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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Compact Heat Exchanger Design and Testing for Advanced Reactors and Advanced Power Cycles

The goal of the proposed research is to demonstrate the thermal hydraulic performance of innovative surface geometries in compact heat exchangers used as intermediate heat exchangers (IHXs) and recuperators for the supercritical carbon dioxide (s-CO 2 ) Brayton cycle. Printed-circuit heat exchangers (PCHEs) are the primary compact heat exchangers of interest. The overall objectives are: To develop optimized PCHE designs for different working fluid combinations including helium to s-CO 2 , liquid salt to s-CO 2 , sodium to s-CO 2 , and liquid salt to helium; To experimentally and numerically investigate thermal performance, thermal stress and failure mechanism of PCHEs under various transients; and To study diffusion bonding techniques for elevated-temperature alloys and examine post-test material integrity of the PCHEs. The project objectives were accomplished by defining and executing five different tasks corresponding to these specific objectives. The first task involved a thorough literature review and a selection of IHX candidates with different surface geometries as well as a summary of prototypic operational conditions. The second task involved optimization of PCHE design with numerical analyses of thermal-hydraulic performances and mechanical integrity. The subsequent task dealt with the development of testing facilities and engineering design of PCHE to be tested in s-CO 2 fluid conditions. The next task involved experimental investigation and validation of the thermal-hydraulic performances and thermal stress distribution of prototype PCHEs manufactured with particular surface geometries. The last task involved an investigation of diffusion bonding process and posttest destructive testing to validate mechanical design methods adopted in the design process. The experimental work utilized the two test facilities at The Ohio State University (OSU) including one existing High-Temperature Helium Test Facility (HTHF) and the newly developed s-CO 2 test loop (STL) facility and s-CO 2 test facility at University of Wisconsin – Madison (UW).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Task-Based Neuromodulation Architecture for Lifelong Learning

The conventional supervised deep learning frameworks for image classification focus on finding the minimizer of an objective function. However, for a lifelong learning system, a network has to be able to dynamically learn new tasks from very few examples. In biological neural networks, it is observed that neuromodulation acts as a key factor in continual learning. In this work, we propose a dynamic learning system, ModNet, wherein a modulatory unit regulates the learning depending on the context and the internal state of the system. The modulatory unit, which consists of a group of neurons, exercises the use of neuromodulation and Hebbian plasticity mechanisms over a population of neurons instead of relying on backpropagation of error gradients. An efficient digital architecture with on-chip training and resource sharing is proposed to facilitate the efficacy of task-based processing.

brain-inspired↗
DOE OSTI2017

Fast synchronization-free algorithms for parallel sparse triangular solves with multiple right-hand sides

The sparse triangular solve kernels, SpTRSV and SpTRSM, are important building blocks for a number of numerical linear algebra routines. Parallelizing SpTRSV and SpTRSM on today's many-core platforms, such as GPUs, is not an easy task since computing a component of the solution may depend on previously computed components, enforcing a degree of sequential processing. As a consequence, most existing work introduces a preprocessing stage to partition the components into a group of level-sets or colour-sets so that components within a set are independent and can be processed simultaneously during the subsequent solution stage. However, this class of methods requires a long preprocessing time as well as significant runtime synchronization over-heads between the sets. To address this, we

HPC↗
DOE OSTI2025

TPCpp-10M: Simulated proton-proton collisions in a time projection chamber for AI foundation models

Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of $\sqrt{s}$ = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.

Data Analysis, Statistics and Probability (physics↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2012

Semantic Features for Classifying Referring Search Terms

When an internet user clicks on a result in a search engine, a request is submitted to the destination web server that includes a referrer field containing the search terms given by the user. Using this information, website owners can analyze the search terms leading to their websites to better understand their visitors needs. This work explores some of the features that can be used for classification-based analysis of such referring search terms. We present initial results for the example task of classifying HTTP requests countries of origin. A system that can accurately predict the country of origin from query text may be a valuable complement to IP lookup methods which are susceptible to the obfuscation of dereferrers or proxies. We suggest that the addition of semantic features improves classifier performance in this example application. We begin by looking at related work and presenting our approach. After describing initial experiments and results, we discuss paths forward for this work.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

A summary of the Advanced WEC Dynamics and Control project

This report serves as a comprehensive summary of the work completed by the "Advanced WEC Dynamics and Controls projecr during the period of 2013-2019. This project was first envisioned to simply consider the question of designing a controller for wave energy converters (WECs), without a complete recognition of the broader considerations that such a task must necessarily examine. This document describes both the evolution of the project scope and the key findings produced. The basic goal of the project has been to deliver tractable methodologies and work flows that WEC designers can use to improve the performance of their machines. Engineering solutions, which may offer 80% of the impact, but require 20% of the effort compared to a perfect result (which may be many years of development down the road) were preferred. With this doctrine, the work of the project often involved translating existing methods that have been successfully developed and applied for other fields, into the application area of wave energy.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

