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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,567 records · Page 87

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NASA NTRS2003

Evaluation of Models for Diffuse Continuum Gamma Rays in EGRET Range

The GALPROP model for cosmic-ray propagation produces explicit predictions for the angular distribution of gamma-rays. We compare our current models with EGRET spectra for various regions of the sky. This allows a critical test of alternative hypotheses for the observed GeV excess. We show that a population of hard-spectrum sources cannot be solely responsible for the excess since it also appears at high latitudes; on the other hand the 'hard electron' spectrum model cannot explain the gamma-ray excess in the inner Galaxy. Hence some combination of these explanations is required.

Strong, A.↗

DOE OSTI2024

Towards predictive control of reversible nanoparticle assembly with solid-binding proteins

Although a broad range of ligand-functionalized nanoparticles and physico-chemical triggers have been exploited to create stimuli-responsive colloidal systems, little attention has been paid to the reversible assembly of unmodified nanoparticles with non-covalently bound proteins. Previously, we reported that a derivative of green fluorescent protein engineered with oppositely located silica-binding peptides mediates the repeated assembly and disassembly of 10-nm silica nanoparticles when pH is toggled between 7.5 and 8.5. We captured the subtle interplay between interparticle electrostatic repulsion and their protein-mediated short-range attraction with a multiscale model energetically benchmarked to collective system behavior captured by scattering experiments. Here, in this work, we show that both solution conditions (pH and ionic strength) and protein engineering (sequence and position of engineered silica-binding peptides) provide pathways for reversible control over growth and fragmentation, leading to clusters ranging in size from 25 nm protein-coated particles to micrometer-size aggregate. We further find that the higher electrolyte environment associated with successive cycles of base addition eventually eliminates reversibility. Our model accurately predicts these multiple length scales phenomena. The underpinning concepts provide design principles for the dynamic control of other protein- and particle-based nanocomposites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Predicting the Solubility of Inorganic Ion Pairs in Water

Abstract Polyoxometalates (POMs), ranging in size from 1 to 10’s of nanometers, resemble building blocks of inorganic materials. Elucidating their complex solubility behavior with alkali‐counterions can inform natural and synthetic aqueous processes. In the study of POMs ([Nb 24 O 72 H 9 ] 15− , Nb 24 ) we discovered an unusual solubility trend (termed anomalous solubility) of alkali‐POMs, in which Nb 24 is most soluble with the smallest (Li + ) and largest (Rb/Cs + ) alkalis, and least soluble with Na/K + . Via computation, we define a descriptor (σ‐profile) and use an artificial neural network (ANN) to predict all three described alkali‐anion solubility trends: amphoteric, normal (Li + >Na + >K + >Rb + >Cs + ), and anomalous (Cs + >Rb + >K + >Na + >Li + ). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution‐phase snapshots of alkali–POM interactions, yielded a new POM formulated [Ti 6 Nb 14 O 54 ] 14− , and provides guidelines to exploit alkali–POM interactions for new POMs discovery.

Rahman, Tasnim↗

DOE OSTI2023

Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows

This report details recent progress for the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows”. We refer to the project as IPPD/2, reflecting the 2017 renewal under expanded scope and partners In IPPD/2, we increased our research scope to include data motion. We are focusing on three major aspects: a) observe how data is generated, distributed, and used; b) analyze how data is (repeatedly) consumed with a focus both on repeated patterns and anomalies; and c) explore how to optimize data motion. This new work on data motion will augment and complement IPPD/2’s research that focused on the computational aspects of tasks. We leverage and extend our existing tools and demonstrate our work on the Belle II workflow suite as well as on workflows from NSLS-II. The highlights of our work are as follows: Provenance for Workflows: Provenance is used to provide information enabling quality control, re-run computational workflows, and reproduce results. IPPD/2 has been building a scalable provenance management system that enables the capture of provenance from the high-level workflow through all relevant system levels in one integrated environment. Leveraging this work, our recent efforts have included using provenance as an enabling technique. Workload characterization: Leveraging provenance and analysis, we characterize data movement within network, storage, and memory over a variety of workloads. This characterization enables an understanding by performance analysts and application developers of the range of behaviors that could be expected. Performance Prediction for Workflows: The goal of modeling distributed workflows is to understand performance bottlenecks and enable more intelligent task scheduling to optimize selected metrics of interest (e.g., task throughput or output data rate). IPPD/2 has utilized both analytical and AI/ML modeling methodologies for performance modeling. Advanced Scheduling and Fault Modeling for Workflows: Scheduling of large-scale scientific workflows on geographically distributed resources is a challenging problem. To improve workflow throughput, we combined novel scheduling algorithms with task predictions from performance modeling and fault modeling. Dynamically Alleviating Bottlenecks in Workflows: Exploiting our provenance, analysis, and modeling efforts, we have explored and developed several techniques for dynamically detecting and alleviating bottlenecks in data movement. In particular, we have spent considerable effort demonstrating our techniques on production-like workflow configurations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Characterization of Lanthanum Monazite Surface Chemistry and Crystal Morphology through Density Functional Theory and Experimental Approaches

