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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Completing and Adapting Models of Biological Processes

We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.

Margaria, Tiziana↗
NASA NTRS2006

Managing Analysis Models in the Design Process

Design of large, complex space systems depends on significant model-based support for exploration of the design space. Integrated models predict system performance in mission-relevant terms given design descriptions and multiple physics-based numerical models. Both the design activities and the modeling activities warrant explicit process definitions and active process management to protect the project from excessive risk. Software and systems engineering processes have been formalized and similar formal process activities are under development for design engineering and integrated modeling. JPL is establishing a modeling process to define development and application of such system-level models.

design↗
NASA NTRS2005

Modeling Atmospheric CO2 Processes to Constrain the Missing Sink

We report on a NASA supported modeling effort to reduce uncertainty in carbon cycle processes that create the so-called missing sink of atmospheric CO2. Our overall objective is to improve characterization of CO2 source/sink processes globally with improved formulations for atmospheric transport, terrestrial uptake and release, biomass and fossil fuel burning, and observational data analysis. The motivation for this study follows from the perspective that progress in determining CO2 sources and sinks beyond the current state of the art will rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. The major components of this effort are: 1) Continued development of the chemistry and transport model using analyzed meteorological fields from the Goddard Global Modeling and Assimilation Office, with comparison to real time data in both forward and inverse modes; 2) An advanced biosphere model, constrained by remote sensing data, coupled to the global transport model to produce distributions of CO2 fluxes and concentrations that are consistent with actual meteorological variability; 3) Improved remote sensing estimates for biomass burning emission fluxes to better characterize interannual variability in the atmospheric CO2 budget and to better constrain the land use change source; 4) Evaluating the impact of temporally resolved fossil fuel emission distributions on atmospheric CO2 gradients and variability. 5) Testing the impact of existing and planned remote sensing data sources (e.g., AIRS, MODIS, OCO) on inference of CO2 sources and sinks, and use the model to help establish measurement requirements for future remote sensing instruments. The results will help to prepare for the use of OCO and other satellite data in a multi-disciplinary carbon data assimilation system for analysis and prediction of carbon cycle changes and carbodclimate interactions.

Kawa, S. R.↗
DOE OSTI2020

Coupling of Spark Plasma Sintering with Advanced Modeling to Enable Process Scale-Up: Presentation to DOE-NE [Slides]

The research goal of this project is to develop at Idaho National Laboratory (INL) a first-of-its-kind Multiphysics Object-Oriented Simulation Environment (MOOSE)-based, multiscale, multiphysics spark plasma sintering (SPS) modeling and simulation code application, termed “Freya.” Freya will simulate the thermo-mechanical-electrical aspects of the SPS fabrication process and will be paired with lower length scale sub-models, such as phase-field, to predict the resulting microstructure. SPS is an advanced manufacturing process that can be used to solve a variety of material manufacturing challenges; however, this process is an extremely challenging problem for modeling and simulation. The SPS process is inherently multiphysics and multi-scale, with the macroscale electro-thermo-mechanical behavior linked intricately to the microstructure evolution of the part being sintered. Accurate modeling and simulation tools, specifically geared towards the SPS process, are needed to predict the influence of the multiple variables involved in the manufacturing process. Modeling and simulation accuracy is achieved and demonstrated through comparison to multiple validation experiments. The validation efforts for Freya include both separate effects and complete multiphysics SPS process experiments. One of the key benefits this Laboratory Directed Research & Development (LDRD) project offers stems from the emphasis placed on experimental validation of the Freya models, both on the individual length scales and of the final coupled multiscale multiphysics simulations. Experimental validation of Freya’s multiscale coupling capability provides the technical credibility necessary for potential future industry and research partners to accept the simulation predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Parallel simulation via SPPARKS of on-lattice kinetic and Metropolis Monte Carlo models for materials processing

Abstract SPPARKS is an open-source parallel simulation code for developing and running various kinds of on-lattice Monte Carlo models at the atomic or meso scales. It can be used to study the properties of solid-state materials as well as model their dynamic evolution during processing. The modular nature of the code allows new models and diagnostic computations to be added without modification to its core functionality, including its parallel algorithms. A variety of models for microstructural evolution (grain growth), solid-state diffusion, thin film deposition, and additive manufacturing (AM) processes are included in the code. SPPARKS can also be used to implement grid-based algorithms such as phase field or cellular automata models, to run either in tandem with a Monte Carlo method or independently. For very large systems such as AM applications, the Stitch I/O library is included, which enables only a small portion of a huge system to be resident in memory. In this paper we describe SPPARKS and its parallel algorithms and performance, explain how new Monte Carlo models can be added, and highlight a variety of applications which have been developed within the code.

