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NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗

NASA NTRSDate not supplied

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.

Spacecraft Structural Health Monitoring↗

NASA NTRS2011

TPSAS-NF1676L-12387-DND

Fast-time wake prediction models are semi-empirical, and are designed to predict aircraft wake vortex information behind a generating aircraft. They are developed using theoretical concepts and often calibrated with data obtained from field measurements, laboratory studies, or numerical simulations. They rely upon inputs representing the generating aircraft (such as weight, airspeed, wingspan, lateral position, and altitude) and atmospheric environment (such as ambient winds, turbulence, and temperature lapse rate). Other input may include the type of aircraft (e.g., B-747- 400). Output from these models may include time histories of the wake vortex strength, and position, as well as a prediction of the uncertainties in strength and position. Fast-time models should provide solutions in a fraction of a second, and hence is an enabling technology able to perform in a real-time operational system, such as dynamic spacing systems envisioned by the Aircraft Vortex Spacing System or other Super Density Operations concepts. Other applications of fast-time models include 1) guidance for wake separation standards for new aircraft, 2) optimization of separation standards for existing aircraft, 3) guidance for setting vertical separation standards during cruise, 4) accident reconstructions, and 5) testing of new concepts for safe increases in airport capacity for both single and multiple runway configurations. Fast-time models are based on a number of assumptions which limit their accuracy and range of application. For example, they do not predict the meandering vortex paths that can result from the wake interaction with atmospheric turbulence. However, these models should account for the mean vortex position and may predict the level of uncertainty. This presentation describes a study in which comparative evaluations of fast-time wake transport and decay models were conducted using data sets from previous field programs taken at MEM (1995), DFW (1997, 1999, 2000), and Denver (2005, 2006) airports. These data sets contain, meteorological data, lidar measurements of wake vortices, and the aircraft parameters associated with these vortices.

Fred H Proctor↗

DOE OSTI2024

Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model

Abstract Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species–environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold‐adapted species was predicted to have high probabilities of extirpation across its current range—with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

SIGHT: Stacked Integration of Geospatial Hierarchical Typologies for Inferring Building Characteristics

Building characteristics are often absent in building stock datasets, particularly in regions most vulnerable to climate change and requiring effective disaster management strategies. Traditional machine learning approaches, while widely used to predict building attributes, typically neglect the spatial context of the data, leading to less accurate and reliable outcomes. To address these challenges, this paper introduces a novel algorithm, the Stacked Integration of Geospatial Hierarchical Typologies. This algorithm adapts a meta-learning framework to incorporate geospatial context into the predictive modeling process. We demonstrate the utility of the algorithm through two primary use cases: building use type classification and building height prediction. The algorithm consistently achieved or exceeded a 0.94 macro average F1 score across five geographically distinct countries for building use type classification. For building height prediction, it accurately predicted heights with a root mean square error of 3.01 in a comprehensive study using roughly 3.6 million buildings in Japan. These results underscore the benefits of integrating spatial hierarchies into machine learning models, enhancing both predictive accuracy and reliability in geospatial modeling. This work introduces a new algorithm to address the pervasive data sparsity issue in existing building stock datasets.

Adams, Daniel [ORNL] (ORCID:0000000196950577)↗

NASA NTRS1981

A single-channel model does not predict visibility of asynchronous gratings

A single channel model is shown to lack the capability to predict the visibility of asynchronous compound gratings. A constant Beta was assumed, based on Monte-Carlo simulations, and used to show that an SE of 6.73 (Limb, 1981) lies outside of an acceptable confidence interval. Potential imbalances between two eyes cannot be as large as 1.5 dB, which can only affect one eye, or one region of a retina. An average unbalance of 0.53 dB (SE = 0.12) employed in experiments to measure thresholds was compensated for by estimated threshold reductions. Trial-to-trial variability was included in the single channel model, as were considerations of retinal inhomogeneity. Temporal sensitivity for 2 and 4 c/deg grating patches were measured, and the amplitude response of a single channel at the two spatial frequencies was obtained. Threshold reductions with a Beta of 3.1 are then determined to not be significant enough to warrant adoption of the single channel model predictions.

