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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Modern data analytics approach to predict creep of high-temperature alloys

A breakthrough in alloy design often requires comprehensive understanding in complex multi-component/multi-phase systems to generate novel material hypotheses. We introduce a modern data analytics workflow that leverages high-quality experimental data augmented with advanced features obtained from high-fidelity models. Herein, we use an example of a consistently-measured creep dataset of developmental high-temperature alloy combined with scientific alloy features populated from a high-throughput computational thermodynamic approach. Extensive correlation analyses provide ranking insights for most impactful alloy features for creep resistance, evaluated from a large set of candidate features suggested by domain experts. We also show that we can accurately train machine learning models by integrating high-ranking features obtained from correlation analyses. Consequently, the demonstrated approach can be extended beyond incorporating thermodynamic features, with input from domain experts used to compile lists of features from other alloy physics, such as diffusion kinetics and microstructure evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A Simple Analytical Model for Predicting the Detectable Ion Current in Ion Mobility Spectrometry Using Corona Discharge Ionization Sources

Corona discharge ionization sources are often used in ion mobility spectrometers (IMS) when a non-radioactive ion source with high ion currents is required. Typically, the corona discharge is followed by a reaction region where analyte ions are formed from the reactant ions. In this work, we present a simple yet sufficiently accurate model for predicting the ion current available at the end of this reaction region when operating at reduced pressure as in High Kinetic Energy Ion Mobility Spectrometers (HiKE-IMS) or most IMS-MS instruments. It yields excellent qualitative agreement with measurement results and is even able to calculate the ion current within an error of 15%. Additional interesting findings of this model are the ion current at the end of the reaction region being independent from the ion current generated by the corona discharge and the ion current in High Kinetic Energy Ion Mobility Spectrometers (HiKE-IMS) growing quadratically when scaling down the length of the reaction region. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Residual Stress in Cold Spray SS304L Measured Via Neutron Diffraction and Comparison of Analytical Models to Predict the Residual Stress

Here, this study employs neutron diffraction to investigate the relationship between residual stress and coating thickness in cold sprayed 304L austenitic stainless steel. Results show that shot peening predominantly impacts the residual stress profile, leading to substantial in-plane compressive force. The impact of laser heating, a widely used method to alter cold spray's microstructural properties, on the coating's residual stress is also analyzed. The findings indicate that the maximum compressive residual stress in the in-plane component is mainly independent of coating thickness, which suggests that the material properties determine the maximum residual stress. The cold sprayed deposits possessed compressive, nearly biaxial strain and stresses. After laser heating, these stresses were replaced by tensile residual stresses. Two analytical models, the Tsui and Clyne and the Boruah models, for predicting residual stresses are also evaluated, and both models provide reasonable fits to the experimental data. At this point, the deviations between the experimental results and the models are principally caused by the inability of the current models to address plastic deformation and relaxation, and the residual stresses generated by thermal gradients.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data Analytics for Catalysis Predictions: Are We Ready Yet?

Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. Finally, we also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Validation of Combined Analytical Methods to Predict Slip in Cylindrical Roller Bearings

This paper presents a combination of models that together calculate the cage and roller speeds of a cylindrical roller bearing. The models consider elastohydrodynamic lubrication and contact elasticity between the roller and raceway, roller centrifugal forces, hydrodynamic lubrication at the cage pocket, and frictional forces. Using these models, the predicted cage and roller speeds and the extent of slip are compared to measurements acquired on cylindrical roller bearings in a commercial gearbox in steady-state and transient operating conditions of a wind turbine. In steady-state conditions at low wind speeds and low lubricant temperatures, cage and roller slip up to 60% occur in the loaded zone of the bearing. In the unloaded zone, up to 80% roller slip occurs. Cage and roller slip then decrease as the lubricant temperature and wind speed increases. In general, the analytical model results match experimental measurements within 10% for lubricant temperatures above 40 degrees C and wind speeds over 10 meters per second. The analytical model is further evaluated during a transient start-up event and highly dynamic emergency stop event and also used to examine changes in the bearing design or lubricant properties. Furthermore, roller and cage slip in cylindrical bearings is a combined effect of the bearing design, applied load, shaft speed, and lubricant properties and temperature, and can be quickly evaluated with the combined analytical models.

