum Eddy conversion 12 h earlier is noted.

Baker, W. E.↗
DOE OSTI2023

Strengthening the US Department of Energy’s Recruitment Pipeline: The DOE/NNSA Predictive Science Academic Alliance Program (PSAAP) Experience

The US Department of Energy (DOE) oversees a system of 17 national laboratories responsible for developing unique scientific capabilities beyond the scope of academic and industrial institutions. These labs strive to keep America at the forefront of discovery and are home to some of the Nation’s best minds and the world’s best scientific and research facilities. Collaborations between national laboratories and academic institutions are critical to develop and recruit talent for the DOE workforce. Academia’s cooperative education model poses challenges for DOE recruitment pipelines centered around traditional internships. This paper discusses a promising DOE recruitment pipeline, the National Nuclear Security Administration’s (NNSA) Predictive Science Academic Alliance Program (PSAAP) initiative. As a part of this, experiences capturing the successes and challenges faced by the University of Utah’s Carbon Capture Multidisciplinary Simulation Center (CCMSC) through their participation in the PSAAP-II initiative are shared. These experiences demonstrate the success of Utah’s PSAAP center as a recruitment pipeline with approximately 43% of CCMSC students going to a national laboratory after graduation. Potential opportunities to strengthen the DOE’s recruitment pipeline are also discussed.

Holmen, John↗
DOE OSTI2022

Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings

Abstract Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec’s ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques.

97 MATHEMATICS AND COMPUTING↗