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NASA NTRS2011

Contributions of TetrUSS to Project Orion

The NASA Constellation program has relied heavily on Computational Fluid Dynamics simulations for generating aerodynamic databases and design loads. The Orion Project focuses on the Orion Crew Module and the Orion Launch Abort Vehicle. NASA TetrUSS codes (GridTool/VGRID/USM3D) have been applied in a supporting role to the Crew Exploration Vehicle Aerosciences Project for investigating various aerodynamic sensitivities and supplementing the aerodynamic database. This paper provides an overview of the contributions from the TetrUSS team to the Project Orion Crew Module and Launch Abort Vehicle aerodynamics, along with selected examples to highlight the challenges encountered along the way. A brief description of geometries and tasks will be discussed followed by a description of the flow solution process that produced production level computational solutions. Four tasks conducted by the USM3D team will be discussed to show how USM3D provided aerodynamic data for inclusion in the Orion aero-database, contributed data for the build-up of aerodynamic uncertainties for the aero-database, and provided insight into the flow features about the Crew Module and the Launch Abort Vehicle.

Mcmillin, Susan N.↗

DOE OSTI2024

Development of a machine learning model for polyethylene pyrolysis using a detailed reaction mechanism

Waste plastics have recently received significant attention as the issue of waste generation continues to increase. Thermal conversion processes, such as pyrolysis and gasification, are attractive potential technologies for utilizing waste plastics and reducing overall waste generation. Efficient utilization of plastics requires a detailed understanding of the conversion process such as pyrolysis and gasification. However, a mechanistic understanding of these processes lead to large and complex kinetic schemes that are not suited for large-scale and long-time simulation methods. Currently, most modeling approaches for pyrolysis and gasification rely on globally lumped, simplified kinetic schemes that provide results that are classified by their product type and not individual species, which limit the level of fidelity achieved via modeling. A machine learning (ML) model has been developed for the primary reactions of high-density polyethylene (HDPE) in an attempt to increase computational efficiency while still maintaining a high level of detail and accuracy. The ML model is trained on a detailed reaction mechanism containing 42 total species and 737 chemical reactions. A DeepONet branch and trunk architecture was adopted to train the model using time-steps relevant to computational fluid dynamics simulations. The ML used physics-informed loss functions to ensure mass conservation. The surrogate model has been deployed in simple MFiX CFD simulations, single particle and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗

NASA NTRSDate not supplied

CFD Simulations of the AEDC H3 Arc-Jet Flow: Mach 3.3 Nozzle, Flow Surveys, Wedge Models

This paper reports computational simulations and analysis of tests in a high enthalpy arc-jet facility at Arnold Engineering Development Center. These tests were conducted using wedge models placed in a free jet downstream of a 4.5-inch diameter contoured Mach 3.3 nozzle in the H3 facility. For calibration of test conditions, one wedge calibration plate instrumented with an array of pressure and coaxial thermocouple heat flux gages, one stagnation pressure probe and two null-point heat flux calorimeters were used. The pitot pressure and null-point probe measurements give experimental surveys of the test flow, providing assessment of the flow uniformity and valuable data for flow characterization. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzle, into the test box and over the wedge models, and comparisons with the experimental measurements. Several issues related to testing are considered: test flow characterization, prediction of surface quantities for three wedge models, effects of model shape change and test article recession, and prediction of surface quantities for the test articles with two circular cavities. These simulations take into account non-uniform total enthalpy profiles at the nozzle inlet as well as the expansion waves emanating from the nozzle exit and their effects on the wedge model flowfields.

