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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 541 records · Page 30

Multiplanar Stiffness of Commercial Carbon Composite Ankle-Foot Orthoses

The mechanical properties of an ankle-foot orthosis (AFO) can impact how a user's movement is either restricted or augmented by the device. However, standardized methods for assessing stiffness properties of AFOs are lacking, posing a challenge for comparing between devices and across vendors. Therefore, the purpose of this study was to quantify the rotational stiffness of thirteen commercial, nonarticulated, carbon composite ankle-foot orthoses. A custom, instrumented test fixture, for evaluating mechanical properties in rotating exoskeletons (EMPIRE), deflected an AFO through 20 deg of plantar/dorsiflexion motion about a specified, but adjustable, ankle axis. Sagittal, frontal, and transverse plane rotational stiffness were calculated, and reliability was assessed between cycles, sessions, and testers. The EMPIRE demonstrated good-to-excellent reliability between testers, sessions, and cycles (intraclass correlation coefficients all ≥0.95 for sagittal plane stiffness measures). Sagittal plane AFO stiffness ranged from 0.58 N·m/deg to 3.66 N·m/deg. AFOs with a lateral strut demonstrated frontal plane stiffnesses up to 0.71 N·m/deg of eversion while those with a medial strut demonstrated frontal plane stiffnesses up to 0.53 N·m/deg of inversion. Transverse plane stiffnesses were less than 0.30 N·m/deg of internal or external rotation. These results directly compare AFOs of different models and from different manufacturers using consistent methodology and are intended as a resource for clinicians in identifying a device with stiffness properties for individual patients.

Biophysics↗

Machine learning and atomic layer deposition: Predicting saturation times from reactor growth profiles using artificial neural networks

In this work, we explore the application of deep neural networks to the optimization of atomic layer deposition (ALD) processes. In particular, we focus on a one-shot optimization problem, where we try to predict the optimal dose time that leads to saturation everywhere in the reactor based on thickness values measured at different points of an ALD reactor after a single trial growth. In order to tackle this problem, we introduce a dataset designed to train neural networks to predict saturation times based on these inputs for a cross-flow ALD reactor. Here, we then explore the predictive ability of artificial neural networks of different depths and sizes using a separate testing dataset to evaluate their accuracies. The results obtained show that networks trained using stochastic gradient descent methods can accurately predict saturation times without requiring any additional information on the surface kinetics. This provides a viable approach to minimize the number of experiments required to optimize new ALD processes in a known reactor, and it highlights the way machine learning can be leveraged for thin film growth and manufacturing. While the datasets and training procedure depend on the reactor geometry, the trained neural networks provide a general surrogate model connecting thickness values and trial dose times with optimal saturation times that can be reused for different ALD processes within the same reactor.

36 MATERIALS SCIENCE↗

Insights into the thermo-hydraulic properties of compacted MX80 bentonite during hydration under elevated temperature

In high-level radioactive waste geological repositories, compacted bentonite undergoes coupled thermo-hydraulic processes due to heat released from a central waste canister and groundwater imbibition from the surrounding host rock. An understanding of these processes is essential for long-term simulations of radionuclide migration and canister corrosion, which requires an understanding of temperature effects on the coupled thermo-hydraulic properties governing these processes. In this study, a tank-scale radial infiltration test was used to investigate water imbibition processes in compacted bentonite under a central heater temperature of 200 °C that simulates high thermal gradients in a repository. Interpretation of this test focuses on evaluation of the liquid water wetting front during hydration and the interpretation of the temperature-dependent transient soil water retention curve (SWRC), thermal conductivity function (TCF), and hydraulic conductivity function (HCF). The SWRC during imbibition follows a temperature-dependent wetting path. Temperature effects on the hydraulic conductivity of bentonite in saturated conditions had the greatest effect on the shape of the HCF, with minimal temperature effects at higher suctions. The transient thermal conductivity data matched well with a new TCF linked with the SWRC shape. Although the bentonite layer was restrained, local deformations during hydration may have affected the shapes of the TCF and HCF.

Engineering↗

Semi-Automatic Geographic Information System Framework for Creating Photo-Realistic Digital Twin Cities to Support Autonomous Driving Research