17 WIND ENERGY↗
DOE OSTI2019

An Analytical Model for Predicting Turn-ON Overshoot in Normally-OFF GaN HEMTs

Recently, a major challenge in the adoption of wide bandgap semiconductors for power electronic applications is the need to trade device performance for device safety. In this article, methods for predicting gate voltage overshoot in normally-OFF gallium nitride (GAN) high electron mobility transistors (HEMTs) are derived in order to deliver optimal device performance. Two models are proposed; a simple, yet less accurate second order model and a complex, yet more accurate fourth order model. These models allow for the calculation of gate resistances necessary for a desired amount of gate voltage overshoot. The nonlinear capacitances of the device are considered in the analysis. The models are validated with an experimental double-pulse tester. Furthermore, these newly developed models allow design engineers to extract the best possible performance of commercially available GaN devices while keeping the devices in their safe-operating region.

42 ENGINEERING↗
DOE OSTI2021

Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction

Data analytics methods have been increasingly applied to understanding materials chemistry, processing due to the manufacturing approach, and uni-axial and cyclic property relationships in the highly complex space of alloy design. There are several benefits to applying data analytics to this space, including the ability to manage non-linearities in the responses of the alloy attributes and the resulting mechanical properties. However, key difficulties in applying and understanding the results of data analytics include the often lack of reported assumptions and data processing steps necessary to improve interpretation and reproducibility in derived results. In this work, the methods used to generate clustering and correlation analyses for experimental 9% Cr ferritic-martensitic steel data were investigated and the resulting implications for mechanical property predictions were assessed. This work uses principal component analysis, partitioning around medoids, t-SNE, and k-means clustering to investigate trends in composition, processing and microstructure information with creep and tensile properties, building on work done previously using a smaller version of the same dataset. The initial assumptions, preprocessing steps and methods are investigated and outlined in order to depict the fine level of detail required to convey the steps taken to process data and produce analytical results. Here, the variations in the resulting analyses are explored due to the influence of new and more varied data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A combined experimental and numerical approach that eliminates the non-uniqueness associated with the Johnson-Cook parameters obtained using inverse methods

Abstract Johnson-Cook constitutive model is a commonly used material model for machining simulations. The model includes five parameters that capture the initial yield stress, strain-hardening, strain-rate hardening, and thermal softening behavior of the material. These parameters are difficult to determine using experiments since the conditions observed during machining (such as high strain-rates of the order of $$10^5$$ 10 5 /sec - $$10^6$$ 10 6 /sec) are challenging to recreate in the laboratory. To address this problem, several researchers have recently proposed inverse approaches where a combination of experiments and analytical models are used to predict the Johnson-Cook parameters. The errors between the measured cutting forces, chip thicknesses and temperatures and those predicted by analytical models are minimized and the parameters are determined. In this work, it is shown that only two of the five Johnson-Cook parameters can be determined uniquely using inverse approaches. Two different algorithms, namely, Adaptive Memory Programming for Global Optimization (AMPGO) and Particle Swarm Optimization (PSO), are used for this purpose. The extended Oxley’s model is used as the analytical tool for optimization. For determining a parameter’s value, a large range for each parameter is provided as an input to the algorithms. The algorithms converge to several different sets of values for the five Johnson-Cook parameters when all the five parameters are considered as unknown in the optimization algorithm. All of these sets, however, yield the same chip shape and cutting forces in FEM simulations. Further analyses show that only the strain-rate and thermal softening parameters can be determined uniquely and the three parameters present in the strain-hardening term of the Johnson-Cook model cannot be determined uniquely using the inverse method. A combined experimental and numerical approach is proposed to eliminate this determine all parameters uniquely.