Arc-jet testing↗

NASA NTRS2016

Assessment of Preconditioner for a USM3D Hierarchical Adaptive Nonlinear Method (HANIM) (Invited)

Enhancements to the previously reported mixed-element USM3D Hierarchical Adaptive Nonlinear Iteration Method (HANIM) framework have been made to further improve robustness, efficiency, and accuracy of computational fluid dynamic simulations. The key enhancements include a multi-color line-implicit preconditioner, a discretely consistent symmetry boundary condition, and a line-mapping method for the turbulence source term discretization. The USM3D iterative convergence for the turbulent flows is assessed on four configurations. The configurations include a two-dimensional (2D) bump-in-channel, the 2D NACA 0012 airfoil, a three-dimensional (3D) bump-in-channel, and a 3D hemisphere cylinder. The Reynolds Averaged Navier Stokes (RANS) solutions have been obtained using a Spalart-Allmaras turbulence model and families of uniformly refined nested grids. Two types of HANIM solutions using line- and point-implicit preconditioners have been computed. Additional solutions using the point-implicit preconditioner alone (PA) method that broadly represents the baseline solver technology have also been computed. The line-implicit HANIM shows superior iterative convergence in most cases with progressively increasing benefits on finer grids.

Pandya, Mohagna J.↗

NASA NTRS2001

Automated quantification of mitral valve regurgitation based on normalized centerline velocity distribution

Previous echocardiographic techniques for quantifying valvular regurgitation are limited by factors including uncertainties for orifice location and a hemispheric convergence assumption that often results in over- and underestimation of flow rate and regurgitant orifice area. Using computational fluid dynamics simulations, these factors were eliminated, allowing a more accurate assessment of regurgitation. A model was developed to allow automated quantification of regurgitant orifice diameter based on the centerline velocity data available from color M-mode echocardiography. The model, validated using in vitro unsteady flow data, demonstrated improved accuracy for orifice diameter (y=0.95x + 0.38, r=0.96) and volume (y=1.18x - 4.72, r=0.93).

NASA Program Biomedical Research and Countermeasur↗

NASA NTRS2017

CFD Simulations of the IHF Arc-Jet Flow: Compression-Pad/Separation Bolt Wedge Tests

This paper reports computational analyses in support of two wedge tests in a high enthalpy arc-jet facility at NASA Ames Research Center. These tests were conducted using two different wedge models, each placed in a free jet downstream of a corresponding different conical nozzle in the Ames 60-MW Interaction Heating Facility. Panel test articles included a metallic separation bolt imbedded in the compression-pad and heat shield materials, resulting in a circular protuberance over a flat plate. As part of the test calibration runs, surface pressure and heat flux measurements on water-cooled calibration plates integrated with the wedge models were also obtained. Surface heating distributions on the test articles as well as arc-jet test environment parameters for each test configuration are obtained through computational fluid dynamics simulations, consistent with the facility and calibration measurements. The present analysis comprises simulations of the non-equilibrium flow field in the facility nozzle, test box, and flow field over test articles, and comparisons with the measured calibration data.

TPS↗

NASA NTRS1998

How Human Factors Drove the Design and Implementation of the Virtual Windtunnel

This viewgraph presentation describes decisions made at the NASA Ames Research Center during its development of a virtual windtunnel to assist humans in visualizing computational fluid dynamics simulations. User requirements for the system include the simulation of vortical structure, pressure distribution, and overall sense of flow. In addition, the system needs to support a variety of interfaces, excluding head mounts, and needs to use an object oriented approach. Direct manipulation of the system is most user-friendly when limited to only grab and point gestures. 'Visualization control tools' (vtools) improve the realism of the system. The necessary object oriented programming is in C++ and openGL. Users interact with objects called 'tools', which include vtools and tools which control the virtual wind tunnel environment. Other objects include data objects accessed by the visualizations. Visualizations can be added to the system by the user. The presentation includes a discussion of run-time architecture, and issues related to computation and implementation.