Monazite is a rare earth element (REE)-containing mineral that consists of (REE)PO 4 formal units and is one of the most important sources of these critical materials. The concentration of REEs from mined monazite ore often involves froth flotation, which is a beneficiation process that enhances the efficiency of downstream processing. The effectiveness of froth flotation is largely governed by the ability of collector agents to selectively bind to monazite particles. Thus, a molecular-level understanding of monazite interfacial chemistry is integral to the design of effective collector agents. To address this need, we performed density functional theory (DFT) calculations and a variety of experimental techniques to characterize La-monazite and elucidate its crystal morphology. Interestingly, we find minimal differences in the predicted morphologies of La-monazite for hydrous and anhydrous environments, which are largely dominated by low-index facets (e.g., {110}, {100}, and {010}). Indexing of synthesized La-monazite crystals via X-ray diffraction also uncovers {110} and {100} as the predominant facets. The average surface energies of 0% and 100% water coverage La-monazite crystals were predicted to be 0.87 and 0.76 J/m 2 , respectively, while calorimetry suggests values of 1.30 and 1.15 J/m 2 , respectively. The apparent discrepancies between the theoretical and experimental values are expected and attributed to defects present in physical crystals, in contrast to the perfect mineral surfaces in simulations. The difference in surface energy between the 0% and 100% water coverage morphologies predicted by theory is consistent with the value measured via calorimetry. DFT reveals a wide range of adsorption energies for water across the studied facets, but in all cases, water is predicted to strongly bind to monazite surfaces with an average adsorption energy of -92.7 kJ/mol for a La-monazite single crystal. Finally, this study provides the groundwork necessary for the rational design of froth flotation collector agents by granting molecular-level insight into the predominant facets of monazite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Enhanced Surgical Decision-Making Tools in Breast Cancer: Predicting 2-Year Postoperative Physical, Sexual, and Psychosocial Well-Being following Mastectomy and Breast Reconstruction (INSPiRED 004)

Background: We sought to predict clinically meaningful changes in physical, sexual, and psychosocial well-being for women undergoing cancer-related mastectomy and breast reconstruction 2 years after surgery using machine learning (ML) algorithms trained on clinical and patient-reported outcomes data. Patients and Methods: We used data from women undergoing mastectomy and reconstruction at 11 study sites in North America to develop three distinct ML models. We used data of ten sites to predict clinically meaningful improvement or worsening by comparing pre-surgical scores with 2 year follow-up data measured by validated Breast-Q domains. We employed ten-fold cross-validation to train and test the algorithms, and then externally validated them using the 11th site’s data. We considered area-under-the-receiver-operating-characteristics-curve (AUC) as the primary metric to evaluate performance. Results: Overall, between 1454 and 1538 patients completed 2 year follow-up with data for physical, sexual, and psychosocial well-being. In the hold-out validation set, our ML algorithms were able to predict clinically significant changes in physical well-being (chest and upper body) (worsened: AUC range 0.69–0.70; improved: AUC range 0.81–0.82), sexual well-being (worsened: AUC range 0.76–0.77; improved: AUC range 0.74–0.76), and psychosocial well-being (worsened: AUC range 0.64–0.66; improved: AUC range 0.66–0.66). Baseline patient-reported outcome (PRO) variables showed the largest influence on model predictions. Conclusions: Machine learning can predict long-term individual PROs of patients undergoing postmastectomy breast reconstruction with acceptable accuracy. This may better help patients and clinicians make informed decisions regarding expected long-term effect of treatment, facilitate patient-centered care, and ultimately improve postoperative health-related quality of life.