Watson, A. B.↗

DOE OSTI2024

Modelling the ejection of primary aerosols during the fast pyrolysis of biomass anisotropic particles

A model for the fast pyrolysis of anisotropic biomass particles is presented which considers bubbling dynamics within the liquid intermediate phase (metaplast) and aerosol ejection from this phase. The model employs the population balance equation and the method of moments to estimate the production rate and resultant size distribution of aerosol ejections, incorporating a detailed CRECK reaction mechanism, and considers the effect of anisotropic biomass microstructure on the intraparticle transport of mass and energy. Here, this study investigates the impact of particle size, heating rate (heat transfer coefficient), and lignocellulosic composition on aerosol ejection. The model predicts that, at high heating rates (convective heat transfer coefficient of 359 W/m 2 .K), aerosols can contribute over 20% to the heavy fraction yield in bio-oil for small particles (1 mm diameter, 4 mm length). The model can predict aerosol size distribution and surface area, indicating an average size of 20 μm for bubbles and 5 μm for aerosols during increased bubble production and aerosol ejection rates. These findings are consistent with prior experimental results and provide essential information for future modeling of extra-particle reactions of the aerosols as they progress through the reactor.

09 BIOMASS FUELS↗

NASA NTRSDate not supplied

AI-Enhanced Computational Tools for Entry Systems Modeling

To advance the understanding of complex atmospheric entry phenomena, NASA’s Entry Systems Modeling (ESM) team [1] has developed high-fidelity computational tools addressing multiscale challenges, from material microstructures to full-scale heatshield response. This abstract highlights a subset of ESM tools, focusing on AI integration to enhance workflows and predictive modeling. - PuMA [2] computes effective material properties from high-resolution micro-CT scans, supporting TPS analysis for NASA missions. - TomoSAM [3] automates 3D tomography dataset segmentation for PuMA using the Segment Anything Model, reducing manual effort and improving accuracy. - PATO [4] models porous reactive materials under extreme conditions, with advancements such as unified solvers, mechanical erosion, and TPS coatings for NASA missions. - arcjetCV [5] employs deep learning to analyze arc jet test footage, measuring recession rates, shape changes, and shock standoff distances, bridging simulations, and experiments to reveal TPS ablation behavior. - ARCHeS [6] simulates arc heater plasma flows, modeling turbulence, radiation, and electromagnetic interactions to optimize arc heater performance, validate TPS under extreme conditions, and serve as a foundation for developing digital twins of arc heater facilities. - SPARTA [7] simulates rarefied hypersonic flows and gas-surface interactions for planetary entry missions, leveraging GPU architectures for scalable and efficient aerothermal and ablation analyses. AI-driven solutions, such as deep learning segmentation, have streamlined workflows in ESM tools and still hold significant potential to further accelerate processes and enhance automation in entry systems modeling. [1] Haskins, J.B. (2023), [2] Ferguson, J.C. (2018), [3] Meurisse, J.B.E. (2018), [4] Semeraro, F. (2023), [5] Quintart, A. (2024) [6] Meurisse, J.B.E. (2022), [7] Plimpton, S.J. (2019)

Predictive Modeling↗

NASA NTRS1983

Coulomb thermal properties and stability of the Io plasma torus

Coulomb collisional energy exchange rates are computed for a model of the Io plasma torus consisting of newly created pickup ions, a background of thermally degraded intermediary ions, and a population of cooler electrons. The electrons are collisionally heated by both the pickup ions and background ions and are cooled by electron impact excitation of plasma ions which radiate in the EUV. It is found that a relative concentration of S III pickup ions forbidden S III/electrons = 0.1 with a temperature of 340 eV can deliver energy to the electrons at a rate of 3 x 10 to the -13th erg/cu cm per sec, sufficient to power the EUV emissions in the Io torus. The model predicts a background ion temperature Ti of about 53 eV and an electron temperature Te of about 5.5 eV on the basis of steady-state energy balance relations at Coulomb rates. The model also predicts electron temperature fluctuations at the 30 percent level on a time scale of less than 11 hours, consistent with recent observations of this phenomenon.

