Results for “data fidelity” — Page 26

NASA NTRS1981

Recent Progress Towards Predicting Aircraft Ground Handling Performance

The significant progress which has been achieved in development of aircraft ground handling simulation capability is reviewed and additional improvements in software modeling identified. The problem associated with providing necessary simulator input data for adequate modeling of aircraft tire/runway friction behavior is discussed and efforts to improve this complex model, and hence simulator fidelity, are described. Aircraft braking performance data obtained on several wet runway surfaces is compared to ground vehicle friction measurements and, by use of empirically derived methods, good agreement between actual and estimated aircraft braking friction from ground vehilce data is shown. The performance of a relatively new friction measuring device, the friction tester, showed great promise in providing data applicable to aircraft friction performance. Additional research efforts to improve methods of predicting tire friction performance are discussed including use of an instrumented tire test vehicle to expand the tire friction data bank and a study of surface texture measurement techniques.

Yager, T. J.↗

NASA NTRS2000

First-Order Model Management With Variable-Fidelity Physics Applied to Multi-Element Airfoil Optimization

First-order approximation and model management is a methodology for a systematic use of variable-fidelity models or approximations in optimization. The intent of model management is to attain convergence to high-fidelity solutions with minimal expense in high-fidelity computations. The savings in terms of computationally intensive evaluations depends on the ability of the available lower-fidelity model or a suite of models to predict the improvement trends for the high-fidelity problem, Variable-fidelity models can be represented by data-fitting approximations, variable-resolution models. variable-convergence models. or variable physical fidelity models. The present work considers the use of variable-fidelity physics models. We demonstrate the performance of model management on an aerodynamic optimization of a multi-element airfoil designed to operate in the transonic regime. Reynolds-averaged Navier-Stokes equations represent the high-fidelity model, while the Euler equations represent the low-fidelity model. An unstructured mesh-based analysis code FUN2D evaluates functions and sensitivity derivatives for both models. Model management for the present demonstration problem yields fivefold savings in terms of high-fidelity evaluations compared to optimization done with high-fidelity computations alone.

Alexandrov, N. M.↗

NASA NTRS1998

Development of a Low Cost Data Acquisition System for the Solid Rocket Booster Program

The Space Transportation System's (STS) Solid Rocket Boosters (SRBs) experience a severe environment during their brief flight. During the last few years several SRB's have sustained noticeable structural damage. The environmental characteristics (vibration, structure, and thermal) encountered by the SRB's during ascent, descent and water impact are in most cases unknown. A developmental flight instrumentation (DFI) system collected data from the SRB's first four flights in the early 1980's, and after the first three flights during the shuttle return-to-flight phase after the Challenger accident. However, the DFI data collected are of low fidelity and do not correlate well with the observed structural damage. The DFI system was evaluated for reuse, but the cost to fly it was prohibitive. The STS is presently scheduled to fly until 2030. To support the STS schedule the avionics on SRB's will be upgraded. The environments on the different sections of the SRB will need to be defined to properly qualify the avionics for multiple flights. The DFI data previously gathered does not provide enough information to properly qualify the avionics. Marshall Space Flight Centers (MSFC) SRB Project Office requested the Science and Engineering Directorate to develop a self contained data acquisition system that could collect data from any area of the booster. This paper summarizes the effort to develop, test, qualify and fly an Enhanced Data Acquisition System (EDAS) using state-of-the-art commercial off the shelf (COTS) equipment. The first flight of this system occurred on shuttle mission STS-91 in June 1998.

Crawford, Kevin↗

DOE OSTI2020

Deep Learning Prediction of Interspecies Interactions from Self-organized Spatiotemporal Patterns of Co-evolving Organisms

