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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enabling Command-and-Control in Advanced In Situ Workflows

Scientific discovery is progressing towards autonomous science with the combination of scientific instruments, high-performance computing, and artificial intelligence in complex workflows. This evolution introduces new requirements for managing scientific workflows, including feedback loops, near real-time constraints, and the ability to dynamically control workflow execution. In situ workflows that analyze and visualize data as it is generated are well-suited to satisfy stringent time constraints and their iterative nature offers greater opportunities for command-and-control. However, only a few of the many workflow management systems available have been specifically designed to manage in situ workflows and often lack support for automated feedback loops that allow analysis and visualization components to interact with the main scientific data producer. To address this need, we present in this paper how to add command-and-control capabilities to a workflow management system. We identify the functional design requirements of such a command-and-control system, detail its architecture, interface, and core mechanisms, and illustrate how advanced in situ workflows can leverage command-and-control in three use cases: graceful termination with checkpoint, dynamic and adaptive data reduction, and event-triggered analysis.

Mehta, Kshitij [ORNL] (ORCID:0000000297149981)↗
DOE OSTI2024

National Security Programs - Cyber: MMAREJBLIGE – Modular Multi Agent Grid Emulation for Joined Breakdowns in Linked Generative Emulations - 23-0644

Modular Multi Agent Grid Emulations for Joined Breakdowns in Linked Generative Emulations (MMAREJBLIGE) introduces an agent-based modeling framework into real-time cyber-physical emulation to achieve a context-aware environment that introduces operator/attacker/external-condition variability to improve emulation fidelity and testing rigor. We detail our agent framework design, internal communication via message passing, and time synchronization, as well as the individual components of the system. We include a brief analysis of several scenarios run on a real-time, hardware-in-the-loop, Industrial Control Systems (ICS) test-bed which include normal operation, physical disruption, disruption with mitigation, and disruption with mitigation during a cyber denial-of-service (DOS) attack.

42 ENGINEERING↗
DOE OSTI2022

Intraspecific Variation for Leaf Physiological and Root Morphological Adaptation to Drought Stress in Alfalfa ( Medicago sativa L.)

Drought stress reduces crop biomass yield and the profitability of rainfed agricultural systems. Evaluation of populations or accessions adapted to diverse geographical and agro-climatic environments sheds light on beneficial plant responses to enhance and optimize yield in resource-limited environments. This study used the morphological and physiological characteristics of leaves and roots from two different alfalfa subspecies during progressive drought stress imposed on controlled and field conditions. Two different soils (Experiments 1 and 2) imposed water stress at different stress intensities and crop stages in the controlled environment. Algorithm-based image analysis of leaves and root systems revealed key morphological and physiological traits associated with biomass yield under stress. The Medicago sativa subspecies (ssp.) sativa population, PI478573, had smaller leaves and maintained higher chlorophyll content (CC), leaf water potential, and osmotic potential under water stress. In contrast, M. sativa ssp. varia, PI502521, had larger leaves, a robust root system, and more biomass yield. In the field study, an unmanned aerial vehicle survey revealed PI502521 to have a higher normalized difference vegetation index (vegetation cover and plant health characteristics) throughout the cropping season, whereas PI478573 values were low during the hot summer and yielded low biomass in both irrigated and rainfed treatments. RhizoVision Explorer image analysis of excavated roots revealed a smaller diameter and a narrow root angle as target traits to increase alfalfa biomass yield irrespective of water availability. Root architectural traits such as network area, solidity, volume, surface area, and maximum radius exhibited significant variation at the genotype level only under limited water availability. Different drought-adaptive strategies identified across subspecies populations will benefit the plant under varying levels of water limitation and facilitate the development of alfalfa cultivars suitable across a broad range of growing conditions. The alleles from both subspecies will enable the development of drought-tolerant alfalfa with enhanced productivity under limited water availability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Analysis of Grid-Forming Inverter Controls for Grid-Connected and Islanded Microgrid Integration

