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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dual-season thermal energy storage-integrated heat pump system for load shifting: experiments, modeling and nationwide analysis

Here, this paper proposes and validates a dual-season thermal energy storage-integrated heat pump (TES-HP) system that shifts building electric load in both cooling and heating modes using a single phase-change TES. The design uses the TES as a heat sink for the condenser in summer and a heat source for the evaporator in winter, thereby reducing on-peak electric demand while ensuring compatible with existing air-distribution systems. A 14-kW TES-HP prototype with six operating modes and novel refrigerant charge management strategies was experimentally tested under varied conditions. Subsequently, data-driven polynomial performance curves were developed and validated against experiments, then coupled with a phase change material (PCM) model and a calibrated building model within a rule-based controller. Single-day case studies demonstrated effective on-peak demand reduction while maintaining thermal comfort. During cooling, hourly power was lowered by up to 1.5 kW; in heating, average hourly power decreased from over 3.5 kW to 1.8 kW, compared to an HP-only system, achieving 64.5 % load shifting, and the need for resistance heating was eliminated. Seasonal simulations showed typical on-peak electricity savings of 25–35 % in cooling and 40–65 % in heating, with the largest benefits on the hottest and coldest days. Extended response-surface analysis and nationwide mapping quantified load shifting as functions of ambient and TES temperatures, revealing a seasonal trade-off in TES phase-change temperature. These results demonstrate the TES-HP system as a practical and scalable solution for grid-interactive buildings that reduces on-peak demand and electricity use while maintaining thermal comfort and enhancing grid flexibility and reliability.

Grid-interactive buildings↗
DOE OSTI2020

Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations

This article presents an application of the Kalman filtering technique to estimate loads on a wind turbine. The approach combines a mechanical model and a set of measurements to estimate signals that are not available in the measurements, such as wind speed, thrust, tower position, and tower loads. The model is severalfold faster than real time and is intended to be run online, for instance, to evaluate real-time fatigue life consumption of a field turbine using a digital twin, perform condition monitoring, or assess loads for dedicated control strategies. The mechanical model is built using a Rayleigh–Ritz approach and a set of joint coordinates. We present a general method and illustrate it using a 2-degrees-of-freedom (DOF) model of a wind turbine and using rotor speed, generator torque, pitch, and tower-top acceleration as measurement signals. The different components of the model are tested individually. The overall method is evaluated by computing the errors in estimated tower-bottom-equivalent moment from a set of simulations. From this preliminary study, it appears that the tower-bottom-equivalent moment is obtained with about 10 % accuracy. The limitation of the model and the required steps forward are discussed.

17 WIND ENERGY↗
DOE OSTI2026

Unified Modeling Architecture for Load Management in Extreme Heat: The New York City Case

Integration of renewable resources to meet growing energy demand is becoming a global priority under decarbonization mandates. This study contributes to ongoing efforts on this key subject by assessing the feasibility of using coastal-urban renewable energy resources, namely, offshore wind and rooftop photovoltaic systems, to meet electricity demand of New York City during the intense recent heat wave period of June 2025. A unified modeling framework, based on the urbanized weather research and forecasting model, is used to simulate climate, renewable resources, and energy demand variables. Findings show significant energy load mismatch of approximately 1150 GWh over the month, between the demand and the combined renewable generation outcome. Three storage integration scenarios are analyzed to mitigate the deficits, reducing said deficits by a minimum of approximately 9% over the duration of the month. This study provides a transferable modeling framework tool for evaluating renewable integration in dense urban environments that can be used by grid operators to support grid resilience during extreme heat events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Wetlands Geoprocessor (v 1.0)

Wetlands have often been described as the “Earth’s kidneys” because wetlands have the ability filter pollutants and purify water. Based upon wetland storage capacity, average daily flow, and inflow nutrient loading this model estimates potential load reductions of nitrate and orthophosphate) from a constructed surface flow wetland (CW). The model relies upon equations, coefficients, and models provided by Cheng et al. 2020 and Cheng & Basu 2017. Nitrogen (N) and phosphorus are important nutrients to ecosystems, but high concentrations can lead to water quality issues. Major sources of these nutrients come from agricultural areas where fertilizer is applied in large quantitates and/or animal waste is present. Excess nitrate and phosphorus may result in overgrowth of algae, which can decrease the dissolved oxygen in water, thereby harming and killing fish and other aquatic species. This model evaluates water purification results and estimates cost to install a surface flow constructed wetlands.

