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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Mathematical Model for Li-S Cell with Shuttling-Induced Capacity Loss Approximation

Lithium sulfur (Li-S) batteries have the potential to outperform the current lithium ion batteries and transform the technology of the future. However, dissolution, diffusion, and shuttling of the dissolved polysulfides result in parasitic reactions and substantial capacity loss. To provide a better understanding of the shuttling process, a 1D porous electrode mathematical model has been developed in this paper. An approximation method is used to account for the shuttling-induced capacity loss by adding an extra source/sink term in the material balance equations for the species involved in the parasitic reactions. Shuttling constants used in the source terms can be determined by fitting the model predictions to the experimental measurements. Here, the results showed that by including the approximation method, the model was able to predict the active material loss and the continuous decrease of volume fractions of Li 2 S on the cathode surface. The model sheds light on the capacity loss mechanism occurring inside the cell as a result of the shuttling of polysulfides.

25 ENERGY STORAGE↗
DOE OSTI2020

B 2 H 6 splitting on catalytic surfaces and role of BH 3 towards hydrogen spillover

A fundamental understanding of the spillover mechanism is an open and challenging problem and plays an important role in catalysis. In particular, bond-exchange spillover mechanism is considered to be effective for reversible storage and release of hydrogen at near ambient conditions. For this, three critical steps are needed: finding the right support (acceptor), the right catalyst to split H 2 , and ensuring that once H 2 is split, the H atoms can migrate on the surface with the help of secondary catalysts and eventually hydrogenate the entire material. In this paper we address these challenges using density functional theory. Here we show that BH 3 , a secondary catalyst, can be produced by symmetrically splitting its stable precursor, B 2 H 6 , on doped metal-free surfaces such as graphene and h-BN as well as on MOF5. In addition, to reduce computational cost, we develop structural descriptor and predictive model equation to effectively screen potential BH 3 binding sites. Symmetrical splitting of B 2 H 6 on different types of materials can address the hydrogen spillover challenge, making efficient storage of hydrogen possible.

08 HYDROGEN↗
DOE OSTI2021

Bridging Multiscale Processes in Earth System Models with Physics-Guided Hierarchical Machine Learning

Focal Area(s): The focal area of this whitepaper is, “Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection).” The ideas and frameworks described herein are deemed site agnostic. As a use case, this group will initially focus on the coupling between land processes, surface/subsurface hydrological processes, coastal processes, and human activities in the U.S. Gulf states.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments

We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

30 cm Drop Modeling

This report documents modeling and analysis of the 30 cm cask drop scenario for spent nuclear fuel (SNF) casks. The 30 cm drop is an important hypothetical dynamic loading scenario for SNF because 10 CFR 71.71 defines testing requirements for normal conditions of transport (NCT) of SNF packages, and free drop (of 30 cm for the heaviest packages) tests are required. Other NCT conditions specified in 10 CFR 71.71 include vibration normally incident to transport, and DOE’s multimodal transportation test (MMTT) and related analysis concluded that mechanical shock and vibration incident to transportation is negligible for SNF structural integrity because the loads are so small. The SNF cask 30 cm drop has a lot more potential to cause damage to SNF because the kinetic energy and momentum of the system is stopped abruptly during the cask impact event. Test data and model predictions both agree that the SNF assemblies are subjected to significantly higher loads in a cask drop event than normal transportation shock and vibration, but currently there is no reason to expect gross structural damage of the fuel assembly or widespread SNF cladding failure as a result of a 30 cm cask drop. The ultimate goal of this work is to close the knowledge gap of stress profiles on SNF rods and fuel assembly components during NCT, and this report describes the progress toward reaching that goal as well as a plan for finishing this work over the next year or two. Key remaining tasks include refining the fuel assembly model, calculating a selection of specific fuel assembly cases to populate the damage model, and finally running the damage model to predict the full general response range of 30 cm drop loads on SNF.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Control of Behind-the-Meter Resources for Enhancing the Resilience of Residential Buildings

There is increased concern about the impacts of frequent power outages, caused primarily by extreme weather conditions. With increasing behind-the-meter resources such as solar photovoltaics (PV), battery energy storage, and controllable loads, these resources - if properly coordinated - can meet critical loads even during an outage. Resilience building controls can coordinate and operate these resources to enhance the resiliency of buildings supporting critical loads for longer duration. In this paper, we present two resilience building controls: rule-based control and model predictive control (MPC). We simulated various scenarios considering different locations, seasons, outage types, and times of outages to evaluate the performance of resilience controls. The results show that MPC-based control can enhance resilience up to 65% compared to rule-based control. Similarly, PV self-consumption and occupant thermal comfort both increase during outages with MPC-based control.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2024

PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites

Abstract Ultra‐relativistic electrons with energies greater than or equal to two megaelectron‐volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those highly energetic electrons has a significant meaning to space weather communities. Here we report the latest progress in developing our predictive model for MeV electrons in the outer radiation belt. The new version, primarily driven by electron measurements made along medium‐Earth‐orbits (MEO), is called PREdictive MEV Electron (PreMevE)‐MEO model that nowcasts ultra‐relativistic electron flux distributions across the whole outer belt. Model inputs include >2 MeV electron fluxes observed in MEOs by a fleet of GPS satellites as well as electrons measured by one Los Alamos satellite in the geosynchronous orbit. We developed an innovative Sparse Multi‐Inputs Latent Ensemble NETwork (SmileNet) which combines convolutional neural networks with transformers, and we used long‐term in situ electron data from NASA's Van Allen Probes mission to train, validate, optimize, and test the model. It is shown that PreMevE‐MEO can provide hourly nowcasts with high model performance efficiency and high correlation with observations. This prototype PreMevE‐MEO model demonstrates the feasibility of making high‐fidelity predictions driven by observations from longstanding space infrastructure in MEO, thus has great potential of growing into an invaluable space weather operational warning tool.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Geometric Measures of Trustworthiness for Machine Learning Predictions

his report details the findings from the research and investigation of Geometric Measures of Trustworthiness for Machine Learning Predictions. We explored the trustworthiness of machine learning (ML) models’ predictions using geometric measures to quantify the similarity of a query point with the training data. Predictive uncertainty in ML can originate from at least three sources: (1) Model uncertainty, which represents the uncertainty in model form (e.g. decision tree, vs neural network) and estimating the model parameters from the training data, (2) Data uncertainty, which represents the natural complexities of the data such as class overlap and inherent noise, and (3) Distributional uncertainty, which represents the mismatch between the training and operational distributions. The proposed measures focus on measuring and explaining the data and distributional uncertainties by measuring the relationships of operational data with the training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Fault isolation and fault-tolerant control for nonlinear stochastic distribution control systems with multiplicative faults

Here, in this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced to discover the fault occurrence time. Then a fault isolation observer is built to produce the residual, and the error system is separated to subsystems affected only by disturbance and multiplicative faults. Moreover, a fault estimation scheme is presented to obtain the fault magnitude information. When faults occur, the system output probability density function will deviate from the desired distribution. So the model predictive control fault tolerant control scheme is needed to minimize the impact of faults as much as possible to make sure that the post fault output probability density function track the desired probability density function. The validity of the designed algorithm is demonstrated through a simulation example, where the fault tolerant control algorithm ensures that the system output probability density function still track the given output probability density function despite the complex case of multiple multiplicative faults occurring simultaneously.

42 ENGINEERING↗
DOE OSTI2020

Optimization-Based Fast-Frequency Estimation and Control of Low-Inertia Microgrids

The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Convex Relaxation of Grid-Connected Energy Storage System Models With Complementarity Constraints in DC OPF

Including complementarity constraints in energy storage system (ESS) models in optimization problems ensure an optimal solution will not produce a physically unrealizable control strategy where there is simultaneous charging and discharging. However, the current approaches to impose complementarity constraints require the use of non-convex optimization methods. Here, we propose a convex relaxation for a common ESS model that has terms for both charging and discharging based on a penalty reformulation for use in a model predictive control (MPC) based optimal power flow (DC OPF) problem. In this approach, the complementarity constraints are omitted and a penalty term is added to the optimization objective function. For the DC OPF problem, we provide analysis for the conditions under which the convex relaxation of the complementarity constraint ensures that a solution with simultaneous ESS charging and discharging operation is suboptimal. Simulation results demonstrating ESS behavior with and without the penalty reformulation are provided for an MPC-based DC OPF problem on multiple IEEE test systems.

25 ENERGY STORAGE↗
DOE OSTI2020

Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control

In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to the grid operation as well as the consumers, such as reducing the stress on the distribution network, minimizing voltage fluctuations and two-way power flows in the distribution network, and reducing the required battery storage capacity for PV integration. This would result in increasing the solar PV generation penetration levels. The aims of this study are twofold. First, spectral (frequency) analyses of solar PV power generation together with the power consumption of multiple building TCLs (such as heating, ventilation, and air conditioning (HVAC) systems, water heaters, and refrigerators) are performed. These analyses define the bandwidth over which these TCLs can operate and also describe the PV generation frequency bandwidth. Such spectral analyses, in frequency domain, can help identify the flexible components of PV generation that can be consumed by the various TCLs through optimal building load utilization. Second, a quadratic optimization problem based on model predictive control is formulated to allow consuming most of the low and medium frequency content of the PV power locally by building TCLs, while maintaining occupants’ comfort and TCLs’ physical constraints. The solution to the proposed optimization problem is achieved using optimal control strategies. Numerical results show that most of the low and medium frequency content of the PV generation can be consumed locally by building TCLs. The remaining high-frequency content of the PV generation can then be stored/offset using energy storage systems.

