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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Community-Informed Urban Flood Modeling for Impact Mitigation

Climate change is intensifying the hydrologic cycle, leading to more frequent and severe rainfall-driven (pluvial) flooding in urban areas. In the mid-Atlantic US cities, aging and under-designed stormwater infrastructure is increasingly strained by these events, resulting in recurring damage to property and disruptions to transportation networks. In this study, we combine community engagement with hydrologic modeling to develop and evaluate potential urban flood adaptation strategies. Over a three-year period, local technical experts and community representatives met regularly to discuss flooding concerns, identify priorities, and co-develop adaptation strategies. These discussions informed the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed, the focus location of this study. The flooding model integrates complex surface and subsurface stormwater infrastructure data, local expert knowledge, and community insights. We simulate stakeholder-prioritized adaptations, such as green and gray infrastructure strategies. Model results demonstrate that enhanced infrastructure maintenance is the most effective adaptation for reducing flood depths, but has varied effects across the watershed, and can increase flooding in some locations. Spatially concentrated greening provides limited benefit to the watershed as a whole, but moderate benefit in community priority areas. Together, these adaptations have the potential to reduce flood depths by as much as 58% in some locations, greatly reducing property damage and mobility impacts, primary concerns of stakeholders. Future work will implement robust optimization tools to search for adaptations which meet stakeholder objectives and perform highly under varied future climate conditions. This work contributes to the expanding literature on collaborative modeling and demonstrates that community-engaged approaches can enhance model credibility and generate more actionable insights for communities seeking to strengthen climate resilience.

Baltimore MD↗
DOE OSTI2026

Building the Business Case with JUSTIFI: Quantifying Multiple Benefits in the Automotive Industry

The Michigan State University Industrial Training and Assessment Center (MSU ITAC) conducted a pilot study at an automotive parts manufacturer in Michigan. The study identified energy-productivity enhancements through the application of the JUSTIFI software. Key recommendations included replacing six inefficient rooftop units (RTUs) with a new air rotational unit. By quantifying operational savings for this project, the expected payback period went from 6.6 years to 1.4 years. Additionally, the installation of variable frequency drives (VFDs) on condenser tower motors was suggested. By including all operational benefits, the payback period was reduced from 8.2 years to 1.3 years. This comprehensive analysis aims to bolster the manufacturer's goals of reducing energy while enhancing overall operational efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Spectrally accurate, reverse-mode differentiable bounce-averaging algorithm and its applications

We present a fast, spectrally (exponentially) accurate, automatically differentiable bounce-averaging algorithm that is used to simplify kinetic models. Using this algorithm, implemented in the DESC stellarator optimisation suite, we can perform efficient optimisation of many objectives to improve stellarator performance, such as the effective ripple 𝜖 eff metric for the neoclassical transport coefficient in the low collisionality regime and proxies for energetic particle confinement. For the first time, we optimise a finite-beta stellarator to directly reduce neoclassical ripple transport using reverse-mode differentiation. This ensures the computational cost of differentiation is independent of the number of controllable parameters.

fusion plasma↗
DOE OSTI2025

Leveraging Pre-Built Catalogs and Object-Level Scheduling to Eliminate I/O Bottlenecks in HPC Environments

Modern High-Performance Computing (HPC) environments face mounting challenges due to the shift from large to small file datasets, along with an increasing number of users and parallelized applications. As HPC systems rely on Parallel File Systems (PFS), such as Lustre for data processing, performance bottlenecks stemming from Object Storage Target (OST) contention have become a significant concern. Existing solutions, such as LADS with its object-level scheduling approach, fall short in large-scale HPC environments due to their inability to effectively address metadata I/O bottlenecks and the growing number of I/O processes. This study highlights the pressing need for a comprehensive solution that tackles both OST contention and metadata I/O challenges in diverse HPC workloads. To address these challenges, we propose SwiftLoad, an object-level I/O scheduling framework that leverages a metadata catalog to enhance the performance and efficiency of parallel HPC utilities. The adoption of the metadata catalog mitigates the metadata I/O bottlenecks that commonly occur in HPC utilities, a challenge that is particularly pronounced in object-level I/O scheduling. SwiftLoad addresses OST contention and the uneven distribution of I/O processes across different OSTs through mathematical modeling and incorporates a Loader Configuration Module to regulate the number of I/O processes. Evaluated with two representative utilities—data deduplication profiling and data augmentation—SwiftLoad achieved performance improvements of up to 5.63x and 11.0x, respectively, on a production supercomputer.