Autonomous grid-forming (GFM) inverter testbeds with scalable platforms have attracted interest recently. In this study, a self-synchronized universal droop controller (SUDC) was adopted, tested, and scaled in a small network and a test feeder using a real-time simulation tool to operate microgrids without synchronous generators. We presented a novel GFM inverter control adoption to better understand the dynamic behavior of the inverters and their scalability, which can impact the distribution system (DS). This paper provides a steady-state and transient analysis of the GFM power inverter controller via simulation to better understand voltage and frequency stabilization and ensure that the critical electric loads are not affected during a prolonged power outage. The controllers of the GFM inverter are simulated in HYPERSIM to examine voltage and frequency fluctuations. This analysis includes assessing the black start capability for photovoltaic microgrids, both grid-connected and islanded, during transient fault conditions. The high photovoltaic PV penetration levels open exciting opportunities and challenges for the DS. The GFM inverter control demonstrated appropriate response times for synchronization, connection, and disconnection to the grid. The DS has become more resilient and independent of fossil fuels by increasing the penetration of inverter-based distributed energy resources (DERs).

Ward, Laura (ORCID:0000000345501581)↗
DOE OSTI2023

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Mixed-State Entanglement from Local Randomized Measurements

In this work, we propose a method for detecting bipartite entanglement in a many-body mixed state based on estimating moments of the partially transposed density matrix. The estimates are obtained by performing local random measurements on the state, followed by postprocessing using the classical shadows framework. Our method can be applied to any quantum system with single-qubit control. We provide a detailed analysis of the required number of experimental runs, and demonstrate the protocol using existing experimental data [Brydges et al., Science 364, 260 (2019)].

1-dimensional spin chains↗
DOE OSTI2024

The Design and Evaluation of Zero Trust Architecture for Electric Vehicle Charging Infrastructure: EVs @ Scale Series on EV Charging Station Cybersecurity

Implementing a zero trust architecture can significantly bolster the security of electric vehicle (EV) charging infrastructure. EV charging infrastructure includes numerous networked interfaces, each of which can present potential vulnerabilities. When these vulnerabilities are exploited, they can compromise the entire system, leading to severe operational and security risks. Zero trust is a security model that operates on the principle of "never trust, always verify," which helps manage the attack surface and limit the scope of any potential compromises. Fundamentally, this model ensures that no entity, whether inside or outside the network, is trusted by default. The design principles of zero trust include continuous verification, strict deny-by-default access controls, and micro-segmentation. Continuous verification ensures that every request is thoroughly checked, regardless of its origin. Strict access controls enforce the principle of least privilege, allowing users and devices only the minimum necessary access to perform their functions. Micro-segmentation involves dividing the network into smaller, isolated segments to prevent lateral movement in case of a breach. In the context of EV charging infrastructure, zero trust can be implemented through various strategies. For example, multi-factor authentication (MFA) can be required for engineers to access the management interfaces and control systems of charging stations. Real-time monitoring and analysis of network traffic can help detect and respond to anomalies. Systems that do not need to communicate with each other can be micro-segmented to enhance security. All communications should adhere to predefined policies to be permitted. Additionally, encrypting communications can protect sensitive information exchanged between chargers and management systems. This paper presents a zero trust architecture specifically designed for EV charging infrastructure. Implementing zero trust not only mitigates risks but also builds a resilient infrastructure capable of withstanding and quickly recovering from cyber threats. The architecture addresses six defined security objectives. A comprehensive test plan is developed to assess the architecture against these objectives, and the results of the evaluation are reported. This approach is essential for maintaining the reliability and integrity of EV charging services in an increasingly interconnected and vulnerable digital landscape. This is the first in a planned series of papers exploring the implementation of zero trust in EV charging infrastructure. Each paper will delve into different aspects and applications of zero trust, highlighting how various work processes and requirements can lead to distinct architectural designs. These architectures will be tailored to address specific security challenges and operational needs within the EV charging ecosystem, ensuring a robust and adaptable security framework.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to purely data-driven methods, {PIML} models can be trained from additional information obtained by enforcing physical laws such as energy and mass conservation. More broadly, {PIML} models can include abstract properties and conditions such as stability, convexity, or invariance. The basic premise of {PIML} is that the integration of ML and physics can yield more effective, physically consistent, and data-efficient models. This paper aims to provide a tutorial-like overview of the recent advances in {PIML} for dynamical system modeling and control. Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of {PIML} models; and 4) physics-informed digital twins. The paper is concluded with a perspective on open challenges and future research opportunities.