36 MATERIALS SCIENCE↗
NASA NTRS2020

Modeling the Transport Processes of a Pair of Solar Energetic Particle Events Observed by Parker Solar Probe near Perihelion

We present model calculations of the transport processes of solar energetic particles in the corona and interplanetary medium for two events detected by Parker Solar Probe near its second perihelion on 2019 April 2 and April 4. In the 2019 April 2 event, the <100 keV proton differential intensity measured by the ISOIS EPI-Lo instrument increased by more than a factor of 10 above the pre-event intensity, whereas the ~1 MeV proton differential intensity detected by the EPI-Hi instrument did not show any intensity enhancement. In the 2019 April 4 event, the ~1 MeV proton intensity showed an increase of more than a factor of 100 above the pre-event intensity, but the <100 keV proton intensity enhancement was rather low and gradual. The observational fact that only the 2019 April 4 event displayed a high-energy intensity enhancement indicates that the associated acceleration process in the second event was more energetic than the first event. However, the gradual and low enhancement of the low-energy proton intensity in the second event is at odds with the scenario. In this paper, we investigate the injection and transport processes of protons in the corona and interplanetary magnetic fields with numerical model simulations. Our model calculations suggest that the gradual and low rise of the low-energy protons in the April 4 event was probably due to the different diffusion conditions in these two events and the fact that the spacecraft was moving away from the source region during these two events.

Solar energetic particles↗
NASA NTRS1992

Chemical reaction path modeling of hydrothermal processes on Mars: Preliminary results

Hydrothermal processes are thought to have had significant roles in the development of surficial mineralogies and morphological features on Mars. For example, a significant proportion of the Martian soil could consist of the erosional products of hydrothermally altered impact melt sheets. In this model, impact-driven, vapor-dominated hydrothermal systems hydrothermally altered the surrounding rocks and transported volatiles such as S and Cl to the surface. Further support for impact-driven hydrothermal alteration on Mars was provided by studies of the Ries crater, Germany, where suevite deposits were extensively altered to montmorillonite clays by inferred low-temperature (100-130 C) hydrothermal fluids. It was also suggested that surface outflow from both impact-driven and volcano-driven hydrothermal systems could generate the valley networks, thereby eliminating the need for an early warm wet climate. We use computer-driven chemical reaction path calculation to model chemical processes which were likely associated with postulated Martian hydrothermal systems.

Plumlee, Geoffrey S.↗
NASA NTRS2012

Biomedical Simulation Models of Human Auditory Processes

Detailed acoustic engineering models that explore noise propagation mechanisms associated with noise attenuation and transmission paths created when using hearing protectors such as earplugs and headsets in high noise environments. Biomedical finite element (FE) models are developed based on volume Computed Tomography scan data which provides explicit external ear, ear canal, middle ear ossicular bones and cochlea geometry. Results from these studies have enabled a greater understanding of hearing protector to flesh dynamics as well as prioritizing noise propagation mechanisms. Prioritization of noise mechanisms can form an essential framework for exploration of new design principles and methods in both earplug and earcup applications. These models are currently being used in development of a novel hearing protection evaluation system that can provide experimentally correlated psychoacoustic noise attenuation. Moreover, these FE models can be used to simulate the effects of blast related impulse noise on human auditory mechanisms and brain tissue.

Bicak, Mehmet M. A.↗
NASA NTRS1973

Digital modeling of vibroacoustical processes generated by gearing

The statistical nature of vibroacoustical processes in transmission gears was studied using mathematical models. Special attention was given to high frequency components of vibroacoustical processes generated by spur gearing. Equations are given for describing torsional and tranverse vibrations of pairs of gear wheels when meshing, taking into account the flexibility of their supports.

Balitskiy, F. Y.↗
DOE OSTI2025

Constraining nuclear mass models using 𝑟-process observables with multiobjective optimization

Modeling nuclear masses, particularly for nuclei far from stability, remains a key objective in nuclear physics. One contemporary approach is machine learning (ML), which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (𝑟 process), which in principle could inform ML models. Here, we introduce a multiobjective optimization approach using the Pareto front algorithm. We show that this technique, capable of identifying models that generate 𝑟-process abundances aligning with both solar and stellar data, is a promising method to select ML models with reliable extrapolation power.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1991

Comparisons of elastic and rigid blade-element rotor models using parallel processing technology for piloted simulations

A piloted comparison of rigid and aeroelastic blade-element rotor models was conducted at the Crew Station Research and Development Facility (CSRDF) at Ames Research Center. A simulation development and analysis tool, FLIGHTLAB, was used to implement these models in real time using parallel processing technology. Pilot comments and quantitative analysis performed both on-line and off-line confirmed that elastic degrees of freedom significantly affect perceived handling qualities. Trim comparisons show improved correlation with flight test data when elastic modes are modeled. The results demonstrate the efficiency with which the mathematical modeling sophistication of existing simulation facilities can be upgraded using parallel processing, and the importance of these upgrades to simulation fidelity.

