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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Reinforcement learning for online adaptation of model predictive controllers: Application to a selective catalytic reduction unit

Here we present a novel application of reinforcement learning (RL) for online dynamic tuning of model predictive controllers (MPC). Applying a state-action-reward-state-action (SARSA) algorithm for temporal difference learning with a control-specific reward function improves the error tracking performance of a standard MPC formulation. The proposed RL approach is also readily adaptable to other MPCs, or entirely different control approaches. Practical details for the implementation of the RL-MPC algorithm are also presented. The proposed algorithm is applied to a case study of controlling nitrogen oxide (NO x ) emissions in an industrial selective catalytic reduction (SCR) unit, a control problem characterized by significant nonlinearity and time delay. Along with an RL-MPC formulation for NOx control, another MPC is proposed to mitigate ammonia slip and decrease ammonia consumption in the SCR. Results showing the efficacy of the RL-MPC for NO x control through learning and implementation on the nonlinear SCR dynamic model are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

BMINN: Learning chemical potentials and parameters from voltage data for multi-phase battery modeling

Free-energy landscapes and chemical potentials govern the dynamics of phase transitions, transport, and stability in functional materials, yet they remain experimentally inaccessible under realistic operating conditions. Here we introduce a Bayesian model-integrated neural network (BMINN) that embeds physics-based formulations of non-autonomous partial differential-algebraic equations into probabilistic learning. This approach reconstructs hidden thermodynamics directly from macroscopic current-voltage data, providing quantitative access to metastable states, staging transitions, and energy barriers without synchrotron probes. Demonstrated on lithium-graphite electrodes, BMINN recovers full Gibbs free-energy landscapes with fidelity validated against operando X-ray diffraction. The framework generalizes across dynamical regimes, enabling accurate voltage prediction, internal state estimation, and inference of governing parameters. Beyond batteries, BMINN exemplifies a broadly applicable strategy for learning missing physics in multiphase, non-equilibrium systems, offering a new pathway to uncover hidden thermodynamic functions across condensed matter and materials physics.

25 ENERGY STORAGE↗
DOE OSTI2020

A machine learning model for predicting the minimum miscibility pressure of CO 2 and crude oil system based on a support vector machine algorithm approach

CO 2 enhanced oil recovery (EOR) is a potential way for carbon capture, utilization and storage (CCUS). Though, the effect of CO 2 injection is greatly influenced by the reservoir conditions. Typically, Minimum miscible pressure (MMP) is selected as one of the key parameters for the screening and evaluation of prospective CO 2 flooding. Conventional slim tube test is both accurate and widely accepted but it is inefficient. Existing empirical formulas for MMPs are easy to be used but have been proved inaccurate and unreliable. Machine learning-based methods have great advantages in predicting MMP. However, only predication accuracy is discussed for most models without the screening of the main control factors and further validation of the model reliability. In this paper, a new prediction model based on support vector machine (SVM) was developed for pure/impure CO 2 and crude oil system. This study was based on 147 sets of MMP data from the literature with full information on reservoir temperature, oil composition and gas composition. The main control factors were screened by several statistical methods. Unlike the conventional prediction models that verified by only prediction accuracy, learning curve and single factor control variable analysis are further validated to obtain the optimum model.

02 PETROLEUM↗
DOE OSTI2021

Machine learning assisted rediscovery of methane storage and separation in porous carbon from material literature

Porous carbon (PC) has been widely regarded as one of the most promising absorbents for methane storage. Studies show that its uptake capacity and selectivity highly depend on textural structures. Although much effort has been made, unveiling their detailed structure-performance relationship remains a challenge. Here, we propose an innovative study where, with the assistance of machine learning, the hidden relationship of the textural structures of PC with the methane uptake and separation can be derived from existing data in material literature. Machine learning models were trained by the data, including specific surface area, micropore volume, mesopore volume, temperature, and pressure as the input variables and methane uptake as the output variable for prediction. Among the tested models, the multilayer perceptron (MLP) shows the highest accuracy in predicting the methane uptake. In addition, the model enables to automatically construct a uptake performance map in terms of micropore volume and mesopore volume. The obtained MLP model was also extended to explore the CO 2 /CH 4 selectivity by retraining it with the data collected from literature of PC for the CO 2 uptake. Finally, the constructed 2D selectivity map shows that the high selectivity can be achieved in the low CH 4 uptake region.

42 ENGINEERING↗
DOE OSTI2021

Nonadiabatic Molecular Dynamics Study of the Relaxation Pathways of Photoexcited Cyclooctatetraene

In the current study, we present nonadiabatic (NAMD) and adiabatic molecular dynamics simulations of the transition-state dynamics of photoexcited cyclooctatetraene (COT). The equilibrium-state structure and absorption spectra are analyzed using the semiempirical Austin Model 1 potential. The NAMD simulations are obtained by a surface-hopping algorithm. We analyzed in detail an active excited to ground state relaxation pathway accompanied by an S 2 /S 3 (D 2d ) → S 1 (D 8h ) → S o (D 4h ) → S o (D 2d ) double-bond shifting mechanism. The simulated excitation lifetime is in good agreement with experiment. The first excited singlet state S1 plays a crucial role in the photochemistry. Here, the obtained critical molecular conformations, energy barrier, and transition-state lifetime results will provide a basis for further investigations of the bond-order inversion and photoswitching process of COT.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Uniform accuracy of implicit-explicit Runge-Kutta (IMEX-RK) schemes for hyperbolic systems with relaxation