A Literature Review: Smart Grids Impacts on Nuclear Power Plants Task 4: Project Final Report

This report describes the results of a literature review performed to study the current state of electric power industry efforts and research work regarding Smart Grid technologies, renewable energy integration, grid reliability standards, and USNRC electric grid regulations and guidance. The information gathered in the literature review is intended to: (1) Develop a better understanding of the impacts that Smart Grid implementation and the integration of renewable energy sources can have on the availability and reliability of the electric grid as a source of offsite power, (2) Provide an understand of the present grid reliability standards and how they will address the challenges of Smart Grid implementation, (3) Identify electrical system vulnerabilities and failure modes that may result from the implementation of Smart Grid technologies and cybersecurity threats, (4) Provide an understand of the actions being taken by FERC, NERC and others to identify, understand and address potential impacts, (5) Provide an understand of existing NRC regulation and guidance addressing requirements of grid design, operation and reliability supporting NPP design requirements, and (6) Recommend any additional actions or studies that should be undertaken by the NRC to ensure compliance of NPPs to GDC 17 and any other applicable regulations and guidance as the electric grid evolves into the Smart Grid.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Chemistry and Reactivity of Oxide Films on Aluminum Clad Test Reactor Fuels

This paper describes recent work performed to assess the degradation of aluminum spent nuclear fuel (ASNF) as part of an ongoing Department of Energy project. As part of a multi-task effort, this paper specifically describes efforts initiated in 2018 to better understand chemical phenomena and degradation in ASNF). In this paper experimental and modeling activities are described. The modeling effort presents initial results in two areas potentially influencing stability. Using an established electrolyte model based on experimental data, the solubility of aluminum as a function of temperature and pH was examined. This work describes interesting results for the minimum pH providing relevant data that was not available from the literature. It was found that solubility increases with temperature and that the minimum pH shifts to low pH with increase in temperature. The second area involves understanding the metallurgy of aluminum alloys during fuel drying. In this study, the temperature range from 100°C to 300°C was analyzed. Thermodynamic models predict that AA1100 will not experience significant phase changes while the more highly alloyed AA6061 and AA5052 may experience significant phase changes. These thermodynamic measurements establish a potential for phase changes during drying, however they do not predict the kinetics for such processes. Phase 1 of the experimental program has initiated and results are forthcoming. The test program will examine aluminum oxidation under anticipated dry storage conditions (temperature and humidity). Examination of localized corrosion due to tight crevices and galvanic contact (with stainless steel) will be included. Finally, water deliquescence will be assessed using thermogravimetric analysis with dynamic humidity profiles.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Chemistry and Reactivity of Oxide Films on Aluminum Clad Test Reactor Fuels - 19294

This paper describes recent work performed to assess the degradation of aluminum spent nuclear fuel (ASNF) as part of an ongoing Department of Energy project. As part of a multi-task effort, this paper specifically describes efforts initiated in 2018 to better understand chemical phenomena and degradation in ASNF). In this paper experimental and modeling activities are described. The modeling effort presents initial results in two areas potentially influencing stability. Using an established electrolyte model based on experimental data, the solubility of aluminum as a function of temperature and pH was examined. This work describes interesting results for the minimum pH providing relevant data that was not available from the literature. It was found that solubility increases with temperature and that the minimum pH shifts to low pH with increase in temperature. The second area involves understanding the metallurgy of aluminum alloys during fuel drying. In this study, the temperature range from 100 deg. C to 300 deg. C was analyzed. Thermodynamic models predict that AA1100 will not experience significant phase changes while the more highly alloyed AA6061 and AA5052 may experience significant phase changes. These thermodynamic measurements establish a potential for phase changes during drying, however they do not predict the kinetics for such processes. Phase 1 of the experimental program has initiated and results are forthcoming. The test program will examine aluminum oxidation under anticipated dry storage conditions (temperature and humidity). Examination of localized corrosion due to tight crevices and galvanic contact (with stainless steel) will be included. Finally, water deliquescence will be assessed using thermogravimetric analysis with dynamic humidity profiles. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Final Report: Sampling-Based Algorithms for Estimating Structure in Big Data.