Digital twin cities are frequently used in vehicle and traffic simulations to render realistic on-road driving scenarios under various traffic and environmental conditions. These digital twins provide a high-fidelity replica of the physical world (e.g., buildings, roads, infrastructures, traffic) to create three-dimensional (3D) virtual-physical environments to support various emerging vehicle and transportation technologies such as connected and automated vehicles. These virtual environments provide a cost-effective digital proving ground to evaluate, validate, and test emerging technologies that include control algorithms, localization, perception, and sensors. Replicating a real-world traffic scenario in a digital twin using a traditional 3D modeling approach is a time-consuming and labor-intensive effort. Here this paper presents a semi-automated spatial framework to construct realistic 3D digital twin cities to support autonomous driving research using readily available geographic information system (GIS) data and 3D prefabricated (prefab) models. We start with a comprehensive review of geospatial data sources of essential digital entities required in a 3D digital twin city and present an integrated GIS-3D modeling pipeline using customized QGIS/GDAL and Blender scripting in Python. The pipeline outputs are realistic 3D digital twin cities compatible with common vehicle simulation software, such as CARLA and IPG CarMaker. The paper closes with a showcase to demonstrate the quality and usability of a digital twin city created to replicate the Shallowford Road corridor in Chattanooga in both Unity and Unreal engine-based virtual environment. The generated digital twin city can be applied to a hardware-in-the-loop simulation environment with an actual testing vehicle to facilitate autonomous driving research.

33 ADVANCED PROPULSION SYSTEMS↗

TULIP: An RNA-seq-based Primary Tumor Type Prediction Tool Using Convolutional Neural Networks

Background: With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years. Methods In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation. Additionally, we adapted the models to take as input either all Ensembl genes (60,483) or protein coding genes only (19,758). Unlike previous work, we avoided selection bias by not filtering genes based on expression values. RNA-seq count data expressed as FPKM-UQ of 9,025 and 10,940 samples from The Cancer Genome Atlas (TCGA) were downloaded from the Genomic Data Commons (GDC) corresponding to 17 and 32 primary tumor types respectively for training and validating the models. Results: All 4 1D-CNN models had an overall accuracy of 94.7% to 97.6% on the test dataset. Further evaluation indicates that the models with protein coding genes only as features performed with better accuracy compared to the models with all Ensembl genes for both 17 and 32 primary tumor types. For all models, the accuracy by primary tumor type was above 80% for most primary tumor types. Conclusions: We packaged all 4 models as a Python-based deep learning classification tool called TULIP (TUmor CLassIfication Predictor) for performing quality control on primary tumor samples and characterizing cancer samples of unknown tumor type. Further optimization of the models is needed to improve the accuracy of certain primary tumor types.

Jones, Sara↗

Adhesively-bonded Metal-CFRTP Bi-materials: Enhanced Crack Growth Resistance via Plasma and Quantified Fracture via Size Effect Method

This work investigated air plasma effect on the Mode I crack growth resistance of adhesively-bonded metal-CFRTP dissimilar joints by using aluminum alloy (AA6061) and short-carbon-fiber-reinforced polyamide 66 (CFRPA66) as an example, and the Double Cantilever Beam (DCB) fracture testing as an evaluation method. The results show that air plasma treatment can significantly improve the fracture resistance of adhesively-bonded AA6061-CFRPA66 dissimilar joints with about 140% enhanced Mode I fracture energy in maximum compared to non-treated ones. The quantified fracture energies from size effect method for both non-treated and plasma-treated cases are geometry-independent, whereas this is not true for modified beam theory causing geometry-dependent results. The foregoing improvement was confirmed from the failure surface morphology of plasma-treated specimens, showing fibers peeling off from CFRPA66 surface due to plasma-enhanced bonding between CFRPA66 and adhesive by the formation of covalent bonds at their interface. This study demonstrated air plasma as an efficient surface modification method to enhance the fracture resistance of adhesively-bonded metal-CFRTP dissimilar joints, and size effect method as a characterization method to properly quantify their fracture properties. These aspects are valuable in the area of multi-materials joining.

Plasma, Adhesive Bonding, Slow Crack Growth, fract↗

Ventilator Testbed

A number of open-source ventilator designs have been published online since the outbreak of the COVID-19 pandemic. These ventilator designs have been created and released to help address the limitations in manufacturing and distribution timelines, and to help drive availability of more cost efficient ventilators. Many of these ventilators have been designed to be relatively simple to build and to use components that can be easily-sourced and acquired in large numbers from local suppliers. A test bed for evaluating the performance of these open-source ventilators was developed at Los Alamos National Laboratory as part of the Technology Evaluation and Demonstration program.

