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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

Model ensembles↗
DOE OSTI2021

Lithium-ion battery physics and statistics-based state of health model

A pseudo-2d model using COMSOL Multiphysics® software is developed to simulate performance and performance degradation of Li-ion batteries consisting of layered and olivine cathodes with graphite anode when subjected to peak shaving grid service. Multiple degradation pathways are considered, including solid electrolyte interphase (SEI) formation and breakdown at the anode, cathode dissolution and its synergistic effect on SEI formation at the anode. The model is validated by simulating commercial cylindrical cell performance. A global model is developed to simulate performance across all chemistries, along with individual chemistry models using global model parameters as initial values. There is good agreement between these models for various optimization parameters such as SEI equilibrium potential, cathode dissolution exchange current density, solvent diffusivity in the SEI and SEI ionic conductivity. To circumvent time constraints related to the COMSOL model, a 0d global model is developed which fits data well and provides more clarity on differences in cathode dissolution exchange current density. Again, good agreement for various optimization parameters is obtained among the COMSOL global & individual chemistry models and the 0-d model. The lessons learned from the physics-based model is used to develop a top down statistics-based model using current, voltage and anode volumetric change per mole lithium intercalated, along with their interactions as degradation predictors. This model predicts out of sample degradation for multiple grid services and electric vehicle drive cycle with high accuracy and provides the pathway to develop an efficient battery management system combining machine learning and findings from physics-based computationally intensive algorithms.

Crawford, Aladsair J.↗
DOE OSTI2016

Studies of nuclei under the extreme conditions of density, temperature, isospin asymmetry and the phase diagram of hadronic matter

The main emphasis of the entire project is on issues having to do with medium energy and ultra-relativistic energy and heavy ion collisions. A major goal of both theory and experiment is to study properties of hot dense nuclear matter under various extreme conditions and to map out the phase diagram in density or chemical potential and temperature. My studies in medium energy nuclear collisions focused on the liquid-gas phase transition and cluster yields from such transitions. Here I developed both the statistical model of nuclear multi-fragmentation and also a mean field theory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRSDate not supplied

A Framework for Software Health Management Using Bayesian Statistics

Software Health Management (SWHM) must make sure that the software always remains in safe and healthy regions of the state space. Boundaries between healthy and unhealthy regions are important for the detection of violations and health management.In this position paper, we present a framework, which employs techniques from Bayesian statistical modeling and active learning to efficiently characterize health boundaries in high-dimensional spaces. We will discuss, how this framework supports SWHM during design time and during operation of learning/adapting software systems

software health management↗
DOE OSTI2016

TH-CD-207A-05: Lung Surface Deformation Vector Fields Prediction by Monitoring Respiratory Surrogate Signals

Purpose: In this study, we developed and evaluated a method for predicting lung surface deformation vector fields (SDVFs) based on surrogate signals such as chest and abdomen motion at selected locations and spirometry measurements. Methods: A Patient-specific 3D triangular surface mesh of the lung region at end-expiration (EE) phase was obtained by threshold-based segmentation method. For each patient, a spirometer recorded the flow volume changes of the lungs; and 192 selected points at a regular spacing of 2cm X 2cm matrix points over a total area of 34cm X 24cm on the surface of chest and abdomen was used to detect chest wall motions. Preprocessing techniques such as QR factorization with column pivoting (QRCP) were employed to remove redundant observations of the chest and abdominal area. To create a statistical model between the lung surface and the corresponding surrogate signals, we developed a predictive model based on canonical ridge regression (CRR). Two unique weighting vectors were selected for each vertex on the surface of the lung, and they were optimized during the training process using the all other phases of 4D-CT except the end-inspiration (EI) phase. These parameters were employed to predict the vertices locations of a testing data set, which was the EI phase of 4D-CT. Results: For ten lung cancer patients, the deformation vector field of each vertex of lung surface mesh was estimated from the external motion at selected positions on the chest wall surface plus spirometry measurements. The average estimation of 98th percentile of error was less than 1 mm (AP= 0.85, RL= 0.61, and SI= 0.82). Conclusion: The developed predictive model provides a non-invasive approach to derive lung boundary condition. Together with personalized biomechanical respiration modelling, the proposed model can be used to derive the lung tumor motion during radiation therapy accurately from non-invasive measurements.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Deformed Materials: Towards a Theory of Materials Morphology Dynamics

