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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Shielding Analyses for VISION Beam Line at SNS

Full-scale neutron and gamma transport analyses were performed to design shielding around the VISION beam line, instrument shielding enclosure, beam stop, secondary shutter including a temporary beam stop for the still closed neighboring beam line to meet requirement is to achieve dose rates below 0.25 mrem/h at 30 cm from the shielding surface. The beam stop and the temporary beam stop analyses were performed with the discrete ordinate code DORT additionally to Monte Carlo analyses with the MCNPX code. Comparison of the results is presented.

Popova, Irina↗
DOE OSTI2013

Advanced Research Projects Agency-Energy Strategic Vision 2013

This report is in response to the requirements set forth in the America COMPETES Act, Public Law 110-69, section 5012(g)(2)(2007) as amended, which has been codified as 42 U.S.C. § 16538 (h)(2), wherein it is stated: "…the Director shall provide to the relevant authorizing and appropriations committees of Congress a roadmap describing the strategic vision that ARPA-E will use to guide the choices of ARPA-E for future technology investments over the following 3 fiscal years."

Martin, C.↗
DOE OSTI2015

A Vision for Systems Engineering Applied to Wind Energy (Presentation)

This presentation was given at the Third Wind Energy Systems Engineering Workshop on January 14, 2015. Topics covered include the importance of systems engineering, a vision for systems engineering as applied to wind energy, and application of systems engineering approaches to wind energy research and development.

17 WIND ENERGY↗
DOE OSTI2015

2015 Enterprise Strategic Vision

This document aligns with the Department of Energy Strategic Plan for 2014-2018 and provides a framework for integrating our missions and direction for pursuing DOE’s strategic goals. The vision is a guide to advancing world-class science and engineering, supporting our people, modernizing our infrastructure, and developing a management culture that operates a safe and secure enterprise in an efficient manner.

Source record↗
DOE OSTI2016

Decadal Vision Progress Report Implementation Plans and Status for the Next Generation ARM Facility

The reconfiguration of the ARM facility, formally initiated in early 2014, is geared toward implementing the Next Generation of the ARM Facility, which will more tightly link ARM measurements and atmospheric models. The strategy is outlined in the ARM Climate Research Facility Decadal Vision (DOE 2014a). The strategy includes the implementation of a high-resolution model, initially at the Southern Great Plains (SGP) site, and enhancements at the SGP and North Slope of Alaska (NSA) sites to provide additional observations to support modeling and process studies. Enhancements at the SGP site focus on ground-based instruments while enhancements at the NSA make use of Unmanned Aerial Systems (UAS) and Tethered Balloon Systems (TBS). It is also recognized that new data tools and data products will need to be developed to take full advantage of these improvements. This document provides an update on the status of these ARM facility enhancements, beginning with the measurement enhancements at the SGP and NSA, followed by a discussion of the modeling project including associated data-processing activities.

Southern Great Plains, North Slope of Alaska, Unma↗
DOE OSTI2016

Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.

Jha, Sumit Kumar↗
DOE OSTI2013

A Vision on the Status and Evolution of HEP Physics Software Tools

This paper represents the vision of the members of the Fermilab Scientific Computing Division's Computational Physics Department (SCD-CPD) on the status and the evolution of various HEP software tools such as the Geant4 detector simulation toolkit, the Pythia and GENIE physics generators, and the ROOT data analysis framework. The goal of this paper is to contribute ideas to the Snowmass 2013 process toward the composition of a unified document on the current status and potential evolution of the physics software tools which are essential to HEP.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

National Nuclear Security Administration Strategic Vision Strengthening our Nation through Nuclear Security

The National Nuclear Security Administration’s enduring mission is to protect our nation by maintaining a safe, secure, and effective nuclear weapons stockpile, reducing global nuclear threats, and providing our submarines and aircraft carriers with militarily effective nuclear propulsion. Although we have numerous strategic partners that enable, contribute to, and benefit from our efforts, no other organization, government or civilian, can accomplish our unique mission on behalf of the American people. In this area, we stand alone. This strategic vision pursues urgent change at a significant scale to develop creative approaches, make sustained investments, and be disciplined in our approach to develop a nuclear capability for our time and for the foreseeable future. It is incumbent on us to anticipate our future security challenges and ensure our country is ready to meet them. Not doing so would have far-reaching consequences. Failure to meet our vital mission objectives will erode confidence in the security that the United States strategic deterrent umbrella provides. In assessing the current mission environment in NNSA, we can count on one thing—change: The geopolitical environment for our deterrent is ever-changing Threats from rogue states and terrorists are evolving Our nuclear weapons and the infrastructure that supports them are aging Science and technology continue to move forward at an ever-increasing pace In this dynamic environment, it is not sufficient to remedy today’s situation and meet our current mission requirements. We must lean forward in the development, planning, and execution of programs that address our current challenges while developing the workforce, infrastructure, tools, and governance systems that will enable us to meet our deterrence, non-proliferation, and naval propulsion mission requirements for the next 50 years. We must embrace a next-generation enterprise that is flexible enough to respond to future challenges. With decades of stagnation following the end of the Cold War, we find ourselves in the unenviable position of implementing near-term fixes while using those lessons learned to better prepare for the future. Without sustained and predictable investments to restore and modernize our infrastructure, we will rapidly lose our ability to deliver on our promise of a safe and reliable nuclear weapons stockpile, which will degrade our deterrent capability and render legacy weapon systems irrelevant to the defense of our nation. We must not and will not allow the next generation of our nuclear security enterprise to simply maintain the status quo. We have a storied past that guides us. For 75 years our enterprise has overcome every challenge, led the country in incredible scientific and engineering endeavors and discoveries, and successfully ensured constant vigilance in maintaining an effective deterrent, reducing global nuclear threats, and powering our nuclear navy. Now is the time for us to take decisive action in forging the enterprise of the future. As I have traveled to all of our labs, plants, and sites and met with people across our complex, I have been left with one overwhelming impression: the incredible workforce that makes up our nuclear security enterprise is up to all of the challenges we have before us. Together we can and will ensure that those in the future workforce have the resources and the responsive, agile infrastructure they need to maintain the systems of today and to design and field the systems of tomorrow.