42 ENGINEERING↗

Results of the Analyses of SMECT Mercury Sample Collected During Mercury Pump (MB1) Functional Check

A pump added to remove mercury from the sump of the Slurry Mix Evaporator Condensate Tank (SMECT) in DWPF was successfully tested in 2020, resulting in a sample of contaminated mercury obtained from the Mercury Purification Process (MPP) cell. The mercury in the sump of the SMECT was present as a result of steam stripping of sludge slurry in the Sludge Receipt and Adjustment Tank (SRAT) at DWPF. A schematic of the MPP system provided by DWPF personnel is shown in Appendix A. DWPF personnel supplied the following detailed description of the Hg sampling: “The sample provided is the result of DWPF extraction of mercury by means of the Mercury Pump-Water Cart. The Mercury Pump extracted the sample from the SMECT mercury sump. The pump uses pressurized water to transfer a small amount of mercury to a leachate bucket located at the Lab Mercury cell. The mercury and water were allowed to be self separated, and mercury was gravity decanted from the leachate bucket.” DWPF personnel indicate that the leachate bucket is equipped with both a top stopcock/drain and a lower/bottom stopcock/drain from which the mercury was decanted or drained out, while the separated water layer remained above the mercury during draining. The DWPF customer requested SRNL to analyze the sample for the following: gross alpha/nonvolatile beta analysis and perform a Toxicity Characteristic Leaching Procedure (TCLP) per the Environmental Protection Agency's Test Methods for Evaluating Solid Waste: Physical/Chemical Methods, SW-846. While the TCLP was preferred, a Total Metals analysis of the eight Resource Conservation and Recovery Act (RCRA) metals was deemed acceptable.2 The analytical data collected on this mercury sample will be used in planning for sample disposition as well to start a profile for future Hg disposition of Hg collected from steam stripping.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Production-Ready CsPbBr3 Module Fabrication

The purpose of this SBIR was to work on evaluation, development, and testing of CsPbBr3 semiconductors for use in commercial gamma-ray detection. The overarching goal was to demonstrate the capability to fabricate functional detectors through resources available to H3D, Inc in pursuit of an alternative to CdZnTe for room-temperature semiconductor detectors. Throughout the project, CdZnTe crystals from Redlen were used as a control group to demonstrate whether the fabrication processes could produce reasonable results. The CdZnTe crystals were diced into roughly 6 mm x 6 mm x 4.5 mm pieces and electrodes were evaporated onto planar faces. In one case, pixels were patterned through a laser ablation method; in the other, a rudimentary pixel shadow-mask was fabricated, and the pixels were generated in the evaporation process. A 16 mm x 16 mm x 14 mm CsPbBr3 crystal was diced into four smaller crystals. These CsPbBr3 had electrodes applied in a similar way to the CdZnTe samples with a rudimentary mask used for pixels on the cathode. Tested CdZnTe pieces were found to generally function with reasonable, low-energy performance. CsPbBr3 crystals were found to be non-functional under forward bias with a large dark-current. A separate attempt, using reverse bias in a non-H3D, Inc test system, observed a dark current similar to previous observations, and some active pixels but no waveforms were observed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Search for Lepton Number Violation in 136 Xe and 134 Xe Decay

The EXO-200/nEXO group at Stony Brook University led by Krishna Kumar searched for neutrinoless double beta decay of 134 Xe and 136 Xe via participation in the final phase of the EXO-200 experiment and calibration pre-R&D for the next generation nEXO concept. On EXO-200, we carried out an improved analysis in the search for 134 Xe using data from Phase-II running. On nEXO, we completed a paper on our first phase of measurements demonstrating the feasibility of using photoelectricity from a gold photocathode to produce controlled amounts of charged clusters in liquid xenon. This has set the stage to develop a new test stand to evaluate the stability of a gold photocathode in order to explore the sensitivity of this method to monitor long electron lifetimes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

PACT Perovskite PV Module Stress Testing Protocol (Version 0.0)

The purpose of this protocol is to use accelerated stress testing to assess the durability of metal halide perovskite (MHP) photovoltaic (PV) modules. The protocol aims to apply field relevant stressors to packaged MHP modules to screen for early failures that may be observed in the field. The current protocol has been designed with a glass/glass-PIB edge seal, no encapsulant package in mind. PACT anticipates adding additional testing sequences to evaluate additional stressors (e.g., PID, reverse bias) in the future.

42 ENGINEERING↗

ACES: Infrastructure As Code. Model Optimization and Performance Capability

Infrastructure as Code (IaC) refers to managing infrastructure (networks, physical/virtual machines, storage, and connection topology) in a descriptive model/language, rather than configuring it manually or using interactive configuration tools. Just like source code can be compiled to generate the same binary code, IaC enables generating the same environment every time it is applied. IaC is a key DevOps practice and is generally used in conjunction with continuous integration (CI) and continuous delivery (CD). In CI, all code changes are merged into a mainline branch and validated multiple times a day as developers check in their changes to the source code. In CD on the other hand, code changes are automatically packaged for a new release-to-production on a regular basis. This typically enables teams to deliver software changes much more quickly and often. Developing and managing the ACES platform using (IaC) is vital for the robust deployment and continued sustainment of this foundational computing capability. IaC and DevOps practices will help us solve many of the common challenges often encountered in developing and maintaining compute infrastructure. First, it will make the provisioning, deployment, and maintenance of the compute infrastructure across multiple environments much more efficient. Second, these processes help make the overall system much more stable by continuously testing new changes as they are introduced to the system. Third, it allows us to be much more confident of the security controls in place since they can be tested as part of the CI process and all new changes can be audited and tracked. Finally, IaC enables the ACES Platform to be adaptable to the emerging technologies due to its ability to spin up different test beds to evaluate and incorporate these technologies. This document addresses common infrastructure-management challenges, describes what happens if they are not addressed, and highlights the value of utilizing IaC to tackle them. Finally, we will provide a high-level overview of the IaC and DevOps practices being utilized by the ACES Platform team.