This grant supported work on the response of crystals to external stress. Our primary work described how disordered structural materials break in two (statistical models of fracture in disordered materials), studied models of deformation bursts (avalanches) that mediate deformation on the microscale, and developed continuum dislocation dynamics models for plastic deformation (as when scooping ice cream bends a spoon, Fig. 9). Glass is brittle -- it breaks with almost atomically smooth fracture surfaces. Many metals are ductile -- when they break, the fracture surface is locally sheared and stretched, and it is this damage that makes them hard to break. Bone and seashells are made of brittle material, but they are strong because they are disordered -- lots of little cracks form as they are sheared and near the fracture surface, diluting the external force. We have studied materials like bone and seashells using simulations, mathematical tools, and statistical mechanics models from physics. In particular, we studied the extreme values of fracture strengths (how likely will a beam in a bridge break far below its design strength), and found that the traditional engineering tools could be improved greatly. We also studied fascinating crackling-noise precursors -- systems which formed microcracks of a broad range of sizes before they broke. Ductile metals under stress undergo irreversible plastic deformation -- the planes of atoms must slide across one another (through the motion of dislocations) to change the overall shape in response to the external force. Microscopically, the dislocations in crystals move in bursts of a broad range of sizes (termed 'avalanches' in the statistical mechanics community, whose motion is deemed 'crackling noise'). In this grant period, we resolved a longstanding mystery about the average shape of avalanches of fixed duration (using tools related to an emergent scale invariance), we developed the fundamental theory describing the shapes of avalanches and how they are affected by the edges of the microscope viewing window, we found that slow creep of dislocations can trigger an oscillating response explaining recent experiments, we explained avalanches under external voltage, and we have studied how avalanches in experiments on the microscale relate to deformation of large samples. Inside the crystals forming the metal, the dislocations arrange into mysterious cellular structures, usually ignored in theories of plasticity. Writing a natural continuum theory for dislocation dynamics, we found that it spontaneously formed walls -- much like models of traffic jams and sonic booms. These walls formed rather realistic cellular structures, which we examined in great detail -- our walls formed fractal structures with fascinating scaling properties, related to those found in turbulent fluids. We found, however, that the numerical and mathematical tools available to solve our equations were not flexible enough to incorporate materials-specific information, and our models did not show the dislocation avalanches seen experimentally. In the last year of this grant, we wrote an invited review article, explaining how plastic flow in metals shares features with other stressed materials, and how tools of statistical physics used in these other systems might be crucial for understanding plasticity.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Revisiting the photodissociation dynamics of the phenyl radical

We have reinvestigated the photodissociation dynamics of the phenyl radical at 248 nm and 193 nm via photofragment translational spectroscopy under a variety of experimental conditions aimed at reducing the nascent internal energy of the phenyl radical and eliminating signal from contaminants. Under these optimized conditions, slower translational energy (P(E{sub T})) distributions for H-atom loss were seen at both wavelengths than in previously reported work. At 193 nm, the branching ratio for C{sub 2}H{sub 2} loss vs. H-atom loss was found to be 0.2 ± 0.1, a significantly lower value than was obtained previously in our laboratory. The new branching ratio agrees with calculated Rice-Ramsperger-Kassel-Marcus rate constants, suggesting that the photodissociation of the phenyl radical at 193 nm can be treated using statistical models. The effects of experimental conditions on the P(E{sub T}) distributions and product branching ratios are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte

Solid polymer electrolytes have yet to achieve the an ionic conductivity > 1 mS/cm at room temperature for realistic applications. This target implies the need to reduce the effective energy barriers of ion transport in polymer electrolytes to around 20 kJ/mol. In this work, we combine information extracted from existing experimental results with theoretical calculations to provide insights into ion transport in single-ion conductors (SICs) with a focus on lithium ion SICs. Through the analysis of temperature-dependent ionic conductivity data obtained from the literature, we evaluate different methods of extracting energy barriers for lithium transport. The traditional Arrhenius fit to the temperature-dependent ionic conductivity data indicates that the Meyer-Neldel rule holds for SICs. However, the values of the fitting parameters remain unphysical. Our modified approach based on recent work (Macromolecules, 56, 15, 6051(2023)), which incorporates a fixed pre-exponential factor, reveals that the energy barriers exhibit temperature dependence over a wide range of temperatures. Using this approach, we identify a series of anions leading to the energy barriers less than 30 kJ/mol, which include trifluoromethane sulfonimide (TFSI), fluoromethane sulfonimide (FSI), and boron-based organic anions. In our efforts to design the next generation of anions, which can exhibit the energy barriers less than 20 kJ/mol, we focused on boron-containing SICs, and performed density functional theory (DFT) based calculations to connect the chemical structures via the binding energy of cation (lithium)-anion pairs with the experimentally derived effective energy barriers for ion transport. Not only have we identified a correlation between the binding energy and the energy barriers, but we also propose a strategy to design new boron-based anions by using the correlation. This combined approach involving experiments and theoretical calculations is capable of facilitating the identification of promising new anions, which can exhibit ionic conductivity $> 1$ mS/cm near room temperature, thereby expediting the development of novel superionic single-ion conducting polymer electrolytes. The published datasets include all the temperature-dependent ionic conductivity collected from the literature with literature DOIs, DFT calculated binding energies, and python scripts to analyze data, construct statistical models, and generate plots.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Uncertainty Quantification and Management for Multi-scale Nuclear Materials Modeling

Understanding and improving microstructural mechanical stability in metals and alloys is central to the development of high strength and high ductility materials for cladding and cores structures in advanced fast reactors. Design and enhancement of radiation-induced damage tolerant alloys are facilitated by better understanding the connection of various unit processes to collective responses in a multiscale model chain, including: dislocation nucleation, absorption and desorption at interfaces; vacancy production, radiation-induced segregation of Cr and Ni at defect clusters (point defect sinks) in BCC Fe-Cr ferritic/martensitic steels; investigation of interaction of interstitials and vacancies with impurities (V, Nb, Ta, Mo, W, Al, Si, P, S); time evolution of swelling (cluster growth) phenomena of irradiated materials; and energetics and kinetics of dislocation bypass of defects formed by interstitial clustering and formation of prismatic loops, informing statistical models of continuum character with regard to processes of dislocation glide, vacancy agglomeration and swelling, climb and cross slip.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Effect of Holding Pressure on Microstructure and Mechanical Properties of A356 Aluminum Alloy

In this study, the effect of holding pressure on microstructure and mechanical properties of low-pressure die cast A356 aluminum alloy was investigated. The results showed that the application of high holding pressure (300 kPa) generated castings with denser structure and superior mechanical properties. By increasing the holding pressure up to 300 kPa, the size of secondary dendrite arm spacing greatly reduced by 22.7% at the cooling rate of 1°C/s and decreased by 12.8% at 10°C/s. The Feret’s diameter and aspect ratio of eutectic silicon particles decreased by 8.4 and 5.1% at the cooling rate of 1°C/s and decreased by 9.3 and 6.4% at 10°C/s, respectively. Meanwhile, the density of A356 aluminum alloy increased to 2.678 g/cm{sup 3} and the area fraction of porosity decreased to 0.035%. Thus, tensile properties of A356 aluminum alloy obtained at high holding pressure were enhanced, especially the ductility. All these could be associated with the better filling capability and faster cooling rate caused by high holding pressure. In the analytical range of experimental conditions, the correlation of mechanical properties with process parameters was established by statistical models to predict the ultimate tensile strength and elongation of low-pressure die cast A356 aluminum alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Simulation of the dissipated and stored energy under deformation and failure of metallic materials

