Search NASA⌕ Search

SEARCH · Search NASA

Results for “Policy optimization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Using Flory–Huggins-informed human-in-the-loop Bayesian optimization to map the phase diagram of polymer blends

Mapping the phase diagram of polymer blends is an essential step in controlling the structure–property relationship of polymer-based materials. However, traditional grid-based approaches are inefficient and rely on subjective judgements for terminating the experimental campaign. Artificial intelligence-guided experimentation offers a compelling alternative, especially when data-driven decision-making is interfaced with established polymer thermodynamics to improve efficiency and interpretability. Here, we introduce a physics-informed Bayesian optimization approach to guide the mapping of the phase diagram of a model blend containing poly(methyl methacrylate) and poly(styrene-ran-acrylonitrile). Physical information is derived from a Flory–Huggins representation of the spinodal curve, which is integrated into the Bayesian optimization process as a structured prior mean that acts as a soft constraint. Implemented as a human-in-the-loop workflow, the approach leverages optical imaging of film cloudiness with iterative Gaussian process surrogate modeling and a parameter selection decision policy to identify the composition-temperature conditions for sequential iterations. Convergence of kernel and Flory–Huggins-based hyperparameters provided a stopping criterion, ensuring an objective and interpretable termination of the experimental campaign. The framework recovered the known lower critical solution temperature (∼160 °C), while increasing material efficiency through targeted sampling. This work establishes a proof-of-concept for the application of Bayesian optimization workflows to study polymer blend miscibility.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Nuclear Hybrid Energy Systems Initial Integrated Case Study Development and Analysis

The US Department of Energy Office of Nuclear Energy established the Nuclear Hybrid Energy System (NHES) project to develop a systematic, rigorous, technically accurate set of methods to model, analyze, and optimize the integration of dispatchable nuclear, fossil, and electric storage with an industrial customer. Ideally, the optimized integration of these systems will provide economic and operational benefits to the overall system compared to independent operation, and it will enhance the stability and responsiveness of the grid as intermittent, nondispatchable, renewable resources provide a greater share of grid power.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Optimal Design of Sustainable Ammonia-Based Food–Energy–Water Systems with Nitrogen Management

As the basis for virtually any form of nitrogen fertilizers, ammonia plays a vital role in agriculture; in addition, there has been an increased interest in its use as a carbon-free energy carrier. However, ammonia is also associated with two major environmental concerns: CO 2 emissions from the conventional production process and nitrogen pollution from the excessive use of ammonia-based fertilizers. To mitigate these environmental impacts, we develop an optimization framework for the design of a sustainable ammonia-based agricultural system that synergistically integrates the production of ammonia from renewable resources and effective measures for nitrogen management. The proposed model captures the effect of intermittency by incorporating both design and detailed operational decisions. Here, by applying a multiscale time representation that reduces the problem size and a tailored surrogate model that accurately approximates model nonlinearity, we are able to achieve optimal solutions within reasonable computation times. A computational case study is conducted using real-world data from a local farm in Morris, Minnesota, and the results indicate the trade-off between cost and nitrogen loss. Importantly, we show that practicing effective nitrogen management can significantly reduce the nitrogen loss with only a small increase in net present cost.

10 SYNTHETIC FUELS↗
DOE OSTI2020

A Clustering-Based Scenario Generation Framework for Power Market Simulation with Wind Integration

A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant numbers of scenario generation methods exist in the literature. This paper develops a clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind sites into clusters according to their cross-correlations. Copula based models are utilized to model spatiotemporal correlations and the Gibbs sampling is then used to generate scenarios for day-ahead markets. Our results show that the generated scenarios based on clustered wind sites outperform existing approaches in terms of reliability and sharpness and can reduce the total computational time for scenario generation and reduction significantly. The clustering-based framework can therefore provide a better support for real-world market simulations with high wind penetration.

data visualization↗
NASA NTRSDate not supplied

Understanding Resilience Optimization Architectures With an Optimization Problem Repository