HPC↗
DOE OSTI2024

Microgrid Design Toolkit (MDT) User Guide: Software v1.4

The MDT is a decision support software that can provide the information needed to identify optimal microgrid designs in the early stages of the design process. MDT searches the trade space of alternative microgrid designs in terms of user-defined objectives, such as performance, reliability, and cost. It produces a Pareto frontier of solutions embodying the efficient tradeoffs amongst multiple user-defined objectives.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Factorized visual representations in the primate visual system and deep neural networks

Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether (‘invariance’), represented in non-interfering subspaces of population activity (‘factorization’) or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters – lighting, background, camera viewpoint, and object pose – in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Dry Deactivation of Sodium Metal in a Molten LiCl-KCl-CsCl-NaCl System

An experimental study was performed with the objective of investigating and characterizing a dry technique for deactivation of sodium metal in a molten salt system. The study was performed in three parts. First, proof-of-principle testing of the technique was performed at bench scale. It involved loading and melting tens of grams of clean sodium metal atop a pool of LiCl-KCl-CsCl eutectic at ∼300°C and then adding ammonium chloride particles while mixing. The ammonium chloride reacted with sodium to form sodium chloride and nitrogen/hydrogen off-gases. The sodium chloride assimilated into the salt pool, forming a quaternary salt mixture of LiCl-KCl-CsCl-NaCl. The proof-of-principle testing repeatedly exhibited complete deactivation of the loaded sodium metal. Second, characterization of a LiCl-KCl-CsCl-NaCl system was performed using differential scanning calorimetry to produce a partial phase diagram of LiCl-KCl-CsCl eutectic versus NaCl, which identified the liquidus, solidus, and two-phase regions of the quaternary system. Third, the dry deactivation technique was demonstrated at kg-scale in an inert atmosphere radiological glovebox with sodium metal that was previously separated in the same glovebox from uranium metal in unirradiated blanket elements for the Fermi-1 nuclear reactor. The quaternary salt product at the end of the demonstration was sampled and showed complete deactivation of the sodium metal. Here, in short, this study qualified a technique to completely deactivate batches of sodium metal in the absence of air or water. While this study focused on the deactivation of sodium metal, including bond sodium from unirradiated blanket elements, the technique is applicable to other sources of sodium metal, such as sodium metal from batteries. Furthermore, the technique could also be extended to other alkali metal systems, including lithium, sodium, potassium, rubidium, cesium, and mixtures thereof.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Life-Cycle Analysis of Residential Windows Retrofits: Net GHG Emission Reduction and Payback Periods

Windows are a critical envelope component that plays an important role in the overall performance and environmental impact of a building life cycle. These implications can be embedded in the window lifecycle related to its design, manufacturing, raw materials and transportation, performance during the building’s use (operational), replacements, maintenance and end-of-life. Windows may impact 25% of the heating and cooling energy use, 10% of total building energy use and 45% of the envelope heat transfer (Harris 2022). The impacts of windows on the energy consumption of buildings have been extensively discussed, however, its embodied life-cycle impacts, such as greenhouse gas (GHG) emissions, and the trade-offs between the embodied and its operational emissions are less explored. Understanding the life cycle impacts of windows may subsidize decision making process and inform the development of emerging windows technologies. BTO’s Windows Program has played an important role to increase the adoption of emerging technologies as high-performance windows in the U.S. (Harris 2022) and to consider the GHG emission impacts of the those windows is an important aspect that can support the strategic objectives and the performance targets from the national blueprint for decarbonizing the buildings sector and to reduce the on-site emissions and embodied life cycle emissions from building materials and construction (US DOE 2024).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