This work is devoted to the development of the statistical model of the structural defect evolution which was developed at the Institute of continuous media mechanics UB RAS. This model takes into account stochastic properties of the defect evolution process, nonlinear interaction of defects, and connection between microplasticity and damage accumulation. The obtained constitutive equations allow us to propose a model of the energy storage and dissipation in the process of plastic deformation and failure of metallic materials. The obtained relations were adapted for standard finite-element package. Applicability of this model was demonstrated in three-dimensional simulation of the strain localization and crack propagation in metals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

THE STATISTICAL MECHANICS OF PLANET ORBITS

The final “giant-impact” phase of terrestrial planet formation is believed to begin with a large number of planetary “embryos” on nearly circular, coplanar orbits. Mutual gravitational interactions gradually excite their eccentricities until their orbits cross and they collide and merge; through this process the number of surviving bodies declines until the system contains a small number of planets on well-separated, stable orbits. In this paper we explore a simple statistical model for the orbit distribution of planets formed by this process, based on the sheared-sheet approximation and the ansatz that the planets explore uniformly all of the stable region of phase space. The model provides analytic predictions for the distribution of eccentricities and semimajor axis differences, correlations between orbital elements of nearby planets, and the complete N-planet distribution function, in terms of a single parameter, the “dynamical temperature,” that is determined by the planetary masses. The predicted properties are generally consistent with N-body simulations of the giant-impact phase and with the distribution of semimajor axis differences in the Kepler catalog of extrasolar planets. A similar model may apply to the orbits of giant planets if these orbits are determined mainly by dynamical evolution after the planets have formed and the gas disk has disappeared.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SU-D-202-02: Quantitative Imaging: Correlation Between Image Feature Analysis and the Accuracy of Manually Drawn Contours On PET Images

Purpose: To evaluate the correlation between image features and the accuracy of manually drawn target contours on synthetic PET images Methods: A digital PET phantom was used in combination with Monte Carlo simulation to create a set of 26 simulated PET images featuring a variety of tumor shapes and activity heterogeneity. These tumor volumes were used as a gold standard in comparisons with manual contours delineated by 10 radiation oncologist on the simulated PET images. Metrics used to evaluate segmentation accuracy included the dice coefficient, false positive dice, false negative dice, symmetric mean absolute surface distance, and absolute volumetric difference. Image features extracted from the simulated tumors consisted of volume, shape complexity, mean curvature, and intensity contrast along with five texture features derived from the gray-level neighborhood difference matrices including contrast, coarseness, busyness, strength, and complexity. Correlation between these features and contouring accuracy were examined. Results: Contour accuracy was reasonably well correlated with a variety of image features. Dice coefficient ranged from 0.7 to 0.90 and was correlated closely with contrast (r=0.43, p=0.02) and complexity (r=0.5, p<0.001). False negative dice ranged from 0.10 to 0.50 and was correlated closely with contrast (r=0.68, p<0.001) and complexity (r=0.66, p<0.001). Absolute volumetric difference ranged from 0.0002 to 0.67 and was correlated closely with coarseness (r=0.46, p=0.02) and complexity (r=0.49, p=0.008). Symmetric mean absolute difference ranged from 0.02 to 1 and was correlated closely with mean curvature (r=0.57, p=0.02) and contrast (r=0.6, p=0.001). Conclusion: The long term goal of this study is to assess whether contouring variability can be reduced by providing feedback to the practitioner based on image feature analysis. The results are encouraging and will be used to develop a statistical model which will enable a prediction of contour accuracy based purely on image feature analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Dependence of the compensation error on the error of a sensor and corrector in an adaptive optics phase-conjugating system