14 SOLAR ENERGY↗
DOE OSTI2022

Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization

Thermal energy storage (TES) for a cooling plant is a crucial resource for load flexibility. Traditionally, simple, heuristic control approaches, such as the storage priority control which charges TES during the nighttime and discharges during the daytime, have been widely used in practice, and shown reasonable performance in the past benefiting both the grid and the end-users such as buildings and district energy systems. However, the increasing penetration of renewables changes the situation, exposing the grid to a growing duck curve, which encourages the consumption of more energy in the daytime, and volatile renewable generation which requires dynamic planning. The growing pressure of diminishing greenhouse gas emissions also increases the complexity of cooling TES plant operations as different control strategies may apply to optimize operations for energy cost or carbon emissions. This paper presents a model predictive control (MPC), site demonstration and evaluation results of optimal operation of a chiller plant, TES and behind-meter photovoltaics for a campus-level district cooling system. The MPC was formulated as a mixed-integer linear program for better numerical and control properties. Compared with baseline rule-based controls, the MPC results show reductions of the excess PV power by around 25%, of the greenhouse gas emission by 10%, and of peak electricity demand by 10%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously.

model predictive control↗
DOE OSTI2025

Hybrid Energy Management with Real-Time Control of a High-Power EV Charging Site

Decarbonization of transportation systems is driving higher capacity energy storage and faster charging power requirements in electric vehicles (EVs). Given the potential advantages - such as increased efficiency, reduced inverter capacity, and less total cable mass - there is a demand in the industry for more DC distribution for high-power charging (HPC) hubs. However, the cost-effective, adaptive, and robust operation of the DC-coupled HPC hub necessitates a robust site energy management system (SEMS). Validating SEMS operation using a digital twin of an HPC hub in a real-time simulator (RTS) platform is crucial before field deployment. In this study, we propose a hybrid energy management site controller designed to achieve high-level, long-term operational objectives while managing low-level power sharing control between hub assets. We develop a centralized model predictive controller (MPC) to optimize hub operating points and use these points to update the droop parameters of the site energy storage system (ESS). This approach ensures the hub follows an optimal operating point while maintaining the flexibility to respond to load surges. We tested and verified our proposed approach both offline and on a Controller Hardware-in-the-loop (C-HIL) simulation platform integrated with a SEMS framework, demonstrating real-time site operation and validating a cost-effective and robust site controller.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Improving Common PV Module Temperature Models by Incorporating Radiative Losses to the Sky

PV module operating temperature is the second-most important factor influencing PV system yield–after irradiance–and a substantial contributor to uncertainty in energy system yield predictions. Models commonly used to predict operating temperature in system simulations are based on a simplified energy balance that lumps together different heat loss mechanisms–including radiation–and assumes an overall linear behavior. Radiative heat loss to the sky is usually substantial, but modeling it accurately requires additional information about down-welling long-wave radiation or sky temperature and increases the complexity of temperature model equations. In this work we show how radiative losses to the sky can be separated into two parts to improve the accuracy of modeling without additional complexity. We also predict and demonstrate the variation of these losses at different tilt angles and show that the effective view factor is reduced by the non- isotropic distribution of down-welling long-wave radiation. Finally, we demonstrate substantial reduction in bias (MBE) and scatter (RMSE) when the new radiative loss term is added to the Faiman model using one year of measurements at Sandia National Labs.

14 SOLAR ENERGY↗
DOE OSTI2024

Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data

This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory. Two distinct deep learning models, conditional Multi-Layer Perceptron (MLP) and unconditional MLP, were developed to forecast power outage probabilities, leveraging a rich array of input features gathered from publicly available sources including weather data, weather station locations, power infrastructure maps, socio-economic and demographic statistics, and power outage records. Given a one-hour-ahead weather forecast, the models predict the power outage probability for each census tract, taking into account both the weather prediction and the location's characteristics. The deep learning models employed different loss functions to optimize prediction performance. Our experimental results underscore the significance of socio-economic factors in enhancing the accuracy of power outage predictions at the census tract level.

24 POWER TRANSMISSION AND DISTRIBUTION↗