Bryson, Steve↗

NASA NTRS2019

CFD Simulations of the IHF Arc-Jet Flow: 9-Inch Nozzle, Flow Surveys, LEAF Wedge Calibration Data

This paper reports computational analyses and flow characterization studies in a high enthalpy arc-jet facility at NASA Ames Research Center. These tests were conducted using a wedge model placed in a free jet downstream of new 9-inch diameter conical nozzle in the Ames 60-MW Interaction Heating Facility. Both the nozzle and wedge model were specifically designed for testing in the new Laser-Enhanced Arc-jet Facility. Data were obtained using stagnation calorimeters and wedge models placed downstream of the nozzle exit. Two instrumented wedge calibration plates were used: one water-cooled and the other RCG-coated tile plate. Experimental surveys of arc-jet test flow with pitot and heat flux probes were also performed at three arc-heater conditions, providing assessment of the flow uniformity and valuable data for the flow characterization. The present analysis comprises computational fluid dynamics simulations of the nonequilibrium flowfield in the facility nozzle and test box, including the models tested, and comparisons with the experimental measurements. By taking into account nonuniform total enthalpy and mass flux profiles at the nozzle inlet as well as the expansion waves emanating from the nozzle exit and their effects on the model flowfields, these simulations approximately reproduce the probe survey data and predict the wedge model surface pressure and heat flux measurements.

Arc-jets↗

NASA NTRS2017

CFD Simulations of the IHF Arc-Jet Flow: Compression-Pad/Separation Bolt Wedge Tests

This paper reports computational analyses in support of two wedge tests in a high enthalpy arc-jet facility at NASA Ames Research Center. These tests were conducted using two different wedge models, each placed in a free jet downstream of a corresponding different conical nozzle in the Ames 60-MW Interaction Heating Facility. Each panel test article included a metallic separation bolt imbedded in Orion compression-pad and heatshield materials, resulting in a circular protuberance over a flat plate. The protuberances produce complex model flowfields, containing shock-shock and shock-boundary layer interactions, and multiple augmented heating regions on the test plate. As part of the test calibration runs, surface pressure and heat flux measurements on water-cooled calibration plates integrated with the wedge models were also obtained. Surface heating distributions on the test articles as well as arc-jet test environment parameters for each test configuration are obtained through computational fluid dynamics simulations, consistent with the facility and calibration measurements. The present analysis comprises simulations of the nonequilibrium flowfield in the facility nozzle, test box, and flowfield over test articles, and comparisons with the measured calibration data.

CFD↗

NASA NTRS2017

CFD Simulations of the IHF Arc-Jet Flow: Compression-Pad Separation Bolt Wedge Tests

This paper reports computational analyses in support of two wedge tests in a high enthalpy arc-jet facility at NASA Ames Research Center. These tests were conducted using two different wedge models, each placed in a free jet downstream of a corresponding different conical nozzle in the Ames 60-MW Interaction Heating Facility. Each panel test article included a metallic separation bolt imbedded in Orion compression-pad and heatshield materials, resulting in a circular protuberance over a flat plate. The protuberances produce complex model flowfields, containing shock-shock and shock-boundary layer interactions, and multiple augmented heating regions on the test plate. As part of the test calibration runs, surface pressure and heat flux measurements on water-cooled calibration plates integrated with the wedge models were also obtained. Surface heating distributions on the test articles as well as arc-jet test environment parameters for each test configuration are obtained through computational fluid dynamics simulations, consistent with the facility and calibration measurements. The present analysis comprises simulations of the non-equilibrium flow field in the facility nozzle, test box, and flow field over test articles, and comparisons with the measured calibration data.

CFD↗

NASA NTRS2003

Software Aids Visualization of Computed Unsteady Flow

Unsteady Flow Analysis Toolkit (UFAT) is a computer program that synthesizes motions of time-dependent flows represented by very large sets of data generated in computational fluid dynamics simulations. Prior to the development of UFAT, it was necessary to rely on static, single-snapshot depictions of time-dependent flows generated by flow-visualization software designed for steady flows. Whereas it typically takes weeks to analyze the results of a largescale unsteady-flow simulation by use of steady-flow visualization software, the analysis time is reduced to hours when UFAT is used. UFAT can be used to generate graphical objects of flow visualization results using multi-block curvilinear grids in the format of a previously developed NASA data-visualization program, PLOT3D. These graphical objects can be rendered using FAST, another popular flow visualization software developed at NASA. Flow-visualization techniques that can be exploited by use of UFAT include time-dependent tracking of particles, detection of vortex cores, extractions of stream ribbons and surfaces, and tetrahedral decomposition for optimal particle tracking. Unique computational features of UFAT include capabilities for automatic (batch) processing, restart, memory mapping, and parallel processing. These capabilities significantly reduce analysis time and storage requirements, relative to those of prior flow-visualization software. UFAT can be executed on a variety of supercomputers.

