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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

GIM3E: Condition-specific Models of Cellular Metabolism Developed from Metabolomics and Expression Data

Motivation: Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been reported. Results: GIMMME (Gene Inactivation Moderated by Metabolism, Metabolomics, and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics, and intracellular metabolomics data. GIMMME establishes metabolite utilization requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions, and also provides calculations of the turnover (production / consumption) flux of metabolites. GIMMME was employed to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. Availability: GIMMME has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/

Environmental Molecular Sciences Laboratory↗
DOE OSTI2023

DeepNeo: a webserver for predicting immunogenic neoantigens

Abstract Non-self epitopes, whether originated from foreign substances or somatic mutations, trigger immune responses when presented by major histocompatibility complex (MHC) molecules and recognized by T cells. Identification of immunogenically active neoepitopes has significant implications in cancer and virus medicine. However, current methods are mostly limited to predicting physical binding of mutant peptides and MHCs. We previously developed a deep-learning based model, DeepNeo, to identify immunogenic neoepitopes by capturing the structural properties of peptide-MHC pairs with T cell reactivity. Here, we upgraded our DeepNeo model with up-to-date training data. The upgraded model (DeepNeo-v2) was improved in evaluation metrics and showed prediction score distribution that better fits known neoantigen behavior. The immunogenic neoantigen prediction can be conducted at https://deepneo.net.

Kim, Jeong Yeon↗
DOE OSTI2021

Dynamics of voltage-driven oscillating insulator-metal transitions

Recent experiments demonstrated emerging alternating insulator and metal phases in Mott insulators actuated by a direct bias voltage, leading to oscillating voltage outputs with characteristic frequencies. Here, we develop a physics-based nonequilibrium model to describe the dynamics of oscillating insulator-metal phase transitions and experimentally validate it using a VO 2 device as a prototype. The oscillation frequency is shown to scale monotonically with the bias voltage and series resistance and terminate abruptly at lower and upper device-dependent limits, which are dictated by the nonequilibrium carrier dynamics. Here, we derive an approximate analytical expression for the dependence of the frequency on the device operating parameters, which yields a fundamental limit to the frequency and may be utilized to provide guidance to potential applications of insulator-metal transition materials as building blocks of brain-inspired non-von Neumann computers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Dirac-electron-mediated magnetic proximity effect in topological insulator/magnetic insulator heterostructures

The possible realization of dissipationless chiral edge current in a topological insulator/magnetic insulator heterostructure is based on the condition that the magnetic proximity exchange coupling at the interface is dominated by the Dirac surface states of the topological insulator. We report a polarized neutron reflectometry observation of Dirac-electron-mediated magnetic proximity effect in a bulk-insulating topological insulator (Bi 0.2 Sb 0.8 ) 2 Te 3 /magnetic insulator EuS heterostructure. We are able to maximize the proximity-induced magnetism by applying an electrical back gate to tune the Fermi level of topological insulator to be close to the Dirac point. A phenomenological model based on diamagnetic screening is developed to explain the suppressed proximity-induced magnetism at high carrier density. Our work paves the way to utilize the magnetic proximity effect at the topological insulator/magnetic insulator heterointerface for low-power spintronic applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Strain localization in dry sheared granular materials: A compactivity-based approach

Shear banding is widely observed in natural fault zones as well as in laboratory experiments on granular materials. Understanding the dynamics of strain localization under different loading conditions is essential for quantifying strength evolution of fault gouge, energy partitioning during earthquakes and characterizing rheological transitions and fault zone structure changes. To that end, we develop a physics-based continuum model for strain localization in sheared granular materials. The grain-scale dynamics is described by the Shear Transformation Zone (STZ) theory, a non-equilibrium statistical thermodynamic framework for viscoplastic deformation in amorphous materials. Using a finite strain computational framework, we investigate the initiation and growth of complex shear bands under a variety of loading conditions and identify implication for strength evolution and ductile to brittle transition. Our numerical results show similar localization patterns to field and lab observations and suggest that shear zones show more ductile response at higher confining pressures, lower dilatancy and loose initial conditions. Lower pressures, higher dilatancy and dense initial conditions favor a brittle response and larger strength drops. These findings shed light on a range of mechanisms for strength evolution in dry sheared granular materials and provide a critical input to physicsbased multiscale models of fault zone instabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Characterization and Analysis of Insulated Metal Substrate-Based SiC Power Module for Traction Application