Source record↗
DOE OSTI2022

Development of an end state vision to implement digital monitoring in nuclear plants

Transitioning from an onsite Maintenance & Diagnostics Center to cloud-based services offers many new opportunities with computing power and storage, but also new challenges in terms of networking and security. This report will cover everything required for that transition including data processing and uploading to cloud services, feature selection, model creation, and result visualization for decision making. Although there are several other cloud-based services (e.g. Amazon Web Services and Google Cloud), this report explores Microsoft Azure to simplify nomenclature and maintain a consistent focus. Many of the services offered by Microsoft Azure are also available in the other cloud-based services, and their differences have been recorded in other literature. The Azure services most important to a nuclear power plant including networking & security, storage & databases, and Artificial Intelligence (AI) are reviewed here. Networking covers all aspects related to communication to Azure resources including security, privacy, and redundancy. Storage & databases includes data storage, upgrading, patching, backups, and monitoring. The AI services allows the user access to the machine learning (ML) techniques developed with Azure including automated ML, anomaly detection, computer vision, and natural language processing. This report summaries the features, capabilities, and challenges when using cloud-based services in a user-friendly manner.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Cameco Corporation's Vision in Motion Project - 19128

Cameco Corporation is a world leader in uranium production. Cameco is also a major supplier of uranium processing services required to produce fuel for the generation of clean energy. These operations are based in Blind River, Cobourg and Port Hope, Ontario and are collectively referred to as the Fuel Services Division. The Fuel Services Division produces uranium trioxide from uranium ore concentrate at the Blind River Refinery. Cameco produces uranium hexafluoride and uranium dioxide at the Port Hope Conversion Facility. Cameco operates a fuel manufacturing facility in Port Hope, Ontario and a metal fabrication facility located in Cobourg, Ontario. The company manufactures fuel bundles utilized in the Candu reactors. Cameco is undertaking a major site cleanup and renewal of its Port Hope Conversion Facility (PHCF) which is located in the Municipality of Port Hope (MPH). This undertaking is known as the Vision in Motion (VIM) project. This project is unique and timely opportunity that has been made possible because of the Port Hope Area Initiative (PHAI). The PHAI Port Hope project is being undertaken by Canadian Nuclear Laboratories (CNL) and includes the construction of a long-term waste management facility (LTWMF) in the MPH. VIM entails an extensive remediation and redevelopment of much of the PHCF and includes: - the cleanup and demolition of a number of old or underutilized buildings; - the removal of contaminated soils, building materials and stored wastes; and - modifications to existing buildings at the PHCF with associated landscaping and infrastructure. Overall, the VIM project will allow for environmental remediation, improved site operations, enhancement of site safety and security through site design and improved aesthetics. This paper will discuss the status of this project underway at the Port Hope Conversion Facility. After years of community consultation, planning, and a successful environmental assessment and licensing of the project, the VIM project at the PHCF is now underway. Physical transfer of waste materials began in June 2018. Cameco is committed to seeing the project through to completion and is working with CNL to ensure this happens within the operating time frame of the LTWMF. Cameco keeps the CNSC and MPH well informed of all project related activities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images

Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs). We ask if pre-trained VLMs can aid scientists in answering a range of biologically relevant questions without any additional fine-tuning. In this paper, we evaluate the effectiveness of 12 state-of-the-art (SOTA) VLMs in the field of organismal biology using a novel dataset, VLM4Bio, consisting of 469K question8 answer pairs involving 30K images from three groups of organisms: fishes, birds, and butterflies, covering five biologically relevant tasks. We also explore the effects of applying prompting techniques and tests for reasoning hallucination on the performance of VLMs, shedding new light on the capabilities of current SOTA VLMs in answering biologically relevant questions using images

Maruf, M [Virginia Tech, Blacksburg]↗
DOE OSTI2025

SHF: Symmetrical Hierarchical Forest with Pretrained Vision Transformer Encoder for High-Resolution Medical Segmentation

