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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Deep reinforcement learning for optimal control of induction welding process

Optimizing induction welding (IW) process parameters for the application of joining thermoplastic composites is challenging as it requires achieving complex spatiotemporal thermal characteristics along the weld-line to obtain desired weld quality. We formulate an optimal control problem which captures these requirements and seeks to optimize the IW coil speed using a fast-acting dynamic IW process model. We develop a novel Deep Reinforcement Learning (DRL) framework to solve this computationally challenging control problem and demonstrate via simulation study that the learned DRL feedback control policy results in better spatiotemporal thermal characteristics as compared to the current state-of-the-art.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors

This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors (Phase-I)

With an ever increasing demand for high precision data from modern detectors for discovery science and precision measurements, all major high energy nuclear and particle experiments, current and future, are facing the challenge on how to deal with the large volume of raw data generated from sophisticated state-of-the-art detectors in high rate collisions. These goals need to be balanced with available hardware and cost limits on DAQ (Data AcQuisition system) bandwidth and offline computing resources to capture, store and process the signal events. Two prototypical examples are the upcoming sPHENIX experiment, the DOE next generation heavy ion physics experiment at the Relativistic Heavy Ion Collider at BNL, and the future EIC experiments that are planned to be online circa 2030.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

An Agile/XP software development process for modernizing the accelerator control system at Fermilab

Fermilab is undergoing the most ambitious upgrade to its accelerator control system of the 21st century. As part of the ACORN project, hundreds of legacy control system applications written in C/C++ will be re-imagined and developed from the ground up. In addition, applications to support Fermilab’s new super-conducting linear accelerator are already under construction. To manage the development of modern controls applications, the Controls department has adopted an Agile software development process based on eXtreme Programming. In this paper we will describe our process and detail our experience applying it to the development of two case studies.

Diamond, John [Fermilab]↗
DOE OSTI2024

Instant Startup and Grid Synchronization of Inverter Based Resources

This paper introduces an innovative approach for instant startup of inverter-based resources (IBRs), which attains synchronization with the grid via the already embedded direct current feedback control. The startup process utilizes direct current control throughout each switching cycle, enabling a process that is free from inrush current. Uniquely, this method determines the grid phase angle by evaluating the current differences across switching cycles, a significant difference from traditional methods that often rely on grid or points of common coupling (PCC) voltage. An analytical model has been constructed, highlighting that the grid phase angle can be obtained in just two switching cycles. The paper details the principle of instant grid synchronization and presents both simulation and experimental results to substantiate the proposed method.

He, Yuchen↗
DOE OSTI2024

Transformer Neural Networks with Spatiotemporal Attention for Predictive Control and Optimization of Industrial Processes

In the context of real-time optimization and model predictive control of industrial systems, machine learning, and neural networks represent cutting-edge tools that hold promise for enhancing dynamic modeling. This work presents a novel transformer neural network architecture for real-time optimization and model predictive control. This network design includes a modified attention mechanism inspired by positional embedding attention from vision transformers and task-specific modifications to the input-output structure of the transformer’s decoder stack. Experiments were conducted using data from a 450 MW coal-fired power plant to evaluate this approach's effectiveness. The transformer neural network was compared with conventional recurrent models, including GRU and LSTM. The transformer exhibited a 6% increase in the R-squared (R2) value of predictions and an 83% reduction in mean squared error (MSE). Computation time was also reduced by 84% compared to conventional recurrent models.

Gallup, Ethan R.↗
DOE OSTI2025

Controlling Oxidation of Nb in Oxygen Abundant Environments.

Superconducting Radio Frequency (SRF) cavities, the core of modern particle accelerators, rely on high-purity niobium (Nb) to achieve high quality factors and accelerating gradients. However, native oxidation in ambient conditions introduces a complex oxide layer with increased surface defects and contamination, particularly from carbon. This research explores a controlled oxidation processes in an oxygen-abundant environment to try to control this complex oxide stack, with the goal of improving surface purity and uniformity. Using standard surface preparation techniques—including electropolishing, vacuum baking, and sonication—followed by a custom oxidation protocol, we compared structural and chemical changes through Confocal Microscopy, Scanning Electron Microscopy (SEM), and X-Ray Photoelectron Spectroscopy (XPS). Results show that iterative oxidation cycles can significantly reduce carbon-related defects, even without baking. Aditionally, baked samples with a specific oxidation technique demonstrated a measurable 25\% decrease in carbide formation.