Nghiem, Truong↗
DOE OSTI2023

Fiber-Optic Raman Probe for On-line EAF Slag Analysis

Here, in Electric Arc Furnace (EAF) steelmaking, the push for improved efficiency requires accurate analysis of the chemical composition of its slag system to control slag foaming, provide refractory protection, and maintain high furnace iron yield. Therefore, the ability to obtain real-time slag chemistry data would provide a useful tool to improve the control and efficiency of the process. The work reported here aims to assess the structure and chemistry of EAF slags at high temperatures using a portable fiber-optic Raman probe. As a result, the ability to relate Raman spectra peaks to chemistry and structure is demonstrated in experimental result with EAF slags at 1550°C.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Fast Modular Reactor Nuclear Design Parameters of Fuel Cycle and Power Distributions

This paper describes conceptual design of a 112 MW thermal (50 MW electric) Fast Modular Reactor (FMR) system operating at 7 MPa with inlet/outlet temperatures of 509/800 ºC. The reactor system includes the reactor core, fuel assemblies, fuel rods, reactor internals, reflector, neutron control system, flow control, and structural components. The nuclear design and analysis were conducted to search for a baseline core with a cycle length greater than 8-year and the power peaking factor less than 1.5 by adjusting the fuel assembly, reactor core, and reflector configurations. The neutronics calculations of the baseline core showed that a refueling interval of 9-year is achieved with a total peaking factor of 1.47 and a fuel rod average linear power of 3.6 kW/m.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Fast modular reactor nuclear design parameters of fuel cycle and power distributions

This paper describes conceptual design of a 112 MW thermal (50 MW electric) Fast Modular Reactor (FMR) system operating at 7 MPa with inlet/outlet temperatures of 509/800 Celsius degrees. The reactor system includes the reactor core, fuel assemblies, fuel rods, reactor internals, reflector, neutron control system, flow control, and structural components. The nuclear design and analysis were conducted to search for a baseline core with a cycle length greater than 8-year and the power peaking factor less than 1.5 by adjusting the fuel assembly, reactor core, and reflector configurations. The neutron transport calculations of the baseline core showed that a refueling interval of 9-year is achieved with a total peaking factor of 1.47 and a fuel rod average linear power of 3.6 kW/m. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Using occupant interaction with advanced lighting systems to understand opportunities for energy optimization: Control data from a hospital NICU

For this study, lighting system control data from five patient rooms in a neonatal intensive care unit (NICU) over a 25-week monitoring period were analyzed to better understand how the NICU staff and families interacted with an automatic tunable lighting system (variable spectrum and intensity) with manual override options. The data were analyzed to determine the amount of time spent in available lighting modes, estimate energy consumption, and to observe any patterns in how the lighting system was being used by occupants. Further analysis revealed opportunities for optimizing the system for occupant needs and energy savings. The patient room occupants were clearly engaged in using the lighting system throughout the 24-hour day to meet needs and preferences. Giving occupants control over the lighting system did not result in a considerable increase in energy use. Implementing a time-out would minimize the extended periods of time when the lights were left on without need at the highest brightness level, further decreasing energy use. This analysis reveals the potential of lighting control data to contribute to energy savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Data and scripts associated with a manuscript analyzing ELM-FATES parameter sensitivity under pre-fire and postfire scenarios using machine learning