Kao, David↗

NASA NTRS2000

Interactive Computer Graphics

Aerospace data analysis tools that significantly reduce the time and effort needed to analyze large-scale computational fluid dynamics simulations have emerged this year. The current approach for most postprocessing and visualization work is to explore the 3D flow simulations with one of a dozen or so interactive tools. While effective for analyzing small data sets, this approach becomes extremely time consuming when working with data sets larger than one gigabyte. An active area of research this year has been the development of data mining tools that automatically search through gigabyte data sets and extract the salient features with little or no human intervention. With these so-called feature extraction tools, engineers are spared the tedious task of manually exploring huge amounts of data to find the important flow phenomena. The software tools identify features such as vortex cores, shocks, separation and attachment lines, recirculation bubbles, and boundary layers. Some of these features can be extracted in a few seconds; others take minutes to hours on extremely large data sets. The analysis can be performed off-line in a batch process, either during or following the supercomputer simulations. These computations have to be performed only once, because the feature extraction programs search the entire data set and find every occurrence of the phenomena being sought. Because the important questions about the data are being answered automatically, interactivity is less critical than it is with traditional approaches.

Kenwright, David↗

DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗

DOE OSTI2023

Scientific machine learning for modeling and simulating complex fluids

The formulation of rheological constitutive equations—models that relate internal stresses and deformations in complex fluids—is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these “digital fluid twins” to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation—a task that is not achievable using any previously developed data-driven rheological equation of state.

Science & Technology - Other Topics↗

DOE OSTI2024

A Simulated Evaluation of Powder Flowability Through a Partially Obstructed Consumable in Blown Powder Directed Energy Deposition Systems

Abstract In the interest of continued industrialization of metal additive manufacturing in modern production environments, cost is often referenced as a primary deterrent to new adopters. Conventional economic models for additive systems, processes, and supply chains often focus on specific process applications with little generalizability, or they neglect significant costs associated with production such as machine maintenance and consumable part replacement. Compounding the latter issue are substantial knowledge gaps in consumable part wear characterization for additive and other convergent manufacturing systems. In coaxial blown powder directed energy deposition systems, gas atomized metal powder is wasted during material deposition at a rate that is partly dependent on present wear phenomena in a consumable nozzle housed in the cladding head assembly. The price and lead time required to replace the nozzle incentivizes its reuse even when visibly worn. Often this initiates a process quality decline in the form of underbuilt geometry and internal defects due to losses in powder catchment efficiency. While depositing H13 steel using a hybrid manufacturing machine tool equipped with such a deposition system, a unique partial clog with a bridge-like structure formed at the consumable nozzle exit when supporting argon gas flows failed mid-process. To further understand coaxial multi-phase powder flow in the event of support gas failure, a computational fluid dynamics simulation is tailored to relevant process parameters, H13 powder material profile, and machine operator observations collected after the incident. The resulting differences in powder flow compared to control gas flow parameters is presented and discussed. The powder flowability and performance of the clogged nozzle is then assessed by using an optical profilometer to extract the profile of the clog and recreate the clog geometry within the simulation environment. In past work this simulation has been experimentally validated for a 316L steel powder material profile and used specifically for analyzing powder stream geometry and catchment efficiency. After the initial powder flow characterization, the clog is removed, and the nozzle is reprofiled. After removing the obstructing clog, the newly unobstructed nozzle geometry, the original off the shelf nozzle geometry, and additional nozzle profiles exploring different consumable refurbishment strategies are reevaluated in the simulation. Powder catchment efficiency for all variant nozzle geometries and relevant flow variables are compared and discussed, along with potential mitigation strategies for optimizing powder flowability with worn consumables. This work expands on the known morphology of blown powder obstructions and wear defects present in consumable coaxial nozzles while discussing pragmatic simulation driven responses to unanticipated subsystem failure in hybrid manufacturing machining platforms.

