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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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The research described consisted of developing and refining the continuous flow laser model program including the creation of a working model. The mathematical development of a two pass amplifier for an iodine laser is summarized. A computer program for the amplifier's simulation is included with output from the simulation model.
As part of the Game Changing Development (GCD) Program, funded by NASA’s Space Technology Mission Directorate (STMD), the development of simulation capability for the prediction of extra-terrestrial Plume Surface Interaction (PSI) environments has been undertaken by the Fluid Dynamics Branch at NASA/MSFC. The Predictive Simulation Capability (PSC) Element is focused on creating simulation capability for the reliable and accurate prediction of PSI in Martian (~650 Pa) and Lunar (vacuum) ambient environments. In addition to the predictive simulation capability, the GCD Program also contains a companion Ground Testing Element for development of focused datasets for validation of predictive capability as well as a Flight-focused Instrumentation Element. This paper will present the status of implementing and maturing particle-particle interaction constituent physics models essential in simulating the landing surface granular material flow under PSI effects. This gas-particle multi-phase interaction modeling of plume impingement flow on the extra-terrestrial soil material is performed with the Gas-Granular Flow Solver (GGFS) addressed in a companion paper. The response of regolith particle flow induced by lander PSI requires accurate representation of the regolith granular material fluidic behavior and gas-granular interactions. The lunar regolith, as the extreme example, is poorly sorted with broad particle size distributions and large fines content. It has significant cohesion, due to interlocking particle shapes for the very jagged particles. The combination of particle shape and size distribution has been identified as major drivers in the complex particle flow response and resulting crater shape characteristics of extraterrestrial granular material. Constituent models for spherical particles can be formulated directly from particle kinetics theory. Complex particle shapes can be modeled by gluing together elemental spherical shapes into composite particles, requiring a Discrete Element Model (DEM) particle kinetics modeling approach to extract data and formulate constituent models. Mixture constituent models for poly-disperse mixtures (i.e, containing distribution of particle sizes) have recently been developed. The required non-spherical particle mixture granular material response closure models are then obtained through small-scale unit physics DEM simulations for the range of particle shapes, mixtures and packing densities. The granular material response closure models are then implemented in the Eulerian granular flow formulation. This DEM-based constituent model extraction process and formulation of poly-disperse particle mixtures has been successfully developed by small business and academic partners in the development of the Gas-Granular Flow Solver (GGFS) simulation program simulation framework. The currently implemented capabilities have reached the capability level of modeling bi-disperse, non-spherical particle mixtures is being continuously extended towards computational modeling of full range irregular particle mixtures. Under the GCD project, this technology is being further developed, transferred to NASA analysts, and matured towards application readiness. The predictive simulation capability team under the GCD project has acquired the modeling tools and processes of the DEM based constituent model formulation from the GGFS development team and is developing the capability to replicate the existing process. This is the first important step towards the ability of the NASA team to independently perform such model development in a production setting. Further efforts are underway to migrate the DEM based model simulation process performed with the academic based tools to more capable Open Source, highly parallelized simulation tools for efficient operation on NASA HPC assets. Evaluation of the currently implemented (such as mono-disperse and bi-disperse spherical and irregular shape particle constituent model applications) and continuously evolving full-range particle physics models in the GGFS tool is performed by the NASA team to advance application readiness of the simulations. Application testing for complex PSI erosions and cratering scenarios such as the Apollo LM is performed for axi-symmetric and full 3D simulations to aid the tool developers in achieving practical application readiness for NASA projects. Important validation and application testing will further be performed against experimental data generated under the GCD PSI project experimental component.