Optimizing a system’s resilience can be challenging, especially when it involves considering both the inherent resilience of a robust design and the active resilience of a health management system to a set of computationally-expensive hazard simulations. While prior work has developed specialized architectures to effectively and efficiently solve combined design and resilience optimization problems, the comparison of these architectures has been limited to a single case study. To further study resilience optimization formulations, this work develops a problem repository which includes previously-developed resilience optimization problems and additional problems presented in this work: a notional system resilience model, a pandemic response model, and a cooling tank hazard prevention model. This work then uses models in the repository at large to understand the characteristics of resilience optimization problems and study the applicability of optimization architectures and decomposition strategies. Based on the comparisons in the repository, applying an optimization architecture effectively requires understanding the alignment and coupling relationships between the design and resilience models, as well as the efficiency characteristics of the algorithms. While alignment determines the necessity of a surrogate of resilience cost in the upper-level design problem, coupling determines the overall applicability of a sequential, alternating, or bilevel structure. Additionally, the application of decomposition strategies is dependent on there being limited interactions between variable sets, which often does not hold when a resilience policy is parameterized in terms of actions to take in hazardous model states rather than specific given scenarios.

Resilience↗
DOE OSTI2026

Responsible Adoption of Artificial Intelligence (AI) in Electric Grid Operations

The future of the grid will be powered by AI—or undermined by it. Artificial intelligence is rapidly reshaping grid operations, improving fault detection, forecasting accuracy, and real-time optimization. As AI systems move closer to operational decision loops, however, they introduce new consequence pathways: expanded attack surfaces, model integrity risks, regulatory exposure, and human-automation challenges. This talk presents a consequence-driven framework for deploying AI responsibly in the electric grid. Attendees will gain practical strategies to strengthen resilience, boost reliability, and deploy AI securely — ensuring the grid of the future is not only smarter but safer.

25 - ENERGY STORAGE↗
DOE OSTI2021

Decomposing Loosely Coupled Mixed-Integer Programs for Optimal Microgrid Design

Microgrids are frequently employed in remote regions, in part because access to a larger electric grid is impossible, difficult, or compromises reliability and independence. Although small microgrids often employ spot generation, in which a diesel generator is attached directly to a load, microgrids that combine these individual loads and augment generators with photovoltaic cells and batteries as a distributed energy system are emerging as a safer, less costly alternative. In this work, we present a model that seeks the minimum-cost microgrid design and ideal dispatched power to support a small remote site for one year with hourly fidelity under a detailed battery model; this mixed-integer nonlinear program (MINLP) is intractable with commercial solvers but loosely coupled with respect to time. A mixed-integer linear program (MIP) approximates the model, and a partitioning scheme linearizes the bilinear terms. We introduce a novel policy for loosely coupled MIPs in which the system reverts to equivalent conditions at regular time intervals; this separates the problem into subproblems that we solve in parallel. We obtain solutions within 5% of optimality in at most six minutes across 14 MIP instances from the literature and solutions within 5% of optimality to the MINLP instances within 20 minutes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Models and Simulation Environment for the Design and Optimization of Hybrid Energy Systems

The energy landscape in the U.S. is changing with proposed federal regulations limiting carbon dioxide emissions and with government credits for 'clean' energy technologies. This shift in the market conditions, especially in deregulated markets, has already resulted in the closing of coal power plants. Previously these coal plants served to balance variation in load. The resulting loss of electricity production is being made up in part with increased renewable generation. This set of governmental policy changes potentially presents expanded opportunities for both renewable energy and nuclear energy to become significant energy sources in the future. Potential synergies exist that can reduce greenhouse gas (GHG) emissions and provide affordable, sustainable energy. Combining multiple energy production, conversion, storage, and consumption facilities can leverage these potential synergies. This research focuses on integrating nuclear power plants within systems with increased renewable penetration, referred to as a nuclear renewable hybrid energy system (NRHES). Industrial-scale nuclear renewable hybrid energy systems have the potential to provide grid services such as dispatchable generation, storage, ancillary services, and power regulation. The problem of designing, analyzing, and optimizing the NHRES is being undertaken by a multi-laboratory team. The effort requires the collaboration of experts across engineering disciplines with methods and results to be shared by these experts. To facilitate collaborations a common system modeling and analysis language is adopted along with open-source libraries and interface standards that support data exchange among the models. Modelica was selected as the common language based on its prior use by team members at each of Idaho National Laboratory, Oak Ridge National Laboratory, and Argonne National Laboratory.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Economic Dispatch Model of Nuclear High-Temperature Reactor with Hydrogen Cogeneration in Electricity Market