60 APPLIED LIFE SCIENCES↗

NASA NTRSDate not supplied

Multi-Anvil Experimentation Applied to Planetary Differentiation

Planets undergo differentiation that includes segregation of metal from silicate at high temperatures and pressures ranging from deep planetary core pressures (>300 GPa for Earth)to very shallow conditions of asteroids (<100 MPa).The multi-anvil solid media apparatus accesses the middle part of this range from 3 to 30 GPa –pressure relevant to the interior of Mercury, Venus, Earth, Earth’s Moon, and Mars. Early planets are thought to have experienced high temperatures from a combination of heat sources including radioactive decay, gravitational and accretional heating, and impact processes. These heating events led to melting of mantles and cores, thus requiring an understanding of solid-liquid equilibria in metal-silicate systems. In 2006 we established a multi-anvil facility at NASA-JSC combining an 880 ton press and a Kawai/Walker type module from Rockland Research. Our high PT work has been greatly facilitated by use of the COMPRES multi-anvil assemblies (1). Our recent work has included studies of element partitioning between liquid metal and liquid silicate(e.g., 2), as well as between minerals and melts(e.g., 3), both of which have led to better constraints on the timing and conditions of planetary differentiation(e.g. 4). Several examples involving sustained efforts will be summarized below and for the presentation. The distribution of siderophile (iron-loving) elements between core and mantle is controlled by metal-silicate equilibrium across a wide range of pressures. Therefore, experimentation across this pressure range helps to calibrate elemental partitioning models that can be applied to planets, and used to predict mantle chemistry and composition during planetary differentiation. Our studies have focused on a wide range of siderophile elements (refractory Ni, Co, W, Mo; volatile P, Ga, Cu, Sn, Sb; highly siderophile Au, Pd) that have constrained partitioning, valence, and isotopic fractionation, and applied to Earth, Moon, and Mars. When molten mantles (magma oceans) cool enough to initiate crystallization, the solids precipitate at depth and in large planets this involves high pressure phases like garnet, majorite, akimotoite, and ringwoodite. As these solids precipitate they can segregate from liquid by density contrasts, thus causing elemental fractionation which can be used to decipher timing of differentiation. Mineral/melt and metal/silicate equilibria in our lab have helped to better understand high pressure fractionation of isotopic parent/daughter pairs Hf/W, Mn/Cr, Pd/Ag, Pt/Os, Re/Os, and U/Pb, and their application to Earth, Moon and Mars. There remains great potential for multi-anvil experimentation to shed light on many pressure–dependent aspects of planetary evolution such as core formation, high pressure phase equilibria, redox equilibria, and volatile evolution and storage. References1. Leinenweber, K., et al.(2012) American Mineralogist,97, 353–368. 2.Righter, K., et al.(2020) Geochem. Persp. Lett.15, 1-6.3. Righter, K., et al.(2020) Met. Planet. Sci 55, 2741-2757.4. Righter, K., et al.(2020)Earth and Planetary Science Letters,552, 116590.

pressure↗

DOE OSTI2021

Improved departure from nucleate boiling prediction in rod bundles using a physics-informed machine learning-aided framework