Scheduled for launch in 2001 as part of NASA's Earth Observing System (EOS), the Geoscience Laser Altimeter System (GLAS) will provide continuous laser sounding of the earth's atmosphere from space for the first time. From its polar orbit about 600 km above the surface, GLAS will employ a 40 Hz solid state laser operating at 1064 nm to measure topography to an accuracy of 10 cm. Simultaneously, the atmospheric channels (1064 and 532 nm) of GLAS will provide profiles of atmospheric backscatter from 40 km to the ground with 75 meter vertical resolution (Spinhirne and Palm, 1996). These measurements will give scientists an unprecedented global data set on the vertical structure of clouds and aerosols which will greatly aid research efforts aimed at understanding their effects on climate and their role in climate change (Hartman, 1994). To better understand and predict the performance of the GLAS atmospheric channels, a computer model was developed to simulate the type of signal that the instrument would likely produce. The model uses aircraft lidar data and provides realistic simulated GLAS data sets over large areas spanning a wide range of atmospheric conditions. These simulated GLAS datasets are invaluable for designing and testing algorithms for the retrieval of parameters such as cloud and aerosol layer height, optical depth and extinction cross section. This work is currently proceeding and in this paper we will present results of the cloud and aerosol detection algorithm with emphasis on the detection of Marine Atmospheric Boundary Layer (MABL) aerosol. In addition, we use a recently developed technique to ascertain the feasability of estimating MABL moisture and temperature structure from spaceborne systems such as GLAS.
This study presents an analytical model for steady-state power generation and tool heat loss in additive friction-stir deposition (AFSD), developed to enable part-scale thermal simulation while remaining computationally inexpensive. The model predicts total generated power, yielding 3.7–4.7 kW across deposition temperature setpoints of 400–460 °C for the deposition of AA6061 with a Be-Cu tool. This corresponds to 90–95% of the reported spindle power. Tool heat loss is experimentally determined by calibrating a steady-state energy balance between the generated power, the substrate-deposition thermal gradient, and a temperature dependent tool heat loss term: q tool (T) = a + b (T - 400°C) with a = 2.7 x 10 6 Wm -2 and b = 9.5 x 10 3 Wm -2 K -1 . The calibration indicates that about 69% of the generated heat is conducted into the tool for this configuration, which is much higher than previously reported. The calibrated heat-source is implemented in finite element software (Adamantine) to simulate the transient thermal history of a 100 cm 3 representative build in 8 min on a standard desktop (at 0.635 mm build-height resolution). For the first three layers, the substrate temperatures between simulation and experiment are within 10% mean absolute percentage error. Sensitivity analysis indicates that uncertainties in average deposition temperature and deformation localization (stir-zone geometry, depth, and spatial dependance of strain-rate and flow stress) dominate model variance, motivating additional experimental verification.
The process of ASR (Alkali-Silica Reaction) induced expansion and damage in pavement concrete specimens is investigated using laboratory experiments and computational modeling. In the experimental program, the concrete specimens are subject to CS-CPT (climate simulation concrete prism test) to obtain ASR induced expansion with and without external supply of alkali. The dissolution rates of the granodiorite used in the concrete mix and the gel formation rates are determined under concrete-like conditions (pH 13.8, with/without Ca(OH){sub 2} and NaCl) at different temperatures. A micromechanics based computational model with aggregate-scale diffusion and reaction kinetics coupled to an Eigenstrain based micromechanics damage model is developed for the simulation of ASR induced expansion and damage. Data from the experimental program are used to calibrate and validate the computational model. Model predictions show that for the given concrete mixture, ASR induced expansion in the specimen exposed to water is predominantly governed by microcracking in the aggregate, while the expansion in the specimen subjected to external alkali supply is governed by microcracking in both the aggregates and the cement paste.
As actual CO2 injection is unlikely to take place at Kevin Dome, Montana, the Big Sky Carbon Sequestration Partnership has turned to maximizing the value of existing data acquired at the site. We present the risk assessment work done using the National Risk Assessment Partnership (NRAP) to Kevin Dome, Montana. Geologic CO2 sequestration in saline aquifers poses certain risks including CO2/brine leakage through wells or non-sealing faults into ground water or land surface. These risks are difficult to quantify due to data availability and uncertainty. One solution is running large numbers of numerical simulations on the primary CO2 injection reservoir, shallow reservoirs/aquifers, faults, and wells to assess leakage risks and uncertainties. However, a full-physics simulation is usually too computationally expensive. NRAP integrated assessment model (NRAP-IAM) uses reduced order models (ROMs) developed from numerical reservoir simulations of a primary CO2 injection reservoir to address this issue. A powerful stochastic framework allows NRAP-IAM to explore complex interactions among many uncertain variables and evaluate the likely performance of potential sequestration sites. In this study, we investigate the sensitivity of a variety of uncertain parameters to CO2/brine leakage through (1) legacy wellbore and (2) fault pathways. We found major uncertain parameters to which the potential CO2 leakage through legacy wellbore is sensitive including values of fracture permeability, end-point CO2 relative permeability, capillary pressure, and permeability of confining rocks. CO2 and brine leakage through fault pathways is sensitive to fracture permeability, length of the faults, and fault displacement.
