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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 OSTI2024

Center for Tokamak Transient Simulations (RPI Unstructured Mesh Developments for FES SciDAC4 Partnerships) (Final Report)

The goal of this project was the development of unstructured mesh technologies for fusion simulation codes” for FES SciDAC partnerships and to integrate those technologies into the simulation workflows of those partnerships. Specific developments were executed in support of the following FES SciDAC4 partnerships: Partnership Center for High‐fidelity Boundary Plasma Simulation (HBPS), Center for Integrated Simulation of Fusion Relevant RF (RF‐SciDAC), Center for Plasma Surface Interactions: Predicting the Performance and Impact of Dynamic PFC Surfaces (PSI2), and Center for Tokamak Transient Simulations (CTTS). The key unstructured mesh development areas addressed in this project include (i) methods to most effectively perform PIC calculations on unstructured meshes; (ii) creating meshes for fusion systems accounting for any desired level of geometric complexity and providing physics aware mesh configurations, (iii) adapting unstructured meshes to control the discretization errors, (iv) executing unstructured mesh calculations on GPU accelerated systems, (v) supporting physics/application‐specific PIC operations including surface/wall interactions of particles, (vi) providing infrastructure tools to support the interactions of solvers with unstructured meshes, and (vii) providing advanced methods for coupling plasma physics codes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network

Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications. Current detection methods operate offline, introducing latency incompatible with in-the-loop qubit control. In this paper, an online detector of charge jumps for superconducting qubits, based on a dilated causal convolutional neural network (DCCNN) designed for in-the-loop deployment on the Quantum Instrumentation Control Kit (QICK) platform, is presented. The network is trained on synthetic Ramsey tomography scans generated from qubit templates measured at the Northwestern Experimental Underground Site (NEXUS) at Fermilab, and translated to FPGA firmware via hls4ml with ap_fixed$\langle 16,6 \rangle$ quantization, reaching a per-inference latency of $6.19 μ$s on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline $χ^2$ algorithm ($0.843 \pm 0.022$ vs. $0.866 \pm 0.020$ on $|Δq| \in [0.1, 0.5] e$ at matched false-positive rate), while requiring no per-qubit hyperparameter tuning. This shifts charge-jump detection from a post-hoc diagnostic to a control-loop primitive, enabling adaptive protocols that respond to radiation-induced events in situ, with applications to quantum-computing error mitigation and to the use of superconducting qubits as particle detectors.

Gaytan-Villarreal, Daniel [Carnegie Mellon U.]↗
DOE OSTI2024

Wideband Impedance Passivation of MMCs for Suppressing Harmonic Oscillations

Harmonic instability and associated oscillation events have become one of the main concerns in MMC-based HVdc systems. These oscillations can appear in the range from a few hundred hertz to several kilohertz, and the root cause is identified as delay-induced negative damping of MMC impedance appearing at the system resonance frequency. Here, this paper introduces a wideband impedance reshaping method through MMC control to eliminate such negative damping for both grid-following (GFL) and grid-forming (GFM) MMCs, which makes the MMC impedance completely passive from the second harmonic frequency upward, thus preventing all harmonic oscillations. First, simplified impedance models of MMCs are derived for the harmonic stability analysis and the impedance reshaping control design. Next, a passivity-based impedance reshaping method is presented, as well as practical considerations for its implementation. In addition, to maintain the MMC's disturbance ride-through capability, an adaptive activation scheme is developed, which enables the wideband impedance reshaping control only in the presence of harmonic oscillation events. The effectiveness of the proposed method is validated by frequency domain analysis and by electromagnetic transient (EMT) simulations of two typical MMC-based power systems.

17 WIND ENERGY↗
DOE OSTI2025

Virtual to Physical: Reinforcement Learning to Optimize SNS Particle Accelerator Controls

Complex accelerators must have control systems that can handle dynamic nonlinear environments. This makes traditional control methods unsuitable as they can struggle to adapt to these uncertainties. This provides an ideal environment for reinforcement learning algorithms as they are adaptable and generalizable. We present a reinforcement learning pipeline that can effectively handle the dynamics of a complex accelerator. We test and prove our pipelines capabilities on multiple environments including the Spallation Neutron Source (SNS) and the Beam Test Facility (BTF) at Oakridge National Lab (ORNL). Due to the limited time available to train an online algorithm like reinforcement learning on a real accelerator, we utilize a virtual twin accelerator (VIRAC) developed by ORNL to pretrain the policy and show its ability to converge in the virtual environment. We then test the adaptability of the pretrained RL model by applying it on the real accelerator and comparing the results. Utilizing our Scientific Optimization and Controls Toolkit (SOCT) and open-source standards such as Gymnasium we create and solve for a MEBT orbit correction problem in the SNS and an emittance maximization problem in the BTF. We show how Twin Delayed Deep Deterministic Policy Gradient (TD3) can solve this optimization environment in the virtual accelerator and transfer this policy onto the real accelerator for inference and model retraining. We show how reinforcement learning can be utilized as a control system for complex accelerators and provide a model pipeline for how an implementation performs and can be adapted to new accelerator control problems.

