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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Considerations for using Privacy Preserving Machine Learning Techniques for Safeguards

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A reduced-order model for nonlinear radiative transfer problems based on moment equations and POD-Petrov-Galerkin projection of the normalized Boltzmann transport equation

A data-driven projection-based reduced-order model (ROM) for nonlinear thermal radiative transfer (TRT) problems is presented. The TRT ROM is formulated by (i) a hierarchy of low-order quasidiffusion (aka variable Eddington factor) equations for moments of the radiation intensity and (ii) the normalized Boltzmann transport equation (BTE). The multilevel system of moment equations is derived by projection of the BTE onto a sequence of subspaces which represent elements of the phase space of the problem. Exact closure for the moment equations is provided by the Eddington tensor. A Petrov-Galerkin (PG) projection of the normalized BTE is formulated using a proper orthogonal decomposition (POD) basis representing the normalized radiation intensity over the whole phase space and time. The Eddington tensor linearly depends on the solution of the normalized BTE. By linear superposition of the POD basis functions, a low-rank expansion of the Eddington tensor is constructed with coefficients defined by the PG projected normalized BTE. The material energy balance (MEB) equation is coupled with the effective gray low-order equations which exist on the same dimensional scale as the MEB equation. The resulting TRT ROM is structure and asymptotic preserving. A detailed analysis of the ROM is performed on the classical Fleck-Cummings (F-C) TRT multigroup test problem in 2D geometry. Numerical results are presented to demonstrate the ROM's effectiveness in the simulation of radiation wave phenomena. Importantly, the ROM is shown to produce solutions with sufficiently high accuracy while using low-rank approximation of the normalized BTE solution. Essential physical characteristics of supersonic radiation wave are preserved in the ROM solutions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2005

Concrete Model Checking with Abstract Matching and Refinement

We propose an abstraction-based model checking method which relies on refinement of an under-approximation of the feasible behaviors of the system under analysis. The method preserves errors to safety properties, since all analyzed behaviors are feasible by definition. The method does not require an abstract transition relation to he generated, but instead executes the concrete transitions while storing abstract versions of the concrete states, as specified by a set of abstraction predicates. For each explored transition. the method checks, with the help of a theorem prover, whether there is any loss of precision introduced by abstraction. The results of these checks are used to decide termination or to refine the abstraction, by generating new abstraction predicates. If the (possibly infinite) concrete system under analysis has a finite bisimulation quotient, then the method is guaranteed to eventually explore an equivalent finite bisimilar structure. We illustrate the application of the approach for checking concurrent programs. We also show how a lightweight variant can be used for efficient software testing.

Pasareanu Corina S.↗
DOE OSTI2020

Nevada Higher Education Benefits from US Department of Energy (DOE) Environmental Management (EM) Nevada Program's Transfer of Geologic Samples - 20442

The Nevada National Security Site (NNSS), formerly the Nevada Test Site, was the location of 100 historic atmospheric and 828 historic underground nuclear tests from 1951 to 1992. Related to this historic nuclear testing, geologic and hydrologic studies of the site were conducted utilizing the skills and the expertise of the U.S. Geological Survey (USGS) and national laboratories (Los Alamos National Laboratory, Lawrence Livermore National Laboratory), who were principal in leading the development of weapons and were responsible for specific underground testing programs. The subsurface samples and technical/scientific data associated with NNSS geologic studies (past and present) are preserved and stored at the USGS Mercury Core Library and Data Center located at the NNSS. Currently, the facility stores over 2,000,000 linear feet of cores and cuttings from more than 2,600 drill holes that can be accessed for study. Most of the samples and historic geologic work was focused directly on subsurface geologic settings that relate directly to historic underground nuclear testing. Since 1992, the United States has observed a unilateral moratorium on full-scale nuclear testing, and the U.S. Department of Energy (DOE) Environmental Management (EM) Nevada Program is now responsible for hydrogeologic characterization of the potential impacts to the natural groundwater systems that may have resulted from historic underground nuclear tests. As part of this effort, the EM Nevada Program Underground Test Area (UGTA) Activity has drilled and completed over 58 deep (2,000 - 7,000 ft.) characterization wells, totaling in excess of 170,000 linear feet of cuttings and core samples. Cutting samples were collected as triplicate samples for each respective depth interval, to account for potential later nondestructive/destructive analysis and to preserve samples for regulatory purposes. Recently, it was recognized by the EM Nevada Program that opportunities may exist to reduce the cost and floor space required for the storage of geologic samples at the Mercury Core Library without impacting the integrity and representative nature of the samples necessary for project execution. An initiative was sponsored by EM Nevada Program to evaluate several options: 1) disposal of a portion of cuttings and cores in a land fill setting; 2) reduction in sample volume through skeletonizing core and cuttings from wells to a representative but much smaller number of samples; and 3) solicit potential interest in the academic community where other geoscientists could freely access the samples for studies. The Nevada state university system through the Nevada Bureau of Mines and Geology responded positively to the opportunity to receive these NNSS samples from the EM Nevada Program. In June 2019, approximately 17,000 geologic samples, representing greater than 170,000 linear feet of drilling and weighing over 20,000 pounds, were shipped from the NNSS to the Great Basin Science Sample and Records Library located in Reno, Nevada. The benefits resulting from this transfer were realized by the EM Nevada Program in terms of cost and space savings for geologic sample storage at the NNSS. The transfer did not impact the EM Nevada Program mission as access to representative geologic samples for regulatory and scientific purposes was preserved. Moreover, the Nevada Bureau of Mines and Geology acquired a significant resource of subsurface geologic samples and supporting technical data to support academic and scientific studies in a complex volcanic setting in southwest Nevada. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2020