DeWitte, Lisa↗

DOE OSTI2022

Quantifying Equiaxed vs Epitaxial Solidification in Laser Melting of CMSX-4 Single Crystal Superalloy

The competition between epitaxial vs. equiaxed solidification has been investigated in CMSX-4 single crystal superalloy during laser melting as practiced in additive manufacturing. Single-track laser scans were performed on a powder-free surface of directionally solidified CMSX-4 alloy with several combinations of laser power and scanning velocity. Electron backscattered diffraction (EBSD) mapping facilitated identification of new orientations, i.e., “stray grains” that nucleated within the fusion zone along with their area fraction and spatial distribution. Using high-fidelity computational fluid dynamics simulations, both the temperature and fluid velocity fields within the melt pool were estimated. This information was combined with a nucleation model to determine locations where nucleation has the highest probability to occur in melt pools. In conformance with general experience in metals additive manufacturing, the as-solidified microstructure of the laser-melted tracks is dominated by epitaxial grain growth; nevertheless, stray grains were evident in elongated melt pools. It was found that, though a higher laser scanning velocity and lower power are generally helpful in the reduction of stray grains, the combination of a stable keyhole and minimal fluid velocity further mitigates stray grains in laser single tracks.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Which way does the dendrite grow? Competition among epitaxy, preferred growth direction, and thermal gradients in powder bed fusion additive manufacturing

The as-processed microstructure of metal alloy parts manufactured through laser powder bed fusion (LPBF) is heavily derived from the cellular dendritic solidification. The growth direction of dendrites within the melt pool is determined through competition among epitaxial growth, preferred growth directions, and maximum thermal gradients. However, the dominant factor and the specific role of each in developing melt pool microstructures remain unknown. Here, in this study, we performed single laser track scans on an SS316L single crystal substrate and combined experimental characterization of microstructure and crystal orientations with Computational Fluid Dynamics simulations of thermal gradients to evaluate the role of each factor in determining dendritic growth direction and evolution. Our results reveal that epitaxial growth dominates microstructure development by preferentially growing along a single 〈100〉 variant of the single crystal substrate adjacent to the melt pool boundary. Under LPBF’s highly curved and rapidly evolving thermal field, this preferential dendrite variant selection and its continued growth from the melt pool boundary to the centerline are governed by the local temperature gradient magnitude at the solid-liquid interface, rather than by the instantaneous maximum temperature gradient direction alone. Using these findings, we successfully predict changes in the dendrite growth direction with changing laser scan direction on a single crystal substrate, and show that the geometric melt pool centerline can deviate from the microstructural centerline because asymmetric local temperature gradient magnitudes transiently limit growth, resulting in different dendrite travel distances on each side of the melt pool.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Macroscale Control of Reactivity using 3D Printed Materials with Intrinsic Catalytic Properties

The morphology of heterogeneous catalysts can impact their performance. However, standard manufacturing methods like extrusion or pelleting offer little options for tailoring catalyst shape. Herein, stereolithographic 3D printing is used to produce catalysts with controlled topologies to enhance their performance. A series of magnetic stir-bar compartments (SBC) were 3D printed and tested as catalysts for sucrose hydrolysis. The SBC were printed using acrylic acid (AA) and 1,6-hexanediol diacrylate (HDDA) as acid sites and hydrophobic crosslinking domains, respectively. Variations in the number and tilt direction of the SBC blades produced significant changes in their apparent catalytic activities. These changes resulted from differences in the fraction of active surface effectively interacting with the reactants in solution, as revealed by computational fluid dynamics simulations. Moreover, varying HDDA:AA ratios in SBC regulated reactant-surface interactions to control catalytic activity. Overall, 3D printing catalysts enables quick performance optimization by simultaneously controlling macroscopic structure and molecular composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