The purpose of this project was to develop sampling-based algorithms to discover hidden struc- ture in massive data sets. Inferring structure in large data sets is an increasingly common task in many critical national security applications. These data sets come from myriad sources, such as network traffic, sensor data, and data generated by large-scale simulations. They are often so large that traditional data mining techniques are time consuming or even infeasible. To address this problem, we focus on a class of algorithms that do not compute an exact answer, but instead use sampling to compute an approximate answer using fewer resources. The particular class of algorithms that we focus on are streaming algorithms , so called because they are designed to handle high-throughput streams of data. Streaming algorithms have only a small amount of working storage - much less than the size of the full data stream - so they must necessarily use sampling to approximate the correct answer. We present two results: * A streaming algorithm called HyperHeadTail , that estimates the degree distribution of a graph (i.e., the distribution of the number of connections for each node in a network). The degree distribution is a fundamental graph property, but prior work on estimating the degree distribution in a streaming setting was impractical for many real-world application. We improve upon prior work by developing an algorithm that can handle streams with repeated edges, and graph structures that evolve over time. * An algorithm for the task of maintaining a weighted subsample of items in a stream, when the items must be sampled according to their weight, and the weights are dynamically changing. To our knowledge, this is the first such algorithm designed for dynamically evolving weights. We expect it may be useful as a building block for other streaming algorithms on dynamic data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A parametric approach to identify synergistic domains of process intensification for reactive separation

Process intensification aims to combine multiple tasks within multi-functional units to drastically improve economic, energy or sustainability metrics of a chemical process. Limited work exists to systematically identify the synergistic domains where intensification outperforms its nonintensified counterpart. In this work, we computationally derive the synergistic domains of a reactive separation system. Specifically, we first postulate general models for both intensified and nonintensified systems. We use these models to generate data to train a ReLU-type artifical neural network (ANN). Further, the trained ReLU-NN model is formulated as a multi-parametric mixed-integer linear program (mp-MILP), and the critical regions of this mp-MILP define the synergistic feasible domains of intensification. We have derived these synergistic domains of vapor–liquid equilibrium (VLE)-based reactive separation for several industrial applications. These synergistic domains enable quick screening of properties that favor intensification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Application configuration selection for energy-efficient execution on multicore systems

Balanced performance and energy consumption are incorporated in the design of modern computer systems. Several runtime factors, such as concurrency levels, thread mapping strategies, and dynamic voltage and frequency scaling (DVFS) should be considered in order to achieve optimal energy efficiency fora workload. Selecting appropriate run-time factors, however, is one of the most challenging tasks because the run-time factors are architecture-specific and workload-specific. And while most existing works concentrate on either static analysis of the workload or run-time prediction results, we present a hybrid two-step method that utilizes concurrency levels and DVFS settings to achieve the energy efficiency configuration for a worldoad. The experimental results based on a Xeon E5620 server with NPB and PARSEC benchmark suites show that the model is able to predict the energy efficient configuration accurately. On average, an additional 10% EDP (Energy Delay Product) saving is obtained by using run-time DVFS for the entire system. An off-line optimal solution is used to compare with the proposed scheme. Finally, the experimental results show that the average extra EDP saved by the optimal solution is within 5% on selective parallel benchmarks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks

Advanced nuclear reactors often exhibit complex thermal-fluid phenomena during transients. To accurately capture such phenomena, a coarse-mesh three-dimensional (3-D) modeling capability is desired for modern nuclear-system code. In the coarse-mesh 3-D modeling of advanced-reactor transients that involve flow and heat transfer, accurately predicting the turbulent viscosity is a challenging task that requires an accurate and computationally efficient model to capture the unresolved fine-scale turbulence. In this work, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor. The model has a coarse mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data to ensure accuracy. A novel neural network architecture, combining a densely connected convolutional network and a long-short-term-memory network, is developed that can efficiently learn from the spatial temporal CFD transient simulation results. The neural network model was trained and optimized on a loss-of flow transient and demonstrated high accuracy in predicting the turbulent viscosity field during the whole transient. The trained model's generalization capability was also investigated on two other transients with different inlet conditions. The study demonstrates the potential of applying the proposed data-driven approach to support the coarse-mesh multi-dimensional modeling of advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Surface characterization and methane activation on SnO x /Cu 2 O/Cu(111) inverse oxide/metal catalysts