Hydrogen produced without carbon emissions could be a useful fuel as nations look to decarbonize their electricity, transport, and industry sectors. Using the iodine–sulfur (IS) cycle coupled with a nuclear heat source is one method for producing hydrogen without the use of fossil fuels. An economic dispatch model was developed for a nuclear-driven IS system to determine hydrogen sale prices that would make such a system profitable. The system studied is the HTTR-GT/H2, a design for power and hydrogen cogeneration at the Japan Atomic Energy Agency’s High Temperature Engineering Test Reactor. This study focuses on the development of the economic model and the role that input data plays in the final calculated values. Using a historical price duration curve shows that the levelized cost of hydrogen (LCOH) or breakeven sale price of hydrogen would need to be 98.1 JPY/m3 or greater. Synthetic time histories were also used and found the LCOH to be 67.5 JPY/m3. The price duration input was found to have a significant effect on the LCOH. As such, great care should be used in these economic dispatch analyses to select reasonable input assumptions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1972

An approximate method for the synthesis of optimal control of distributed systems.

An approximate method is presented for the synthesis of an optimal control of distributed parameter systems, based on a combination of the ideas of Lyapunov's direct method and those of Bellman's successive approximation in policy space. The method is such that the approximate equation is improved after each iteration in the sense of the performance index being used.

Park, K. E.↗
DOE OSTI2025

Safe Physics-Informed Machine Learning for Dynamics and Control

This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.

Drgona, Jan↗
DOE OSTI2025

HD ADOPT: Heavy-Duty Vehicle Choice Model Documentation

HD ADOPT is a logit consumer vehicle choice and stock model that analyzes the Class 8 tractor market. The model projects future technology shares, fuel consumption, and greenhouse gas (GHG) emissions under input assumptions of technology progress, energy prices, and policies. ADOPT is distinguished from other vehicle choice models through inclusion of non-linear and heterogenous consumer preferences and characterization of the full range of market options rather than use of composite vehicles. In addition, ADOPT has integrated vehicle simulation capabilities that enable performance assessment and optimization of endogenous technology evolution. Optionally, the model is able to adjust this evolution to enforce compliance with fuel economy and GHG emissions regulations. Primary results include projection of technology shares and future in-use fleet energy demand, petroleum consumption, and GHG emissions. This enables analysis and comparison of future scenarios of technology improvements, economic conditions, and national policies. Recent new features for the HD modeling also enable examination of different on-board hydrogen fuel storage technologies from the lens of consumer preferences for vehicle cost and range. This report documents current ADOPT capabilities and methodologies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

Optimizing and Diversifying Electric Vehicle Driving Range for U.S. Drivers

Properly determining the driving range is critical for accurately predicting the sales and social benefits of battery electric vehicles (BEVs). This study proposes a framework for optimizing the driving range by minimizing the sum of battery price, electricity cost, and range limitation cost referred to as the "range-related cost" as a measurement of range anxiety. The objective function is linked to policy-relevant parameters, including battery cost and price markup, battery utilization, charging infrastructure availability, vehicle efficiency, electricity and gasoline prices, household vehicle ownership, daily driving patterns, discount rate, and perceived vehicle lifetime. Qualitative discussion of the framework and its empirical application to a sample (N=36664) representing new car drivers in the United States is included. The quantitative results strongly suggest that ranges of less than 100 miles are likely to be more popular in the BEV market for a long period of time. The average optimal range among U.S. drivers is found to be largely inelastic. Still, battery cost reduction significantly drives BEV demand toward longer ranges, whereas improvement in the charging infrastructure is found to significantly drive BEV demand toward shorter ranges. In conclusion, the bias of a single-range assumption and the effects of range optimization and diversification in reducing such biases are both found to be significant.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

BuildingIQ Technology Field Validation

This document describes a field validation and verification of the BuildingIQ Predictive Energy Optimization (PEO) technology based on a five-site study. BuildingIQ describes its PEO technology as a software-as-a-service (SaaS) platform that optimizes commercial building HVAC control for system efficiency, occupant comfort, and cost. It is targeted for use in large, complex buildings, and integrates with the building automation system (BAS) to conduct supervisory control. The PEO algorithm defines optimal space air temperature setpoints that are automatically implemented at the variable air volume (VAV) terminal units when possible, or through supply air temperature and duct static pressure setpoints at the air handling unit (AHU) level. The optimization is built upon a learned predictive model that provides a 24-hour ahead forecast of the building’s power profile, using weather forecasts and historical operational data; this model is updated every 4 to 6 hours. Demand-responsive load reductions may also be implemented.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Market-Integrated Optimization of Wind-Battery-Hydrogen Hybrids for Peaking Capacity via Storage

