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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2025

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.

14 SOLAR ENERGY↗
DOE OSTI2021

Real-time estimation and control of divertor surface heat flux on the DⅢ-D tokamak

We report Future tokamaks will require robust technologies for the mitigation of heat exhaust onto the plasma-facing components. As a first step towards this development, a system has been developed at DIII-D that estimates and controls in real-time the heat flux to the PFCs. Real-time estimation of the peak power flux from this model-based approach is validated with off-line infra-red measurements for various DIII-D plasma discharges. A nonlinear free-boundary simulation code (GSevolve) is used for simulating the closed loop response and for the off-line determination of the control parameters. The implementation and first experimental results of the application during the DIII-D plasma campaign are reported.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Multiscale model predictive control of battery systems for frequency regulation markets using physics-based models

In this work, we propose a multiscale model predictive control (MPC) framework for stationary battery systems that exploits high-fidelity models to trade-off short-term economic incentives provided by energy and frequency regulation (FR) markets and long-term degradation effects. We find that the MPC framework can drastically reduce long-term degradation while properly responding to FR and energy market signals (compared to MPC formulations that use low-fidelity models). Our results also provide evidence that sophisticated battery models can be embedded within closed-loop MPC simulations by using modern nonlinear programming solvers (we provide an efficient and easy-to-use implementation in Julia). We use insights obtained with our simulations to design a low-complexity MPC formulation that matches the behavior obtained with high-fidelity models. This is done by designing a suitable terminal penalty term that implicitly captures long-term degradation. The results suggest that complex degradation behavior can be accounted for in low-complexity MPC formulations by properly designing the cost function. We believe that our proof-of-concept results can be of industrial relevance, as battery vendors are seeking to participate in fast-changing electricity markets while maintaining asset integrity.

42 ENGINEERING↗
DOE OSTI2024

H ∞ Control for Energy Dispatch in Autonomous Nanogrid With Communication Delays

This paper proposes an optimal controller and estimator for energy dispatch to balance the power supply and demand considering communication delays. The proposed algorithm involves modeling an autonomous nanogrid (ANG) consisting of distributed energy resources, energy storage systems, loads, an $H$ ∞ controller with a reference power modulation technique, and a state estimator. The ANG was developed to express the dynamic supply-demand energy balance of a nanogird system. Reference power modulation was designed to generate the desired ESS power based on the imbalanced energy. Random communication delays were modeled using a stochastic variable satisfying the Bernoulli random binary distribution. The optimal $H$ ∞ controller and estimator were developed using a linear matrix inequality approach to exponentially stabilize the closed-loop system. Simulations were performed using real daily demand forecasts obtained from the Korea Meteorological Administration to demonstrate the effectiveness of the proposed real-time optimization algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Establishing Models for Digital Twin of Hydropower Systems Using Probability Density Function Shaping

This paper introduces a digital twin modeling method for hydropower systems with Kaplan turbines using probability density function (PDF) shaping. We first use multilayer perceptron (MLP) model to build the discretized openloop Kaplan unit, where the MLP is trained by historical data. Then we use a proportional integral double derivative (PIDD) controller and a lead-lag exciter to test the obtained digital twin model in a closed-loop fashion. Simulation results show that the proposed digital twin modeling method can accurately capture the dynamics of the Kaplan hydropower unit. Finally, we show that the obtained digital twin can help to optimize the PIDD parameters. Compared with the original PIDD controller, the optimized one can achieve an over 90% improvement on the mean square tracking error.

Yin, Zhun [New York University]↗
DOE OSTI2022

Design and Closed‐Loop Motion Planning of an Untethered Swimming Soft Robot Using 2D Discrete Elastic Rods Simulations

Despite tremendous progress in the development of untethered soft robots in recent years, existing systems lack the mobility, model‐based control, and motion planning capabilities of their piecewise rigid counterparts. As in conventional robotic systems, the development of versatile locomotion of soft robots is aided by the integration of hardware design and control with modeling tools that account for their unique mechanics and environmental interactions. Here, a framework for physics‐based modeling, motion planning, and control of a fully untethered swimming soft robot is introduced. This framework enables offline co‐design in the simulation of robot parameters and gaits to produce effective open‐loop behaviors and enables closed‐loop planning over motion primitives for feedback control of a frog‐inspired soft robot testbed. This pipeline uses a discrete elastic rods (DERs) physics engine that discretizes the soft robot as many stretchable and bendable rods. On hardware, an untethered aquatic soft robot that performs frog‐like rowing behaviors is engineered. Hardware validation verifies that the simulation has sufficient accuracy to find the best candidates for sets of parameters offline. The simulator is then used to generate a trajectory library of the robot's motion in simulation that is used in real‐time closed‐loop path following experiments on hardware.

