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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Workflows Community Summit 2022: A Roadmap Revolution

Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from the execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing (often referred to as a computing continuum) and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large-scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC, enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and applications, as well as enable the publication of workflows and their associated products according to the FAIR principles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers

Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning attacks manipulate training data to induce unreliability of an ML model, whereas backdoor data poisoning attacks maintain system performance unless the MLmodel is presented with an input containing an embedded“trigger” that provides a predetermined response advantageous to the adversary. Our work builds upon prior back-door data-poisoning research for ML image classifiers and systematically assesses different experimental conditions including types of trigger patterns, persistence of trigger patterns during retraining, poisoning strategies, architectures (ResNet-50, NasNet, NasNet-Mobile), datasets (Flowers, CIFAR-10), and potential defensive regularization techniques (Contrastive Loss, Logit Squeezing, Manifold Mixup,Soft-Nearest-Neighbors Loss). Experiments yield four key findings. First, the success rate of backdoor poisoning at-tacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique. Second, we find that poisoned models are hard to detect through performance inspection alone. Third, regularization typically reduces backdoor success rate, although it can have no effect or even slightly increase it, depending on the form of regularization. Finally, backdoors inserted through data poisoning can be rendered ineffective after just a few epochs of additional training on a small set of clean data without affecting the model’s performance.

Truong, Loc T.↗
DOE OSTI2021

Performance evaluation of a Terry GS-2 steam impulse turbine with air-water mixtures

Terry steam turbines are widely used in various industries because of their robust design. Within the nuclear power generation industry, they are used in the Reactor Core Isolation Cooling System to remove decay heat during reactor isolation events. During the Fukushima Daiichi nuclear power station disaster in Japan in 2011, the Reactor Core Isolation Cooling System and associated Terry turbine operated for over 70 hours in Unit 2; this runtime is well beyond the expected operating duration. Theories suggest the turbine was subjected to a two-phase inlet flow, which could degrade the turbine performance. In this work, an experimental test rig was constructed to test a full-scale Terry model GS-2 steam turbine under two-phase air/water flows. Steady-state efficiency and torque performance maps of the turbine were developed over a range of turbine inlet pressures (1.38–4.83 bar or 20–70 psia), air mass fractions (0.05–1.0) and rotational speeds up to 4000 RPM. Furthermore, turbine performance followed expected trends with torque varying linearly and efficiency varying quadratically with rotational speed. In addition, high-speed images of the two-phase flow entering the turbine were also analyzed to understand how changes in inlet pressure and air mass fraction affect the flow regime and homogenization. The present tests with air–water two-phase mixtures are an important step towards providing an understanding of the full-scale Terry turbine’s behavior and performance curves under two-phase conditions. The results of this work will be combined with air/water and steam/water data gathered using a small-scale Terry ZS-1 steam turbine in order to understand the scaling relationship between large and small size Terry turbines and fluid pairs. The combined data set will enable further development of analytical models over a wide range of conditions and may be used to provide technical justification for expanded use of the Terry turbines in nuclear power plant safety systems and other systems.

42 ENGINEERING↗
DOE OSTI2022

DIAS: A Data-Informed Active Subspace Regularization Framework for Inverse Problems

This paper presents a regularization framework that aims to improve the fidelity of Tikhonov inverse solutions. At the heart of the framework is the data-informed regularization idea that only data-uninformed parameters need to be regularized, while the data-informed parameters, on which data and forward model are integrated, should remain untouched. We propose to employ the active subspace method to determine the data-informativeness of a parameter. The resulting framework is thus called a data-informed (DI) active subspace (DIAS) regularization. Four proposed DIAS variants are rigorously analyzed, shown to be robust with the regularization parameter and capable of avoiding polluting solution features informed by the data. They are thus well suited for problems with small or reasonably small noise corruptions in the data. Furthermore, the DIAS approaches can effectively reuse any Tikhonov regularization codes/libraries. Though they are readily applicable for nonlinear inverse problems, we focus on linear problems in this paper in order to gain insights into the framework. Various numerical results for linear inverse problems are presented to verify theoretical findings and to demonstrate advantages of the DIAS framework over the Tikhonov, truncated SVD, and the TSVD-based DI approaches.

