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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assisted computational framework is presented to identify High-Temperature Reservoir Thermal Energy Storage (HT-RTES) site with optimal performance metrics by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems. All the simulations and the neural network model were done by Idaho National Laboratory. A detailed description of the work was reported in publication linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Significant slowdown of plasma-optimized surface energy deactivation by vacuum sealing for efficient adhesive bonding

This work proposes an approach to minimize surface energy deactivation of plasma-treated metal and carbon fiber-reinforced polymer (CFRP) surfaces by vacuum sealing. Plasma treatments enhance adhesive wettability on post-treated surfaces for adhesive joints, but the treated surfaces deactivate quickly in air. The surface energy of aluminum alloy AA6061 and carbon fiber-reinforced polymer-polyamide (CFRP-PA66) optimally treated by a blown air plasma instrument returns to the original surface energy within one hour. Vacuum sealing of AA6061 and CFRP-PA66 reduced the surface energy deactivation over 7 days by at least 230 times and 970 times compared to in air. Double Cantilever Beam (DCB) tests performed on adhesively-bonded AA6061/CFRP-PA66 joints showed that the total energy release and energy dissipation before failure of plasma-treated and vacuum-sealed materials was up to 60% more than plasma-treated materials without vacuum sealing and up to 125% more than non-plasma-treated materials.

42 ENGINEERING↗
DOE OSTI2022

Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles

Due to the high upfront cost of electric vehicles, many public transit agencies can afford only mixed fleets of internal combustion and electric vehicles. Optimizing the operation of such mixed fleets is challenging because it requires accurate trip-level predictions of electricity and fuel use as well as efficient algorithms for assigning vehicles to transit routes. We present a novel framework for the data-driven prediction of trip-level energy use for mixed-vehicle transit fleets and for the optimization of vehicle assignments, which we evaluate using data collected from the bus fleet of CARTA, the public transit agency of Chattanooga, TN. We first introduce a data collection, storage, and processing framework for system-level and high-frequency vehicle-level transit data, including domain-specific data cleansing methods. We train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy. Based on these predictions, we formulate the problem of minimizing energy use through assigning vehicles to fixed-route transit trips. We propose an optimal integer program as well as efficient heuristic and meta-heuristic algorithms, demonstrating the scalability and performance of these algorithms numerically using the transit network of CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Data-Driven Co-optimization of Energy Efficiency and Indoor Environmental Quality in Commercial Buildings

In this paper, we use publicly available data of a highly instrumented building to estimate how zonal temperature and carbon dioxide (CO2) concentration are related to some key operational and environmental measurements. Subsequently, we have developed, simulated, and evaluated an optimization framework for minimizing the energy consumption of the central heating, ventilation and air conditioning (HVAC) unit while meeting zonal temperature and indoor air quality (IAQ) standards. Finally, we have evaluated the achievable energy savings for our proposed approach as compared to a baseline approach and reported significant savings potential.

Naqvi, Syed Ahsan Raza↗
DOE OSTI2023

Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or for with a variety of distributed energy generation and storage systems. One challenging factor for the development of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total costs over the lifetime of the systems; this calculation is difficult in simple cases, where a battery is costed independently, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or allow for batteries to be operated in a degradation-aware manner that optimizes battery dispatch based on operating costs. This work incorporates a battery life model into REopt. This battery life model is simple, so that it may be solvable within the constrains of a mixed-integer linear optimization problem, but is fit to accelerated aging data recorded in the lab. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model to ensure validity of lifetime estimates made by REopt. Battery life and cost is optimized by controlling three decision to minimize system life cost: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cell reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to control battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries with degradation costs are always operated using 'just-in-time' charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, oversizing of battery storage systems is not a cost burden when battery storage is an optimal solution, as any additional battery capacity can simply be utilized to avoid costs of purchasing energy from a utility.

battery↗
DOE OSTI2022

Energy-dependent optimization of the prompt fission neutron spectrum with CGMF

Throughout the course of FY21, significant effort was put into investigating models within the LANL developed Hauser-Feshbach fission fragment decay code, CGMF, to understand and potentially solve the long-standing challenge of a too-soft prompt fission neutron spectrum, PFNS. Several inputs and models to CGMF were investigated, including the discrete nuclear levels, the optical model potential, level densities, and the fission fragment initial conditions. Some of the global models within CGMF led to a slight hardening of the neutron spectrum—particularly the likely incomplete discrete levels in through which γ-rays decay—but none of the changes where large enough for the tail of the PFNS to reproduce experimental data. A significant hardening of the spectrum tail was observed when the fission fragment initial conditions were optimized based on their sensitivities to the PFNS data for thermal incident neutrons. In this way, the parameters for the CGMF mass and total kinetic energy distributions, along with the spin cutoff factor were adjusted to better reproduce the experimental PFNS measurements. This optimization hardened the tail of the PFNS slightly but led to unphysical mass distributions for the fission fragments before neutron emission. It was clear from the above that we do not expect to be able to produce an evaluation-quality PFNS with CGMF in the near future. Challenges at thermal will persist–and possibly worsen–with increasing incident energy, where more models are needed to completely describe the fission. Basic-science research funding exceeding the amount available and scope of our NCSP funds would be needed to tackle this decade-long challenge impacting many fission-fragment event generator. And, in fact, Amy Lovell won LDRD ECR funding to do so over the next few years. Therefore, we focused in FY22 on extending evaluation capabilities beyond thermal incident neutrons, to take into account the incident energy dependence of the PFNS and fission fragment initial condition distributions in CGMF. We chose to set up the evaluation methodology to perform PFNS evaluations with CGMF across incident-neutron energies, in order to have it readily available for future NCSP evaluations when the PFNS from CGMF has improved. In this report, we outline the evaluation methodology, along with the results of the optimization, including full model calculations with CGMF using the evaluated parameters.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Are Deep Energy Retrofits in Commercial Buildings Including Window Upgrades?