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Fire Severity-Dependent Shifts in Vegetation Parameter Sensitivity: A Pre- and Post-Fire Analysis Using ELM-FATES and Explainable AI” submitted to Journal of Advances in Modeling Earth Systems (Zahura et al. 2026). The study examines vegetation physiological parameters controlling pre-fire and post-fire vegetation dynamics. To support this analysis, 73 vegetation parameters in Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (Fisher et al., 2018) , which is coupled with E3SM (Energy Exascale Earth System Model) land model (ELM, ELM-FATES), were perturbed using a Sobol sequence to generate 1,024 ensemble members for two plant functional types: needleleaf evergreen extratropical trees (NEET) and C3 grass. Simulations were conducted for the pre-fire period (2016) and post-fire period (2018–2023). Burn severity was represented by modifying the Nesterov index in FATES to 75,000, 150,000, and 300,000 for low, moderate, and high severity, respectively. A no-fire scenario was also included. Simulations were performed for 16 grid cells in the American River Watershed across different burn severities and plant functional types. XGBoost (eXtreme Gradient Boosting) models were trained using the parameter ensembles and ELM-FATES-simulated outputs, including leaf area index (LAI), gross primary productivity (GPP), aboveground biomass, vegetation evaporation, transpiration, and soil evaporation. Models were trained separately for each year and burn severity, followed by SHAP (SHapley Additive exPlanations) analysis to identify changes in dominant parameters after fire disturbance. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. The data package contains the ELM-FATES simulation data. The scripts and data related to the analysis will be added later. The inputs and outputs from ELM-FATES are inside the “FATES” folder. “FATES_domain_surface” contains the domain and surface netcdfs that were used to run ELM-FATES in the study area. “FATES_parameters” contains the 1024 ensembles that were generated using Sobol sequence. “FATES_outputs” folder contains ELM-FATES simulated variables. All files are .csv and .nc (NetCDF).

Aboveground biomass↗
DOE OSTI2025

Stability Analysis of Power Systems with High Penetration of State-of-the-Art Inverter Technologies

With the increasing level of inverter-based resources (IBRs) in modern power systems, this paper presents a small-signal stability analysis for power systems comprising synchronous generators (SGs) and IBRs. Four types of inverter controls are considered: two grid-following (GFL) controls, with or without grid support functions; droop-based grid-forming (GFM) controls; and virtual oscillator control-based GFM. We also analyze the impact of STATCOM and synchronous condensers on system stability to assess their role in the energy mix transition. With the small-signal dynamic behavior of the major technologies modeled, this paper provides stringent stability assessments using the IEEE 39-bus benchmark system modified to simulate future power systems. The exhaustive test cases allow for (a) assessing the impacts of different types and controls of generation and supplementary grid assets, as well as system inertia and line impedance on grid stability, and (b) elucidating pathways for the stabilization of IBR-dominated power systems. The analysis also indicates that future power systems can be stabilized with only a fraction of the total generation as voltage sources without SGs or significant system inertia if they are well distributed. This study provides insights into future power system operations with a high level of IBRs that can also be used for planning and operation studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

SQuaD: Smart Quantum Detection for Photon Recognition and Dark Count Elimination

Quantum detectors of single photons are an essential component for quantum information processing across computing, communication and networking. Today's quantum detection system, which consists of single photon detectors, timing electronics, control and data processing software, is primarily used for counting the number of single photon detection events. However, it is largely incapable of extracting other rich physical characteristics of the detected photons, such as their wavelengths, polarization states, photon numbers, or temporal waveforms. This work, for the first time, demonstrates a smart quantum detection system, SQuaD, which integrates a field programmable gate array (FPGA) with a neural network model, and is designed to recognize the features of photons and to eliminate detector dark-count. The SQuaD is a fully integrated quantum system with high timing-resolution data acquisition, onboard multi-scale data analysis, intelligent feature recognition and extraction, and feedback-driven system control. Our \name experimentally demonstrates 1) reliable photon counting on par with the state-of-the art commercial systems; 2) high-throughput data processing for each individual detection events; 3) efficient dark count recognition and elimination; 4) up to 100% accurate feature recognition of photon wavelength and polarization. Additionally, we deploy the SQuaD to an atomic (erbium ion) photon emitter source to realize noise-free control and readout of a spin qubit in the telecom band, enabling critical advances in quantum networks and distributed quantum information processing.