Implicit-explicit Runge-Kutta (IMEX-RK) schemes are popular methods to treat multiscale equations that contain a stiff part and a non-stiff part, where the stiff part is characterized by a small parameter. Here, in this work, we prove rigorously the uniform stability and uniform accuracy of a class of IMEX-RK schemes for a linear hyperbolic system with stiff relaxation. The result we obtain is optimal in the sense that it holds regardless of the value of and the order of accuracy is the same as the design order of the original scheme, i.e., there is no order reduction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Cybersecurity Value-at-Risk Framework

As more variable renewable energy sources are added to the grid, the role of hydropower as a reliable baseline and firming resource is growing more critical. However, the U.S hydropower fleet is not fully prepared to face modern issues such as cybersecurity threats. Hydropower accounts for 37% of U.S. utility-scale renewable electricity but is challenged by diverse infrastructure and legacy devices that predate modern security practices. While new cybersecurity solutions cannot simply be added to current hydropower generation and operation technologies, custom cybersecurity assessments can reveal system-specific threats and risk probabilities and identify mitigating enhancements.

cybersecurity valuation methodology↗
DOE OSTI2021

gamut: A Geospatial R Package to Analyze Multisectoral Urban Teleconnections

Most cities in the United States withdraw surface water to meet public water supply needs. The lands on which this water is generated are often developed for human activities -- such as agriculture, mining, and industry -- that may compete for water resources or contaminate water supplies. Cities are thereby connected to other sectors through their water supply catchments. This connection is an example of an multisectoral urban teleconnection. The Geospatial Analytics for Multisectoral Urban Teleconnections (``gamut``) package provides national-scale information on these teleconnections by combining land use data with hydrological analysis to characterize urban source watershed human interactions across the conterminous United States.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Dragonstone Strategy Kickoff Report [Slides]

The oil & natural gas (ONG) system like any other heavy industrial process, relies on a complex system of information technology (IT) and operational technology (OT) devices. Cyber-preparedness varies across the ONG industry from organization to organization. Common challenges include remote locations, long-lived field assets, and insufficient capabilities to find and track malware. ONG companies tend to be concerned with lack of cyber-awareness from employees, risk stemming from remote access for operations & maintenance, and software vulnerabilities within third-party equipment. For a variety of reasons, the general consensus from stakeholders interviewed by LLNL is that the state of cybersecurity within the electric grid is currently outpacing its ONG cousin. Existing strategies for the resilience and cybersecurity of the electric grid can and should be leveraged to provide immediate benefits to the ONG system. In LLNL’s view, there are two key factors currently limiting the development of necessary cyber-practices within the ONG industry: regulatory overlap and inefficiencies, and lack of industry awareness of potentially useful technologies for solving known issues. In the months following this report, LLNL will develop a cyber-resilience roadmap for the oil & gas pipeline sector, coupling threat intelligence, industry knowledge base, and ongoing outreach

02 PETROLEUM↗
DOE OSTI2022

Open-source Tools for Solving Grid Optimization Problems: ARPA-e Benchmark Algorithm Overview [Slides]

This document contains the official formulation that will be used for evaluation in Challenge 2 of the Grid Optimization (GO) Competition. Minor changes may occur within the formulation. Entrants will be notified when a new version is released. Changes are not expected to be of a significance that would cause a change in approach for the Entrants. This formulation builds upon the Challenge 1 formulation published in ARPA-E DE-FOA-0001952. Entrants will be judged based on the current official Challenge 2 formulation posted on the GO Competition website (this document, which is subject to change), not the formulation posted in DE-FOA-0001952. Entrants are permitted and encouraged to use any alternative problem formulation and modeling convention within their own software (such as convex relaxation, decoupled power flow formulations, current-voltage formulations, etc.) in an attempt to produce an exact or approximate solution to this particular mathematical program. However, the judging of all submitted approaches must conform to the official formulation presented here.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Hydropower Cybersecurity Value-at-Risk Framework

Hydropower remains one of the strongest forms of renewable energy generation methods. It is crucial to address the increasing risks associated with the rapid digitization. The push towards decarbonization also factors in the need to ensure security and resilience for grid-connected renewable energy resources. This report summarizes the U.S. Department of Energy's Water Power Technologies Office's effort to develop a cybersecurity valuation methodology that assists hydropower stakeholders in assessing risks associated with plan operations and gathers valuation guidance through a web-based application. The Hydropower Cybersecurity Value-at-Risk Framework delivers a platform for industry members to perform self-assessments and make informed decisions on their cybersecurity investments.

13 HYDRO ENERGY↗
DOE OSTI2026

Zero Order Reactioin Kinetics: Enabling the use of Detailed Chemical Kinetics in Combustion Simulations (Final CRADA Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS), as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Gamma Technologies, LLC (GT or Participante), to incorporate the ability to access LLNL chemical kinetics technologies while using GT-SUITE, GT’s market leading engine simulation software. At the end of the project, LLNL has released Zero-RK version 3.5 with zero- and one-dimensional (0-D and 1-D) solver functionality that interfaces with GT’s GT-SUITE v2023 and later releases. GT has tested its product to assure their customers that the interface can provide reduction in chemistry solution time for detailed chemistry simulations. The process has also positioned GT to easily benefit from future improvements of the Zero-RK suite of tools developed under the DOE Vehicle Technologies Office.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