Romero, Juan [Fermilab]↗
DOE OSTI2026

Synthetic soils for ecological and synthetic biology applications

Soils are heterogeneous and dynamic systems characterized by complex physical, chemical, and biological interactions. Understanding these interactions is critical, as they influence plant productivity, global biogeochemical cycles, and ecosystem resilience. While ecologists have long studied soils in field, greenhouse, and laboratory settings, their complexity and heterogeneity make it challenging to pinpoint key properties driving biological processes and derive mechanistic insights. Advancements in synthetic biology, which seeks to engineer and control biological processes in soils, have increased the demand for standardized and controllable experimental platforms. These platforms, referred to here as ‘synthetic soils’, are systems designed to reproduce selected physicochemical characteristics of natural soils in a simplified and defined format, allowing scientists to systematically change soil physicochemical properties (i.e. texture, mineralogy, pH) to study how biological components (i.e. microbes, plants, soil fauna, etc.) respond to, modify, or interact within these controlled environments. This review explores existing synthetic soils, their advantages, limitations, and applications in ecology and synthetic biology, and discusses potential directions for their future development.

artificial soils↗
DOE OSTI2024

Constitutive model development of aluminum alloy 1100 for elevated temperature forming process

Commercially pure aluminum alloy, AA1100, presents good electrical and thermal conductivity, high formability, and low cost. Those favorable characteristics have the potential to enable bipolar plates with improved economics and enhanced performance compared to current stainless steel bipolar plates for proton exchange membrane fuel cells. An accurate constitutive model is essential to develop and optimize processing parameters and effectively control the forming process. Here, the objective of this work is to develop a constitutive model of AA1100 that is able to simulate stress-strain relation, formed geometry, and predict the onset of fracture strain to avoid forming failure. Initially, a set of tensile tests at temperature between 300 and 500°C and strain rate between 0.005 and 1.0/s were conducted to examine the deformation behavior. Then, a set of damage-based unified visco-plastic constitutive equations is proposed and calibrated based on the results of stress-strain data. A genetic algorithm optimization method is applied to search for best fitting material constants in constitutive equations. The proposed model shows good predictability of both the stress-strain relation and fracture strain at low strain rate and high temperature conditions. The accuracy of proposed model is also evaluated statistically. A comparison of the proposed model with three popular models (Arrhenius-type mode, Johnson-Cook model and Zerilli-Armstrong model) was made. The proposed model shows the best experimental agreement with correlation coefficient of 0.96 in contrast to 0.25, 0.38 and 0.75 for the popular models, respectively. The proposed model can help to optimize the elevated temperature forming process and guide die design to enable optimal geometric features in the formed components.

08 HYDROGEN↗
DOE OSTI2025

Mk-IV Salt Crystallization Hot Finger Apparatus for Partitioning Used Electrorefiner Salt

Electrorefining is a controlled redox process used to regulate the behavior of ionic species. Through this process, metals can be deposited onto a cathode from an electrolyte solution in a controlled manner. The Mk-IV electrorefiner (Mk-IV ER) at Idaho National Laboratory is an engineering-scale, molten salt-based electrorefining cell that has been used for decades to recover metallic uranium from spent fuel. As a result, highly stable fission product chlorides have accumulated in the electrolyte. This accumulation results in changes to the salt’s properties, such as melting temperature, thermal conductivity, and density, as well as elevated product impurity and fissile materials criticality margin. These factors prompt the need for a salt regeneration process, such as melt-crystallization and species drawdown. This work focuses on providing a conceptual design to regenerate ER salt from used Mk-IV-ER salt in-situ, while minimizing salt waste volumes by concentrating the fission products in a final processed salt heal. We propose using a hot-finger crystallization apparatus design to fractionally crystallize salt in the Mk-IV-ER head space (or baffle space), allowing the collection of solid and liquid fractions. By using a cup-drain design, the used salt will be allowed to slowly solidify on the walls of a stainless-steel cup. The apparatus drain plug will then open to allow the liquid salt phase to drain to a lower cup, effectively separating the liquid phase from the solid phase. Under the hypothesis that the liquid phase salt concentrates the fission products, which is under examination in the accompanying work package, this separation allows the recovered solid salt to be reused while minimizing the high-level salt waste volume of used ER salt.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Variation in Cation Adsorption Mechanism Controlled by Chemical and Structural Heterogeneities at the Quartz (101)–Water Interface