INR-TEM: Robust cavity detection in multifocus TEM images via implicit neural representations

When characterizing materials using transmission electron microscopy (TEM) images, detecting and quantifying small features in microstructures, such as cavities, pose significant challenges. Off-the-shelf object detection models, including YOLOv8, show considerable performance degradation, particularly when images vary in resolution and the objects of interest possess a low percentage of the total image region of interest. In this study, we introduce a novel detection pipeline that incorporates an implicit neural representation (INR)-based detection method, INR-TEM, and two-modality imaging (e.g., under-focused and over-focused images typically acquired during materials characterization) to improve object detection performance. The INR-TEM method incorporates a pixel-wise prediction principle inspired by pixel-wise centerness weighting. INR-TEM demonstrates superior robustness to resolution variability, maintaining high detection accuracy even at low image resolutions compared to YOLOv8. To leverage INR-TEM effectively in real-world two-modality characterization applications, we further integrate a two-stage motion correction pipeline designed explicitly for aligning multifocus TEM images. The alignment process, comprising keypoint (based on scale-invariant feature transform, SIFT) and intensity matching, significantly mitigates the adverse effects of perceived motion-induced image degradation during through-focal TEM imaging, directly enhancing INR-TEM’s detection capability over conventional single-focus images. Our integrated INR-TEM cavity detection framework notably improves performance across various cavity sizes, outperforming off-the-shelf YOLOv8 detections that rely on a single image modality.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

CoCoMET v1.0: a unified open-source toolkit for atmospheric object tracking and analysis

Advances in performance and analysis capabilities have accelerated the development of object tracking algorithms for atmospheric research. This has resulted in a growing number of studies using Lagrangian tracking techniques to analyze the evolution of atmospheric phenomena and the underlying processes. However, the increasing complexity and variety of tracking algorithms present a steep learning curve for new users and make it difficult for existing users to compare algorithm performance. We introduce CoCoMET (Community Cloud Model Evaluation Toolkit), an open-source toolkit that addresses these issues. CoCoMET simplifies the process of running multiple tracking algorithms simultaneously and analyzing objects in both model and observational datasets by specifying parameters in a single configuration file. It standardizes input data from different sources into a consistent format and unifies the tracking output across algorithms. CoCoMET enhances the functionality of existing tracking methods by calculating additional properties such as cell growth and dissipation rates, perimeter, surface area, convexity, and irregularity. In addition, CoCoMET includes a novel method for identifying mergers and splits in 2D and 3D tracks and supports the integration of Eulerian/stationary datasets external to the tracking data for process studies. Its potential utility is demonstrated through examples of model intercomparison, model evaluation against observations, and comparisons between tracking algorithms. Designed for open-source environments, CoCoMET will continue to expand with future releases, incorporating more input data types and tracking algorithms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

SLAB: simultaneous labeling and binding affinity prediction for protein–ligand structures

Machine learning models are often used as scoring functions to predict the binding affinity of a protein–ligand complex. These models are trained with limited amounts of data with experimentally measured binding affinity values. A large number of compounds are labeled inactive through single-concentration screens without measuring binding affinities. These inactive compounds, along with the active ones, can be used to train binary classification models, while regression models are trained using compounds with binding affinities only. However, the classification and regression tasks are often handled separately, without sharing the learned feature representations. In this paper, we propose a novel model architecture that jointly performs regression and classification objectives, aiming to maximize data utilization and improve predictive performance by leveraging two complementary tasks. In our setup, the regression yields the binding affinity, whereas the classification task yields the label as active or inactive. We demonstrate our method using PDBbind, the standard 3D structure database, as well as a dataset of flavivirus protease compounds with binding affinity data. Our experiments show that the new joint training strategy improves the accuracy of the model, increasing applicability in various practical drug screening scenarios.