Prokaryotic and Fungal Characterization of the Facilities Used to Assemble, Test, and Launch the OSIRIS-REx Spacecraft

To characterize the ATLO (Assembly, Test, and Launch Operations) environment of the OSIRIS-REx spacecraft, we analyzed 17 aluminum witness foils and two blanks for bacterial, archaeal, fungal, and arthropod DNA. Under NASA’s Planetary Protection guidelines, OSIRIS-REx is a Category II outbound, Category V unrestricted sample return mission. As a result, it has no bioburden restrictions. However, the mission does have strict organic contamination requirements to achieve its primary objective of returning pristine carbonaceous asteroid regolith to Earth. Its target, near-Earth asteroid (101955) Bennu, is likely to contain organic compounds that are biologically available. Therefore, it is useful to understand what organisms were present during ATLO as part of the larger contamination knowledge effort—even though it is unlikely that any of the organisms will survive the multi-year deep space journey. Even though these samples of opportunity were not collected or preserved for DNA analysis, we successfully amplified bacterial and archaeal DNA (16S rRNA gene) from 16 of the 17 witness foils containing as few as 7 ± 3 cells per sample. Fungal DNA (ITS1) was detected in 12 of the 17 witness foils. Despite observing arthropods in some of the ATLO facilities, arthropod DNA (COI gene) was not detected. We observed 1,009 bacterial and archaeal sOTUs (sub-operational taxonomic units, 100% unique) and 167 fungal sOTUs across all of our samples (25–84 sOTUs per sample). The most abundant bacterial sOTU belonged to the genus Bacillus. This sOTU was present in blanks and may represent contamination during sample handling or storage. The sample collected from inside the fairing just prior to launch contained several unique bacterial and fungal sOTUs that describe previously uncharacterized potential for contamination during the final phase of ATLO. Additionally, fungal richness (number of sOTUs) negatively correlates with the number of carbon-bearing particles detected on samples. The total number of fungal sequences positively correlates with total amino acid concentration. These results demonstrate that it is possible to use samples of opportunity to characterize the microbiology of low-biomass environments while also revealing the limitations imposed by sample collection and preservation methods not specifically designed with biology in mind.

Aaron B. Regberg↗
DOE OSTI2025

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Critical heat flux (CHF) is a key quantity in nuclear system modeling due to its impact on heat transfer, safety margins, and reactor performance. This study develops and validates an uncertainty-aware hybrid modeling approach that combines machine learning with physics-based models to predict CHF in cases of dryout. The Biasi and Bowring empirical correlations were paired with three ML uncertainty quantification (UQ) techniques: deep neural network (DNN) ensembles, Bayesian neural networks (BNNs), and deep Gaussian processes (DGPs). A pure ML model without a base model was evaluated for comparison. Model performance was assessed under plentiful (7,350 points) and limited (9 points) training data scenarios using parity, uncertainty distributions, and calibration curves. Results show that the Biasi hybrid DNN ensemble achieved the best overall performance, with a mean absolute relative error of 1.846%, and well-calibrated uncertainty estimates. The BNN-based hybrids showed slightly higher error (2.14%) but superior uncertainty calibration. DGP models underperformed, with over 6% error and poor uncertainty calibration. All hybrid models outperformed pure machine learning configurations, demonstrating resistance against data scarcity. These findings indicate that hybrid modeling significantly improves predictive accuracy, interpretability, and resilience to data scarcity. The integration of uncertainty awareness provides actionable confidence in CHF predictions, which is vital for safety-critical decisions in nuclear applications. This hybrid approach offers a viable pathway for deploying ML models in reactor analysis tools while preserving domain knowledge and physical consistency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