The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is a regulatory limit for the licensing of pressurized water reactors (PWRs) worldwide. Despite the abundance of predictive tools available to the reactor thermal-hydraulics community, the path for an accurate CHF model remains elusive. This work approaches the prediction of DNB through a physics-informed machine learning-aided framework (PIMLAF) with the objective of achieving superior predictive capabilities for a rod bundle. In view of the limitations in existing macro-scale physics-driven tools, an improved mechanistic model is first proposed, leveraging key concepts in the liquid sublayer dryout and bubble crowding mechanisms. Furthermore, the proposed mechanistic model is able to predict DNB in different heater geometries for a broad range of flow conditions without the need for recalibration. This model is then incorporated as the physics-informed component of the hybrid framework PIMLAF, which takes advantage of established understanding in the field (i.e., domain knowledge [DK]) and uses machine learning (ML) to capture undiscovered information from the mismatch between the actual and DK-predicted output. Two bundle-related case studies using the PWR subchannel and bundle tests (PSBT) database are carried out to illustrate the PIMLAF’s improved performance over traditional approaches for both interpolation and extrapolation purposes. In light of the PIMLAF’s promising potential to reduce prediction error, reactor vendors are encouraged to leverage their in-house experimental efforts and apply the hybrid framework to potentially achieve margin reductions in the minimum DNB ratio (MDNBR) for the designs of interest.

42 ENGINEERING↗

NASA NTRS1974

A filter spectrometer concept for facsimile cameras

A concept which utilizes interference filters and photodetector arrays to integrate spectrometry with the basic imagery function of a facsimile camera is described and analyzed. The analysis considers spectral resolution, instantaneous field of view, spectral range, and signal-to-noise ratio. Specific performance predictions for the Martian environment, the Viking facsimile camera design parameters, and a signal-to-noise ratio for each spectral band equal to or greater than 256 indicate the feasibility of obtaining a spectral resolution of 0.01 micrometers with an instantaneous field of view of about 0.1 deg in the 0.425 micrometers to 1.025 micrometers range using silicon photodetectors. A spectral resolution of 0.05 micrometers with an instantaneous field of view of about 0.6 deg in the 1.0 to 2.7 micrometers range using lead sulfide photodetectors is also feasible.

Jobson, D. J.↗

NASA NTRS2003

The Chandra X-Ray Observatory Radiation Environmental Model Update

CRMFLX (Chandra Radiation Model of ion FLUX) is a radiation environment risk mitigation tool for use as a decision aid in planning the operation times for Chandra's Advanced CCD Imaging Spectrometer (ACIS) detector. The accurate prediction of the proton flux environment with energies of 100 - 200 keV is needed in order to protect the ACIS detector against proton degradation. Unfortunately, protons of this energy are abundant in the region of space where Chandra must operate. In addition, on-board particle detectors do not measure proton flux levels of the required energy range. CRMFLX is an engineering environment model developed to predict the proton flux in the solar wind, magnetosheath, and magnetosphere phenomenological regions of geospace. This paper describes the upgrades to the ion flux databases for the magnetosphere, magnetosheath, and solar wind regions. These data files were created by using Geotail and Polar spacecraft flux measurements only when the Advanced Composition Explorer (ACE) spacecraft's 0.14 MeV particle flux was below a threshold value. This new database allows for CRMFLX output to be correlated with both the geomagnetic activity level, as represented by the Kp index, as well as with solar proton events. Also, reported in this paper are results of analysis leading to a change in Chandra operations that successfully mitigates the false trigger rate for autonomous radiation events caused by relativistic electron flux contamination of proton channels.

Blackwell, William C.↗

NASA NTRS1980

Galileo probe forebody entry thermal protection - Aerothermal environments and heat shielding requirements

Solutions are presented for the aerothermal heating environments and the material thermal response for the forebody heatshield on the candidate 242 kg Galileo probe entering the modeled nominal and cold-dense Jovian atmospheres. In the flowfield analysis, a finite difference procedure was employed to obtain benchmark predictions of pressure, radiation and convective heating rates (both laminar and turbulent) and the corresponding wall blowing obtained under the steady state approximation. The fluxes over the probe flank were found to be in a range where spallation is an important mass loss mechanism. The predicted heating rates were also used as boundary conditions for a charring materials ablation which was used to predict thermochemical based surface recession, mass loss and bondline temperatures. The contingency factor of 30% currently employed by NASA was found to be insufficient for entry into the cold-dense atmosphere.