Mineral–water interfacial reactions are central to chemical processes that control the fate of nutrients and contaminants in natural environments. Mineral surfaces commonly have complex structures and compositions whose impact on interfacial reactivity is poorly understood. Here, in this work, we investigated the effects of surface heterogeneities on Rb + sorption at the quartz (101)–10 mM RbCl solution interface at pH 9.8 using in situ high-resolution X-ray reflectivity. Two surface locales (i.e., Spots A and B) having distinct interfacial structures were chosen: Spot A was characterized by its low defect density (≤20% topmost Si vacancies) and Rb + adsorption occurred predominantly as an inner-sphere complex. In comparison, Spot B had a higher defect density (~50% vacancies) and was covered with poorly crystalline SiO 2 . A substantially larger Rb + uptake (i.e., 7-times higher coverage) was observed on this defective surface where Rb + incorporated in the vacancy sites (confirmed by density functional tight binding-based molecular dynamics simulations) or adsorbed directly on the disordered film. These results provide a direct quantification of how surface heterogeneity influences the geochemical behavior of mineral–water interfaces, in particular highlighting the important role of chemical and structural defects on the sorbate speciation and coverage at silicate mineral surfaces.

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

Unlocking the Future of Aircraft Manufacturing: The Environmental Benefits of Laser Patterning for Surface Enhancement of Aircraft-Certified Alloys

Surface protection and functional modification of aircraft-certified aluminum alloys are essential for corrosion resistance, durability, and long-term airworthiness. At the same time, increasingly restrictive environmental regulations motivate the development of alternatives to legacy wet-chemical surface treatments. This study presents an integrated assessment of ultrafast femtosecond laser surface texturing as a surface functionalization approach for Aluminum 6061 alloys within an aerospace manufacturing and sustainability context. Ultrashort-pulse laser processing enables controlled micro- and nano-scale surface topographical modification with limited thermal impact, allowing adjustment of wettability and surface functionality while preserving bulk material integrity. As a dry and contactless process, femtosecond laser treatment eliminates the use of hazardous chemicals, reduces consumable inputs, and generates minimal secondary waste. A streamlined cradle-to-gate life cycle assessment conducted in accordance with ISO 14040/14044 indicates a lower global-warming potential per functional unit compared with conventional surface treatments, including anodization, plasma-assisted coatings, and organic coating systems. Complementary qualitative analyses addressing environmental health and safety, supply-chain risk, and ESG alignment indicate potential advantages related to occupational safety, regulatory compliance, waste management, and end-of-life recyclability. The investigation is performed on planar Aluminum 6061 reference surfaces with a treated area of 25 mm 2 , providing a controlled laboratory-scale basis for analyzing process behavior, functional surface modification, and associated environmental metrics. Within this defined scope, the results support further evaluation of femtosecond laser surface texturing as a surface engineering option for future aerospace manufacturing.