The use of synchrotron X-ray fluorescent imaging to study distribution and content of elements in chemically fixed single cells: a case study using mouse pancreatic beta-cells

Synchrotron X-ray fluorescence microscopy (SXRF) presents a valuable opportunity to study the metallome of single cells because it simultaneously provides high-resolution subcellular distribution and quantitative cellular content of multiple elements. Different sample preparation techniques have been used to preserve cells for observations with SXRF, with a goal to maintain fidelity of the cellular metallome. In this case study, mouse pancreatic beta-cells have been preserved with optimized chemical fixation. We show that cell-to-cell variability is normal in the metallome of beta-cells due to heterogeneity and should be considered when interpreting SXRF data. Additionally, we determined the impact of several immunofluorescence (IF) protocols on metal distribution and quantification in chemically fixed beta-cells and found that the metallome of beta-cells was not well preserved for quantitative analysis. However, zinc and iron qualitative analysis could be performed after IF with certain limitations. To help minimize metal loss using samples that require IF, we describe a novel IF protocol that can be used with chemically fixed cells after the completion of SXRF.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1977

Remote water-monitoring system

General-purpose, water-quality sampling process detects microorganisms and integrates sampling, sample preservation, and sample analysis. System overcomes difficulties in correlation of surface measurements with remote measurements and minimizes human error in handling of samples and instrumentation.

Grana, D. C.↗
NASA NTRS2005

Hadean Crustal Processes Revealed from Oxygen Isotopes and U-Th-Pb Depth Profiling of Pre-4.0 Ga Detrital Zircons from Western Australia

Because physical and chemical processes of the past are determined from analysis of a preserved geologic record, little is known about terrestrial crustal processes of the first 500 Ma during the so-called Hadean Eon. What is known from direct measurements has been derived almost exclusively from the study of greater than 4.0 Ga detrital zircons from the Jack Hills, Western Australia. The geochemistry of these zircons has direct application to understanding the origin and evolution of the rocks during the Hadean because: (i) U-Th-Pb age determinations by ion microprobe suggests the presence of crust as early as 4.37 Ga, or shortly after lunar formation; (ii) high-resolution U-Th-Pb zircon depth profiles reported here reveal several episodes of zircon growth in the Hadean previously unrecognized; (iii) core regions of pre-4.0 Ga zircons with igneous compositions are enriched in O-18 and contain metaluminous and peraluminous mineral inclusions, both features indicative of S-type grainitod protoliths. Study of these ancient zircons provides a unique window into the first half billion years that permits assessment of the potential of the Hadean Earth to host an emergent biosphere.

Trail, D.↗
DOE OSTI2020

NDMAS System and Process Description

The U. S. Department of Energy (DOE) has made a significant investment in research to develop the next generation of reactor technologies as well as to improve the performance and lengthen the life cycle of existing nuclear reactors. Data collected to demonstrate new concepts may also be used in the future to support licensing of these technologies. Provenance of these data must be preserved. The Nuclear Data Management and Analysis System (NDMAS) was established to manage and preserve data collected by fuels and materials research conducted by the high-temperature, gas-cooled reactor program. The scope of NDMAS is expanding to include other nuclear research programs that have the shared need to preserve the provenance of research data. Nuclear research funded by DOE is conducted by Idaho National Laboratory (INL), universities, other national laboratories, foreign research partners, and private companies. This research will generate a large amount of data from a variety of sources over a period of many years. Managing the data generated by the research and development projects presents a significant challenge for retaining data integrity and availability.