Nicolet, W. E.↗

DOE OSTI2021

Development of a Turbulent Liquid Spray Atomization Model for Diesel Engine Simulations (Final Technical Report)

This project addresses the systematic lack of predictive capabilities by spray models within engine CFD codes. We develop a new modeling approach to predict the breakup of diesel sprays based on recent literature showing that liquid turbulence plays a fundamental role in spray atomization. A new body of quantitative validation data is also developed as a critical element of the project, leveraging the joint capabilities of Georgia Tech’s high-pressure continuous-flow spray chamber and Argonne National Lab’s near-nozzle x-ray diagnostics at the Advanced Photon Source. This project contributes spatially-resolved measurements of drop size distribution within well-characterized diesel injectors, Spray A and D, from the Engine Combustion Network (ECN) to the engine combustion community for the first time. Utilizing this new body of measurements, we validate and demonstrate a new spray model for diesel sprays, termed the KH-Faeth model, that predicts global and local spray characteristic more accurately than the widely adopted and employed KH model. Predicted drop size distributions are seen to predict measured drops sizes both quantitatively and predictively, with accurate response in droplet size distributions over a wide range of ambient density, injection pressure, and injector nozzle size (Spray A and D) without model tuning. The KH-Faeth model can reduce error in the predicted centerline droplet size profile by up to 80% for ECN Spray D simulations when compared to use of the widely employed KH model.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1985

Creep fatigue life prediction for engine hot section materials (isotropic)

The first two years of a two-phase program aimed at improving the high temperature crack initiation life prediction technology for gas turbine hot section components are discussed. In Phase 1 (baseline) effort, low cycle fatigue (LCF) models, using a data base generated for a cast nickel base gas turbine hot section alloy (B1900+Hf), were evaluated for their ability to predict the crack initiation life for relevant creep-fatigue loading conditions and to define data required for determination of model constants. The variables included strain range and rate, mean strain, strain hold times and temperature. None of the models predicted all of the life trends within reasonable data requirements. A Cycle Damage Accumulation (CDA) was therefore developed which follows an exhaustion of material ductility approach. Material ductility is estimated based on observed similarities of deformation structure between fatigue, tensile and creep tests. The cycle damage function is based on total strain range, maximum stress and stress amplitude and includes both time independent and time dependent components. The CDA model accurately predicts all of the trends in creep-fatigue life with loading conditions. In addition, all of the CDA model constants are determinable from rapid cycle, fully reversed fatigue tests and monotonic tensile and/or creep data.

Moreno, Vito↗

NASA NTRSDate not supplied

Broadband Predictions of Optimized Proprotors in Axial Forward Flight

A low-fidelity toolchain was used to predict broadband self-noise trends for three proprotors in axial flight: a baseline (C24ND) and two acoustically constrained proprotors (OPT-III and COPR-3). Rotor loads were predicted with the ANOPP Propeller Analysis System (PAS) and self-noise was predicted with the semiempirical method of Brooks, Pope, and Marcolini (BPM) implemented in the ANOPP2 Self-Noise Internal Functional Module (ASNIFM). Comparisons to experimental data revealed that trends for turbulent boundary layer trailing edge (TBL-TE) noise could be modeled across several flight conditions by increasing the boundary layer thicknesses via the trip setting. Since the Mach number range of the BPM method is exceeded in these predictions, a dependence of boundary layer displacement thickness on blade station Mach number was suggested as a possible reason for needing to model thicker boundary layers, suggesting that the TBL-TE model needs to be developed further. Bluntness vortex shedding noise (BVS) predictions required tuning the trailing edge thickness and trailing edge closure angle for each flight condition to match experimental trends, demonstrating that the BVS noise model is incomplete and that BVS noise may vary with the angle of attack. This study indicates that the BPM self-noise method needs to be improved, which will lead to more accurate broadband predictions.

