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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Queue wait time prediction in high performance computing (HPC) systems

High Performance Computing (HPC) systems are critical enablers for groundbreaking scientific research across various domains. Efficient resource allocation, facilitated by job scheduling, is paramount for maximizing the utilization of HPC systems. However, the variability in wait times for queued jobs poses challenges for users, necessitating accurate job wait time estimation. This paper explores the influence of job characteristics, including job size (the number of nodes requested and walltime), the queue to which the job is submitted and other resource requirements, on job wait times in leadership-class HPC systems. Focusing on the Theta Cray XC40 and Polaris machines at Argonne National Laboratory, the study evaluates the performance of different supervised learning algorithms in predicting job wait times. It also evaluates the impact of data preprocessing, including outlier detection, Principal Component Analysis (PCA), and feature selection, on the performance of wait time prediction models. The findings reveal insights into the relationship between job characteristics and wait times, offering a foundation for optimizing resource allocation and enhancing user experience. The methodologies and tools developed in this study are adaptable to other leadership-class HPC systems, providing a valuable contribution to the broader HPC community aiming to improve job scheduling efficiency and user satisfaction.

Okafor, Nwamaka↗
DOE OSTI2024

Roles of Solvent in the Catalytic Hydrogen Release from Liquid Organic Hydrogen Carriers: Chemical, Thermodynamical and Technological Aspects

A Liquid Organic Hydrogen Carrier (LOHC) enables the storage and transport of hydrogen at ambient pressures and temperatures in a safe and convenient form using current infrastructure. However, it is challenging to directly compare reactivity and selectivity for hydrogen release, especially when comparing the catalytic efficiencies of neat LOHCs to highly diluted LOHCs in different solvents, reaction conditions, and catalysts. This work evaluates the role of solvents in catalysis and quantifies the energy efficiency of the overall process. The presence of solvent dilutes the volumetric density of available hydrogen, but may be necessary to achieve optimal catalysts stability, reactivity, and product selectivity. With respect to the reaction conditions as determined by thermodynamics, solvents with higher vapor pressures than that of the carrier can cause the erroneous impression of a more favorable reaction equilibrium. Concerning energy efficiency, solvents can result in increased energy demand for hydrogen release as the inert solvent must be heated to reaction temperatures required for release of H 2 from the LOHC. Further, this work recommends that investigations of catalyst reactivity should be carried out at different ratios of solvent to LOHC to understand how the reactivity changes and what the implications are for maximizing energy density and catalyst stability and reactivity. Investigations should also consider how these implications will affect the technical needs of applications intended for the LOHC system. Based on the results of this study, it is advised to focus research activities on LOHC systems with a gravimetric solvent content below about 50% as the thermodynamic disadvantages become very pronounced beyond this threshold.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Methods to Observe Tribological Failures in Self-Mated Steel Contacts

Scuffing, a type of wear found in highly stressed or poorly lubricated contacts, is characterized by a rapid increase in friction and severe plastic deformation of the near-surface material. Scuffing has proven difficult to study because it initiates unpredictably, progresses rapidly, and typically develops within an inaccessible contact interface. Although there have been successful in-situ studies of scuffing in real-time, the transparent counter body needed for these studies changes the interactions between the surfaces and the lubricant, which affects the scuffing process in unknown ways. This paper describes the development of X-ray-compatible tribometry to study the scuffing of self-mated steels in-situ and in real-time. The method uses a crossed cylinders configuration with a thin (500 μm thick) stationary component and a small (≈200 μm) contact width to maximize X-ray interactions with atoms within the stress field generated by the contact. The resulting instrument and method are used to benchmark the scuffing response of self-mated 52,100 steel under tribologically challenging ‘oil-off’ lubrication conditions. The results demonstrate reliable scuffing in this configuration despite the relatively small contact areas and loads used. Following scuffing, gross plastic deformation was observed on both surfaces along with significant subsurface grain refinement and flow only on the stationary surface, which experienced constant contact. Interestingly, high friction initiated at specific locations of the migratory surface, which experienced intermittent contact, and then propagated across the track over time, suggesting that local conditions of the migratory surface dominated friction leading into the failure event.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets

Introduction Data normalization is crucial for multi-omics integration, reducing systematic errors and maximizing the likelihood of discovering true biological variation. Most studies assess normalization for a single omics type or use datasets from separate experiments. Few address time-course data, where normalization might bias temporal differentiation. In this study, we compared common normalization methods and a machine learning approach, Systematical Error Removal using Random Forest (SERRF), using multi-omics datasets generated from the same experiment—even from the same cell lysate. Objectives To develop a straightforward process to assess normalization effects and identify the most robust methods across multi-omics datasets. Methods We analyzed metabolomics, lipidomics, and proteomics datasets from primary human cardiomyocytes and motor neurons exposed to acetylcholine-active compounds over time. Normalization effectiveness was evaluated based on improvement in QC features consistency and observing the change in treatment and time-related variance. Results Probabilistic Quotient Normalization (PQN) and Locally Estimated Scatterplot Smoothing (LOESS) QC were identified as optimal for metabolomics and lipidomics, while PQN, Median, and LOESS normalization excelled for proteomics. These methods consistently enhanced QC feature consistency in metabolomics and lipidomics, and preserved time-related variance or treatment-related variance in proteomics, demonstrating their effectiveness and robustness. SERRF normalization, applied only to metabolomics in this study, outperformed other methods in some datasets but inadvertently masked treatment-related variance in others. Conclusion Our evaluation identified PQN and LoessQC as the top methods for metabolomics and lipidomics, and PQN, Median, and Loess normalization for proteomics, in multi-omics integration in a temporal study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Dissimilar Material Joining via Interlocking Metasurfaces

Background The integration of dissimilar materials poses a significant challenge in engineering, necessitating innovative solutions for robust and reliable joining. Interlocking metasurfaces (ILMs) are a new joining technology comprising arrays of autogenous features patterned across two surfaces that interlock to form robust structural joints. Objective Here, this study elucidates the factors influencing the tensile performance of ILM joints formed between dissimilar materials. Methods We employed parametric optimization to identify optimal unit cell geometries for maximal yield strength based on the hypothesis that the elastic tensile properties of the materials are the primary determinants of tensile performance. Experimental validation was performed by mechanically testing the theorized optimal ILM geometry and a range of ILM geometries to capture the overall behavior trends of joints between two additively manufactured polymers, VeroPureWhite (VW) and RGDA8430-DM (8430). Results Experimental validation of optimized designs revealed that additional factors, e.g. flexural strength and localized plasticity, also strongly influenced the tensile performance of T-slot ILMs joining dissimilar materials. The proposed optimal design remained the best performer. Conclusions This study demonstrates the viability of ILMs as a joining method for dissimilar materials. ILMs can join dissimilar materials with no loss in joint yield strength compared to joints composed solely of the weaker of the two constitutive materials. ILMs demonstrated their potential as a versatile and effective joining technology in diverse engineering applications.

Elbrecht, Benjamin James [Sandia National Laborato↗
DOE OSTI2024

Sensory integration for neuroprostheses: from functional benefits to neural correlates

In the field of sensory neuroprostheses, one ultimate goal is for individuals to perceive artificial somatosensory information and use the prosthesis with high complexity that resembles an intact system. To this end, research has shown that stimulation elicited somatosensory information improves prosthesis perception and task performance. While studies strive to achieve sensory integration, a crucial phenomenon that entails naturalistic interaction with the environment, this topic has not been commensurately reviewed. Therefore, here we present a perspective for understanding sensory integration in neuroprostheses. First, we review the engineering aspects and functional outcomes in sensory neuroprosthesis studies. In this context, we summarize studies that have suggested sensory integration. We focus on how they have used stimulation-elicited percepts to maximize and improve the reliability of somatosensory information. Next, we review studies that have suggested multisensory integration. These works have demonstrated that congruent and simultaneous multisensory inputs provided cognitive benefits such that an individual experiences a greater sense of authority over prosthesis movements (i.e., agency) and perceives the prosthesis as part of their own (i.e., ownership). Thereafter, we present the theoretical and neuroscience framework of sensory integration. We investigate how behavioral models and neural recordings have been applied in the context of sensory integration. Sensory integration models developed from intact-limb individuals have led the way to sensory neuroprosthesis studies to demonstrate multisensory integration. Neural recordings have been used to show how multisensory inputs are processed across cortical areas. Lastly, we discuss some ongoing research and challenges in achieving and understanding sensory integration in sensory neuroprostheses. Here, resolving these challenges would help to develop future strategies to improve the sensory feedback of a neuroprosthetic system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Eco-driving Profile Optimization by Dynamic Programming for Battery Electric Vehicles