Kasparian, Armen [Thomas Jefferson National Accele↗
DOE OSTI2024

Hardware-in-the-Loop Testing of Wide-Area Damping Controller for Field Implementation in Large-scale Power Grid

In our previous work, an adaptive measurement-driven wide-area damping controller (WADC) for suppressing inter-area oscillations has been proposed and a hardware prototype was developed and validated through hardware-in-the-loop tests. As a continuation of the work, this paper introduces a WADC software prototype to handle the realistic challenges for field implementation in the control room of the power grid. The WADC software is developed and operated as an openPDC adapter with a graphical user interface (GUI) to monitor the WADC inputs and output, the communication delays and other variables. The software prototype has been fully tested through an enhanced hardware-in-the-loop (HIL) test setup. Its performance is verified under various realistic communication uncertainties, such as random time delays and data losses, with different communication protocols. The experiment results have proven the WADC software can deliver sufficient damping to suppress the targeted oscillation mode in handling various communication uncertainties for future field deployment.

Jia, Xinlan↗
DOE OSTI2025

Reinforcement Learning Control for Buildings Co-Optimizing Energy, Comfort, and Indoor Air Quality: An Annual Assessment

Efficient control of Heating, Ventilation, and Air Conditioning (HVAC) systems is crucial for optimizing energy use and maintaining indoor comfort in buildings. Traditional control methods, such as PID control, cannot handle energy use trade-offs among multiple components in the building energy system at a supervisory level. Reinforcement learning (RL) presents a promising solution, offering adaptive and data-driven control strategies that optimize performance over time. However, RL also faces several challenges, including the conflicts encountered in co-optimizing energy savings, occupant comfort, and indoor air quality, and the requirement for extensive interactions with the environment in training. We proposed a flexible simulation platform that integrates a hybrid model for RL training and designed an RL agent to control the entire central HVAC system, focusing on co-optimizing energy consumption, thermal comfort, and indoor air quality ($\text{CO}_{2}$ and PM2.5 concentrations). Finally, we evaluated the RL agent's performance over an annual cycle. Our findings indicate that the RL agent can effectively manage the HVAC system with 14.7 % energy savings annually and balance multiple objectives, which demonstrates significant potential for improving HVAC system control and sustainability in buildings.

Guo, Fangzhou↗
DOE OSTI2025

ARMing the Edge: Demonstration of Edge Computing Field Campaign Report

Edge computing enables “next-to-instrument” control and intelligent data volume reduction and the potential for autonomous, adaptive measurement strategies such as for automated control of scan strategies for Doppler lidar (DL). Instruments with narrow bandwidth connections (e.g., ship and remote sites) can do scene determination and save phenomenon-appropriate data. For example, Doppler spectrum can be saved when clouds are detected by the instrument or automatic moment detection can take place in camera images and only preserve spectrum when non-monomodal spectra are detected. Automated control at the edge involves changing the sampling (temporal or scanning strategy) of an instrument to suit the phenomena both present and being studied (Jackson et al. 2020). Both data processing and instrument control introduces the possibility of a software-defined instrument.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Toward particle accelerator machine state embeddings as a modality for large language models

Understanding and diagnosing the state of a particle accelerator requires navigating high-dimensional control system data, often involving hundreds of interdependent parameters. We propose a novel multimodal embedding framework that jointly learns representations of machine states from both numerical control system readouts and natural language descriptions. This enables the translation of complex machine conditions into human-readable summaries while maintaining fidelity to the underlying physical system. The obtained embeddings are subsequently adapted to an open-weights large language model via cross-attention conditioning. We demonstrate a first implementation trained on European XFEL machine state data. This work covers the embedding model architecture, training methodology, and presents initial examples demonstrating the model's capabilities in action. Due to the general concept of machine state, the model can be easily adapted to other facilities and control system environments.