Broadband self-noise↗

NASA NTRSDate not supplied

Broadband Predictions of Optimized Proprotors in Axial Forward Flight

Broadband self-noise↗

DOE OSTI2025

Grain Boundary Segregation Suppresses Local Short‐Range Ordering in Nanocrystalline High‐Entropy Alloys

Multi-principal-element alloys like high-entropy alloys (HEAs) have potential applications in many engineering fields due to their unique mechanical/functional properties. While HEAs are generally considered random solid solutions, recent studies revealed that they are prone to short-range-ordering (SRO) due to the complex multi-pair-wise interactions among the constituent elements. Meanwhile, SROs' evolution can sometimes be deleterious, and it is necessary to have control over their evolution. Examining the AlCoCrFe-Zr model alloy, long-range ordering occurs following the expectation of enthalpic predictions. Advanced characterization techniques—transmission electron microscopy, high-energy synchrotron X-ray diffraction/pair distribution function, and atom probe tomography, reveal that SRO is suppressed in as-milled and GB-decorated NC-(AlCoCrFe)100-xZrx (x = 0–1.5 atomic %). Warren-Cowley coefficient calculations are further used to validate the suppression of SRO. Besides the low segregation enthalpies of Cr, Fe, and Zr, and the high-mixing enthalpy of Cr and Fe, the short diffusion path to GBs due to high-GB density in the NC-HEAs and the higher energy state of the GBs than the matrix promotes GB-segregation that further alters the matrix chemistry and consequently disfavors SRO formation within the matrix. Despite the GB-segregation of Cr, Fe, and Zr, the matrices and GBs remain in a random solid solution.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Autonomous nondestructive evaluation of resistance spot welded joints

The application of non-destructive evaluation approaches has attracted strong interests in modern automotive industries. Here, we present an autonomous deep-computing framework to analyze raw videos from infrared systems and to predict weld nugget shape and size with unprecedented accuracy and speed. In a comprehensive training and testing experiment with 90 videos (seven sets of welding material stack-ups), a new method was developed to assemble sufficient datasets for neural network training. Our framework successfully predicts all the nugget shapes with F1 scores that range from 0.84 to 0.92. The total training time on Nvidia DGX station takes less than 10 min for each set of welding material stack-up. The real inference time of an individual dataset (with 30 video frames) takes about 0.005 s. The procedure and methods developed in the study can be applied to other image-based weld property prediction, as well as other manufacturing processes. Furthermore, our well-trained neural networks take limited memory resources (2.3 MB) and are suitable for embedded microprocessors for in-situ welding quality control as edge computing within an intelligent welding framework.

42 ENGINEERING↗

NASA NTRSDate not supplied

Fan Noise Predictions of the NASA Source Diagnostic Test using Unsteady Simulations with LAVA Part II - Tonal and Broadband Noise Assessment

The NASA Source Diagnostic Test (SDT) campaign experimental data is used for validation of a sliding mesh technique recently implemented within the Launch, Ascent, and Vehicle Aerodynamics computational framework for time-accurate simulation of rotating fans. The far-field acoustics are analyzed in this work, building upon the aerodynamic validation studies previously published in Part I. Two modeling approaches are explored: the unsteady Reynolds-averaged Navier Stokes (uRANS) with the negative Spalart-Allmaras (SA-neg) turbulence model closure, and a hybrid Reynolds-averaged Navier Stokes/large-eddy simulation (RANS/LES) paradigm employing a zonal detached-eddy simulation (ZDES) closure with enhanced shielding protection. Two convective flux scheme approaches with different dissipation properties are also explored with ZDES. The Ffowcs-Williams and Hawkings (FW-H) permeable surface approach is used for propagation of the near-field acoustics to the far-field microphone locations. This work analyzes the low-speed (approach) condition, characterized by a fan rotation speed of 7808 rotations-per-minute (RPM). Three different grid levels ranging between 200 million and 1.1 billion grid points are considered. Results show good prediction of the broadband noise levels at sideline angles ranging between 70° and 110°. The forward arc observers show an under-prediction of the overall sound pressure levels (OASPL) even at the fine grid level. A breakdown of the inlet and exhaust contributions reveals a steep drop-off in the broadband noise levels past a blade-passing frequency (BPF) of 1.5, potentially caused by a lack of resolved small-scale turbulent fluctuations in the interstage region. Past 110° the OASPL are over-predicted by up to 10 dB due to an over-prediction of the low-frequency broadband noise levels in the aft arc. The detuning of BPF2 caused by small deviations in the blade stagger angle around the wheel is captured, and a corresponding decrease in the sound power level for this cut-on tone is observed.

AATT↗