Huang, Xiaonan↗
DOE OSTI2023

Development of a hardware-in-the-loop testbed for laboratory performance verification of flexible building equipment in typical commercial buildings

The goals of reducing energy costs, shifting electricity peaks, increasing the use of renewable energy, and enhancing the stability of the electric grid can be met in part by fully exploiting the energy flexibility potential of buildings and building equipment. The development of strategies that exploit these flexibilities could be facilitated by publicly available high-resolution datasets illustrating how control of HVAC systems in commercial buildings can be used in different climate zones to shape the energy use profile of a building for grid needs. This article presents the development and integration of a Hardware-In-the-Loop Flexible load Testbed (HILFT) that integrates physical HVAC systems with a simulated building model and simulated occupants with the goal of generating datasets to verify load flexibility of typical commercial buildings. Compared to simulation-only experiments, the hardware-in-the-loop approach captures the dynamics of the physical systems while also allowing efficient testing of various boundary conditions. The HILFT integration in this article is achieved through the co-simulation among various software environments including LabVIEW, MATLAB, and EnergyPlus. Although theoretically viable, such integration has encountered many real-world challenges, such as: 1) how to design the overall data infrastructure to ensure effective, robust, and efficient integration; 2) how to avoid closed-loop hunting between simulated and emulated variables; 3) how to quantify system response times and minimize system delays; and 4) how to assess the overall integration quality. Lessons-learned using the examples of an AHU-VAV system, an air-source heat pump system, and a water-source heat pump system are presented.

Chen, zhelun↗
DOE OSTI2020

Virtual Pressure Sensor for Electronic Expansion Valve Control in a Vapor Compression Refrigeration System

Virtual sensor technology, which uses simulation models/algorithms to calculate a value to represent an unmeasured variable or replace a directly measured reading, has attracted many studies in the heating, ventilation, air conditioning and refrigeration (HVAC&R) industry. However, most virtual sensor technologies are developed for fault detection and diagnostics (FDD) purposes, which generally compare the virtual sensor values with actual measured values to detect if any fault occurred and identify the causes that led to the fault. It is rare to see studies focus on control performance of virtual sensors after substituting an actual sensor. This is particularly important for the system with no redundant sensor since a virtual sensor is the most effective way to operate the system in the desirable region when any sensor failure occurs. To address this gap, this paper develops a new virtual pressure sensor technology to substitute the actual pressure measurement for electronic expansion valve (EXV) control in a vapor compression refrigeration system by integrating compressor and valve characteristics. The control performance of this proposed virtual pressure sensor technology under various operating conditions is validated with experimental data. Closed loop EXV control simulations with the proposed virtual pressure sensor are conducted, and the results are analyzed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Plasma flux expansion control on the DIII-D tokamak

A new controller has been developed with help of the flexible divertor poloidal-field coil set of the DIII-D tokamak, to aid in the precise control of the flux expansion in the scrape-off layer (SOL). Here, the single-input multiple-output (SIMO) architecture ensures flexibility through a complementary set of orthogonal actuator direction to guarantee minimum effect on existing controlled variables, e.g. radial and vertical position control of the X-point. A non-linear free-boundary simulation code (GSevolve) is used for simulating the closed-loop response and for verifying the implementation of the control algorithm on the DIII-D plasma control system (PCS). First results of the experimental commissioning of the new controller during 2020 DIII-D campaign are also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Framework for assessment of magnetic equilibrium controller performance on the MAST upgrade spherical tokamak

Here, in this work we present the assessment framework for magnetic equilibrium controllers on MAST Upgrade spherical tokamak (MAST-U) spherical tokamak. Such controllers are essential for the MAST-U since exhaust physics and core-edge integration studies require advanced divertor plasma configurations. The developed framework is based on the TokSys suite of plasma control codes, which was adapted and upgraded for MAST-U. However, extra capabilities were added on top of TokSys to support the development of new control algorithms, deployment of controllers to the plasma control system (PCS) and evaluation of their performance. The controller assessment was realized via closed-loop integrated control simulations with the actual MAST-U PCS and different physics-based plasma models. Since all components of the assessment chain were experimentally validated, these simulations provide qualified controllers applicable for direct use in the experiment. This resulted in the successful experimental demonstration of advanced plasma shape control on MAST-U with minimal on-machine development time. A similar methodology would be beneficial to other tokamaks, both existing and future.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Crystalline-symmetry-protected Majorana modes in coupled quantum dots