Efforts to build a Predictive Material Modeling (PMM) framework from the micro-scale to the macro-scale are presented in this abstract. To reduce the need for extensive testing, accelerate the design cycle process, and reduce uncertainty margins applied to final designs, NASA is developing simulation and modeling tools that enable characterization of material properties and response to high-enthalpy environments. The Porous Microstructure Analysis (PuMA) code has been developed for computing macroscale (volume averaged) properties of porous materials using microscale images from micro-computed tomography (micro-CT). Microscale modeling requires a realistic representation of a material microstructure; these are obtained either synthetically during the design of the material or through X-ray micro-CT. Volume averaged properties are then used to inform macroscale material response models, such as those implemented in the Porous-material Analysis Toolbox based on OpenFOAM (PATO) software, also actively developed by NASA. The computational model in PATO is a generic heat and mass transfer model for porous reactive materials containing several solid phases and a single gas phase. The detailed chemical interactions occurring between the solid phases and the gas phase are modeled at the pore scale assuming local thermal equilibrium. These tools were developed to efficiently interface with other pre-existing codes such as SPARTA (direct simulation Monte Carlo), DPLR (hypersonic CFD), NEQAIR (radiative transport) and DAKOTA (uncertainty quantification and optimization). Detailed flight data (Mars Science Laboratory [MSL] Entry Descent and Landing Instrument [MEDLI]) is critical for validating these computational tools for NASA applications. Examples of modeling ablative material response using these codes will be presented including 3D simulations of the full-scale heatshield of the MSL capsule. The simulations demonstrate the ability of the modern material response code, PATO, to handle the material response of geometrically complex and large domains, through the use of massively parallel computations.
Efforts to build a Predictive Material Modeling (PMM) framework from the micro-scale to the macro-scale are presented in this abstract. To reduce the need for extensive testing, accelerate the design cycle process, and reduce uncertainty margins applied to final designs, NASA is developing simulation and modeling tools that enable characterization of material properties and response to high-enthalpy environments. The Porous Microstructure Analysis (PuMA) code has been developed for computing macroscale (volume averaged) properties of porous materials using microscale images from micro-computed tomography (micro-CT). Microscale modeling requires a realistic representation of a material microstructure; these are obtained either synthetically during the design of the material or through X-ray micro-CT. Volume averaged properties are then used to inform macroscale material response models, such as those implemented in the Porous-material Analysis Toolbox based on OpenFOAM (PATO) software, also actively developed by NASA. The computational model in PATO is a generic heat and mass transfer model for porous reactive materials containing several solid phases and a single gas phase. The detailed chemical interactions occurring between the solid phases and the gas phase are modeled at the pore scale assuming local thermal equilibrium. These tools were developed to efficiently interface with other pre-existing codes such as SPARTA (direct simulation Monte Carlo), DPLR (hypersonic CFD), NEQAIR (radiative transport) and DAKOTA (uncertainty quantification and optimization). Detailed flight data (Mars Science Laboratory [MSL] Entry Descent and Landing Instrument [MEDLI]) is critical for validating these computational tools for NASA applications. Examples of modeling ablative material response using these codes will be presented including 3D simulations of the full-scale heatshield of the MSL capsule. The simulations demonstrate the ability of the modern material response code, PATO, to handle the material response of geometrically complex and large domains, through the use of massively parallel computations.
The model is a combined, steady-state cardiovascular and thermal model. It was originally developed for interactive use, but was converted to batch mode simulation for the Sigma 3 computer. The model has the purpose to compute steady-state circulatory and thermal variables in response to exercise work loads and environmental factors. During a computer simulation run, several selected variables are printed at each time step. End conditions are also printed at the completion of the run.