Hill, Gary↗
NASA NTRS1991

Comparison of elastic and rigid blade-element rotor models using parallel processing technology for piloted simulations

A piloted comparison of rigid and aeroelastic blade-element rotor models was conducted at the Crew Station Research and Development Facility (CSRDF) at Ames Research Center. A simulation development and analysis tool, FLIGHTLAB, was used to implement these models in real time using parallel processing technology. Pilot comments and qualitative analysis performed both on-line and off-line confirmed that elastic degrees of freedom significantly affect perceived handling qualities. Trim comparisons show improved correlation with flight test data when elastic modes are modeled. The results demonstrate the efficiency with which the mathematical modeling sophistication of existing simulation facilities can be upgraded using parallel processing, and the importance of these upgrades to simulation fidelity.

Hill, Gary↗
DOE OSTI2020

Modeling land surface processes over a mountainous rainforest in Costa Rica using CLM4.5 and CLM5

This study compares the performance of the Community Land Models (CLM4.5 and CLM5) against tower and ground measurements from a tropical montane rainforest in Costa Rica. The study site receives over 4000 mm of mean annual precipitation and has high daily levels of relative humidity. The measurement tower is equipped with eddy-covariance and vertical profile systems able to measure various micrometeorological variables, particularly in wet and complex terrain. In this work, results from point-scale simulations for both CLM4.5 and its updated version (CLM5) are compared to observed canopy flux and micrometeorological data. Both models failed to capture the effects of frequent rainfall events and mountainous topography on the variables of interest (temperatures, leaf wetness, and fluxes). Overall, CLM5 alleviates some errors in CLM4.5, but CLM5 still cannot precisely simulate a number of canopy processes for this forest. Soil, air, and canopy temperatures, as well as leaf wetness, remain too sensitive to incoming solar radiation rates despite updates to the model. As a result, daytime vapor flux and carbon flux are overestimated, and modeled temperature differences between day and night are higher than those observed. Slope effects appear in the measured average diurnal variations of surface albedo and carbon flux, but CLM5 cannot simulate these features. This study suggests that both CLMs still require further improvements concerning energy partitioning processes, such as leaf wetness process, photosynthesis model, and aerodynamic resistance model for wet and mountainous regions.

58 GEOSCIENCES↗
DOE OSTI2021

Multi-physics Peridynamic Modeling of Damage Processes in Protective Coatings

Protective coatings can revolutionize the aerospace industry by providing a way to build a fuel-efficient and low-emission gas turbine engine operating at elevated temperature. Currently, significant challenges persist in the multi-physics modeling of damage processes in protective coatings exposed to the hot corrosive environment of gas turbine engine. Examples of such detrimental processes include an interface damage between different material layers forming protective coating, a formation of the through-the-thickness vertical cracks, a diffusion of oxygen and moisture through those cracks, an oxidation, and a delamination. In this paper, a feasibility of a multi-physics peridynamic model to provide insight to these detrimental processes has been demonstrated. 3-Dimensional peridynamic equations which are valid everywhere, including dynamically evolving discontinuities, have been presented. In conclusion, validation and demonstration have been shown for conduction heat transfer, reactive oxidation, and delamination of protective coating with a composition borrowed from the open literature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1982

Random element method for numerical modeling of diffusional processes

The random element method is a generalization of the random vortex method that was developed for the numerical modeling of momentum transport processes as expressed in terms of the Navier-Stokes equations. The method is based on the concept that random walk, as exemplified by Brownian motion, is the stochastic manifestation of diffusional processes. The algorithm based on this method is grid-free and does not require the diffusion equation to be discritized over a mesh, it is thus devoid of numerical diffusion associated with finite difference methods. Moreover, the algorithm is self-adaptive in space and explicit in time, resulting in an improved numerical resolution of gradients as well as a simple and efficient computational procedure. The method is applied here to an assortment of problems of diffusion of momentum and energy in one-dimension as well as heat conduction in two-dimensions in order to assess its validity and accuracy. The numerical solutions obtained are found to be in good agreement with exact solution except for a statistical error introduced by using a finite number of elements, the error can be reduced by increasing the number of elements or by using ensemble averaging over a number of solutions.

Ghoniem, A. F.↗
NASA NTRS1979

Development of a Model and Computer Code to Describe Solar Grade Silicon Production Processes

Mathematical models and computer codes based on these models, which allow prediction of the product distribution in chemical reactors for converting gaseous silicon compounds to condensed-phase silicon were developed. The following tasks were accomplished: (1) formulation of a model for silicon vapor separation/collection from the developing turbulent flow stream within reactors of the Westinghouse (2) modification of an available general parabolic code to achieve solutions to the governing partial differential equations (boundary layer type) which describe migration of the vapor to the reactor walls, (3) a parametric study using the boundary layer code to optimize the performance characteristics of the Westinghouse reactor, (4) calculations relating to the collection efficiency of the new AeroChem reactor, and (5) final testing of the modified LAPP code for use as a method of predicting Si(1) droplet sizes in these reactors.

Srivastava, R.↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