Microorganisms colonizing natural habits such as soils co-evolve to form specific spatial patterns through interspecies interactions. These self-organized patterns are a key ecological phenotype, which provides critical information on their interaction mechanisms. However, conventional network inference techniques that analyze species population data in bulk have yet to be extended to account for such spatial heterogeneity. Here we proposed supervised deep learning as a new network inference tool for predicting interspecies interactions from spatiotemporal patterns of microbial evolution. Due to lack of biological imaging data that can be used for training deep learning networks, we used in silico data generated from high-fidelity agent-based models to determine model structure and parameters. Even though networks were trained under simple configurations where interaction coefficients are assumed to be spatially invariant, we demonstrated that the resulting model can be utilized to successfully predict spatial variation of interactions in more complex domains (i.e., configured with a context-dependent mixture of interaction coefficients) as well as in simple domains without further training. In the further test against real biological data obtained through imaging experiments of a binary consortium (Pseudomonas fluorescens and a mutant of Escherichia coli), our model also predicted the dramatic shifts in interactions of the two organisms across different environmental contexts. Through various successful demonstrations in this work, the combined use of the agent-based model and machine learning algorithm provides a means to use new type of data - microscopic images - for extracting microbial interactions, therefore presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Lee, Joon-Yong↗

NASA NTRS1999

Update of the Development of a Low Cost Data Acquisition System for the Space Shuttle Solid Rocket Booster Program

The space shuttle solid rocket boosters (SRBs) experience a severe environment during their brief flight. During the last few years several SRB's have sustained noticeable structural damage. The environmental characteristics (vibration, structure, and thermal) encountered by the SRB's during ascent, descent and water impact are in most cases unknown. A developmental flight instrumentation (DFI) system collected data from the SRBs' first four flights in the early 1980's, and after the first three flights during the shuttle return-to-flight phase after the Challenger accident. However, the DFI data collected are of low fidelity and do not correlate well with cases of observed structural damage. The DFI system was evaluated for reuse, but the cost to fly it was prohibitive. The space shuttle is presently scheduled to fly until 2030. To support the shuttle flight schedule, avionics on the SRB's will be upgraded. The environments on the different sections of the SRB will need to be defined more completely to properly qualify the avionics for multiple flights. The DFI data previously gathered do not provide enough information to properly qualify the avionics. Marshall Space Flight Center's (MSFC) SRB Project Office requested the Science and Engineering (S&E) Directorate to develop a stand-alone data acquisition system that could collect data from any area of the booster. In answer to this requirement, S&E developed the Enhanced Data Acquisition System (EDAS). To minimize development time and cost, the development team used state-of-the-art commercial off the shelf (COTS) equipment. The first two flights of this system occurred on shuttle mission STS-91 in June 1998 and STS-95 in October 1998. Twenty-one measurements were successfully recorded on the STS-91 right hand booster, providing new accelerometer, strain, temperature, and heating rate data to analysts. Twenty-four measurements were successfully recorded on the STS-95 left hand booster, providing data from the booster and the external tank. This paper summarizes the effort to develop, test, qualify and fly the EDAS to meet SRB flight and data collection requirements.

Crawford, Kevin↗

NASA NTRS1999

Using a Commerical off the Shelf Data Acquisition System for the Space Shuttle Solid Rocket Booster Program

The space shuttle solid rocket boosters (SRBs) experience a severe environment during their brief flight. During the last few years several SRB's have sustained noticeable structural damage. The environmental characteristics (vibration, structure, and thermal) encountered by the SRB's during ascent, descent and water impact are in most cases unknown. A developmental flight instrumentation (DFI) system collected data from the SRBs' first four flights in the early 1980's, and after the first three flights during the shuttle return-to-flight phase after the Challenger accident. However, the DFI data collected are of low fidelity and do not correlate well with cases of observed structural damage. The DFI system was evaluated for reuse, but the cost to fly it was prohibitive. The space shuttle is presently scheduled to fly until 2030. To support the shuttle flight schedule, avionics on the SRB's will be upgraded. The environments on the different sections of the SRB will need to be defined more completely to properly qualify the avionics for multiple flights. The DFI data previously gathered do not provide enough information to properly qualify the avionics. Marshall Space Flight Center's (MSFC) SRB Project Office requested the Science and Engineering (S&E) Directorate to develop a stand-alone data acquisition system that could collect data from any area of the booster. In answer to this requirement, S&E developed the Enhanced Data Acquisition System (EDAS). To minimize development time and cost, the development team used state-of-the-art commercial off the shelf (COTS) equipment. The first two flights of this system occurred on shuttle mission STS-91 in June 1998 and STS-95 in October 1998. Twenty-one measurements were successfully recorded on the STS-91 right hand booster, providing new accelerometer, strain, temperature, and heating rate data to analysts. Twenty-four measurements were successfully recorded on the STS-95 left hand booster, providing data from the booster and the external tank. This paper summarizes the effort to develop, test, qualify and fly the EDAS to meet SRB flight and data collection requirements.