To activate methane at low or medium temperatures is a difficult task and a pre-requisite for the conversion of this light alkane into high value chemicals. In this work, we report the preparation and characterizations of novel SnO x /Cu 2 O/Cu(111) interfaces that enable low-temperature methane activation. Scanning tunneling microscopy identified small, well-dispersed SnO x nanoclusters on the Cu 2 O/Cu(111) substrate with an average size of 8 Å, and such morphology was sustained up to 450 K in UHV annealing. Ambient pressure X-ray photoelectron spectroscopy showed that hydrocarbon species (CH x groups), the product of methane activation, were formed on SnO x /Cu 2 O/Cu(111) at a temperature as low as 300 K. An essential role of the SnO x –Cu 2 O interface was evinced by the SnO x coverage dependence. Systems with a small amount of tin oxide, 0.1–0.2 ML coverage, produced the highest concentration of adsorbed CH x groups. Calculations based on density functional theory showed a drastic reduction in the activation barrier for C–H bond cleavage when going from Cu 2 O/Cu(111) to SnO x /Cu 2 O/Cu(111). On the supported SnO x , the dissociation of methane was highly exothermic (ΔE ~ –35 kcal mol –1 ) and the calculated barrier for activation (~20 kcal mol –1 ) could be overcome at 300–500 K, target temperatures for the conversion of methane to high value chemicals.

36 MATERIALS SCIENCE↗
DOE OSTI2026

String-Breaking Dynamics in Quantum Adiabatic and Diabatic Processes

Confinement prohibits isolation of color charges, e.g., quarks, in nature via a process called string breaking : the separation of two charges results in an increase in the energy of a color flux, visualized as a string, connecting those charges. Eventually, creating additional charges is energetically favored, hence breaking the string. Such a phenomenon can be probed in simpler models, including quantum spin chains, enabling enhanced understanding of string-breaking dynamics. A challenging task is to understand how string breaking occurs as time elapses, in an out-of-equilibrium setting. This work establishes the phenomenology of dynamical string breaking induced by a gradual increase of string tension over time. It, thus, goes beyond instantaneous quench processes and enables tracking the real-time evolution of strings in a more controlled setting. We focus on domain-wall confinement in a family of quantum Ising chains. Our results indicate that, for sufficiently short strings and slow evolution, string breaking can be described by the transition dynamics of a two-state quantum system akin to a Landau-Zener process. For longer strings, a more intricate spatiotemporal pattern emerges: the string breaks by forming a superposition of bubbles (domains of flipped spins of varying sizes), which involve highly excited states. We finally demonstrate that string breaking driven only by quantum fluctuations can be realized in the presence of sufficiently long-ranged interactions. This work holds immediate relevance for studying string breaking in quantum-simulation experiments.

Ising model↗
DOE OSTI2024

IRIS-GNN: Leveraging Graph Neural Networks for Scheduling on Truly Heterogeneous Runtime Systems

The diversity of accelerators in computer systems poses significant challenges for software developers, such as managing vendor-specific compiler toolchains, code fragmentation requiring different kernel implementations, and performance portability issues. To address these, the Intelligent Runtime System (IRIS) was developed. IRIS works across various systems, from smartphones to supercomputers, enabling automatic performance scaling based on available accelerators. It introduces abstract tasks for seamless execution transitions between accelerators while ensuring memory consistency and task dependencies. Although IRIS simplifies system details, optimal dynamic scheduling still requires user input to understand workload structures. To address this, we introduce a new scheduling policy for IRIS, termed IRIS-GNN, which is the first IRIS hybrid policy that operates in conjunction with the dynamic policies. This policy employs a Graph-Neural Network (GNN) to conduct Graph Classification of any task graphs submitted to IRIS. This GNN analyzes the structure and attributes of the task graph, categorizing it as either locality, concurrency, or mixed. This classification subsequently guides the selection of the dynamic policy used by IRIS. We provide a comparison of the performance of IRIS-GNN against the complete spectrum of IRIS’s dynamic policies, assess the overhead introduced by the GNN within this scheduling framework, and ultimately explore its practical application in real-world scenarios.

Johnston, Beau↗
DOE OSTI2018

Assessment of In-situ Proppant Placement in SRV Using Through-Fracture Core Sampling at HFTS

The primary aim of this study was to develop robust methods aimed at detecting and quantifying the subsurface proppant distribution as it relates to the completed wells in both the upper and the middle Wolfcamp formations at the HFTS site. There were two sources of proppant deemed useful for this task. The first source was the actual scrapings from the fracture faces that were collected during the core description work. The second sample source was the scrapings and sludge from the cut core tubes collected during core handling. This study highlights the analysis done on the second set of sludge samples. Apart from developing a method for detection and quantification of proppant and other particles contained within this sludge, the study was aimed at the following broad objectives: Determine the spatial distribution of proppant in the created SRV along the cored interval, including size distribution and proppant concentration. Determine if pay zones of interest are sufficiently propped/ stimulated. Determine if fracture and cluster spacing is optimal for thorough lateral reservoir coverage. Determine if well spacing is optimal based on propped SRV length.

Maity, Debotyam↗