42 ENGINEERING↗
DOE OSTI2017

Phase-field based Multiscale Modeling of Heterogeneous Solid Electrolytes: Applications to Nanoporous Li 3 PS 4

Modeling the effective ion conductivities of heterogeneous solid electrolytes typically involves the use of a computer-generated microstructure consisting of randomly or uniformly oriented fillers in a matrix. But, the structural features of the filler/matrix interface, which critically determine the interface ion conductivity and the microstructure morphology, have not been considered during the microstructure generation. In using nanoporous β-Li 3 PS 4 electrolyte as an example, we develop a phase-field model that enables generating nanoporous microstructures of different porosities and connectivity patterns based on the depth and the energy of the surface (pore/electrolyte interface), both of which are predicted through density functional theory (DFT) calculations. Room-temperature effective ion conductivities of the generated microstructures are then calculated numerically, using DFT-estimated surface Li-ion conductivity (3.14×10 -3 S/cm) and experimentally measured bulk Li-ion conductivity (8.93×10 -7 S/cm) of β-Li 3 PS 4 as the inputs. We also use the generated microstructures to inform effective medium theories to rapidly predict the effective ion conductivity via analytical calculations. Furthemore, when porosity approaches the percolation threshold, both the numerical and analytical methods predict a significantly enhanced Li-ion conductivity (1.74×10 -4 S/cm) that is in good agreement with experimental data (1.64×10 -4 S/cm). The present phase-field based multiscale model is generally applicable to predict both the microstructure patterns and the effective properties of heterogeneous solid electrolytes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Recursive Use of the Short-Time Fast Fourier Transform for Signature Analysis in Continuous Processes

Although a nuclear reactor is a hostile environment for sensing and electrical communications, the reactor core is amenable to acoustic communication. An acoustic measurement infrastructure (AMI) has been installed in the Advanced Test Reactor (ATR) to record acoustic signals that can capture its different operating regimes. AMI uses coolant pumps as continuous signal sources, coolant and structural components as transmission lines, and accelerometers to capture system motion. A recursive signal processing technique based on the short-time fast Fourier transform (STFFT) for continuous processes provides unique signatures for diagnostic and prognostic analyses from the system motion data. Here this article presents a recursive STFFT methodology that processes acoustic signals from continuous industrial processes. The article first discusses the initial STFFT use with simulated data to elucidate the basic principles necessary to understand and interpret the STFFT results from actual pump vibration data. Each repetitive use of the STFFT on pump vibration data using the results from the prior STFFT processing will generate additional complimentary time-frequency-based signatures. These signatures are generated by the coolant pumps operating under different process conditions. After each use of the STFFT, the resulting signatures provide exemplary examples of the diversity and intuitive nature of recursively using the STFFT. This article focuses on recursively using the STFFT to provide numerous complimentary and diverse signatures that will ultimately be inputs for machine learning algorithms that provide predictive data analytics. The intuitive nature of the information and signatures from recursive STFFT processing will also bring intuitive interpretation capabilities to machine learning and predictive data analytic techniques.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Single Bimodular Sensor for Differentiated Detection of Multiple Oxidative Gases

Semiconductive metal-oxide sensors suffer from cross-sensitivities under mixed chemical condition, specifically upon mixture of multiple oxidative or reductive gases. Herein, a single bimodular sensor is demonstrated for smart differentiation of multiple oxidative analytes by relating the resistance-metric mode to impedance-metric mode. The sensor construct based on ZnO nanorods readily outputs three response datasets upon exposure of oxidative-gas mixture including O 2 , SO 2 , and NO 2 , the resistance, real part impedance, and imaginary part impedance. The differentiative and correlated nature between these response signals allows such a single sensor platform to differentiate these oxidative gases accurately and robustly. Linear and non-linear decision boundaries are established over a large gas-concentration range from 2 ppm to 3% through a combination of principal component analysis and artificial neural network training. A facile user interface is demonstrated for recognition and measurement of unknown gas analytes, with the error of the predicted analyte-concentration as low as 2%.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Kinetic model for the collisionless sheath of a collisional plasma