Nguyen, Hai↗
DOE OSTI2022

Structural stability of thin overhanging walls during material extrusion additive manufacturing of thermoset-based ink

Recent developments have enabled material extrusion additive manufacturing of thermoset-based composite inks on the large scale. In addition, printing out-of-plane components is of broad interest to the polymer material extrusion community. Here, we address some of the challenges associated with both large-scale and out-of-plane thermoset material extrusion additive manufacturing by studying the height at which thin overhanging walls collapse. Walls at a range of overhang angles were printed until they collapsed. An optical camera captured the profile of each wall throughout the print, allowing the collapse height to be identified and the geometric fidelity to the programmed angle to be evaluated. Using previously measured rheological properties, predictive models were generated to approximate the collapse height and profile of the deflected walls. First, an analytical model was created to predict the height at which the walls would yield. The analytical model assumes the walls exhibit a perfectly linear profile; however, experiments proved this assumption to be false. Therefore, a finite element simulation was developed to account for the elastic deflection that occurs during printing. The finite element simulation predicts both the yield height and the deflected profile after the deposition of each layer. For the properties of the thermoset ink used here, the yield height predicted by the analytical model and finite element simulation are virtually identical. These predictions match experimental data reasonably well, but minor errors are observed. Accounting for the fully plastic moment appears to explain the small mismatch between experimental data and predictions. Additionally, the finite element simulation provides an excellent prediction of the deflected profile before the wall begins to collapse. Finally, by demonstrating that the collapse height and deflected profile of thin overhanging walls can be predicted, this work illustrates how the soft viscoelastic properties of thermoset-based composite inks limit the scale of a key feature required to print some nonplanar components. It also provides a basis to tailor in-process curing systems to suppress deflection and collapse of thin overhanging walls.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Advancing river corridor science beyond disciplinary boundaries with an inductive approach to catalyse hypothesis generation

Abstract A unified conceptual framework for river corridors requires synthesis of diverse site‐, method‐ and discipline‐specific findings. The river research community has developed a substantial body of observations and process‐specific interpretations, but we are still lacking a comprehensive model to distill this knowledge into fundamental transferable concepts. We confront the challenge of how a discipline classically organized around the deductive model of systematically collecting of site‐, scale‐, and mechanism‐specific observations begins the process of synthesis. Machine learning is particularly well‐suited to inductive generation of hypotheses. In this study, we prototype an inductive approach to holistic synthesis of river corridor observations, using support vector machine regression to identify potential couplings or feedbacks that would not necessarily arise from classical approaches. This approach generated 672 relationships linking a suite of 157 variables each measured at 62 locations in a fifth order river network. Eighty four percent of these relationships have not been previously investigated, and representing potential (hypothetical) process connections. We document relationships consistent with current understanding including hydrologic exchange processes, microbial ecology, and the River Continuum Concept, supporting that the approach can identify meaningful relationships in the data. Moreover, we highlight examples of two novel research questions that stem from interpretation of inductively‐generated relationships. This study demonstrates the implementation of machine learning to sieve complex data sets and identify a small set of candidate relationships that warrant further study, including data types not commonly measured together. This structured approach complements traditional modes of inquiry, which are often limited by disciplinary perspectives and favour the careful pursuit of parsimony. Finally, we emphasize that this approach should be viewed as a complement to, rather than in place of, more traditional, deductive approaches to scientific discovery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder

Real-time data collection and analysis in large experimental facilities present a great challenge across multiple domains, including high energy physics, nuclear physics, and cosmology. To address this, machine learning (ML)-based methods for real-time data compression have drawn significant attention. However, unlike natural image data, such as CIFAR and ImageNet that are relatively small-sized and continuous, scientific data often come in as three-dimensional 3D data volumes at high rates with high sparsity (many zeros) and non-Gaussian value distribution. This makes direct application of popular ML compression methods, as well as conventional data compression methods, suboptimal. To address these obstacles, this work introduces a dual-head autoencoder to resolve sparsity and regression simultaneously, called Bicephalous Convolutional AutoEncoder (BCAE). This method shows advantages both in compression fidelity and ratio compared to traditional data compression methods, such as MGARD, SZ, and ZFP. To achieve similar fidelity, the best performer among the traditional methods can reach only half the compression ratio of BCAE. Moreover, a thorough ablation study of the BCAE method shows that a dedicated segmentation decoder improves the reconstruction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