Biological and medical sciences↗
DOE OSTI2024

AEPF: Attention-Enabled Point Fusion for 3D Object Detection

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class’s easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Chemistry↗
DOE OSTI2025

Designing robust energy policy packages under deep uncertainty: A multi-metric decision support framework

The complexity of transitioning to sustainable energy systems requires policy frameworks capable of balancing multiple objectives while addressing deep uncertainty. However, existing approaches often lack systematic methods to identify combinations of policy levers that remain effective across a wide range of uncertain futures. This paper presents a novel decision support framework that guides the selection of robust policy packages based on their performance across multiple objectives under uncertainty. Our method leverages a large ensemble of scenarios and applies scenario discovery techniques to identify influential policy levers. Here, we introduce new indicators to assess the robustness of policies by evaluating their ability to mitigate adverse outcomes across metrics. These indicators support an iterative process to build a robust policy package. Finally, we map the technological and energy pathways associated with the robust policy package by leveraging an energy system optimization model. We illustrate the application of this framework to the Spanish energy system, providing insights into how specific combinations of policy levers shape decarbonization pathways under uncertainty.

Decision-support method↗
DOE OSTI2024

Analysis of Ground Source Heat Pump ECMs: Implemented Under DOE ESPC IDIQ Contract

Several federal performance and decarbonization objectives are driving the U.S. government to transition to net-zero emissions across the federal building portfolio. The overarching purpose of this Federal Energy Management Program (FEMP)-Geothermal Technologies Office (GTO) work is to support federal facilities in accomplishing the administration’s federal building decarbonization goals. Creating awareness of existing geothermal (or ground-source) heat pump (GHP) systems deployed on federal property, implemented using performance contracts and evaluating the energy and environmental impacts of these systems, this work will assist federal facilities in considering inclusion of GHP technology energy conservation measures (ECMs) in future performance contracting project awards. This report summarizes analyses of data from GHP ECMs installed under the DOE ESPC IDIQ contract across various climate areas and geographic locations, agencies, and building use types. Through the analysis of proposed GHP ECMs and understanding key metrics on implemented GHP systems in a variety of applications, FEMP and GTO can further support federal agencies in meeting decarbonization and net-zero building goals by evaluating and incorporating GHP systems into performance contracts and communicate the benefits of this technology.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Exponential Backoff and Its Security Implications for Safety-Critical OT Protocols over TCP/IP Networks

The convergence of Operational Technology (OT) and Information Technology (IT) networks has become increasingly prevalent with the growth of Industrial Internet of Things (IIoT) applications. This shift, while enabling enhanced automation, remote monitoring, and data sharing, also introduces new challenges related to communication latency and cybersecurity. Oftentimes, legacy OT protocols were adapted to the TCP/IP stack without an extensive review of the ramifications to their robustness, performance, or safety objectives. To further accommodate the IT/OT convergence, protocol gateways were introduced to facilitate the migration from serial protocols to TCP/IP protocol stacks within modern IT/OT infrastructure. However, they often introduce additional vulnerabilities by exposing traditionally isolated protocols to external threats. This study investigates the security and reliability implications of migrating serial protocols to TCP/IP stacks and the impact of protocol gateways, utilizing two widely used OT protocols: Modbus TCP and DNP3. Our protocol analysis finds a significant safety-critical vulnerability resulting from this migration, and our subsequent tests clearly demonstrate its presence and impact. A multi-tiered testbed, consisting of both physical and emulated components, is used to evaluate protocol performance and the effects of device-specific implementation flaws. Through this analysis of specifications and behaviors during communication interruptions, we identify critical differences in fault handling and the impact on time-sensitive data delivery. The findings highlight how reliance on lower-level IT protocols can undermine OT system resilience, and they inform the development of mitigation strategies to enhance the robustness of industrial communication networks.