We propose a minimalist architecture for achieving various crystalline-symmetry-protected Majorana modes in an array of coupled quantum dots. Our framework is motivated by the recent experimental demonstrations of two-site and three-site artificial Kitaev chains in a similar setup. We find that introducing a π-phase domain wall in the Kitaev chain leads to a pair of mirror-protected Majorana zero modes located at or near the junction. Joining two π junctions into a closed loop, we can simulate two distinct classes of two-dimensional higher-order topological superconducting phases, both carrying symmetry-protected Majorana modes around the sample corners. As an extension of the π junction, we further consider a general vertex structure where n Kitaev chains meet, i.e., a Kitaev n vertex. We prove that such an n vertex, if respecting a dihedral symmetry group D n , necessarily carries n vertex-bound Majorana modes protected by the D n symmetry. Resilience of the junction and vertex Majorana bound states against disorder and correlation effects is also discussed. Our architecture paves the way for designing, constructing, and exploring a wide variety of artificial topological crystalline phases in quantum-dot experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Interpretable Deep Learning for Advancing Field-Enhanced Catalysis

This DOE Early Career project developed a physics-informed, interpretable AI-and-modeling framework to understand and exploit electric-field effects in heterogeneous catalysis, with ammonia cracking and synthesis as a representative pathway. The team built and validated methods to map local electric fields on metal surfaces and nanoparticles, showing that low-coordination features (tips/edges/corners) can concentrate fields by several-fold relative to flat facets. Using DFT-generated datasets, the project created physics-guided machine learning models that rapidly predict local electric fields and field-dependent adsorption energetics with near-DFT accuracy while reducing computational cost by orders of magnitude. These predictions were integrated with microkinetic modeling to quantify how field-dipole interactions reshape reaction energetics and mechanisms, enabling large increases in predicted catalytic rates and substantial reductions in operating temperature under favorable field conditions. To accelerate discovery of earth-abundant catalysts, the project combined interpretable ML screening (with electronic-structure descriptors identified as key drivers) with a generative inverse-design workflow based on diffusion models and physics constraints. The resulting closed-loop approach, linking simulation, mechanistic modeling, and AI, provides reusable tools and datasets for designing catalysts and operating conditions in field-enhanced catalysis, with broad relevance to electrostatic catalysis, plasma catalysis, electrocatalysis, and other energy-related chemical transformations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Using AI for Wave-front Estimation with the Rubin Observatory Active Optics System

Abstract The Vera C. Rubin Observatory will, over a period of 10 yr, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an active-optics system (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the optical system. To accomplish this, Rubin will use out-of-focus images from sensors located at the edge of the focal plane to learn and correct for perturbations to the wave front. We have designed and integrated a deep-learning (DL) model for wave-front estimation into the AOS pipeline. In this paper, we compare the performance of this DL approach to Rubin’s baseline algorithm when applied to images from two different simulations of the Rubin optical system. We show the DL approach is faster and more accurate, achieving the atmospheric error floor both for high-quality images and low-quality images with heavy blending and vignetting. Compared to the baseline algorithm, the DL model is 40× faster, the median error 2× better under ideal conditions, 5× better in the presence of vignetting by the Rubin camera, and 14× better in the presence of blending in crowded fields. In addition, the DL model surpasses the required optical quality in simulations of the AOS closed loop. This system promises to increase the survey area useful for precision science by up to 8%. We discuss how this system might be deployed when commissioning and operating Rubin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Generic characterization of electrical test benches for AC- and HVDC-connected wind power plants

The electrical test and assessment of wind turbines go hand in hand with standards and network connection requirements. In this paper, the generic structure of advanced electrical test benches, including grid emulator or controllable grid interface, wind torque emulator, and device under test, is proposed to harmonize state-of-the-art test sites. On the other hand, modern wind turbines are under development towards new features, concerning grid-forming, black-start, and frequency support capabilities as well as harmonic stability and control interaction considerations, to secure the robustness and stability of renewable-energy-based power systems. Therefore, it is necessary to develop new and revised test standards and methodologies to address the new features of wind turbines. This paper proposes a generic test structure within two main groups, including open-loop and closed-loop tests. The open-loop tests include the IEC 61400-21-1 standard tests as well as the additional proposed test options for the new capabilities of wind turbines, which replicate grid connection compliance tests using open-loop references for the grid emulator. In addition, the closed-loop tests evaluate the device under test as part of a virtual wind power plant and perform real-time simulations considering the grid dynamics. The closed-loop tests concern grid connection topologies consisting of AC and HVDC, as well as different electrical characteristics, including impedance, short-circuit ratio, inertia, and background harmonics. The proposed tests can be implemented using available advanced test benches by adjusting their control systems. The characteristics of a real power system can be emulated by a grid emulator coupled with real-time digital simulator systems through a high-bandwidth power-hardware-in-the-loop interface.