97 MATHEMATICS AND COMPUTING↗

Preliminary Sensitivity Analysis for Sensors Impacts on Building Control Performance

This report describes the preliminary sensitivity analysis for sensor impacts on building control performance through the US Department of Energy’s Oak Ridge National Laboratory’s Flexible Research Platform (FRP-2) building. The rooftop unit system provides cooling and heating to the building. The main heating coil is a gas heating coil. Each zone is served by a variable air volume box with an electricity reheat coil. The rooftop unit and variable air volume box controls adopted the practical control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. For sensors, the incipient (time-changing) sensor errors, including bias sensor error and precision sensor error, are the inputs of interest. The outputs are energy consumption and thermal comfort (e.g., the predicted percentage of dissatisfied occupants). The large-scale simulation (3,600 cases) was conducted on a cloud platform by integrating sensor errors and ASHRAE Guideline 36 control sequences into an emulator based on the EnergyPlus simulation program with Python energy management system feature. The surrogate models were developed based on cloud simulation results. The uncertainty analysis showed that the sensor errors substantially affect building energy consumption and thermal comfort. The sensitivity analysis shows a ranking of sensor error impacts for each interested output item (e.g., cooling energy, reheat coil heating energy, predicted percentage of dissatisfied occupants). In FY 2022, sensor locations, types, and costs will be evaluated. The field test in Oak Ridge National Laboratory’s Flexible Research Platform building regarding sensor impacts will also be performed. Finally, a comparative analysis will be conducted based on the field test results and emulator results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Simulation of Molecular Hydrogen Jets and Visualization of Associated Hydrodynamic Features [Slides]

A numerical method is presented for solving the Navier-Stokes equations to represent a steady-state hydrogen jet expanding into a vacuum chamber. This numerical method has been evaluated using three test problems with promising results. Simulations of the hydrogen jet indicate the presence of a low temperature region (less than 15 K) within the jet for the high flow rate case. The high flow rate case also gives rise to a large recirculation region within the jet. The conditions at the minimum temperature point within the jet approach the hydrogen sublimation line when the mass flow rate exceeds 2 g/s, which may allow for the formation of hydrogen ice crystals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Light Elements $R$-matrix Analyses with the SAMMY code towards the Foundation of Charged-particle Nuclear Data Libraries [Slides]

This presentation covers newly developed SAMMY module for inverse channel transformation. Additionally covered is the R-matrix analysis of 7 Be compound nucleus and the R-­matrix analysis of 17 O compound nucleus. Further touched on is the evaluated Nuclear Data File generation and processing with the AMPX code. The presentation concludes with talks on future evaluation work and tests on light nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗

Real-Time Wave Energy Converter Control Using Instantaneous Frequency

Wave Energy Converters (WECs) rely on effective Power Take-Off (PTO) control strategies to maximize energy absorption under dynamic sea conditions. Traditional hydrodynamic modeling techniques may require computationally intensive convolution calculations, making real-time control implementation challenging. This paper presents an alternative approach by leveraging instantaneous frequency estimation to dynamically adjust PTO damping in response to varying wave frequencies. Two real-time frequency estimation methods are explored: the Hilbert Transform (HT) and Phase-Locked Loop (PLL). The Hilbert Transform method provides accurate frequency tracking but introduces a delayed response due to its dependence on causal data. Conversely, the PLL approach demonstrates strong potential in frequency tracking but requires careful gain tuning, particularly in complex sea states. Comparative evaluations across multiple test cases—including sinusoidal variations, amplitude steps, frequency step changes, and real-world JONSWAP spectrum waves—highlight the strengths and limitations of each method. The two different PTO control techniques across the various frequency estimation methods were tested under real-sea states using a state-space model of a point-absorbing Wave Energy Converter. The Capture Width Ratio (CWR) is used as a performance metric, with results showing that the HT achieves a 10.6% improvement, while the PLL estimation yields a 0.9% improvement relative to the fixed parameter control baseline. These results highlight the effectiveness of real-time frequency estimation in improving energy absorption compared to static control parameters.

WEC control↗