U.S. Commercial buildings account for about 20% of total U.S. energy consumption. Because the thermal performance of windows significantly affects building energy efficiency and HVAC system performance, best practice guidance often includes window and envelope improvements in conjunction with HVAC upgrades to optimize energy use and improve occupant comfort. It is an open question, however, regarding how often these best practices are implemented in the field. This paper aims to address that gap by conducting a literature review and a series of interviews with commercial building auditing and management professionals to explore the factors that drive window retrofits in commercial buildings. The paper explores a range of case studies from deep energy retrofits across the globe, comparing projects with and without window retrofits. The primary goals of this review are to: (1) provide data from real-world case studies illustrating the role of windows in deep energy renovations and HVAC upgrades, (2) conduct retrofit cost analyses for windows and high-performance HVAC systems and (3) offer insights into how window upgrade decisions are made and when they are implemented as part of deep energy retrofits. Most of the retrofit studies focused exclusively on high performance HVAC upgrades without considering how window upgrades might further enhance the overall energy efficiency of commercial buildings. Interviews with building industry experts shed light on the key factors influencing deep energy retrofit decisions and what factors tip the scales in favor of including window measures with more comprehensive retrofit projects.

Cort, Katherine↗
DOE OSTI2022

Robust bidding strategy for aggregation of distributed prosumers in flexiramp market

Distributed prosumers (DPs) are the grid customers that own energy production/storage assets. Due to the flexibility and fast response of their assets, they can procure ancillary service products (ASP) in the wholesale market. An appealing ASP offered by California ISO in the real-time market (RTM) is flexiramp for which market participants do not submit direct offers, and the compensation is based on their energy opportunity costs. Here in this report, we propose a bidding strategy model for DP aggregator participation in the RTM considering energy and flexiramp. First, we develop a risk-averse optimization to determine the optimal energy and reserve product to trade in day-ahead market while considering proper amounts of flexiramp to trade in the RTM. In the RTM, to obtain optimal amounts of energy and flexiramp, the aggregator must submit hourly multi-level price-quantity energy bids for multiple RTM intervals with 15 min time-steps. On this basis, we propose a robust hourly economic bidding strategy model that determines the optimal energy bids in the RTM. We develop an adjustable robust counterpart of the model to address the RTM energy and flexiramp price uncertainties. The simulation results justify the efficacy of our proposed framework in gaining profits from the wholesale market.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Reinforcement Learning for Energy-Movement Optimization in Arduino-Based Robotics

Our goal is to develop a learning method for an Arduino-based robotthat maximizes travel distance and minimizes energy expenditure. • Will implement State ActionReward State Action (SARSA) reinforcement learning algorithm • Learning steps informed by state of environment • Rewards good decisions and punishes bad ones.

Gilmore, Blake↗
DOE OSTI2024

Optimizing high energy density sulfur cathodes: A multivariate approach to electrode formulation and processing

Lithium-sulfur (Li-S) batteries involve complex solid-liquid-solid phase transformations during both discharging and charging processes, where cathode materials, formulation, and structure play a crucial role. Here, a design of experiments (DoE) methodology and an empirical model are developed to systematically explore the interactions and trade-offs among cathode factors and process variables, and to obtain generalizable effects estimates for the multivariate system. Compared to the conventional one-factor-at-a-time (OFAT) approach, this work demonstrates advantages in both efficiency and accuracy by allowing the data to guide future research and decisions. Further, an optimized cathode formulation and processing parameters are predicted and validated experimentally, achieving over 1000 mAh g -1 in discharge capacity and improved cycling under practical lean electrolyte (4 µL mg -1 S) and high S-loading cathodes (>4 mg cm -2 ) conditions. The optimized cathode was scaled up and assembled into Li-S pouch cells, achieving 316 Wh kg -1 in cell-level energy, proving that the comprehensive and rigorous framework for optimizing complex systems with DoE leads to improved performance in a practical pouch cell system.