In the framework of a statistical model of an adaptive optics system (AOS) of phase conjugation, three algorithms based on an integrated mathematical approach are considered, each of them intended for minimisation of one of the following characteristics: the sensor error (in the case of an ideal corrector), the corrector error (in the case of ideal measurements) and the compensation error (with regard to discreteness and measurement noises and to incompleteness of a system of response functions of the corrector actuators). Functional and statistical relationships between the algorithms are studied and a relation is derived to ensure calculation of the mean-square compensation error as a function of the errors of the sensor and corrector with an accuracy better than 10%. Because in adjusting the AOS parameters, it is reasonable to proceed from the equality of the sensor and corrector errors, in the case the Hartmann sensor is used as a wavefront sensor, the required number of actuators in the absence of the noise component in the sensor error turns out 1.5 – 2.5 times less than the number of counts, and that difference grows with increasing measurement noise. (adaptive optics)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Reactive Dye Adsorption from Aqueous Solution on HPEI-Modified Fe{sub 3}O{sub 4} Nanoparticle as a Superadsorbent: Characterization, Modeling, and Optimization

In this work, the optimum condition for the adsorption of Reactive Blue 19 dye onto Fe{sub 3}O{sub 4} functionalized with hyperbranched polyethylenimine (Fe@HPEI) was determined using response surface methodology. The properties of synthesized magnetic Fe@HPEI nanoparticles were ascertained using FTIR, SEM, TEM, VSM and zeta potential measurement. The experimental adsorption data indicating the positive effect of adsorbent dose and contact time, and the negative effect of pH, initial dye concentration and ionic strength on dye adsorption. At optimal condition; pH 3.4, contact time 25 min, adsorbent dose 0.4 g/L and initial dye concentration 113 mg/L, Fe@HPEI nanoparticles removed dye up to 99.27%. The maximum experimental dye adsorption was near to the predicted value of 100%, which confirmed the reliability of the selected statistical model. The dye adsorption data were fitted well to Langmuir isotherm and pseudo-second-order kinetic, and the maximum adsorption capacity was 500 mg/g. Totally, Fe@HPEI as an effective superadsorbent can be repeatedly utilized for the adsorption of dye from the aqueous environment.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2014

On the Molière theory of multiple scattering of charged particles (1947–1948) and its critique in subsequent years

Several “misconceptions” regarding the theory of multiple scattering of fast charged particles in matter developed by Molière in 1947–1948 and its application in the analysis of experimental results are discussed. It is shown that the critics of this theory misinterpreted the Molière method for determining the cross section of particle scattering by atoms with the screening of their nuclear fields by electron shells described by the Thomas-Fermi statistical model. If the original Molière method is applied consistently, the obtained scattering cross section generally agrees with the results of later classical calculations carried out by Lindhard and his collaborators and other authors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Parallel Integration and Chromosomal Expansion of Metabolic Pathways

Robust fermentation of biomass-derived sugars into bioproducts demands the reliable microbial expression of metabolic pathways. Plasmid-based expression systems may suffer from instability and result in highly variable titers, rates, and yields. An established mitigation approach, Chemical Induced Chromosomal Expansion (CIChE), expands a singly integrated pathway to plasmid-like copy numbers, while maintaining stability in the absence of antibiotic selection pressure. Here, we report Parallel Integration and Chromosomal Expansion (PIACE), extensions to CIChE that enable independent expansions of pathway components across multiple loci, use suicide vectors to achieve high-efficiency site-specific integration of sequence-validated multi-gene components, and introduce a heat-curable plasmid to obviate recA deletion post pathway expansion. We applied PIACE to stabilize an isopentenol pathway across three loci in E. coli DH1, and to then generate libraries of pathway component copy-number variants to screen for improved titers. Furthermore, polynomial regressor statistical modeling of the production screening data suggests that increasing copy numbers of all isopentenol pathway components would further improve titers.

09 BIOMASS FUELS↗