sas-temper

Modeling remains a considerable challenge for practitioners of SAXS and SANS because the materials being studied are not highly ordered, the length scales being studied are large, and the information content of the data is low. Data analysis is both challenging and time consuming. Novices often rely on the assistance of an expert, such as the instrument scientist who supported them at the facility where the experiment was performed, to analyze their data. The high flux provided by modern facilities makes it possible to study dozens of samples or sample conditions during a single trip. Ultimately, the high throughput of modern instruments limits access to instrument scientists. A bottleneck in the research effort results that reduces facility productivity. Sas-temper seeks to address this problem, as well as the intrinsic difficulties of being confident in non-linear least squared fitting of data, by providing tools that automate much of the manual process of initial data fitting and refinement, as well as by providing tools for characterizing the nature of the parameter space that fits the measured data. The tools provided by sas-temper help small-angle scattering practitioners transition data into results.

Heller, William [Oak Ridge National Lab. (ORNL), O↗
DOE OSTI2024

Compositional Variation Tests on DuraMelter 100 with LAW Sub-Envelope C1 Feed (LAWC22) Glass in Support of the LAW Pilot Melter (Final Report)

The Vitreous State Laboratory at The Catholic University of America (VSL) is developing and testing glass formulations for RPP-WTP waste envelopes to provide data to meet the RPP-WTP contract requirements and to support system design activities. That work is based upon small-scale batch melts (“crucible melts”) using waste envelope simulants. Selected formulations have also been tested in small-scale continuously-fed joule-heated melters (DM10 and DM100 systems) and, ultimately, in the LAW Pilot Melter. Such melter tests provide information on key process factors such as feed processing behavior, dynamic effects during processing, sulfate incorporation, processing rates, off-gas amounts and compositions, foaming control, etc., that cannot be reliably obtained from crucible melts. This sequential scale-up approach in the vitrification testing program ensures that maximum benefit is obtained from the more costly pilot-scale tests and that the most effective use is made of that resource. The principal objective of the work described in this report was to collect the necessary small-scale melter test data with a LAW Sub-Envelope C1 waste simulant in order to support the next series of tests with the same melter feed on the LAW Pilot Melter. This work was conducted under a corresponding Test Specification and Test Plan.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery

Abstract We autonomously directed a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or “drone” to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, were varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones.

42 ENGINEERING↗
DOE OSTI2022

First measurement of the $Λ–Ξ$ interaction in proton–proton collisions at the LHC

The first experimental information on the strong interaction between $Λ$ and $Ξ$ - strange baryons is presented in this Letter. The correlation function of $Λ–Ξ$ - and $\overline{Λ}$ –$\overline{Ξ}$ + pairs produced in high-multiplicity proton–proton (pp) collisions at $\sqrt{s}$=13 TeV at the LHC is measured as a function of the relative momentum of the pair. The femtoscopy method is used to calculate the correlation function, which is then compared with theoretical expectations obtained using a meson exchange model, chiral effective field theory, and Lattice QCD calculations close to the physical point. Data support predictions of small scattering parameters while discarding versions with large ones, thus suggesting a weak $Λ–Ξ$ - interaction. The limited statistical significance of the data does not yet allow one to constrain the effects of coupled channels like $Σ–Ξ$ and $N–Ω$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Assembly of JAZ–JAZ and JAZ–NINJA complexes in jasmonate signaling

Jasmonates (JAs) are plant hormones with crucial roles in development and stress resilience. They activate MYC transcription factors by mediating the proteolysis of MYC inhibitors called JAZ proteins. In the absence of JA, JAZ proteins bind and inhibit MYC through the assembly of MYC–JAZ–Novel Interactor of JAZ (NINJA)–TPL repressor complexes. However, JAZ and NINJA are predicted to be largely intrinsically unstructured, which has precluded their experimental structure determination. Through a combination of biochemical, mutational, and biophysical analyses and AlphaFold-derived ColabFold modeling, we characterized JAZ–JAZ and JAZ–NINJA interactions and generated models with detailed, high-confidence domain interfaces. We demonstrate that JAZ, NINJA, and MYC interface domains are dynamic in isolation and become stabilized in a stepwise order upon complex assembly. By contrast, most JAZ and NINJA regions outside of the interfaces remain highly dynamic and cannot be modeled in a single conformation. Our data indicate that the small JAZ Zinc finger expressed in Inflorescence Meristem (ZIM) motif mediates JAZ–JAZ and JAZ–NINJA interactions through separate surfaces, and our data further suggest that NINJA modulates JAZ dimerization. This study advances our understanding of JA signaling by providing insights into the dynamics, interactions, and structure of the JAZ–NINJA core of the JA repressor complex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