Although full automation has not yet been achieved, automated vehicles are a valid research area. Not only would automated vehicles provide ultimate driver convenience, but they would maximize energy efficiency by eliminating undesired human driving behaviors and optimally controlling the powertrain. From the perspective of control related to energy saving, speed profile optimization is important for improving system efficiency and satisfying passenger demands. This study employs Dynamic Programming (DP) to solve the constrained optimal problem for travel time, distance, and speed limit by exploring all possible control options. The solutions obtained by DP demonstrate consistent control patterns combining four control modes-acceleration, cruising, coasting, and braking, with cruising or coasting being selective depending on the boundary conditions. Further, this study introduces DP-based simulation results and attempts to provide comprehensive interpretations of the optimal policy by analyzing the essential factors that affect the control problem, including boundary conditions, road load, and powertrain characteristics. Based on these interpretations, the control concepts can be explained as the optimal policy selecting the best control option based on system efficiency and boundary conditions. The results of DP are compared with a human-like driver model to show that the optimal speed profiles can effectively reduce energy consumption.

Autonomous vehicles↗
DOE OSTI2026

Geothermal-integrated thermally anisotropic building envelope for energy and peak-demand reduction

Buildings consume large amounts of energy for heating and cooling, while peak electricity demand places significant stress on the power grid. This paper presents a reduced-order co-simulation framework and load-oriented supervisory control strategy for a geothermal-integrated thermally anisotropic building envelope with a ground loop (TABE+GL). In TABE+GL, a hydronic loop embedded in the building envelope is directly coupled with a geothermal ground loop, allowing for bidirectional heat exchange between the envelope, the ground, and the indoor environment. A hybrid co-simulation framework was established by coupling a reduced-order resistor–capacitor (RC) thermal network model with EnergyPlus augmented with GHEDesigner modules. The RC model generated feasible heat flux options under three operating modes, and EnergyPlus predicted sensible loads, energy use, and pump energy demand. At each simulation step, a supervisory control algorithm selected the optimal loop configuration and duty factor that maximizes useful TABE geothermal utilization without exceeding the predicted sensible load, thereby avoiding overheating or cooling. Case studies were conducted for Los Angeles, California, Charleston, South Carolina, and Denver, Colorado. Results showed that the proposed framework reduced HVAC electricity consumption by 43%–67%, natural gas use for space heating by 11%–38%, and peak electricity demand by 43%–88%. These results highlight the potential of combining reduced-order envelope modeling, direct geothermal coupling, and load-oriented supervisory control to improve whole building energy performance and reduce peak demand across diverse weather conditions.

Howard, Daniel [Southern Adventist University]↗
DOE OSTI2026

Phenomenological opportunities at the EIC

This review presents a comprehensive overview of key phenomenological opportunities at the future Electron–Ion Collider (EIC), synthesizing discussions and collaborative research efforts developed within the Korean EIC community and the EICφ collaboration. We explore a diverse range of physics topics central to the EIC scientific program, including the multidimensional tomography of nucleon and nuclear structure, precision Quantum Chromodynamics studies through jet physics and event-shape observables, heavy quarkonium production as a probe of partonic dynamics, and the spectroscopy of exotic hadrons. Furthermore, we discuss the transformative potential of emerging technologies—specifically Machine Learning and Quantum Computing—as essential tools for addressing the computational challenges and maximizing the scientific discovery potential of the EIC era.

Electron–Ion collider↗
DOE OSTI2024

Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter

The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Physics-Informed Neural Networks for PDE-Constrained Optimization and Control

The goal of optimal control is to determine a sequence of inputs for maximizing or minimizing a given performance criterion subject to the dynamics and constraints of the system under observation. This work introduces Control Physics-Informed Neural Networks (PINNs), which simultaneously learn both the system states and the optimal control signal in a single-stage framework that leverages the system’s underlying physical laws. While prior approaches often follow a two-stage process-modeling, the system first and then devising its control—the presented novel framework embeds the necessary optimality conditions directly into the network architecture and loss function. We demonstrate the effectiveness of the novel methodology by solving various open-loop optimal control problems governed by analytical, one-dimensional, and two-dimensional partial differential equations (PDEs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2024

Custom-form iron trifluoride Li-batteries using material extrusion and electrolyte exchanged ionogels