Linne, Karl C. [U. Chicago (main)] (ORCID:00090009↗
DOE OSTI2025

Comparative Evaluation of the Single-Phase Shift and Extended-Phase Shift Control for Isolated DC-DC Converter

Abstract: This paper presents a comparative evaluation of the Single-Phase Shift (SPS) and Extended-Phase Shift (EPS) controls for the isolated DC-DC Converter. The analysis is based on the power regulation range, flexibility of operation, current stress on power devices, system efficiency while focusing on the issue of backflow power, control complexity, design and implementation overhead, and stability of the entire system. It is also examined how the backflow influences power circulation and increases current stress. Here, the Dual Active Bridge (DAB) Converter is employed to assess the performance of the control methods. Compared to SPS control, EPS offers a wider power regulation range, greater flexibility, lower current stress, and better system efficiency. However, EPS has increased control complexity, design and implementation overhead, and often requires sophisticated feedback control loops. The comparison is made through mathematical modeling of power transfer, backflow, and current stress. Simulation results validate the comparative analysis presented.

Amir, Aamir [The University of Alabama (UA)]↗
DOE OSTI2024

Simultaneous optimal system and controller design for multibody systems with joint friction using direct sensitivities

Abstract Real-world multibody systems are often subject to phenomena like friction, joint clearances, and external events. These phenomena can significantly impact the optimal design of the system and its controller. This work addresses the gradient-based optimization methodology for multibody dynamic systems with joint friction using a direct sensitivity approach. The Brown–McPhee model has been used to characterize the joint friction in the system. This model is suitable for the study due to its accuracy for dynamic simulation and its compatibility with sensitivity analysis. This novel methodology supports codesign of the multibody system and its controller, which is especially relevant for applications like robotics and servo-mechanical systems, where the actuation and design are highly dependent on each other. Numerical results are obtained using a software package written in Julia with state-of-the-art libraries for automatic differentiation and differential equations. Three case studies are provided to demonstrate the attractive properties of simultaneous optimal design and control approach for certain applications.

Verulkar, Adwait↗
DOE OSTI2026

Multibody for Everybody (M4E): A Symbolic Dynamics Modeling Tool with Applications in Simulation, Control, and Optimization

Developing the analytical model of a multibody system is often the initial step in control and optimization. The analytical model (equations of motion) describes a system’s time evolution under specified forcing conditions. Although developing these equations is easy for simple systems, this process becomes more complex for systems composed of multiple bodies. Deriving equations of motion for complex multibody systems requires specialized expertise in multibody dynamics, is time-consuming, and is susceptible to error. To address this issue, this paper presents an open-source, easy-to-use, systematic framework to derive symbolic equations of motion in both Python and MATLAB using the joint coordinate formulation. This formulation results in a set of ordinary differential equations that use the minimum set of coordinates needed to model a system. The symbolic representation provides better insight into the influence of design parameters on system performance, facilitates sensitivity analysis and parameter studies, and supports direct implementation of control and optimization routines. The tool enables numerical simulation for specified parameter sets, is modular for straightforward integration with other tools and libraries, and allows incorporation of hydrodynamics, mooring, and other external forces. The result is a reproducible, extensible pipeline for modeling, simulation, and design of complex multibody systems. The proposed tool is versatile and can be applied to domains such as robotics, control, and design. In addition, we integrated external libraries that provide capabilities for modeling offshore systems such as underwater robots and marine energy converters.

16 TIDAL AND WAVE POWER↗