SITCOMTN-165: Surface brightness profiles around massive galaxies in LSSTComCam data

Low surface brightness photometry of massive galaxies can probe the extended stellar halos and intracluster light but is often limited by background subtraction and masking systematics. Here we present radial surface brightness profiles for the brightest cluster galaxy of Abell 360 and 2dFGRS TGS323Z113 using Rubin Observatory Commissioning Camera (LSSTComCam) Data Preview 1 in the g, r, and z bands. Our results show that the LSSTComCam data can go down to ~28 mag arcsec^-2 in the r band. Comparison to DECaLS measurements processed with Legacyhalos shows good agreement of surface brightness profiles at small radii while the LSSTComCam data extend deeper at larger radii.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Unveiling mesoscopic structures in distorted lamellar phases through deep learning-based small angle neutron scattering analysis

Hypothesis: The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases. Experiments and Simulations: This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system. Findings: The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. In conclusion, this research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Synthetic Data Generation Using Machine Learning

Robust machine learning techniques for image analysis require a substantial amount of data to yield confident results. In the nuclear domain, data scarcity is a substantial challenge because there are so few facilities worldwide. This research focuses on being able alleviate the data scarcity problem by generating synthetic data to bridge the gap between large and small datasets. This work achieves that goal using a Generative Adversarial Network (GAN) architectural approach, by training a model on real-world data and expands that small dataset through synthetic data amendments. Model performance is impacted by the size of the real-world dataset and the number of training epochs utilized. This means that 1) It is important to develop your GAN to be optimized with the specific data type, and 2) approaches taken when training the GAN should be specialized to encompass important aspects of the dataset that it is generating. By taking a step to improve dataset sizes in this way, the gap between models trained by parties with significant amount of data and those without access to large data, closes, allowing for robust analyses of satellite imagery for nuclear domain applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Experience with Rucio in the wider HEP community

Managing the data of scientific projects is an increasingly complicated challenge, which was historically met by developing experiment-specific solutions. However, the ever-growing data rates and requirements of even small experiments make this approach very difficult, if not prohibitive. In recent years, the scientific data management system Rucio has evolved into a successful open-source project that is now being used by many scientific communities and organisations. Rucio is incorporating the contributions and expertise of many scientific projects and is offering common features useful to a diverse re-search community. This article describes the recent experiences in operating Rucio, as well as contributions to the project, by ATLAS, Belle II, CMS, ES-CAPE, IGWN, LDMX, Folding@Home, and the UK's Science and Technology Facilities Council (STFC).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

DENSS-Multiple

It takes multiple data sets from small-angle neutron scattering experiment at different contrast conditions to synthetically use DENSS algorithm (https://www.nature.com/articles/nmeth.4581) to combine all the information from each data set. As the multiple data set contains more information than a single data, all information in the multiple data set are collectively contribute to the final single synthetic structure that represents a molecular particle structure.

Qian, Shuo (000000024842828X)↗
DOE OSTI2020

Transfer Learning for HVAC System Fault Detection

Faults in HVAC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gaining in popularity. Yet the lack of labeled data, such as normal versus faulty operational status, has slowed the application of machine learning to HVAC systems. In addition, for any particular building, there may be an insufficient number of observed faults over a reasonable amount of time for training. To overcome these challenges, we present a transfer methodology for a novel Bayesian classifier designed to distinguish between normal operations and faulty operations. The key is to train this classifier on a building with a large amount of sensor and fault data (for example, via simulation or standard test data) then transfer the classifier to a new building using a small amount of normal operations data from the new building. We demonstrate a proof-of-concept for transferring a classifier between architecturally similar buildings in different climates and show few samples are required to maintain classification precision and recall.

transfer learning, Building HVAC, Bayesian framewo↗