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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reduction kinetics of hematite powder using argon/hydrogen plasma with prospects for near net shaping of sustainable iron

Direct reduction of iron ore using hydrogen plasma is being explored as a potential solution to decarbonize the iron and steel sector. The current state-of-the-art demonstrated reduction of hematite pellets via hydrogen plasma using Ar + 10% H 2 but had slow reduction kinetics, requiring 30 minutes of plasma exposure for complete reduction. Here we show that using hematite in a powder form, easily obtainable from beneficiated ore, results in 10× faster kinetics using plasma generated from Ar + 2% H 2 shielding gas compared to the current state-of-the art. Additionally, the increased kinetics using powders and a dilute hydrogen concentration can enable the use of advanced manufacturing techniques like blown powder directed energy deposition using a plasma tungsten arc welding torch to manufacture near net shape components directly from the ore concentrates. This ore to part approach will also reduce the emissions associated with downstream processes like rolling, forging, and machining, thereby further aiding in the sectorial decarbonization efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature

The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Alignment of Au nanorods along de novo designed protein nanofibers studied with automated image analysis

In this paper, we focus on exploring the directional assembly of anisotropic Au nanorods along de novo designed 1D protein nanofiber templates. Using machine learning and automated image processing, we analyze scanning electron microscopy (SEM) images to study how the attachment density and alignment fidelity are influenced by variables such as the aspect ratio of the Au nanorods, and the salt concentration of the solution. We find that the Au nanorods prefer to align parallel to the protein nanofibers. This preference decreases with increasing salt concentration, but is only weakly sensitive to the nanorod aspect ratio. While the overall specific Au nanorod attachment density to the protein fibers increases with increasing solution ionic strength, this increase is dominated primarily by non-specific binding to the substrate background, and we find that greater specific attachment (nanorods attached to the nanofiber template as compared to the substrates) occurs at the lower studied salt concentrations, with the maximum ratio of specific to non-specific binding occurring when the protein fiber solutions are prepared in 75 mM NaCl concentration.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding Flow Characteristics in Metal Additive Manufacturing

In fluid flow, the frictional effects from interior surfaces of flow components create pressure loss across the surface. Understanding this inherent pressure loss due to friction and other phenomena is important when designing a flow system. For conventional manufacturing, friction loss has been empirically studied and is well known in the engineering community. However, newer manufacturing techniques, such as laser powder bed fusion (LPBF), an additive manufacturing technique, need to be understood. The inherent flow conditions resulting from the additive processes were studied. Test samples were printed with the flow path oriented in the vertical direction with different channel diameters to characterize the inherent fundamental flow properties of the process. Moody diagrams were replicated for a greater understanding of possibilities and shortfalls when implementing LPBF into design applications. Experimental flow testing revealed that greater effective sand grain roughness resulted from this process when compared to conventional machining methods. This roughness did not correlate well to the average roughness measured with a laser scanning microscope but was consistent with the average particle size of the powder metal feedstock.

42 ENGINEERING↗
DOE OSTI2020

Interface learning of multiphysics and multiscale systems

Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws. To address such challenges, we introduce an interface learning paradigm and put forth a data-driven closure approach based on memory embedding to provide physically correct boundary conditions at the interface. To enable the interface learning for hyperbolic systems by considering the domain of influence and wave structures into account, we put forth the concept of upwind learning toward a physics-informed domain decomposition. The promise of the proposed approach is shown for a set of canonical illustrative problems. Here, we highlight that high-performance computing environments can benefit from this methodology to reduce communication costs among processing units in emerging machine-learning-ready heterogeneous platforms toward exascale era.