Accelerator Physics↗
DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2025

Arms Control Development Criteria (ACDCs)

The Arms Control Development Criteria (ACDC) have been developed to provide a consistent framework for understanding the readiness of a technology or capability for use in arms control as poten>al components of a verifica>on system. More importantly, they are also intended to serve as a guide for technology developers to beCer address the development needs of a technology from the start of the development process, rather than trying to adapt a technology to arms control aDer it is developed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Data-Driven Voltage Regulation of Distribution Grid Using Nonlinear Autoregressive Model with Exogenous Inputs (NARX)

This article proposes data-driven control via a nonlinear autoregressive model with exogenous inputs (NARX) for real-time voltage regulation of a modified feeder using reactive power sources. Traditional voltage control strategies rely on rule-based heuristics or optimization techniques, which often require detailed system models and extensive computational resources. The NARX-based controller learns system dynamics from historical data and predicts optimal reactive power dispatch in real-time for voltage correction. The proposed approach is evaluated on a power system feeder model under varying load and network conditions. Simulation results demonstrate that the NARX-based controller achieves improved voltage regulation, offering higher adaptability to system fluctuations. This study highlights the potential of data-driven control for enhancing the reliability of power distribution networks.

Donge, Vrushabh [ORNL] (ORCID:0000000306062803)↗
DOE OSTI2025

Occupant-Centric Demand Response for Thermostatically-Controlled Home Loads

Efficiently managing energy usage to balance supply and demand on the electric grid is crucial, especially with the widespread deployment of distributed variable renewable electricity generation. This paper introduces two duty-cycle control methods for heating systems, adjusting thermostat setpoints to limit and shift electricity demand. The control approaches employ innovative techniques, such as adaptive duty cycling, to prioritize household thermal comfort while reducing peak demand. These control methods can respond to signals from the electric grid, including demand targets and time-of-use tariffs, and were tested physically on an electric furnace and heat pump in a test home during winter conditions in 2021 and 2022. The results are given as average demand reductions and energy use impacts with respect to the average indoor-outdoor temperature difference during the control period. For heat pumps, demand limiting control reduced power by 18.5% and 23.3% for indoor-outdoor temperature differences of 30°F and 40°F. Preheating-based demand shifting achieved reductions of 34.8% and 33.2% for the same temperature differences. Electric furnace tests showed demand reductions of 33.8% and 25.3% for demand limiting, and 56.1% and 45.7% for preheating-based demand shifting. These findings highlight the potential for innovative control methods to enhance grid efficiency and reduce energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Atmosphere Effects in Laser Powder Bed Fusion: A Review

The use of components fabricated by laser powder bed fusion (LPBF) requires the development of processing parameters that can produce high-quality material. Manipulating the most commonly identified critical build parameters (e.g., laser power, laser scan speed, and layer thickness) on LPBF equipment can generate acceptable parts for established materials and moderately intricate part geometries. The need to fabricate increasingly complex parts from unique materials drives the limited research into LPBF process control using underutilized parameters, such as atmosphere composition and pressure. As presented in this review, manipulating atmosphere composition and pressure in laser beam welding has been shown to expand processing windows and produce higher-quality welds. The similarities between laser beam welding and laser-based AM processes suggest that this atmosphere control research could be effectively adapted for LPBF, an area that has not been widely explored. Tailoring this research for LPBF has significant potential to reveal novel processing regimes. This review presents the current state of the art in atmosphere research for laser beam welding and LPBF, with a focus on studies exploring cover gas composition and pressure, and concludes with an outlook on future LPBF atmosphere control systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

A Compact Cryogenic Environment for In Situ Neutron Diffraction Under Mechanical Loading

Understanding the deformation mechanisms of materials at cryogenic temperatures is crucial for cryogenic engineering applications. In situ neutron diffraction is a powerful technique for probing such mechanisms under cryogenic conditions. In this study, we present the development of a compact cryogenic environment (CCE) designed to facil-itate in situ neutron diffraction experiments under mechanical loading at tempera-tures as low as 77 K. The CCE features a polystyrene foam cryogenic chamber, alumi-num blocks serving as neutron-transparent cold sinks, a liquid nitrogen dosing system for cryogen delivery, a nitrogen gas flow control system for thermal management, a process controller for temperature control, and a pair of thermally isolated grip adapt-ers for mechanical testing. The CCE achieves reliable temperature control with mini-mal neutron attenuation. Utilizing this setup, we conducted three in situ neutron dif-fraction tensile tests on a 316L stainless steel at 77 , 173 , and 298 K, respectively. The results highlight the pronounced effects of cryogenic temperatures on the material's deformation mechanisms, underscoring both the significance of cryogenic deformation studies and the effectiveness of the CCE.