DNP3↗
DOE OSTI2025

Utilization of Data Augmentation Techniques in Automated Inspection Systems for Defect Detection in Metals With Limited Data

Accurate identification of defects on metal surfaces is of great interest to many industry sectors, such as the automotive and aerospace industries. In contrast to conventional manual inspection techniques, recent automated inspection systems employ deep learning models trained to detect defects rapidly and precisely. The development of these models often requires a substantial image dataset to acquire adequate knowledge of defect features and enhance their predictive accuracy. When data is limited, augmentation techniques are often used to improve the precision and accuracy of defect detection systems. This study examined the prediction performance of two object detection models, namely Faster Region‐based Convolutional Neural Network (Faster R‐CNN) and You Only Look Once version 8 (YOLOv8), to identify dent defects in limited images of cast iron cylinder head surfaces. The original image set contains 46 images with 563 dents. To overcome limited data availability, common image augmentation techniques along with a copy‐paste method were applied. Results show that standard augmentation improved YOLOv8 accuracy by 8.00% and average precision (AP) by 3.00%. On the other hand, the copy‐paste technique achieved a 20.00% increase in accuracy and a 1% increase in AP with just 200 synthetic dents. Furthermore, these results provide support for using the copy‐paste augmentation strategy to enhance defect detection performance, with a limited dataset, contributing to more accurate defect identification in remanufacturing processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

DoCeph: DPU-Offloaded Messaging in Ceph for Reduced Host CPU Utilization

Ceph is a widely used distributed object store, but its messenger layer imposes substantial CPU overhead on the host. To address this limitation, we propose DoCeph, a DPU-offloaded storage architecture for Ceph that disaggregates the system by offloading the communication-intensive messaging component to the DPU while retaining the storage backend on the host. The DPU efficiently manages communication, using lightweight RPC for metadata operations and DMA for data transfer. Moreover, DoCeph introduces a pipelining technique that overlaps data transmission with buffer preparation, mitigating hardware-imposed transfer size limitations. We implemented DoCeph on a Ceph cluster with NVIDIA BlueField-3 DPUs. Evaluation results indicate that DoCeph cuts host CPU usage by up to 92% while sustaining stable throughput and providing larger performance benefits for object writes over 1 MB.

Park, Kuri [Sogang University]↗
DOE OSTI2026

Technoeconomic Design Optimization for Fast Reactors. Part II: Impact of Technoeconomic Constraints on Optimal Design

There is a current drive toward optimizing reactors, particularly small/micro reactors to minimize cost and maximize performance. Previous work has investigated the development of technoeconomic workflows for the design optimization of pool-type fast reactors that aim to deploy into district energy grids. Initial scoping studies verified that the workflow was capable of capturing design trends throughout a variety of design configurations and problem formulations while remaining sufficiently flexible. In this paper, this methodology is applied to understand how cost functions and technoeconomic constraints can drive optimal reactor design. Specifically, the UPu10Zr-fueled fast reactor model from Part I is adapted to include changes in the fissile content limits, control rod worth limits, control rod drive cost, and assumed fuel form. In the case of constraint relaxation at fixed power (fissile content and control rod worth limits), cost sensitivities of 5% to 10% were uncovered. Multi-objective optimization at varying reactor power levels with individualized control rod drives for each assembly (as opposed to one operational and one safety drive) increased cost by $\$10$ to $\$25$ million and substantially altered the optimal core geometry, favoring geometries with substantially fewer control rod placements relative to baseline optimization. Finally, a multi-objective optimization was performed at varying power levels with the fuel form overhauled to metallic, high-assay low-enriched uranium–based U10Zr with more refined fuel cost models. In the case of uranium fueling, the costs increased by at least $50 million relative to the baseline case. Furthermore, economic fuel zoning and lower reactivity swing cores were recovered. Each case serves to demonstrate the value of applying technoeconomic workflows to initial reactor design scoping studies to better understand the trade-off for a proposed concept between different design options.

Argonne Reactor Computation (ARC) codes↗