42 ENGINEERING↗
DOE OSTI2021

Efficient Parallel Sparse Symmetric Tucker Decomposition for High-Order Tensors

Tensor based methods are receiving renewed attention in recent years due to their prevalence in diverse real-world applications. There is considerable literature on tensor representations and algorithms for tensor decompositions, both for dense and sparse tensors. Many applications in hypergraph analytics, machine learning, psychometry, and signal processing result in tensors that are both sparse and symmetric, making it an important class for further study. Similar to the critical Tensor Times Matrix chain operation (TTMc) in general sparse tensors, the Sparse Symmetric Tensor Times Same Matrix chain (S3TTMc) operation is compute and memory intensive due to high tensor order and the associated factorial explosion in the number of non-zeros. In this work, we present a novel compressed storage format CSS for sparse symmetric tensors, along with an efficient parallel algorithm for the S3TTMc operation. We theoretically establish that S3TTMc on CSS achieves a better memory versus run-time trade-off compared to state-of-the-art implementations. We demonstrate experimental findings that confirm these results and achieve up to 2.9× speedup on synthetic and real datasets.

Shivakumar, Shruti↗
DOE OSTI2021

Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes

This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SOC)-dependent degradation patterns that would be unobserved in a smaller test matrix. Model structure is informed by incremental capacity analysis that shows loss of lithium inventory and cathode-material loss as the dominant capacity fade mechanisms. The model includes terms attributable to solid-electrolyte interface (SEI) growth, electrode cracking, cycling-driven acceleration of SEI growth, and "break-in" mechanisms that slightly decrease or increase available Li inventory early in life. The study explores what mathematical couplings of these mechanisms best describe calendar aging, cycle aging, and mixed calendar/cycle aging. Various approaches are discussed for extracting relevant stress factors from complex cycling profiles to predict lifetime during real-world battery loads using models trained on constant-current laboratory test results. The complexity of the present human-driven model identification process motivates future work in machine learning to more widely search and statistically discern the optimal model that correctly extrapolates capacity fade based on physical knowledge.

25 ENERGY STORAGE↗
DOE OSTI2023

Hpc Natural Language Understanding (nlu) Dataset

This code provides natural language annotation and labels for multiple activities in high performance. The purpose is to enable machine learning for natural language processing algorithms with high performance computing systems.

Biggs, BrandonS↗
DOE OSTI2020

Annotated Translated Disassembled Code

Procedure for generating function/library embedding based on disassembled binary data from a graph database and processing those embedding through supervised machine learning techniques.

Beckman, BryanR.↗
DOE OSTI2022

DRB-ML-Dataset

This datastet contains kernels extracted from OpenMP microbenchmarks collected in DataRaceBench. The purpose of this dataset is to serve as training data for machine learning with programming language processing (PLP).

Lin, Pei-Hung↗
DOE OSTI2022

A meta-analysis of semantic classification of citations

The aim of this literature review is to examine the current state of the art in the area of citation classification. In particular, we investigate the approaches for characterizing citations based on their semantic type. We conduct this literature review as a meta-analysis covering 60 scholarly articles in this domain. Although we included some of the manual pioneering works in this review, more emphasis is placed on the later automated methods, which use Machine Learning and Natural Language Processing (NLP) for analyzing the fine-grained linguistic features in the surrounding text of citations. The sections are organized based on the steps involved in the pipeline for citation classification. Specifically, we explore the existing classification schemes, data sets, pre-processing methods, extraction of contextual and non-contextual features, and the different types of classifiers and evaluation approaches. The review highlights the importance of identifying the citation types for research evaluation, the challenges faced by the researchers in the process, and the existing research gaps in this field.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Machine learning in analytical spectroscopy for nuclear diagnostics [Invited]

Analytical spectroscopy methods have shown many possible uses for nuclear material diagnostics and measurements in recent studies. In particular, the application potential for various atomic spectroscopy techniques is uniquely diverse and generates interest across a wide range of nuclear science areas. Over the last decade, techniques such as laser-induced breakdown spectroscopy, Raman spectroscopy, and x-ray fluorescence spectroscopy have yielded considerable improvements in the diagnostic analysis of nuclear materials, especially with machine learning implementations. These techniques have been applied for analytical solutions to problems concerning nuclear forensics, nuclear fuel manufacturing, nuclear fuel quality control, and general diagnostic analysis of nuclear materials. The data yielded from atomic spectroscopy methods provide innovative solutions to problems surrounding the characterization of nuclear materials, particularly for compounds with complex chemistry. Implementing these optical spectroscopy techniques can provide comprehensive new insights into the chemical analysis of nuclear materials. In particular, recent advances coupling machine learning methods to the processing of atomic emission spectra have yielded novel, robust solutions for nuclear material characterization. This review paper will provide a summation of several of these recent advances and will discuss key experimental studies that have advanced the use of analytical atomic spectroscopy techniques as active tools for nuclear diagnostic measurements.