Crawford, Kevin↗

NASA NTRS2016

Space Suit Portable Life Support System (PLSS) 2.0 Pre-Installation Acceptance (PIA) Testing

Following successful completion of the space suit Portable Life Support System (PLSS) 1.0 development and testing in 2011, the second system-level prototype, PLSS 2.0, was developed in 2012 to continue the maturation of the advanced PLSS design which is intended to reduce consumables, improve reliability and robustness, and incorporate additional sensing and functional capabilities over the current Space Shuttle/International Space Station Extravehicular Mobility Unit (EMU) PLSS. PLSS 2.0 represents the first attempt at a packaged design comprising first generation or later component prototypes and medium fidelity interfaces within a flight-like representative volume. Pre-Installation Acceptance (PIA) is carryover terminology from the Space Shuttle Program referring to the series of test sequences used to verify functionality of the EMU PLSS prior to installation into the Space Shuttle airlock for launch. As applied to the PLSS 2.0 development and testing effort, PIA testing designated the series of 27 independent test sequences devised to verify component and subsystem functionality, perform in situ instrument calibrations, generate mapping data to define set-points for control algorithms, evaluate hardware performance against advanced PLSS design requirements, and provide quantitative and qualitative feedback on evolving design requirements and performance specifications. PLSS 2.0 PIA testing was carried out from 3/20/13 - 3/15/14 using a variety of test configurations to perform test sequences that ranged from stand-alone component testing to system-level testing, with evaluations becoming increasingly integrated as the test series progressed. Each of the 27 test sequences was vetted independently, with verification of basic functionality required before completion. Because PLSS 2.0 design requirements were evolving concurrently with PLSS 2.0 PIA testing, the requirements were used as guidelines to assess performance during the tests; after the completion of PIA testing, test data served to improve the fidelity and maturity of design requirements as well as plans for future advanced PLSS functional testing.

Watts, Carly↗

NASA NTRS2016

Space Suit Portable Life Support System (PLSS) 2.0 Pre-Installation Acceptance (PIA) Testing

Following successful completion of the space suit Portable Life Support System (PLSS) 1.0 development and testing in 2011, the second system-level prototype, PLSS 2.0, was developed in 2012 to continue the maturation of the advanced PLSS design. This advanced PLSS is intended to reduce consumables, improve reliability and robustness, and incorporate additional sensing and functional capabilities over the current Space Shuttle/International Space Station Extravehicular Mobility Unit (EMU) PLSS. PLSS 2.0 represents the first attempt at a packaged design comprising first generation or later component prototypes and medium fidelity interfaces within a flight-like representative volume. Pre-Installation Acceptance (PIA) is carryover terminology from the Space Shuttle Program referring to the series of test sequences used to verify functionality of the EMU PLSS prior to installation into the Space Shuttle airlock for launch. As applied to the PLSS 2.0 development and testing effort, PIA testing designated the series of 27 independent test sequences devised to verify component and subsystem functionality, perform in situ instrument calibrations, generate mapping data, define set-points, evaluate control algorithms, evaluate hardware performance against advanced PLSS design requirements, and provide quantitative and qualitative feedback on evolving design requirements and performance specifications. PLSS 2.0 PIA testing was carried out in 2013 and 2014 using a variety of test configurations to perform test sequences that ranged from stand-alone component testing to system-level testing, with evaluations becoming increasingly integrated as the test series progressed. Each of the 27 test sequences was vetted independently, with verification of basic functionality required before completion. Because PLSS 2.0 design requirements were evolving concurrently with PLSS 2.0 PIA testing, the requirements were used as guidelines to assess performance during the tests; after the completion of PIA testing, test data served to improve the fidelity and maturity of design requirements as well as plans for future advanced PLSS functional testing.