Custom-form factor batteries fabricated in non-conventional shapes can maximize the overall energy density of the systems they power, particularly when used in conjunction with energy dense materials (e.g., Li metal anodes and conversion cathodes). Additive manufacturing (AM), and specifically material extrusion (ME), have been shown as effective methods for producing custom-form cell components, particularly electrodes. However, the AM of several promising energy dense materials (conversion electrodes such as iron trifluoride) have yet to be demonstrated or optimized. Furthermore, the integration of multiple AM produced cell components, such as electrodes and separators, along with a custom package remains largely unexplored. In this work, iron trifluoride (FeF 3 ) and ionogel (IG) separators are conformally printed using ME onto non-planar surfaces to enable the fabrication of custom-form Li-FeF 3 batteries. Further, to demonstrate printing on non-planar surfaces, cathodes and separators were deposited onto cylindrical rods using a 5-axis ME printer. ME printed FeF 3 was shown to have performance commensurate with FeF 3 cast using conventional means, both in coin cell and cylindrical rod formats, with capacities exceeding 700 mAh/g on the first cycle and ranging between 600 and 400 mAh/g over the next 50 cycles. Additionally, a ME process for printing polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) based IGs directly onto FeF 3 is developed and enabled using an electrolyte exchange process. In coin cells, this process is shown to produce cells with similar capacity to cells built with Celgard separators out to 50 cycles, with the exception that cycling instabilities are observed during cycles 8–20. When using printed and exchanged IGs in a custom cylindrical cell package, 6 stable high-capacity cycles are achieved. Overall, this work demonstrates approaches for producing high-energy-density Li-FeF 3 cells in coin and cylindrical rod formats, which are translatable to customized, arbitrary geometries compatible with ME printing and electrolyte exchange.

25 ENERGY STORAGE↗
DOE OSTI2025

Embedded 3D printing of isotropically enhanced lattice structure with programmable elasticity via three-dimensional fiber alignment

Engineered composites with fiber orientation in a complete three-dimensional (3D) context are highly desired for maximizing structural performance as they mimic the well-organized fiber arrangement in natural composites. However, most fabrication techniques lack the capability for through-plane (z-axis) alignment, limiting the possibility for structural design and performance optimization. In this study, we present an embedded 3D printing approach that enables complete 3D fiber alignment, including region-specific alignment control along the through-plane direction. Our method utilizes the extrusion and suspension of composite inks within a supporting matrix. By regionally adjusting the ink extrusion rate and nozzle translation rate, we locally manipulated the fiber arrangement within the composite 3D architecture. Here, we demonstrate that the 3D alignment of carbon fibers in a structural composite significantly enhances both mechanical properties and thermal conductivity in the in-plane and through-plane directions. Furthermore, by strategically arranging regional fiber orientations within micro-architected materials, we fabricated metamaterial engineered composites with programmable anisotropy ratio and directional moduli.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Integrating Analytical Solutions and U-Net Model for Predicting Groundwater Contaminant Plumes in Pump-and-Treat Systems

Pump-and-treat (P&T) is a common technique for groundwater remediation involving the extraction and treatment of contaminated water above ground. Optimizing the design and operation of the P&T well network is essential for maximizing the system’s effectiveness and efficiency. However, this optimization often necessitates many model evaluations, leading to computationally demanding tasks. This study introduces a novel approach that integrates analytical solutions for groundwater dynamics with the U-Net (Ronneberger et al., 2015) deep learning framework to predict groundwater contaminant plume migration under dynamic pumping conditions. By incorporating the Thiem equation (Thiem, 1906) into the input preprocessing, the U-Net model transforms sparse well data into a continuous spatial field that captures the hydraulic impacts of pumping activities. This integration enables the model to leverage both deep learning capabilities and classical physics-based groundwater theories, enhancing prediction accuracy and computational efficiency. These advancements can facilitate rapid, large-scale evaluations of P&T optimization simulations, allowing for timely and effective decision-making in well placement and system management. We demonstrate the model's robust performance across both simplified transient 2D models and a more complex 3D heterogeneous site model at the 200 West P&T facility at the Hanford Site. The U-Net-based model offers substantial computational advantages, reducing simulation times significantly compared to full physics-based models and providing a powerful tool for rapid site evaluation and P&T system optimization, such as evaluating alternative P&T well network designs. Our findings highlight the potential of advanced machine learning models to significantly enhance the efficiency and sustainability of groundwater remediation efforts, offering a novel application of U-Net architecture in environmental science.