25 ENERGY STORAGE↗
DOE OSTI2024

Control and Optimization of Energy Storage System in Power Distribution System

The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate fast-charging infrastructure for long-distance trips. Extreme fast charging (XFC) technology can recharge EVs in less than 10 minutes for 200 miles range. Firstly, a novel robust optimization-based mixed integer linear programming model is proposed to size a battery energy storage system (BESS) and PV system in an XFCS. In this part, it is assumed that the sizing and location of the XFCS are known. Secondly, the aforesaid assumption is relaxed, and a strategic multi-period coordinated planning model is proposed to optimally site and size BESS-assisted charging stations in a highway transportation network and PV systems in a power distribution network by considering the coupling between both networks. Optimal operation and control of BESS-assisted EV charging stations are vital to alleviate the adverse impact of extreme fast charging of EVs on the host power network. A joint solution is proposed to mitigate the steady state and transient impact of extremefast charging of EVs and ensure grid-friendly integration of XFCSs with the host grid. Lastly, to make the operation of the XFCS cost-effective, a multi-layered energy management framework is proposed for the XFCS by considering forecast uncertainties, monthly demand charges reduction, and BESS degradation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Colorado Alternatively Powered Aircraft Airport Infrastructure Study

Advanced Air Mobility encompasses emerging technologies in propulsion of general aviation and commuter aircraft for the purpose of moving passengers and goods. The State of Colorado has a strong aerospace and aviation legacy, and this research explores the potential energy implications of a transition from traditional aircraft propulsion systems to more-electric, full electric and hydrogen powered aircraft in flight schools, and small community air service utilizing NASA's Regional Air Mobility Model. Effort includes identifying potential energy needs and cost-effective solutions to support those emerging needs analyzing potential benefits on on-site generation and storage to optimize energy systems.Advanced Air Mobility encompasses emerging technologies in propulsion of general aviation and commuter aircraft for the purpose of moving passengers and goods. The State of Colorado has a strong aerospace and aviation legacy, and this research explores the potential energy implications of a transition from traditional aircraft propulsion systems to more-electric, full electric and hydrogen powered aircraft in flight schools, and small community air service utilizing NASA's Regional Air Mobility Model. Effort includes identifying potential energy needs and cost-effective solutions to support those emerging needs analyzing potential benefits on on-site generation and storage to optimize energy systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Beyond Price Taker: Conceptual Design and Optimization of Integrated Energy Systems Using Machine Learning Market Surrogates

Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

Energy management analysis of lunar oxygen production

Energy load models in the process of hydrogen reduction of ilmenite for lunar oxygen production are being developed. The load models will be used as a first step to ultimately determine the optimal energy system needed to supply the power requirements for the process. The goal is to determine the energy requirements in the process of hydrogen reduction of ilmenite to produce oxygen. The general approach is shown, and the objectives are to determine the energy loads of the processes in the system. Subsequent energy management studies will be made to minimize the system losses (irreversibilities) and to design optimal energy system power requirements. A number of processes are being proposed as possible candidates for lunar application and some detailed experimental efforts are being conducted within this project at the University of Arizona. Priorities are directed toward developing the energy models for each of the proposed processes being considered. The immediate goals are to identify the variables that would impact energy requirements and energy sources of supply.

Fazzolari, R.↗
NASA NTRS1989

Near Hartree-Fock quality Gaussian type orbital basis sets for the first- and third-row atoms

Energy-optimized, near Hartree-Fock (NHF) quality Gaussian type orbital (GTO) basis sets are reported for the second-row (Li to Ne) and fourth-row (K to Kr) atoms. The most accurate basis sets reported for the second row are (18s 13p) sets which are with 4 micro E(H) of the numerical Hartree-Fock (NHF) results. For B to Ne basis sets with more than 15s functions are quadruple zeta in the valence space. For the second-row transition metal atoms the (20s 12p 9d) basis sets are triple zeta in the valence space and are approximately equivalent to Clementi and Roetti's accurate Slater type orbital sets. Supplementing the (20s 12p 9d) basis sets optimized for the lowest state with the 4s(2)3d(n) occupation with a diffuse d function gives self-consistent-field energy separations to the 4s(1)3d(n+1) and 3d(n+2) states which are within 100 micro E(H) of the NHF results. The most accurate basis sets for the transition metal atoms are with 30 micro E(H) of the NHF results. In addition, energy optimized sets are reported for He(3P), Li(2P) and Be(3P).

Partridge, Harry↗
DOE OSTI2026

Agent-based Modeling for the Multi-objective Optimization of Energy Production Pathways: Integrated Techno-Economics and Life Cycle Assessment

This project developed an integrated modeling framework to evaluate how purpose-grown bioenergy feedstocks could support large-scale sustainable aviation fuel production in the United States. Led by Colorado State University with project partners, the work combined geographically resolved analysis, techno-economic assessment, life cycle assessment, multi-objective optimization, and agent-based modeling to examine feedstock performance, deployment potential, land-use implications, and adoption dynamics. The overall goal was to identify practical and sustainable pathways for producing aviation fuel from feedstocks such as energy crops, woody crops, oilseeds, and algae while helping inform policy, investment, and research needs tied to national SAF goals. In essence, the project provides a systems-level assessment of the opportunities and constraints associated with scaling bioenergy-based aviation fuels in the U.S.

09 BIOMASS FUELS↗