Davis, Cleve↗
DOE OSTI2024

Field Performance of Commercial Building Load Flexibility Using Model Predictive Control

Model Predictive Control (MPC) applied to buildings is starting to see some commercial adoption by companies. However, it is hard to estimate if relative energy cost savings are enough to justify the cost of MPC implementation with few reported demonstrations. In small commercial and residential buildings, a one size-fits-all solution can help reduce implementation costs, while in very large buildings or districts the potential energy cost savings magnitude can cover more tailored solutions. This estimation becomes harder for medium to large commercial buildings, where a one-size-fits-all solution cannot be adopted and potential energy cost savings might not be sufficient to cover a tailored solution. Therefore, value propositions in addition to energy efficiency alone can make MPC technology more attractive through additional energy cost savings. One such value proposition is load shifting in response to dynamic electricity prices. On this aspect, MPC is a key technology to unlock building thermal mass for energy flexibility in response to electric grid conditions. This study shows the experimental results of MPC control of an office building in Berkeley, where different dynamic electricity price profiles were used in the MPC objective function to shift the building load and to calculate hypothetical electricity costs. Results show potential 50% cost savings with respect to the existing controller with the dynamic price scenario.

Zanetti, Ettore↗
DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A DNN surrogate unsteady aerodynamic model for wind turbine loads calculations

A recurrent deep-neural network (DNN) surrogate model capable of modeling the unsteady aerodynamic response and dynamic stall behavior of wind turbine blades has been developed and validated for use in engineering design codes. The model is trained using a subset of the oscillating airfoil experiments conducted at the Ohio State University wind tunnel. The predictions from our DNN model show excellent agreement with the measured data and, in all cases, a marked improvement over the state-of-the-art unsteady aerodynamic models. The DNN-based unsteady aerodynamics model was integrated with OpenFAST to perform full-turbine load computations for the NREL-5MW rotor. The largest differences are observed for the inboard stations, particularly in the pitching moment response, when using the new surrogate model compared to the other models available in OpenFAST.

17 WIND ENERGY↗
DOE OSTI2021

Reduced Order Model of Transactive Bidding Loads

Transactive energy (TE) has been identified to provide better grid efficiency and reliability by market-based transactive exchanges between energy producers and energy consumers. Simulations of TE systems are crucial to evaluate the benefits and impacts of different transactive mechanisms. However, such simulations can be time consuming due to the information exchange between various participants and complex co-simulation environments. In this paper, we develop a reduced order model to speed up the simulation of transactive systems in TE simulation platform (TESP) while achieving very low error between the reduced order and full model results. Specifically, the developed reduced order model consists of an aggregate responsive load agent which utilizes two Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTMs) to enable transactive elements to collectively participate in the TE system. The proposed aggregate responsive load (ARL) agent is able to produce similar transactive behaviors to the full simulation model while achieving significant simulation time reduction. Finally, we also show that the developed model enables generalization of simulation results across different dates and across different number of loads included in the simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models

This study evaluates the effect of complete nationwide lockdown in 2020 on residential electricity demand across 13 Indian cities and the role of digitalisation using a public smart meter dataset. We undertake a data-driven approach to explore the energy impacts of work-from-home norms across five dwelling typologies. Our methodology includes climate correction, dimensionality reduction and machine learning-based clustering using Gaussian Mixture Models of daily load curves. Results show that during the lockdown, maximum daily peak demand increased by 150-200% as compared to 2018 and 2019 levels for one room-units (RM1), one bedroom-units (BR1) and two bedroom-units (BR2) which are typical for low- and middle-income families. While the upper-middle- and higher-income dwelling units (i.e., three (3BR) and more-than-three bedroom-units (M3BR)) saw night-time demand rise by almost 44% in 2020, as compared to 2018 and 2019 levels. Our results also showed that new peak demand emerged for the lockdown period for RM1, BR1 and BR2 dwelling typologies. We found that the lack of supporting socioeconomic and climatic data can restrict a comprehensive analysis of demand shocks using similar public datasets, which informed policy implications for India's digitalisation. We further emphasised improving the data quality and reliability for effective data-centric policymaking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A robust dynamic state estimation approach against model errors caused by load changes