Yu, Dunji [ORNL] (ORCID:0000000189467851)↗
DOE OSTI2024

Model-predictive optimal control of ferrofluidic microrobots in three-dimensional space

Ferrofluid microrobots have emerged as promising tools for minimally invasive medical procedures. Their unique properties to navigate complex fluids and reach otherwise inaccessible regions of the human body have enabled new applications in targeted drug delivery, tissue engineering, and diagnostics. Here, this paper proposes a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots in three dimensions (3D). The internal optimization routine of the controller determines appropriate changes in the applied electromagnetic field to minimize the deviation between the actual and desired trajectories of the microrobot. A linear system governing locomotion is derived and used as the equality constraints of the optimization problems associated with the feedback index. In addition to ferrofluid droplets, the controller presented in this work may be applied to other magnetically-pulled microrobots. Several experiments are performed to validate the controller and showcase its ability to adapt to changes in system parameters such as the desired tracking trajectory and the size, orientation, deformation, and velocity of the microrobot. The accuracy of the controller is analyzed for each experiment, and the average error is found to be within 0.25 mm for small velocities. An additional experiment is performed to demonstrate significant improvement over a PID controller that is optimally tuned using Bayesian optimization. The results presented in this paper suggest that the proposed control algorithm could enable new microrobotic capabilities in minimally invasive medical procedures, lab-on-a-chip applications, and microfluidics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Estimation of cutting tool wear using an elastomeric tactile sensor

Machining performance of cutting tools and part quality are affected by the geometric condition of the cutting edge, which is influenced by thermomechanical loads experienced during the process. Tool condition monitoring (TCM) systems provide insight for timely replacement of cutting tools. However, existing TCM systems are expensive and require specialized equipment or sensors, hindering widespread adoption. A novel TCM system is developed herein using an elastomeric tactile sensor. Sensor images of the cutting edge are used to quantify wear using two distinct algorithms. In the first algorithm, the unworn and worn edges are identified based on Canny edge detect. In the second, the unworn edge is identified using edge detection while the region of wear is identified using a relative intensity method. In both cases, the maximum wear width is calculated based on an experimentally determined pixel to-physical distance scale. The TCM system is first used to estimate flank wear on a solid carbide helical end mill before evaluating its robustness by employing it to estimate insert wear of an indexable helical end mill. Measurements are also performed manually using an optical microscope and a high-resolution focus variation microscope for verification. The novel technique estimates flank wear in the solid carbide tool with a high accuracy of 98%. Larger discrepancies are observed for the inserts, however, with overlapping uncertainties. In conclusion, the technique shows promise in adaptability, automation, and closed loop control of machine tools.

Machining↗
DOE OSTI2025

Dysregulation of lung epithelial cell homeostasis and immunity contributes to Middle East respiratory syndrome coronavirus disease severity

Coronaviruses (CoV) emerge suddenly from animal reservoirs to cause novel diseases in new hosts. Discovered in 2012, the Middle East respiratory syndrome coronavirus (MERS-CoV) is endemic in camels in the Middle East and is continually causing local outbreaks and epidemics. While all three newly emerging human CoVs from the past 20 years (SARS-CoV, SARS-CoV-2, and MERS-CoV) cause respiratory disease, each CoV has unique host interactions that drive differential pathogeneses. To better understand the virus and host interactions driving lethal MERS-CoV infection, we performed a longitudinal multi-omics analysis of sublethal and lethal MERS-CoV infection in mice. Significant differences were observed in body weight loss, virus titers, and acute lung injury among lethal and sub-lethal virus doses. Virus-induced apoptosis of type I and II alveolar epithelial cells suggests that loss or dysregulation of these key cell populations was a major driver of severe disease. Omics analysis suggested differential pathogenesis was multi-factorial with clear differences among innate and adaptive immune pathways as well as those that regulate lung epithelial homeostasis. Infection of mice lacking functional T and B cells showed that adaptive immunity was important in controlling viral replication but also increased pathogenesis. In summary, we provide a high-resolution host response atlas for MERS-CoV infection and disease severity. Multi-omics studies of viral pathogenesis offer a unique opportunity to not only better understand the molecular mechanisms of disease but also to identify genes and pathways that can be exploited for therapeutic intervention all of which is important for our future pandemic preparedness.

MERS-CoV↗