+↗
NASA NTRS2014

A Technique for Mapping Characteristic Lengths to Preserve Energy Dissipated via Strain Softening in a Multiscale Analysis

It is often advantageous to account for the microstructure of the material directly using multiscale modeling. For computational tractability, an idealized repeating unit cell (RUC) is used to capture all of the pertinent features of the microstructure. Typically, the RUC is dimensionless and depends only on the relative volume fractions of the different phases in the material. This works well for non-linear and inelastic behavior exhibiting a positive-definite constitutive response. Although, once the material exhibits strain softening, or localization, a mesh objective failure theories, such as smeared fracture theories, nodal and element enrichment theories (XFEM), cohesive elements or virtual crack closure technique (VCCT), can be utilized at the microscale, but the dimensions of the RUC must then be defined. One major challenge in multiscale progressive damage modeling is relating the characteristic lengths across the scales in order to preserve the energy that is dissipated via localization at the microscale. If there is no effort to relate the size of the macroscale element to the microscale RUC, then the energy that is dissipated will remain mesh dependent at the macroscale, even if it is regularized at the microscale. Here, a technique for mapping characteristic lengths across the scales is proposed. The RUC will be modeled using the generalized method of cells (GMC) micromechanics theory, and local failure in the matrix constituent subcells will be modeled using the crack band theory. The subcell characteristic lengths used in the crack band calculations will be mapped to the macroscale finite element in order to regularize the local energy in a manner consistent with the global length scale. Examples will be provided with and without the regularization, and they will be compared to a baseline case where the size and shape of the element and RUC are coincident (ensuring energy is preserved across the scales).

softening↗
DOE OSTI2022

Rays for Roots - Integrating Backscatter X-Ray Phenotyping, Modeling and Genetics to Increase Carbon Sequestration and Switchgrass Resource Use (Final Report)

To increase carbon (C) deposition in the soil and enhance crop resource use efficiency, characterizing root form and function is essential. Several root and soil traits have been linked to increased root-to-soil C transfer. Technology that could provide high-resolution characterization of many of these traits in field conditions would revolutionize our ability to study and understand how to increase C sequestration. In this effort, we developed an initial early prototype backscatter X-ray system for non-destructive imaging of root traits. We collected initial backscatter X-ray data in field and lab settings and carried out early analysis of these data. Along with this prototype, we also developed a suite of root phenotyping approaches including advanced minirhizotron image analysis, soil core imaging, and mesocosm imaging. Minirhizotron (MR) tubes are clear tubes inserted into the soil in the field and used to image roots and the surrounding soil. Our team has developed deep learning-based methods that can segment roots from soil that can learn from imprecise image-level labels. The ability to learn or fine-tune our deep learning algorithms from image-level labels allows easier and faster application of these approaches to new locations and new plant species. We have successfully implemented and applied our MR analysis approaches to thousands of switchgrass MR images collected across geographical regions. An advantage of MR imaging is the ability to collect root and soil images over time. Our soil core analysis included collecting hundreds of soil core samples from harvested switchgrass fields and imaging these cores with both X-ray CT and backscatter X-ray imaging. Initial segmentation approaches for the X-ray CT images of these cores have been developed and applied. An advantage of soil core analysis is that it preserves the three-dimensional structures of the roots and soil in the core collected. Our group also developed photogrammetry-based mesocosm root imaging and phenotyping approaches. In this approach, a plant was grown in a large mesocosm with a three-dimensional grid of thin supporting lines inserted throughout the mesocosm. After the plant (and, correspondingly, the root architecture is grown and established) the soil media was removed and the supporting lines approximately preserved the three-dimensional root architecture. Then, we applied photogrammetry techniques to create a three-dimensional digital representation of the root architecture for which we developed analysis algorithms including skeletonization. We carried out our phenotyping development with powerful switchgrass resources and physiological and agroecosystem modeling to deliver novel technology. This project contributes to multiple ARPA-E missions including reduction of foreign imports of energy, reduction of energy-related emissions including greenhouse gases, and ensuring that the United States maintains a technological lead in developing and deploying advanced energy technology. Furthermore, the developed tools could transform public and private plant breeding and could be broadly applicable to other crops and, potentially, other application areas. Our team of engineers, plant and soil scientists, and modelers i) developed an early prototype backscatter X-ray platform that can operate in field conditions; ii) developed a suite of root phenotyping and characterization approaches as described above; iii) developed and carried out plant biology and physiology roots studies and; iv) developed and implemented mechanistic physiological modeling.