Anchondo, Ian↗

NASA NTRSDate not supplied

CFD Validation Study of a Hypersonic Cone-Slice-Flap Configuration

Model validation is the process of determining the degree of accuracy between the real world and the model. The result of model validation can be used to either improve the model through calibration or quantify the model-form uncertainty. This work focuses on estimating the model-form uncertainty through an area metric for a hypersonic cone-slice-flap geometry configuration given the uncertainty in both the simulation and experimental data. A procedure that can give an accurate representation of the model-form uncertainty using a small number of high-fidelity runs is outlined. The work also assesses the impact of using different high-fidelity solvers and different turbulence models. The goal of performing this comparison is to provide a quantifiable measurement of the accuracy of each solver and turbulence model for this type of design. A low-fidelity analysis is also performed to get a model-form uncertainty for this type of analysis. The two high-fidelity CFD solvers used here are VULCAN-CFD and FUN3D, and the low-fidelity results comes from Cart3D.The experimental data comes from the 20-inch Mach 6 Tunnel located at NASA Langley Research Center. The work here shows that the models used tend to under-predict the drag and moment aerodynamic coefficients for this type of design leading to a model-form uncertainty estimate which can account for up to an 86% of the total uncertainty in the model predictions.

Laura M White↗

NASA NTRS2018

Impacts of NASA's Earth Observations on Subseasonal and Seasonal Forecasts

NASA's suite of Earth-observing satellites provides a unique view of many processes on Earth, with relevance on timescales ranging from hours to weeks and even years. NASA's observations span all parts of the Earth system: atmospheric, ocean, land and cryosphere, and include physical, chemical and biological components. This presentation explores use of NASA observations in extended-range prediction, from many days to months, using the Goddard Earth Observing System (GEOS) assimilation and predictive modeling capabilities. The skill of a weather forecast is linked to the fidelity of the initialization process (data assimilation) and the realistic representation of "fast" physical processes in the model. At longer time horizons, the slower "feedback" processes in the Earth System begin to take a more prominent role in the accuracy of the forecast. Simultaneously, forecast-skill attribution transitions away from feature-based metrics that emphasize smaller scales (e.g., representations of fronts and vortices) to metrics that emphasize the statistical distributions of large-scale features (e.g., ENSO diagnostics and teleconnections). This presentation will summarize studies performed using the GEOS-S2S (subseasonal to seasonal) system that explore the impacts of NASA observations on the fidelity of the forecasts. The GEOS-S2S system is configured for the atmosphere-ocean-land-ice model and is initialized using in-situ and space-based observations, including atmospheric aerosols and ozone which are not typically analyzed in such systems. The GEOS-S2S model routinely includes aerosol feedbacks, which systematically impact the realism of the forecasts, providing a first example of how suitable NASA observations impact the performance of the GEOS-S2S system. Studies in which a stratospheric chemistry module is activated in the GEOS-S2S system allow the impacts of ozone radiative feedbacks to be isolated. Space-based observations of sea-surface temperature and altimetry are routinely analyzed for the initialization of the GEOS-S2S system; recent advances allow the use of NASA's sea-surface salinity data, which are shown to impact the long-range skill of the forecasts.

Pawson, Steven↗

NASA NTRS2007

Noise Spectra and Directivity For a Scale-Model Landing Gear

An extensive experimental study has been conducted to acquire detailed noise spectra and directivity data for a high-fidelity, 6.3%-scale, Boeing 777 main landing gear. The measurements were conducted in the NASA Langley Quiet Flow Facility using a 41-microphone directional array system positioned at a range of polar and azimuthal observer angles with respect to the model. DAMAS (Deconvolution Approach for the Mapping of Acoustic Sources) array processing as well as straightforward individual microphone processing were employed to compile unique flyover and sideline directivity databases for a range of freestream Mach numbers (0.11 - 0.17) covering typical approach conditions. Comprehensive corrections were applied to the test data to account for shear layer ray path and amplitude variations. This allowed proper beamforming at different measurement orientations, as well as directivity presentation in free-field emission coordinates. Four different configurations of the landing gear were tested: a baseline configuration with and without an attached side door, and a noise reduction concept "toboggan" truck fairing with and without side door. DAMAS noise source distributions were determined. Spectral analyses demonstrated that individual microphones could establish model spectra. This finding permitted the determination of unique, spatially-detailed directivity contours of spectral band levels over a hemispherical surface. Spectral scaling for the baseline model confirmed that the acoustic intensity scaled with the expected sixth-power of the Mach number. Finally, comparison of spectra and directivity between the baseline gear and the gear with an attached toboggan indicated that the toboggan fairing may be of some value in reducing gear noise over particular frequency ranges.