Pump-and-treat↗
DOE OSTI2024

Innovative control mechanism for research and test reactors using mandrel-shaped control rods

Research and test reactors have historically played a pivotal role in supporting the initial development of nuclear reactors. They continue to provide essential data for enhancing fuel designs and material knowledge. However, with many such reactors aging and the growing demand for data to bolster advanced reactor development, it is more necessary to research potential design attributes of the next generation of research and test reactors. For test reactors dedicated to fuel and material testing, the design of control mechanisms significantly influences the stabilization of neutron flux levels in irradiation positions while sustaining criticality. This study presents an innovative control mechanism for potential research and test reactor designs. It employs small absorber rods that move in opposite axial directions to maintain axial symmetry of power and neutron flux during burnup cycles. These rods maximize reactivity worth while also offering flexibility to flatten the radial power distribution. An axial translation of the control mechanisms’ absorbers, as compared to the rotational movement of absorbers in control cylinders, also provides a benefit to available excess reactivity and cycle length. Additionally, this work utilizes a simplified core model of the Advanced Test Reactor to assess the performance of this control mechanism. Compared to the current control system based on rotating control cylinders, the new control mechanism has the potential to enhance, or at least maintain, neutronic performance parameters in this reactor design.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Genetic algorithm optimization of nuclear criticality experiment for reduction of intermediate-energy 239 Pu nuclear data uncertainties

Nuclear criticality experiments are conducted to investigate specific nuclear data important for safe handling and storage of fissile materials, reactor design and operation, and the validation of radiation transport codes. Incorrect or uncertain nuclear data can prohibitively impact operational safety limits, reactor licensing, and predictive simulation capability; therefore, integral measurements from criticality experiments are necessary and should be performed frequently. To maximize the impact of the integral measurements, it is important to consider experiment geometry, material selection, and component dimensions. When taking these considerations into account, the experiment design process becomes iterative and very time intensive. This work utilizes a genetic algorithm to efficiently explore potential nuclear criticality experiment designs for the Laboratory Directed Research & Development project PARADIGM (PARallel Approach of Differential and InteGral Measurements) at Los Alamos National Laboratory. In this paper, the building blocks of the genetic algorithm are discussed in detail, the genetic algorithm methodology is verified, and the genetic algorithm is used to produce three candidate experiment models for the final PARADIGM design. The three candidate models produced by the genetic algorithm consist of copper-reflected assemblies containing 14 repeating units of alumina, graphite, boron, and plutonium plates. Furthermore, in addition to the optimization results, final design considerations are also discussed for designs with a height and/or weight very close to or slightly above assembly machine operational limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Exsolution of NiCo alloys over Ruddlesden-Popper perovskite for mild electrochemical synthesis of ammonia on protonic ceramic electrochemical cells

Ammonia synthesis from renewable energies on protonic ceramic electrochemical cells (PCECs) shows great potential. The primary challenges in ammonia synthesis on PCECs include sluggish catalytic activity, competition from the hydrogen evolution reaction, and unsatisfactory durability of the cathode, which is the active site of ammonia generation. Here, in this study, we report an elaborate design of the cathode with an intended formula of Pr 4 Ni 1.79 Co 1.2R u 0.01 O 10-δ , where NiCo alloy nanoparticles are exsolved from Ruddlesden-Popper perovskite substrates after the reduction in 5 % H 2 /Ar at 400 °C for 1 h, for electrocatalysis of the nitrogen reduction reaction to ammonia. The host material Pr 4 Ni 1.8 Co 1.2 O 10-δ with embeddable layered structure and stability was deliberately chosen to fix the Ru cation and maximize the catalytic activity of NiCo. Also, density functional theory calculations suggest that Ru doping provides an optimal balance between structural stability and redox activity, facilitating the controlled exsolution of NiCo nanoparticles and enhancing catalytic performance. As a result, the composite electrode with exsolved NiCo alloy and abundant oxygen vacancies on fuel-electrode-supported PCECs achieves a superior electrochemical activity towards ammonia synthesis: a peak ammonia formation rate of 27.84 μg h −1 cm −2 and excellent Faradaic efficiencies of 62.6 % at 350 °C.

30 DIRECT ENERGY CONVERSION↗