42 ENGINEERING↗
NASA NTRS1980

Vortex methods for two- and three-dimensional flow simulations

The point vortex and vortex blob methods for two dimensional flows are presented. Several results are discussed concerning the numerical analysis of the latter scheme, e.g., the preservation of globally conserved quantities and the analysis of the spatial discretization error resulting from the convection of fixed blobs of vorticity. An application to the two dimensional mixing layer is briefly described. The contour dynamics method is also discussed. The simulation of three dimensional flows with vortex methods is discussed. A natural way to represent the vorticity is in the form of closed tubes of filaments of vorticity, although other schemes are examined. Applications to aircraft trailing vortices and to a turbulent spot in a laminar boundary layer are presented. Hybrid schemes that use an Eulerian mesh to solve the Poisson equation for the velocity field are discussed. The goal of these schemes is to avoid the high cost of the Biot-Savart integration if many vortex elements are used while enjoying most of the advantages of pure Lagrangian schemes.

Leonard, A.↗
DOE OSTI2023

TopoSZ: Preserving Topology in Error-Bounded Lossy Compression

Existing error-bounded lossy compression techniques control the pointwise error during compression to guarantee the integrity of the decompressed data. However, they typically do not explicitly preserve the topological features in data. When performing post hoc analysis with decompressed data using topological methods, preserving topology in the compression process to obtain topologically consistent and correct scientific insights is desirable. In this paper, we introduce TopoSZ, an error-bounded lossy compression method that preserves the topological features in 2D and 3D scalar fields. Specifically, we aim to preserve the types and locations of local extrema as well as the level set relations among critical points captured by contour trees in the decompressed data. The main idea is to derive topological constraints from contour-tree-induced segmentation from the data domain, and incorporate such constraints with a customized error-controlled quantization strategy from the SZ compressor (version 1.4). In conclusion, our method allows users to control the pointwise error and the loss of topological features during the compression process with a global error bound and a persistence threshold.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis

Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this paper, we propose a technique that addresses storage costs while improving post-analysis accuracy through spatiotemporal adaptive, error-controlled lossy compression. We investigate the trade-off between data precision and temporal output rates, revealing that reducing data precision and increasing timestep frequency lead to more accurate analysis outcomes. Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor. To evaluate our approach, we conduct experiments using the well-known E3SM climate simulation code and apply our method to compress variables used for cyclone tracking. Our results show a significant reduction in storage size while enhancing the quality of cyclone tracking analysis, both quantitatively and qualitatively, in comparison to the prevalent timestep decimation approach. Compared to three state-of-the-art lossy compressors lacking feature preservation capabilities, our adaptive compression framework improves perfectly matched cases in TC tracking by 26.4-51.3% at medium compression ratios and by 77.3-571.1% at large compression ratios, with a merely 5–11% computational overhead.

Gong, Qian↗
DOE OSTI2022

A Trajectory-Driven Algorithm for Differentiating SRB Measures on Unstable Manifolds

Sinai-Ruelle-Bowen (SRB) measures are limiting stationary distributions describing the statistical behavior of chaotic dynamical systems. Directional derivatives of SRB measure densities conditioned on unstable manifolds are critical in the sensitivity analysis of hyperbolic chaos. These derivatives, known as the SRB density gradients, are by-products of the regularization of Lebesgue integrals appearing in the original linear response expression. In this paper, we propose a novel trajectory- driven algorithm for computing the SRB density gradient defined for systems with high-dimensional unstable manifolds. We apply the concept of measure preservation together with the chain rule on smooth manifolds. Due to the recursive one-step nature of our derivations, the proposed procedure is memory-efficient and can be naturally integrated with existing Monte Carlo schemes widely used in computational chaotic dynamics. Here, we numerically show the exponential convergence of our scheme, analyze the computational cost, and present its use in the context of Monte Carlo integration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

2020 Multiscale Microbial Dynamics Modeling Course

The 2020 Multiscale Microbial Dynamics course is adapted from the virtual 2020 Mutliscale Microbial Dynamics Summer School that was hosted by Environmental Molecular Sciences Laboratory (EMSL), a U.S. Department of Energy (DOE) science user facility located on the Pacific Northwest National Laboratory (PNNL) campus, in collaboration with the Joint Genome Institute (JGI) and the DOE Systems Biology Knowledgebase (KBase). The course course covers how to incorporate microbial metagenomic and environmental metabolite data from watershed ecosystems into metabolic and community modeling using computational frameworks, such as KBase and PFLOTRAN. The curriculum includes lectures and software and data analysis tutorials. All materials are freely accessible to the community as part of the 2020 Microbial Dynamics Summer School Organization in KBase.

54 ENVIRONMENTAL SCIENCES↗