This paper presents the electrical characterization and drive cycle--based thermal analysis of an insulated metal substrate (IMS)--based silicon carbide power module for high-power traction inverters. The substrate was constructed using a thin layer of polymer--ceramic blend dielectric material with a thick copper core to improve transient thermal performance. The cooling performance of this module has already been validated with promising results. In this paper, an experimental test bed was set up to evaluate the dynamic and static electrical performance of the designed module under a wide range of operating conditions. The characterization results were then used to develop a drive cycle--based thermal model to validate the performance compared to the traditional direct bonded copper--based power module. The results indicate that the IMS-based power module is a suitable solution for high-power traction applications.

Chowdhury, Shajjad↗
DOE OSTI2021

Investigating the Impact of Thickness, Calendering and Channel Structures of Printed Electrodes on the Energy Density of LIBs - 3D Simulation and Validation

Current lithium ion batteries (LIBs) are expensive and bulky, limited by relatively low charging rates. To increase the rate of charging and reduce weight, thin electrodes with high energy density are required. The increase in energy density can be achieved by several techniques including boosting electrolyte transport, high loading/utilization of active material, employing high conductive electrolytes and electrodes with advanced architectures, and increasing cell temperature. In this paper, a 3D physics-based electrochemical model of LIBs is developed in COMSOL simulation software for different thickness, calendering steps as well as channel structures (conical, cylindrical) to optimize the electrode design and in turn maximize volumetric energy density. The simulation results demonstrated that calendering the electrodes with high initial porosity increases the volumetric energy density of the cell. In addition, cylindrical channel structures with relatively lower edge-to-edge distance also results in increased volumetric energy density. The simulation results of the 3D model was validated by comparing it with experimental results.

improving volumetric energy density↗
DOE OSTI2013

A Dynamic Photovoltaic Model Incorporating Capacitive and Reverse-Bias Characteristics

Photovoltaics (PVs) are typically modeled only for their forward-biased dc characteristics, as in the commonly used single-diode model. While this approach accurately models the I-V curve under steady forward bias, it lacks dynamic and reverse-bias characteristics. The dynamic characteristics, primarily parallel capacitance and series inductance, affect operation when a PV cell or string interacts with switching converters or experiences sudden transients. Reverse-bias characteristics are often ignored because PV devices are not intended to operate in the reverse-biased region. However, when partial shading occurs on a string of PVs, the shaded cell can become reverse biased and develop into a hot spot that permanently degrades the cell. To fully examine PV behavior under hot spots and various other faults, reverse-bias characteristics must also be modeled. This study develops a comprehensive mathematical PV model based on circuit components that accounts for forward bias, reverse bias, and dynamic characteristics. Using a series of three experimental tests on an unilluminated PV cell, all required model parameters are determined. The model is implemented in MATLAB Simulink and accurately models the measured data.

Kim, KA↗
DOE OSTI2019

Novel PCM Integration with Electrical Heat Pump for Demand Response

In this paper, we examine the potential for using phase change material (PCM) as a thermal storage medium for improving the efficiency as well as enabling grid-responsive control of heat pump (HP) systems. Previous research in PCM integration in buildings has primarily been focused on integration with building envelopes. An objective of our research is to design a novel and practical PCM integration with existing HP systems. High-fidelity models for building, HP and PCM are developed to facilitate the model-based predictive control (MPC) design. To optimize the operation of HP and PCM, we introduce an optimization problem considering various constraints for both comfort and hardware constraints. We include experimental results in which a house fleet are optimized to minimize their power consumption or electricity cost. We numerically demonstrate the designs potential for reducing power consumption or electricity cost by 13% or 19%, respectively.