To study the impact of advanced aeronautical technologies on operations to and from terminal airports, a computer model of air traffic movements was developed. The advantages of fast-time simulation are discussed, and the arrival scheduling and flight simulation are described. A New York area study, user's guide, and programmer's guide are included.
Purpose: A reliable model to simulate nuclear interactions is fundamental for Ion-therapy. We already showed how BLOB (“Boltzmann-Langevin One Body”), a model developed to simulate heavy ion interactions up to few hundreds of MeV/u, could simulate also 12 C reactions in the same energy domain. However, its computation time is too long for any medical application. For this reason we present the possibility of emulating it with a Deep Learning algorithm. Methods: The BLOB final state is a Probability Density Function (PDF) of finding a nucleon in a position of the phase space. We discretised this PDF and trained a Variational Auto-Encoder (VAE) to reproduce such a discrete PDF. As a proof of concept, we developed and trained a VAE to emulate BLOB in simulating the interactions of 12 C with 12 C at 62 MeV/u. To have more control on the generation, we forced the VAE latent space to be organised with respect to the impact parameter (b) training a classifier of b jointly with the VAE. Results: In this work, the distributions obtained from the VAE are similar to the input ones and the computation time needed to use the VAE as a generator is negligible. Conclusions: We show that it is possible to use a Deep Learning approach to emulate a model developed to simulate nuclear reactions in the energy range of interest for Ion-therapy. We foresee the implementation of the generation part in C++ and to interface it with the most used Monte Carlo toolkit: Geant4.
A method is presented to model atomic oxygen erosion of protected polymers in low Earth orbit (LEO). Undercutting of protected polymers by atomic oxygen occurs in LEO due to the presence of scratch, crack or pin-window defects in the protective coatings. As a means of providing a better understanding of undercutting processes, a fast method of modeling atomic-oxygen undercutting of protected polymers has been developed. Current simulation methods often rely on computationally expensive ray-tracing procedures to track the surface-to-surface movement of individual "atoms." The method introduced in this paper replaces slow individual particle approaches by substituting a model that utilizes both a geometric configuration-factor technique, which governs the diffuse transport of atoms between surfaces, and an efficient telescoping series algorithm, which rapidly integrates the cumulative effects stemming from the numerous atomic oxygen events occurring at the surfaces of an undercut cavity. This new method facilitates the systematic study of three-dimensional undercutting by allowing rapid simulations to be made over a wide range of erosion parameters.
Forward simulation is an indispensable tool for evaluation of precipitation retrieval algorithms as well as for studying snow/ice microphysics and their radiative properties. The main challenge of the implementation arises due to the size of the problem domain. To overcome this hurdle, assumptions need to be made to simplify compiles cloud microphysics. It is important that these assumptions are applied consistently throughout the simulation process. ISSARS addresses this issue by providing a computationally efficient and modular framework that can integrate currently existing models and is also capable of expanding for future development. ISSARS is designed to accommodate the simulation needs of the Aerosol/Clouds/Ecosystems (ACE) mission and the Global Precipitation Measurement (GPM) mission: radars, microwave radiometers, and optical instruments such as lidars and polarimeter. ISSARS's computation is performed in three stages: input reconditioning (IRM), electromagnetic properties (scattering/emission/absorption) calculation (SEAM), and instrument simulation (ISM). The computation is implemented as a web service while its configuration can be accessed through a web-based interface.