Collisional plasmas typically have mean-free-path still much greater than the Debye length, so the sheath is mostly collisionless. Once the plasma density, temperature, and flow are specified at the sheath entrance, the profile variation of electron and ion density, temperature, flow speed, and conductive heat fluxes inside the sheath is set by collisionless dynamics, and can be predicted by an analytical kinetic model distribution. These predictions are contrasted here with direct kinetic simulations, showing good agreement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

TH-A-19A-06: Site-Specific Comparison of Analytical and Monte Carlo Based Dose Calculations

Purpose: To investigate the impact of complex patient geometries on the capability of analytical dose calculation algorithms to accurately predict dose distributions and to verify currently used uncertainty margins in proton therapy. Methods: Dose distributions predicted by an analytical pencilbeam algorithm were compared with Monte Carlo simulations (MCS) using TOPAS. 79 complete patient treatment plans were investigated for 7 disease sites (liver, prostate, breast, medulloblastoma spine and whole brain, lung and head and neck). A total of 508 individual passively scattered treatment fields were analyzed for field specific properties. Comparisons based on target coverage indices (EUD, D95, D90 and D50) were performed. Range differences were estimated for the distal position of the 90% dose level (R90) and the 50% dose level (R50). Two-dimensional distal dose surfaces were calculated and the root mean square differences (RMSD), average range difference (ARD) and average distal dose degradation (ADD), the distance between the distal position of the 80% and 20% dose levels (R80- R20), were analyzed. Results: We found target coverage indices calculated by TOPAS to generally be around 1–2% lower than predicted by the analytical algorithm. Differences in R90 predicted by TOPAS and the planning system can be larger than currently applied range margins in proton therapy for small regions distal to the target volume. We estimate new site-specific range margins (R90) for analytical dose calculations considering total range uncertainties and uncertainties from dose calculation alone based on the RMSD. Our results demonstrate that a reduction of currently used uncertainty margins is feasible for liver, prostate and whole brain fields even without introducing MC dose calculations. Conclusion: Analytical dose calculation algorithms predict dose distributions within clinical limits for more homogeneous patients sites (liver, prostate, whole brain). However, we recommend treatment plan verification using Monte Carlo simulations for patients with complex geometries.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Fast Surface Oxygen Release Kinetics Accelerate Nanoparticle Exsolution in Perovskite Oxides

Exsolution is a recent advancement for fabricating oxide-supported metal nanoparticle catalysts via phase precipitation out of a host oxide. A fundamental understanding and control of the exsolution kinetics are needed to engineer exsolved nanoparticles to obtain higher catalytic activity toward clean energy and fuel conversion. Since oxygen release via oxygen vacancy formation in the host oxide is behind oxide reduction and metal exsolution, we hypothesize that the kinetics of metal exsolution should depend on the kinetics of oxygen release, in addition to the kinetics of metal cation diffusion. In this report we probe the surface exsolution kinetics both experimentally and theoretically using thin-film perovskite SrTi 0.65 Fe 0.35 O 3 (STF) as a model system. We quantitatively demonstrated that in this system the surface oxygen release governs the metal nanoparticle exsolution kinetics. As a result, by increasing the oxygen release rate in STF, either by reducing the sample thickness or by increasing the surface reactivity, one can effectively accelerate the Fe 0 exsolution kinetics. Fast oxygen release kinetics in STF not only shortened the prereduction time prior to the exsolution onset, but also increased the total quantity of exsolved Fe 0 over time, which agrees well with the predictions from our analytical kinetic modeling. The consistency between the results obtained from in situ experiments and analytical modeling provides a predictive capability for tailoring exsolution, and highlights the importance of engineering host oxide surface oxygen release kinetics in designing exsolved nanocatalysts.

36 MATERIALS SCIENCE↗