Humphreys, William M., Jr.↗

DOE OSTI2021

Neural network representation of electronic structure from ab initio molecular dynamics

Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. Here we introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials. This predictive representation of ab initio electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling. When it is applied to a one-dimension charge-density wave material, carbyne, we are able to compute the spectral function and optical conductivity in the canonical ensemble. The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born–Oppenheimer limit. The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties, paving the way to previously inaccessible or challenging avenues in materials modeling.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

Point cloud-based diffusion models for the Electron-Ion Collider

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We focus on the events at the future Electron-Ion Collider, but we expect that our results can be extended to proton-proton and heavy-ion collisions. Second, previous generative models often relied on image-based techniques. The sparsity of the data can negatively affect the fidelity and sampling time of the model. We address these issues using point clouds and a novel architecture combining edge creation with transformer modules called Point Edge Transformers. Third, we adapt the foundation model OmniLearn, to generate full collider events. This approach may indicate a transition toward adapting and fine-tuning foundation models for downstream tasks instead of training new models from scratch.

Araz, Jack Y. [Stony Brook Univ., NY (United State↗

NASA NTRS2009

Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine

The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

Schwabacher, Mark A.↗

NASA NTRS2011

Design, Optimization, and Evaluation of Integrally-Stiffened Al-2139 Panel with Curved Stiffeners

A curvilinear stiffened panel was designed, manufactured, and tested in the Combined Load Test Fixture at NASA Langley Research Center. The panel is representative of a large wing engine pylon rib and was optimized for minimum mass subjected to three combined load cases. The optimization included constraints on web buckling, material yielding, crippling or local stiffener failure, and damage tolerance using a new analysis tool named EBF3PanelOpt. Testing was performed for the critical combined compression-shear loading configuration. The panel was loaded beyond initial buckling, and strains and out-of-plane displacements were extracted from a total of 20 strain gages and 6 linear variable displacement transducers. The VIC-3D system was utilized to obtain full field displacements/strains in the stiffened side of the panel. The experimental data were compared with the strains and out-of-plane deflections from a high fidelity nonlinear finite element analysis. The experimental data were also compared with linear elastic finite element results of the panel/test-fixture assembly. Overall, the panel buckled very near to the predicted load in the web regions.

Havens, David↗

DOE OSTI2023

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗

NASA NTRS2008

Constellation Program Life-cycle Cost Analysis Model (LCAM)

The Constellation Program (CxP) is NASA's effort to replace the Space Shuttle, return humans to the moon, and prepare for a human mission to Mars. The major elements of the Constellation Lunar sortie design reference mission architecture are shown. Unlike the Apollo Program of the 1960's, affordability is a major concern of United States policy makers and NASA management. To measure Constellation affordability, a total ownership cost life-cycle parametric cost estimating capability is required. This capability is being developed by the Constellation Systems Engineering and Integration (SE&I) Directorate, and is called the Lifecycle Cost Analysis Model (LCAM). The requirements for LCAM are based on the need to have a parametric estimating capability in order to do top-level program analysis, evaluate design alternatives, and explore options for future systems. By estimating the total cost of ownership within the context of the planned Constellation budget, LCAM can provide Program and NASA management with the cost data necessary to identify the most affordable alternatives. LCAM is also a key component of the Integrated Program Model (IPM), an SE&I developed capability that combines parametric sizing tools with cost, schedule, and risk models to perform program analysis. LCAM is used in the generation of cost estimates for system level trades and analyses. It draws upon the legacy of previous architecture level cost models, such as the Exploration Systems Mission Directorate (ESMD) Architecture Cost Model (ARCOM) developed for Simulation Based Acquisition (SBA), and ATLAS. LCAM is used to support requirements and design trade studies by calculating changes in cost relative to a baseline option cost. Estimated costs are generally low fidelity to accommodate available input data and available cost estimating relationships (CERs). LCAM is capable of interfacing with the Integrated Program Model to provide the cost estimating capability for that suite of tools.

Prince, Andy↗