Dong, Jin↗
DOE OSTI2020

Novel Technique for Developing Linearized Convex System Models from Experimentally Derived Data

This paper presents a novel technique for generating a convex system model from an experimentally derived data set which features variance among repeated measurements. The convex system model developed as a test case characterizes the dynamic system losses of a vanadium redox flow battery as a function of the active power output and the battery state of charge. The technique hinges on a pre-cleaning via clustering procedure which precedes the formation of a planar convex hull comprised of triangular simplices. The clustering procedure efficiently reduces the experimental data set while mitigating variance among repeated measurements and removes outliers. Ultimately, the lower evelope of the planar convex hull serves as the desired convex system model. The proposed technique reduces systematic model error which is otherwise present when directly developing a planar convex hull model based on an unreduced data set.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Simulation App for Time-Resolved Photoluminescence in Thin-Film Solar Cells

We develop a COMSOL Multiphysics-based model and application, or "app", for time-resolved photoluminescence (TRPL) measurements in thin-film solar cells. TRPL is commonly used to measure recombination in thin-film solar cells. However, simulations show that decay times extracted from TRPL data are not, in general, equal to the Shockley-Read-Hall lifetime parameter set in the model. Here, we present a model and app, using graded CdSe y Te 1-y solar cells as an example, that can be used to study impacts of a range of potentially important material and measurement parameters on TRPL results.

minority-carrier lifetime↗
DOE OSTI2018

An Integrated Method of ANFIS and Dempster-Shafer Theory for Fault Diagnosis of Power Transformer

Dissolved gas analysis (DGA) approach is extensively applied to detect incipient faults of power transformers. This paper presents a novel DGA method for power transformer incipient fault diagnosis based on integrated adaptive neuro fuzzy inference system (ANFIS) and Dempster-Shafer Theory (DST). Four out of seven common conventional methods which are studied and compared for better consistency and accuracy, are used to develop new ANFIS based fault diagnosis models. To promote fault diagnostic performance further and make fault decision process more reliable and reasonable, an improved DST is introduced to integrate outputs of each ANFIS based model, and to provide comprehensive and convincing fault diagnosis results. The fault diagnosis capability of the proposed method is validated by a reported fault dataset and 10-fold cross validation experiment. The performance of the proposed method is compared with conventional approaches and ANFIS based models which demonstrate that the proposed method is superior to other methods and is more effective and stable for power transformer fault diagnosis with high accuracy and consistency.

adaptive nuero fuzzy inference system↗
DOE OSTI2022

KokkACC: Enhancing Kokkos with OpenACC

Template metaprogramming is gaining popularity as a high-level solution for achieving performance portability on heterogeneous computing resources. Kokkos is a representative approach that offers programmers high-level abstractions for generic programming while most of the device-specific code generation and optimizations are delegated to the compiler through template specializations. For this, Kokkos provides a set of device-specific code specializations in multiple back ends, such as CUDA and HIP. Unlike CUDA or HIP, OpenACC is a high-level and directive-based programming model. This descriptive model allows developers to insert hints (pragmas) into their code that help the compiler to parallelize the code. The compiler is responsible for the transformation of the code, which is completely transparent to the programmer. This paper presents an OpenACC back end for Kokkos: KokkACC. As an alternative to Kokkos’s existing device-specific back ends, KokkACC is a multi-architecture back end providing a high-productivity programming environment enabled by OpenACC’s high-level and descriptive programming model. Moreover, we have observed competitive performance; in some cases, KokkACC is faster (up to 9×) than NVIDIA’s CUDA back end and much faster than OpenMP’s GPU offloading back end. This work also includes implementation details and a detailed performance study conducted with a set of mini-benchmarks (AXPY and DOT product) and three mini-apps (LULESH, miniFE and SNAP, a LAMMPS proxy mini-app).