Rao, Ashwin P. (ORCID:0000000319312568)↗
DOE OSTI2021

Event-scale predictions of water and nitrogen exports in coastal watersheds

Focal Area(s): The purpose of this white paper is to call for the improvement of the predictability and explainability of the magnitude and timing of water and nutrient exports in coastal watersheds. In this regard, hybrid machine learning approaches and process-based numerical models provide new opportunities to maximize predictability, emulate process-based model components, and explicitly address model uncertainty associated with event-scale predictions in coastal watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Regal Beloit Final Technical Report

The original project proposal submitted from NovaTorque Inc (NovaTorque) in 2016 was to improve the existing motor with 95% efficiency by reducing the losses by 21% to achieve 96%. A few months after the proposal was submitted, NovaTorque lost funding and went out of business. The assets of NovaTorque were then acquired by Regal Beloit Corporation (“Regal Beloit” or “Regal”). When the NovaTorque proposal was selected, the project was transferred to Regal Beloit. Once we had production samples from the new Regal production line, they were tested at the Regal Beloit test lab in Wausau, WI. The original NovaTorque motor had an efficiency of 95%, but when the technology was transferred to Regal Beloit, there were multiple manufacturing improvements made, even though the basic electromagnetic design did not change. The test results in Wausau showed that the motors made at the Regal Beloit plant had an efficiency of 96%. Since the goal of the project was to reduce the losses by 21%, a new efficiency target of 96.8% became the project objective. In the first budget period, motors were tested to get the baseline performance. We then used FEA modeling with ANSYS Maxwell to model the existing motor to get correlation between the FEA simulations and the actual test results. Once the model was validated, we evaluated some changes that could be made to the stator to reduce the losses and improve efficiency without changing the rotor or stator housing, keeping the modifications easy to implement. The changes were primarily in the area of making the stator axially shorter and adding Soft Magnetic Composite (SMC) tooth tips. In the second budget period, we proceeded to design and build the new stator that was identified above and identified additional improvements in the process that included a stator machining modification and the use of rectangular wire. When we actually built the motor, the choice of rectangular wire turned out to be a problem. The wire was made by squishing round wire to get the rectangular shape, which caused work hardening, making the wire too stiff to make the desired coils. We shifted to annealed square wire which was better, but we still could not maintain the proper coil envelope. The result was that we had reduced cross section area for stator laminations. We also had to have a radial offset resulting in a radial misalignment between the stator and rotor because of the oversized coils. After the motor was completed, it was shipped to Texas A&M University for testing. With the loss of flux from these issues, the measured motor efficiency was only 96.3%. The primary focus of this motor was to make sure our FEA simulation model predicted the measured losses and overall efficiency, this we moved on to the FEA simulation. The FEA simulations of the motor “as built” with misalignments had good correlation with the test results, so the next step was to use that model to optimize the design of the motor for a final build. This time we considered changes to the stator and rotor and also minor changes to the housing diameter. In the third budget period, we did the detailed design and construction of the final prototypes. The final prototypes had a slight increase in the stator diameter to fit in a standard Regal Beloit housing. We shortened the stator and use more layers of wire in the coils. We also increased the cone angle of the rotor and stator from the original 110 degrees to 130 degrees to get some additional efficiency and optimized the stator cross section. The predicted efficiency from the FEA simulations was 96.9%. When completed, the motors were tested. We were a little short of reaching our target efficiency goal of 96.8%. We were only able to get to 96.7% efficiency. While it may be possible with additional iterations in designs and future builds to gain that additional 0.1%, we believe that we are close to the best we can achieve from a practical viewpoint, and additional iterations would be more work than the potential gains would be worth. No other motor in this class can even reach the 96% that we started with.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Surface Roughness Calculations of Additively Manufactured Cylinders via X-Ray Computed Tomography