Dynamic state estimation (DSE) plays an important role in power system security monitoring and online control. In practice, there are two approaches to implementing DSE. The first approach is distributed DSE, which is based on the assumption that the terminal bus of each generator can be measured by PMUs (phasor measurement units). The assumption cannot be satisfied currently, however, because PMUs usually are installed at important high-voltage buses such as 500-kV buses installed in portions of the grid overseen by the Western Electricity Coordinating Council. Another issue of this approach is that performance of DSE is vulnerable to bad measurement data. The reason for this vulnerability is that DSE is performed separately through measurements at each terminal bus, and measurements at terminal buses are the only measurement upon which DSE can rely. Therefore, important redundant measurements are not included in this approach. The second approach is centralized DSE. This approach does not have the requirement for PMU location, and redundant measurements can be considered fully. However, load changes and grid topology changes impact centralized DSE. In this paper, we propose a new approach for handling the impact of load changes on DSE. We have developed a new algorithm that includes two sequential steps. In the first step, errors caused by load changes are detected by analyzing the difference between prediction results and measured results. In the second step, once model error is detected, a model optimization procedure is run to correct the error so the state estimation error can be mitigated. Simulation results from the IEEE 68 bus system show that the proposed approach can effectively handle model errors caused by load changes.

robust dynamic state estimation, load change, powe↗
DOE OSTI2023

UNF-ST&DARDS Enhancements for RCCA Data in As-loaded Dual Purpose Cask Models

This report summarizes the work performed to enable detailed modeling of rod cluster control assembly (RCCAs) in dual purpose canisters (DPCs) in as-loaded configurations using the Used Nuclear Fuel – Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS). The goal of this project was to evaluate the reactivity impact that RCCAs have on k eff of DPCs with pressurized water reactor (PWR) fuel to potentially use the additional margin in future post-closure criticality safety analysis. This preliminary evaluation determined the number of DPCs that currently require compensatory actions prior to emplacement in a repository because of their high reactivity under post-closure criticality scenarios, which could be made acceptable by including the as-loaded RCCAs as specified in the Unified Database (UDB). This report briefly describes the modeling methods currently used within UNF-ST&DARDS and the modifications made to automate inclusion of the as-loaded RCCAs in the DPCs for post-closure criticality calculations for the loss of neutron absorber (NA) scenarios, or NA models. For the Zion site, the loss of basket scenarios, or degraded basket (DB) models, are also included. Discussion is also provided regarding the UDB data compared to available site-specific loading map data for the sites, which are specifically evaluated herein. This report compares the modifications to a similar evaluation for the Zion by site conducted by Walker as an initial validation. After the partial validation, all existing PWR sites with applicable NA models within the UDB were evaluated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Modeling The Effects of Loading Scenario and Thermal Expansion Coefficient on Potential Failure of Cryo-compressed Hydrogen Vessels

A multiscale thermomechanical model for a simplified Type-3 cryogenic compressed hydrogen (H2) storage vessel is developed in this paper. The model accounts for the temperature-dependent elastic-plastic behavior of the vessel carbon/epoxy composite overwrap and aluminum alloy liner. The homogenized thermo-elastic-plastic behavior for the individual laminas of the vessel layup is obtained by an incremental Eshelby-Mori-Tanka approach associated with a micromechanical failure criterion to predict lamina failure while a standard elastic-plastic constitutive model is used to describe the behavior of a typical aluminum alloy assumed for the liner. The vessel response to external loadings is achieved by a finite element method. Four loading scenarios representing four thermomechanical cycles applied to the vessel are analyzed to evaluate constituent and lamina stresses as well as the associate failure criterion during the cycle according to these scenarios. The model can provide helpful guidance to mitigate thermal stresses by an adequate selection of loading scenario, optimizing the layup and by tailoring thermomechanical properties of the resin matrix.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Scalable self attraction and loading calculations for unstructured ocean tide models

Self attraction and earth-loading effects are important for accurately modeling global tides. A common approach of handling this forcing is to expand mass anomalies into spherical harmonics, which are scaled by load Love numbers to account for elastic earth deformation. We investigate two different approaches to perform these calculations for ocean models that employ unstructured meshes and distributed memory parallelization. The first approach leverages a highly efficient spherical harmonics library, but requires all-to-one and one-to-all communications and interpolation operations between the unstructured and a structured mesh. This approach is compared to a parallel algorithm that computes the spherical harmonic transformations directly on the unstructured mesh with an all-reduce communication. Here, our results show that although the unstructured mesh calculations are more expensive, the scalability of the unstructured mesh approach allows for more efficient spherical harmonics transforms for high-resolution meshes and large processor counts. This methodology enables the efficient inclusion of tidal dynamics large-scale Earth system model simulations.

54 ENVIRONMENTAL SCIENCES↗