Valero Lara, Pedro↗
DOE OSTI2020

Enhancement of microbiome management by machine learning for biological wastewater treatment

Here we propose to develop microbiome-based machine learning models to predict the response of biological wastewater treatment systems to environmental or operational disturbances or to design specific microbiomes to achieve a desired system function. These machine learning models can be used to enhance the stability of microbiome-based biological systems and warn against the failure of these systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Influence of partial charge on the material removal rate during chemical polishing

A partial charge-based chemical polishing model has been developed, which can serve as metric for describing the relative polishing material removal rate for different combinations of slurries and workpieces. A series of controlled polishing experiments utilizing a variety of colloidal polishing slurries (SiO 2 , CeO 2 , ZrO 2 , MgO, Sb 2 O 5 ) and optical materials [single crystals of Al 2 O 3 (sapphire), SiC, Y 3 Al 5 O 12 (YAG), CaF 2 , and LiB 3 O 5 (LBO); a SiO 2 -Al 2 O 3 -P 2 O 5 -Li 2 O glass ceramic (Zerodur); and glasses of SiO 2 :TiO 2 (ULE), SiO 2 (fused silica), and P 2 O 5 -Al 2 O 3 -K 2 O-BaO (Phosphate)] was performed and its material removal rate was measured. As previously proposed by Cook (J Non-Cryst Solids. 1990;120:152), for many polishing systems, the removal rate is governed by a series of chemical reactions which include the formation of a surface hydroxide, followed by condensation of that hydroxyl moiety with the polishing particle, and a subsequent hydrolysis reaction. The rate of condensation can often be the rate limiting step, thus it can determine the polishing material removal rate. By largely keeping the numerous other factors that influence material removal rate fixed (such as due to particle size distributions, interface interactions, pad topography, kinematics, and applied pressure), the material removal rate is shown to scale exponentially with the partial charge difference (δ wp-s ) between the workpiece and polishing slurry particle for many of the slurry-workpiece combinations indicating that condensation rate is the rate limiting step. The partial charge (δ) describes the equilibrium distribution of electron density between chemically bonded atoms and is related to the electronegativity of the atoms chemically bonded to one another. This partial charge model also explains the age-old experimental finding of why cerium oxide is the most effective polishing slurry for chemical removal of many workpieces. Finally, some of the slurry-workpiece combinations that did not follow the partial charge dependence offer insight to other removal mechanisms or rate limiting reaction pathways.

36 MATERIALS SCIENCE↗
DOE OSTI2018

PhaseChange Material Freezing in an Energy Storage Module for a Micro Environmental ControlSystem

This study analyzes phase change material (PCM) freezing process in a novel latent heat storage device (LHSD). Heat is removed from the PCM with an embedded evaporator. A mathematical model of freezing in a finite-thickness PCM slab is presented. An experimentally validated reduced-order model (ROM) based on the mathematical model was developed to analyze the heat transfer between the freezing PCM and an evaporating refrigerant flowing inside a flat, microchannel tube coil embedded in the PCM. A detailed finite element model (FEM) of the same device was also developed and employed to verify the validity of the ROM over a wider range of conditions. The freezing times and total “cooling” stored in the PCM computed by the ROM agree very well with those computed by the detailed FEM. The ROM executes in ~1 min for a full heat exchanger, compared with more than 10 h for the FEM, making the former much more practical for use in parametric analysis and optimization of design alternatives.