As Integrated Energy Systems (IES) combine multiple energy and storage technologies to provide potentially more value and less risk via resource diversification, complementary overbuild, increased flexibility, and revenue-stacking, IES value is dependent on electricity market dispatch and grid interactions should play an important role in IES design and operation. This study hybridizes and retrofits wind and combustion turbine plants to study the impacts of replacing gas generation capacity with wind, battery, PEM electrolysis, hydrogen tanks and hydrogen turbines. The optimized design is co-simulated in a production cost model with different bidding strategies in order to compare performance and highlight the importance of grid-interactions. We analyze the revenue and dispatch changes as well as the price and cost implications of wind-battery-hydrogen IESs.

electrolysis↗
DOE OSTI2021

Optimized simple and affordable procedure for differentiation of monocyte-derived dendritic cells from LRF: An accessible and valid alternative biological source

Dendritic cells are one of the most popular immune cells, which plays a remarkable role in both immunotherapy and tolerance induction. Due to unwanted side effects of leukocyte presence in donated blood, the policy of blood service is the pre-storage reduction of leukocytes, which today, filtration is the most common method for this purpose. The filtration method has led to diminished access to Buffy coat as a generally used conventional source of biological cells. We developed a simple, affordable, and reproducible method for dendritic cell differentiation from filter-derived monocytes and, the results of the filter study were compared with differentiated DCs from the conventional buffy coat-derived monocytes. The Monocytes were recovered from leukoreduction filter using an optimized protocol with supplemented PBS buffer. Following the adhesion method, CD14{sup +} Monocyte-enriched population with the purity of 94 % was obtained. After cytokine stimulation over a 6-day period and maturation induction by LPS, differentiated DCs were evaluated for morphology, surface markers (CD86, CD40, CD83 and, HLA-DR), antigen uptake potency and IL-12 secretion. Analysis and comparison of the results represented no significant difference between the two groups. Accordingly, we conclude that leukoreduction filters could be introduced as a reliable and research-grade source of monocyte for DC generation in biological research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Optimization of an in Vitro CYP450 Induction Assay Using Cryopreserved Cell Lines

Cytochrome P450 (CYP450) enzymes are responsible for the majority for Phase I metabolism. Induction occurs when a drug increases the rate of CYP450 enzyme production. As a result, the rates of metabolism of subsequent drugs increase, posing concerns for dosing and safety. To further expand the USAMRICD Absorption, Distribution, Metabolism, Excretion, and Toxicity Center of Excellence (ADMET CoE) profiling capabilities, a CYP450 induction assay is being developed using alternative cell lines to primary human hepatocytes to measure changes in gene expression of CYP1A2 and CYP3A4 isoenzymes. Primary human hepatocytes are the gold standard for evaluating P450 induction in vitro ; however, they have inherent limitations such as high costs, fastidious nature, and significant lot‐to‐lot variability. Induction assessments using cryopreserved hepatocytes and two immortalized cell lines, HepaRG and HepatoCells, are being conducted to determine a more robust, cost‐effective alternative model. Each cell line was exposed to the standard inducers rifampicin and omeprazole to assess CYP3A4 and CYP1A2 activity, respectively. Following a 72‐hour incubation period, RNA from the cell cultures was extracted and purified. Fold changes of gene expression were measured using real‐time quantitative PCR (RT‐qPCR). Hepatocytes displayed an average fold change of 92.7 for CYP3A4 and 49.6 for CYP1A2, whereas HepaRG displayed changes of 28.6 and 179.5, respectively. Average values for CYP3A4 and CYP1A2 for HepatoCells were 58.7 and 43.9. Based on the data, HepatoCells most closely mimicked cryopreserved hepatocytes in comparison to HepaRG cells. Additional data on assay parameters and technique optimization will be presented. Support or Funding Information Opinion disclaimer: The views expressed in this abstract are those of the authors and do not reflect the official policy of the Department of the Army, Department of Defense, or the U.S. Government. This project was generously supported in part by an appointment to the Postgraduate Research Participation Program at the U.S. Army Medical Research Institute of Chemical Defense administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and U.S. Army Medical Research and Materiel Command. Support is also provided by the Defense Threat Reduction Agency – Joint Science and Technology Office, Medical S&T Division. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Lim, Tae Young↗