17 WIND ENERGY↗
DOE OSTI2022

NPP Simulators for Coupled Thermal and Electric Power Dispatch

The Light Water Reactor Sustainability (LWRS) program within the United States Department of Energy supports extending the operation of the U.S. commercial nuclear power plant (NPP) fleet. Within the LWRS program, the Flexible Plant Operation and Generation (FPOG) Pathway works to diversify the revenue streams of light water reactors (LWRs) by opening opportunities for the co-generation of non-electric products in addition to supplying electrical power to the grid. Recent events have added greater motivation to these efforts. For example, the recent Inflation Reduction Act (IRA) passed by the U.S. federal government offers substantial tax incentives for producing clean hydrogen, the technology readiness level of dispatchable and high-efficiency hydrogen production has dramatically increased in a short time, and societal response to world climate change is driving a transition away from fossil fuels. Producing hydrogen with maximum efficiency using nuclear power requires dispatching both electrical and thermal power from the nuclear plant to the hydrogen plant, so testing concepts of operations for combined electrical and thermal power dispatch (TPD) from an NNP to a hydrogen plant is of interest. This report documents achievement of the Light Water Reactor Sustainability (LWRS) program milestone “Install and demonstrate a vendor-developed simulator on the Human Systems Simulation Laboratory (HSS) for dispatch of LWR electrical power to a close-coupled electrolysis plant” with a due date of Dec. 22, 2022. Several factors provide motivation for this effort. Coupling the power generation deck of a nuclear power plant to a hydrogen production facility introduces new possibilities for operational transients that must be addressed. In particular, the performance of the integrated system during startup and shutdown of the hydrogen production facility, as well as offnormal conditions, need to be evaluated to ensure there are no adverse effects on the operation of the existing NPP. The concept of operations involving the NPP, the hydrogen plant, and the electric power grid must be tested using NPP simulators and operating procedures that have been modified for TPD operations. These tests must also include dynamic simulations of the coupled tertiary thermal and electric loads as well as coordinated activities with NPP operators, tertiary load operators and grid power coordinators. The report summarizes progress in developing and testing full-scope NPP simulators at the HSSL, including a generic BWR simulator from GSE Systems, Inc. and generic PWR simulator from Westinghouse. In the case of the TPD-GBWR Simulator from GSE Systems, Inc., a BWR is thermally coupled to a high temperature electrolysis (HTE) plant that produces hydrogen and oxygen from de-ionized water. The hydrogen plant is not explicitly simulated but only included as a transient heat sink. A thermal power dispatch (TPD) system transfers heat between the steam systems at the BWR and the hydrogen plant. Operational results from two versions of the modified simulator are presented. The first version uses synthetic oil as a heat transfer fluid in a closed delivery heat loop (DHL) that generates steam at the hydrogen plant. The second version uses steam as the heat transfer fluid in a delivery steam line (DSL) to provide steam to the hydrogen plant. For both versions, the estimated thermal power delivery distance is approximately one kilometer. The amount of thermal power dispatched in the simulators is 15% of the total reactor thermal power such that the simulators provide a tool to study the feasibility of coupling a BWR to industrial processes that benefit from a combination electrical and thermal power dispatch. Ongoing work within a CRADA is also developing a full-scope PWR simulator provided by Westinghouse for both thermal and electric power coupling. This simulator is based on a PWR plant with two three-loop Westinghouse reactors. Westinghouse PWRs are sufficiently similar that a simulator of a three-loop reactor is an appropriate representation for two-loop and four-loop PWR reactors. The three-loop simulator will initially be modified for close-coupling to a 100 MW HTE hydrogen production plant that will require approximately 25 MW of thermal power while operating at its maximum rated capacity. The simulator testing will include full coupling to dynamic simulations of a hydrogen production plant and a representative bulk electric grid. The simulator provided by Westinghouse is similar to the GPWR simulator that INL has already obtained from GSE Systems but has a few important added benefits. First, the Westinghouse simulator is based on digital controls and has additional screens that can be called up to show parameter trends to assist operators in decision-making. The Westinghouse simulator also has upgrades to the controls and hardware representations, such as valve actuators, that make it more realistic and flexible in terms of accurately sim

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Active learning enables generation of molecules that advance the known Pareto front

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.

Chemistry↗
DOE OSTI2022

Dynamic machine learning-based optimization algorithm to improve boiler efficiency

With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. Further, the best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