X-ray Computed Tomography (X-ray CT) is an analytical technique used in materials science to non–destructively characterize features in variety of materials like polymers, metals, composites, and explosives. It also has the capability of imaging additively manufactured, machined and assembled parts and processes. The non-destructive imaging allows for the analysis of features (voids and cracks), which give a fundamental understanding of material characteristics. Additionally, X-ray CT can obtain accurate measurements of dimensional and topographic variations as a result of different stimuli, and assess accuracy of material production. This study will focus on parts manufactured via metal additive manufacturing (AM). Although, AM produces parts faster and easier, the printing process can produce defects (pores and surface roughness) that undermine the part’s mechanical properties. The analysis of 3D printed objects has a strength in analysis in that the material has an STL file from which the item was printed, which is not available in many manufactured materials (i.e. foams) due to stochastic structures. For this study, the print accuracy of several 3D printed cylinders will be assessed via X-ray CT to determine optimal printing parameters for parts with less defects and stronger mechanical properties. This will be accomplished by having the original STL file serve as the baseline surface to compute difference in the CT rendering to calculate surface roughness for each cylinders.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Preliminary Report on Compositional Specification for Printed 316SS

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing (AM). A key challenge for the widespread adoption of AM is the variability in material properties, which can originate from powder feedstock, process parameters, geometry, and the machine. AM powder feedstock compositions are based on those developed for conventional manufacturing. However, the smaller melt pools combined with multiple melt cycles over the course of a build can have substantial effects on the local phase selection resulting from minor batch-to-batch feedstock variability. Therefore, this report focuses on coupling CALPHAD calculations with data mining and visualization to aid the development of compositional specifications for stainless steel 316, an important alloy for nuclear applications and suitable for AM. This work reports that δ-ferrite formation has a stronger dependence on the amount of nickel in the alloy despite nickel being an austenite stabilizer. These findings show the need for exercising a tighter control on the amount of nickel in the alloy compared with the amount of chromium, which is a ferrite stabilizer.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A semi-supervised learning method to produce explainable radioisotope proportion estimates for NaI-based synthetic and measured gamma spectra

Quantifying the radioactive sources present in gamma spectra is an ever-present and growing national security mission and a time-consuming process for human analysts. While machine learning models exist that are trained to estimate radioisotope proportions in gamma spectra, few address the eventual need to provide explanatory outputs beyond the estimation task. In this work, we develop two machine learning models for a NaI detector measurements: one to perform the estimation task, and the other to characterize the first model’s ability to provide reasonable estimates. To ensure the first model exhibits a behavior that can be characterized by the second model, the first model is trained using a custom, semi-supervised loss function which constrains proportion estimates to be explainable in terms of a spectral reconstruction. The second auxiliary model is an out-of-distribution detection function (a type of meta-model) leveraging the proportion estimates of the first model to identify when a spectrum is sufficiently unique from the training domain and thus is out-of-scope for the model. In demonstrating the efficacy of this approach, we encourage the use of meta-models to better explain ML outputs used in radiation detection and increase trust.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

IMPACT: Design of Integrated Multiphysics Producible Additive Components for Turbomachinery

The overall objective of the IMPACT program was to enable a dramatic reduction in design maturation time for an additive hot-section turbomachinery component through the following: • A fast crack-risk producibility surrogate model generated from machine learning applied to additive process simulation data generated via exascale computing, • Linking this surrogate model to multi-physics topology optimization (TO) to enable the creation of producible, near-optimal structural/thermal designs for additive hot-section components, • Maturing this toolset to reduce hot-section component design-for-manufacturing iterations by a large fraction, and eventually, • Using these tools to develop more efficient gas turbines in much shorter design cycle times.

42 ENGINEERING↗