Engineering↗
DOE OSTI2015

SU-E-J-96: Multi-Axis Dose Accumulation of Noninvasive Image-Guided Breast Brachytherapy Through Biomechanical Modeling of Tissue Deformation Using the Finite Element Method

Purpose: Noninvasive image-guided breast brachytherapy delivers conformal HDR Ir-192 brachytherapy treatments with the breast compressed, and treated in the cranial-caudal and medial-lateral directions. This technique subjects breast tissue to extreme deformations not observed for other disease sites. Given that, commercially-available software for deformable image registration cannot accurately co-register image sets obtained in these two states, a finite element analysis based on a biomechanical model was developed to deform dose distributions for each compression circumstance for dose summation. Methods: The model assumed the breast was under planar stress with values of 30 kPa for Young’s modulus and 0.3 for Poisson’s ratio. Dose distributions from round and skin-dose optimized applicators in cranial-caudal and medial-lateral compressions were deformed using 0.1 cm planar resolution. Dose distributions, skin doses, and dose-volume histograms were generated. Results were examined as a function of breast thickness, applicator size, target size, and offset distance from the center. Results: Over the range of examined thicknesses, target size increased several millimeters as compression thickness decreased. This trend increased with increasing offset distances. Applicator size minimally affected target coverage, until applicator size was less than the compressed target size. In all cases, with an applicator larger or equal to the compressed target size, > 90% of the target covered by > 90% of the prescription dose. In all cases, dose coverage became less uniform as offset distance increased and average dose increased. This effect was more pronounced for smaller target-applicator combinations. Conclusions: The model exhibited skin dose trends that matched MC-generated benchmarking results and clinical measurements within 2% over a similar range of breast thicknesses and target sizes. The model provided quantitative insight on dosimetric treatment variables over a range of clinical circumstances. These findings highlight the need for careful target localization and accurate identification of compression thickness and target offset. Prof. Rivard is a shareholder of Advanced Radiation Therapy, LLC, the manufacturer of the AccuBoost noninvasive image-guided breast brachytherapy system examined herein. None of the other authors have any conflicts-of-interest for this work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-BRB-03: Cross-Institutional Validation of An Ultrafast Automatic Planning Platform for Breast Irradiation

Purpose: Recently an ultrafast automatic planning system for breast irradiation using tangential beams was developed by modeling relationships between patient anatomy and achieved dose distribution. This study evaluates the performance of this system when applied to a different patient population and dose calculation algorithm. Methods: The system and its anatomy-to-dose models was developed at institution A based on 20 cases, which were planned using manual fluence painting technique and calculated WITH heterogeneity correction. Institution B uses field-in-field planning technique and dose calculation WITHOUT heterogeneity correction. 11 breast cases treated at Institution B were randomly selected for retrospective study, including left and right sides, and different breast size (irradiated volumes defined by Jaw/MLC opening range from 875cc to 3516cc). Comparisons between plans generated automatically (Auto-Plans) and those used for treatment (Clinical-Plans) included: energy choice (single/mixed), volumes receiving 95%/100%/105%/110% Rx dose (V95%/V100%/V105%/V100%) relative to irradiated volume, D1cc, and LungV20Gy. Results: In 9 out of 11 cases single/mixed energy choice made by the software agreed with Clinical-Plans. For the remaining 2 cases software recommended using mixed energy and dosimetric improvements were observed. V100% were similar (p=0.223, Wilcoxon Signed-Rank test) between Auto-Plans and Clinical-Plans (57.6±8.9% vs. 54.8±9.5%). V95% is 2.3±3.0% higher for Auto-Plans (p=0.027), indicating reduced cold areas. Hot spot volume V105% were significantly reduced in Auto-Plan by 14.4±7.2% (p=0.004). Absolute V105% was reduced from 395.6±359.9cc for Clinical-Plans to 108.7±163cc for Auto-Plans. D1cc was 107.4±2.8% for Auto-Plans, and 109.2±2.4% for Clinical-Plans (p=0.056). LungV20Gy were 13.6±4.0% for Auto-Plan vs. 14.0±4.1% for Clinical-Plans (p=0.043). All optimizations were finished within 1.5min. Conclusion: The performance of this breast auto-planning system remained stable and satisfactory when applied to a different patient population and dose calculation algorithm. The auto-planning system was able to produce clinically similar Rx dose coverage with significantly improved homogeneity inside breast tissue, in less than 1.5min.

60 APPLIED LIFE SCIENCES↗