In this work, the partially averaged Navier–Stokes (PANS) equations are used to predict the variable-density Rayleigh–Taylor (RT) flow at Atwood number 0.5 and maximum Reynolds number 500. This is a prototypical problem of material mixing, featuring laminar, transitional, and turbulent flow, instabilities and coherent structures, density fluctuations, and production of turbulence kinetic energy by both shear and buoyancy mechanisms. These features pose numerous challenges to modeling and simulation, making the RT flow ideal to develop the validation space of the recently proposed PANS Besnard–Harlow–Rauenzahn-linear eddy viscosity model closure. The numerical simulations are conducted at different levels of physical resolution and test three approaches to set the parameters $f_\phi$ defining the range of physically resolved scales. The computations demonstrate the efficiency (accuracy vs cost) of the PANS model predicting the spatiotemporal development of the RT flow. Results comparable to large-eddy simulations and direct numerical simulations are obtained at significantly lower physical resolution without the limitations of the Reynolds-averaged Navier–Stokes equations in these transitional flows. The data also illustrate the importance of appropriate selection of the physical resolution and the resolved fraction of each dependent quantity $\phi$ of the turbulent closure, $f_\phi$. These two aspects determine the ability of the model to resolve the flow phenomena not amenable to modeling by the closure and, as such, the computations’ fidelity.
A real-time, man-in-the loop, simulation of the rotor system research aircraft (RSRA) was conducted. The unique feature of this simulation was that two digital computers were used in parallel to solve the equations of the RSRA mathematical model. The design, development, and implementation of the simulation are documented. Program validation was discussed, and examples of data recordings are given. This simulation provided an important research tool for the RSRA project in terms of safe and cost-effective design analysis. In addition, valuable knowledge concerning parallel processing and a powerful simulation hardware and software system was gained.
The present recursive algorithm for solving molecular systems' dynamical equations of motion employs internal variable models that reduce such simulations' computation time by an order of magnitude, relative to Cartesian models. Extensive use is made of spatial operator methods recently developed for analysis and simulation of the dynamics of multibody systems. A factor-of-450 speedup over the conventional O(N-cubed) algorithm is demonstrated for the case of a polypeptide molecule with 400 residues.
Microorganisms grow differently in spaceflight than they do on Earth. While much remains unexplained about how microgravity affects microbial growth, one dominant hypothesis is that the lack of density-driven convection in the liquid growth environment makes mixing diffusion-limited, and therefore slower. This is supported by evidence that individual microbial strains experience starvation and acid stress in microgravity. However, if it is true, then microgravity would also have measurable effects on microbes in mixed communities, because many interspecies interactions involve the exchange of soluble metabolites through the medium (cross-feeding). Specifically, cooperative cross-feeding communities would grow more slowly in microgravity, and cooperation would be less stable on evolutionary timescales. Here we describe our efforts to test this hypothesis by simulating microgravity in silico and in the lab, using a model system of Eschericia coli and Salmonella enterica that grow only when they can exchange methionine and acetate. We created CAMDLES (CFD-DEM Artificial Microgravity Developments for Living Ecosystem Simulation) as an extension of CFDEM®coupling software, to carry out computational modeling of biological flows, growth, and mass transfer in microgravity and also in laboratory artificial microgravity devices (rotating wall vessels, RWV). Using CAMDLES, we found distinct differences in growth rates between RWV and true microgravity, and we were able to identify several features, such as spatial distribution, biofilm formation, and product yield parameters, that influence the degree to which RWV growth recapitulates microgravity growth. In addition, we report on the development of a laboratory system for monitoring growth rates and species ratios of the community in RWVs, using fluorescent strains. Pairing CAMDLES with the laboratory model system allows us to generate quantitative predictions about the effects of spaceflight on organisms that will be essential to sustaining human space exploration in the long term.
We report a sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical processes. While sophisticated simulation codes are used to model ICF implosions, these tools contain necessary numerical approximation but miss physical processes that limit predictive capability. Identification of relationships between controllable design inputs to ICF experiments and measurable outcomes (e.g., neutron yield, neutron velocity, areal density) from performed experiments can help guide the future design of experiments and development of simulation codes, to potentially improve the accuracy of the computational models used to simulate ICF experiments. We use sparse matrix decomposition methods to identify clusters of a few related design variables. Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule). A variable importance analysis finds that in addition to variables highly correlated with neutron yield, such as picket power and laser energy, variables that represent a dramatic change of the ICF design, such as number of pulse steps, are also very important. The obtained sparse components are then used to train a random forest (RF) regression surrogate for predicting total yield. The RF performance on the training and testing data compares with the performance of the RF trained using all the design variables considered. This work is intended to inform design changes in future ICF experiments by augmenting the expert intuition and simulation results.