This paper presents a novel approach to addressing the long-sequence problem in high-resolution medical images for Vision Transformers (ViTs). Using smaller patches as tokens can enhance ViT performance, but quadratically increases computation and memory requirements. Therefore, the common practice for applying ViTs to high-resolution images is either to: (a) employ complex sub-quadratic attention schemes or (b) use large to medium-sized patches and rely on additional mechanisms within the model to capture the spatial hierarchy of details. We propose Symmetrical Hierarchical Forest (SHF), a lightweight approach that adaptively patches the input image to increase token information density and encode hierarchical spatial structures into the input embedding. We then apply a reverse depatching scheme to the output embeddings of the transformer encoder, eliminating the need for convolution-based decoders. Unlike previous methods that modify attention mechanisms or use a complex hierarchy of interacting models, SHF can be retrofitted to any ViT model to allow it to learn the hierarchical structure of details in high-resolution images without requiring architectural changes. Experimental results demonstrate significant gains in computational efficiency and performance: on the PAIP WSI dataset, we achieved a 3∼32×speedup or a 2.95%∼7.03% increase in accuracy (measured by Dice score) at a 64K2 resolution with the same computational budget, compared to state-of-the-art production models. On the 3D medical datasets BTCV and KiTS, training was 6×faster, with accuracy gains of 6.93% and 5.9%, respectively, compared to models without SHF.

Zhang, Enzhi [Hokkaido University, Japan]↗
DOE OSTI2025

MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models

Mixture of Experts (MoE) models have enabled the scaling of Large Language Models (LLMs) and Vision Language Models (VLMs) by achieving massive parameter counts while maintaining computational efficiency. However, MoEs introduce several inference-time challenges, including load imbalance across experts and the additional routing computational overhead. To address these challenges and fully harness the benefits of MoE, a systematic evaluation of hardware acceleration techniques is essential. We present MoE-Inference-Bench, a comprehensive study to evaluate MoE performance across diverse scenarios. We analyze the impact of batch size, sequence length, and critical MoE hyperparameters such as FFN dimensions and number of experts on throughput. We evaluate several optimization techniques on Nvidia H100 GPUs, including pruning, Fused MoE operations, speculative decoding, quantization, and various parallelization strategies. Our evaluation includes MoEs from the Mixtral, DeepSeek, OLMoE and Qwen families. The results reveal performance differences across configurations and provide insights for the efficient deployment of MoEs.

Chitty-Venkata, Krishna Teja↗
DOE OSTI2025

Compressing Vision Transformers in Geospatial Transfer Learning with Manifold-Constrained Optimization

Deploying geospatial foundation models on resource-constrained edge devices demands compact architectures that maintain high downstream performance. However, their large parameter counts and the accuracy loss often induced by compression limit practical adoption.In this work, we leverage manifold-constrained optimization framework DLRT to compress large vision transformer–based geospatial foundation models during transfer learning. By enforcing structured low-dimensional parameterizations aligned with downstream objectives, this approach achieves strong compression while preserving task-specific accuracy. We show that the method outperforms of-the-shelf low-rank methods as LoRA. Experiments on diverse geospatial benchmarks confirm substantial parameter reduction with minimal accuracy loss, enabling high-performing, on-device geospatial models.

Snyder, Thomas [Yale University]↗
DOE OSTI2026

QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEval, the first VLM benchmark for quantum calibration plots: 243 samples across 87 scenario types from 22 experiment families, spanning superconducting qubits and neutral atoms, evaluated on six question types in both zero-shot and in-context learning settings. The best general-purpose zero-shot model reaches a mean score of 72.3, and many open-weight models degrade under multi-image in-context learning, whereas frontier closed models improve substantially. A supervised fine-tuning ablation at the 9-billion-parameter scale shows that SFT improves zero-shot performance but cannot close the multimodal in-context learning gap. As a reference case study, we release NVIDIA Ising Calibration 1, an open-weight model based on Qwen3.5-35B-A3B that reaches 74.7 zero-shot average score.

Cao, Shuxiang↗
DOE OSTI2026

CTRL-STEER: Closed-Loop Neuron Activation Control in Vision-Language-Action Models

Vision-Language-Action (VLA) models enable test-time behavioral steering via neuron-level interventions, but existing methods use fixed strengths and operate in open loop. This static modulation fails under evolving task dynamics, leading to overcorrection, oscillations, and reduced task success—especially for temporal attributes like speed. We propose CTRL-STEER, a control-theoretic framework that casts activation steering as closed-loop feedback with adaptive, time-varying interventions. Instead of assuming neurons encode temporal concepts, we steer along motion-aligned residual directions and regulate intervention magnitude via feedback. We instantiate this with both PID and reinforcement learning controllers that jointly optimize concept adherence and task success. Experiments on fine-tuned OpenVLA policies across four LIBERO suites show improved stability and a better steering–success trade-off over fixed-coefficient baselines, without retraining the base model.

Babu, Abhijith [Florida International University, ↗