corrosion resistance↗
DOE OSTI2025

Representing lateral groundwater flow from land to river in Earth system models

Lateral groundwater flow (LGF) is an important hydrologic process in controlling water table dynamics. Due to the relatively coarse spatial resolutions of land surface models, the representation of this process is often overlooked or overly simplified. In this study, we developed a hillslope-based lateral groundwater flow model. Specifically, we first developed a hillslope definition model based on an existing watershed delineation model to represent the subgrid spatial variability in topography. Building upon this hillslope definition, we then developed a physical-based lateral groundwater flow using Darcy’s equation. This model explicitly considers the relationships between the groundwater table along the hillslope and the river water table levels. We coupled this intra-grid model to the land component (E3SM Land Model: ELM) and river component (MOdel for Scale Adaptive River Transport: MOSART) of the Energy Exascale Earth System Model (E3SM). We tested both the hillslope definition model and the lateral groundwater flow model and performed sensitivity experiments using different configurations. Simulations for a single grid cell at 0.5°×0.5° within the Amazon basin show that the definition of hillslope is the key to modeling lateral flow processes and the runoff partition between surface and subsurface can be dramatically changed using the hillslope approach. Although our method provides a pathway to improve the lateral flow process, future improvements are needed to better capture the subgrid structure to account for the spatial variability in hillslopes within the simulated grid of land surface models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

New Era Towards Autonomous Additive Manufacturing: A Review of Recent Trends and Future Perspectives

Abstract The Additive Manufacturing (AM) landscape has significantly transformed in alignment with Industry 4.0 principles, primarily driven by the integration of Artificial Intelligence (AI) and Digital Twin (DT). However, current Intelligent Additive Manufacturing (IAM) systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction (HMI). This paper reviews existing IAM solutions, emphasizing control, monitoring, process autonomy, and end-to-end integration, and identifies key limitations, such as the absence of a high-level controller for global decision-making. To address these gaps, we propose a transition from IAM to Autonomous Additive Manufacturing (AAM), featuring a hierarchical framework with four integrated layers: knowledge, generative solution, operational, and cognitive. In the cognitive layer, AI agents notably enable machines to independently observe, analyze, plan, and execute operations that traditionally require human intervention. These capabilities streamline production processes and expand the possibilities for innovation, particularly in sectors like in-space manufacturing (ISM). Additionally, this paper discusses the role of AI in self-optimization and lifelong learning, positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy, fostering a more adaptive, resilient manufacturing ecosystem.

Fan, Haolin↗
DOE OSTI2025

Chemical and Optical Control of Spin Crossover: Ultrafast XUV Spectroscopy of Molecular Magnets in Native Solvation Environments

With support from the US Department of Energy Office of Science, we have developed and utilized extreme ultraviolet (XUV) spectroscopy and sum frequency generation vibrational spectroscopy as probes of charge, spin, and solvation structure and dynamics in molecules and at interfaces. This work is crucial to advancing fundamental understanding of the processes that control the efficiency and speed of energy conversion and information processing in molecules and at interfaces. Accordingly, it has important applications for developing next generation technologies for information storage and processing with increasing data storage density and processing rates as well as developing new methods for efficient energy conversion and storage.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Learning the factors controlling mineral dissolution in three-dimensional fracture networks: applications in geologic carbon sequestration

We perform a set of high-fidelity simulations of geochemical reactions within three-dimensional discrete fracture networks (DFN) and use various machine learning techniques to determine the primary factors controlling mineral dissolution. The DFN are partially filled with quartz that gradually dissolves until quasi-steady state conditions are reached. At this point, we measure the quartz remaining in each fracture within the domain as our primary quantity of interest. We observe that a primary sub-network of fractures exists, where the quartz has been fully dissolved out. This reduction in resistance to flow leads to increased flow channelization and reduced solute travel times. However, depending on the DFN topology and the rate of dissolution, we observe substantial variability in the volume of quartz remaining within fractures outside of the primary subnetwork. This variability indicates an interplay between the fracture network structure and geochemical reactions. We characterize the features controlling these processes by developing a machine learning framework to extract their relevant impact. Specifically, we use a combination of high-fidelity simulations with a graph-based approach to study geochemical reactive transport in a complex fracture network to determine the key features that control dissolution. We consider topological, geometric and hydrological features of the fracture network to predict the remaining quartz in quasi-steady state. We found that the dissolution reaction rate constant of quartz and the distance to the primary sub-network in the fracture network are the two most important features controlling the amount of quartz remaining. This study is a first step towards characterizing the parameters that control carbon mineralization using an approach with integrates computational physics and machine learning.

54 ENVIRONMENTAL SCIENCES↗