Barbosa, D. D.↗

NASA NTRS1993

Verification of a two-dimensional infiltration model for the resin transfer molding process

A two-dimensional finite element model for the infiltration of a dry textile preform by an injected resin was verified. The model, which is based on the finite element/control volume technique, determines the total infiltration time and the pressure increase at the mold inlet associated with the RTM process. Important input data for the model are the compaction and permeability behavior of the preform along with the kinetic and rheological behavior of the resin. The compaction behavior for several textile preforms was determined by experimental methods. A power law regression model was used to relate fiber volume fraction to the applied compaction pressure. Results showed a large increase in fiber volume fraction with the initial application of pressure. However, as the maximum fiber volume fraction was approached, the amount of compaction pressure required to decrease the porosity of the preform rapidly increased. Similarly, a power law regression model was used to relate permeability to the fiber volume fraction of the preform. Two methods were used to measure the permeability of the textile preform. The first, known as the steady state method, measures the permeability of a saturated preform under constant flow rate conditions. The second, denoted the advancing front method, determines the permeability of a dry preform to an infiltrating fluid. Water, corn oil, and an epoxy resin, Epon 815, were used to determine the effect of fluid type and viscosity on the steady state permeability behavior of the preform. Permeability values measured with the different fluids showed that fluid viscosity had no influence on the permeability behavior of 162 E-glass and TTI IM7/8HS preforms. Permeabilities measured from steady state and advancing front experiments for the warp direction of 162 E-glass fabric were similar. This behavior was noticed for tests conducted with corn oil and Epon 815. Comparable behavior was observed for the warp direction of the TTI IM7/8HS preform and corn oil. Mold filling and flow visualization experiments were performed to verify the analytical computer model. Frequency dependent electromagnetic sensors were used to monitor the resin flow front as a function of time. For the flow visualization tests, a video camera and high resolution tape recorder were used to record the experimental flow fronts. Comparisons between experimental and model predicted flow fronts agreed well for all tests. For the mold filling tests conducted at constant flow rate injection, the model was able to accurately predict the pressure increase at the mold inlet during the infiltration process. A kinetics model developed to predict the degree of cure as a function of time for the injected resin accurately calculated the increase in the degree of cure during the subsequent cure cycle.

Hammond, Vincent H.↗

NASA NTRSDate not supplied

Fast Assessment of Metal Performance through Dislocation Physics and Machine Learning

The microstructure of metals is key to their mechanical properties. The types, density, composition and morphology of crystal defects all have pronounced impact on the properties. Changes to the microstructure occurring during processing and use can be very striking. The emerging technology additive manufacturing (AM) has the potential to improve performance by allowing optimized designs, but the process and environments can lead to unusual microscale features whose properties must be understood and characterized to enable higher technological readiness levels and application. Experimentally, an extensive evaluation of mechanical properties of 3D printed metals is a challenge, and anomalous effects related to the AM process add complexity. We present a new machine learning (ML) model predicting mechanical response based on dislocation mediated plasticity simulations. A large set of 3D discrete dislocation dynamics simulations with wide ranges of loading conditions is transformed to preprocessed data ready for training with the ML model. The trained model can predict the mechanical response of Mo30W for a given microstructure evolution, providing key information essential for optimization of AM processing.

Jaehyun Cho↗

NASA NTRSDate not supplied

Fast Assessment of Metal Performance through Dislocation Physics and Machine Learning

Jaehyun Cho↗

NASA NTRS1987

Self-regulating heater application to Shuttle/Centaur hydrazine fuel line thermal control

The Shuttle/Centaur high energy upper stage vehicle thermal environments were more severe than previous Centaur vehicle thermal environments, creating need for a new hydrazine fuel line thermal control technique. Constant power heaters did not satisfy power dissipation requirements, because the power required to maintain fuel line thermal control during cold conditions exceeded the maximum power allowable during hot conditions. Therefore, a Raychem Thermolimit self-regulating heater was selected for this application, and was attached to the hydrazine fuel line with Kapton and aluminum foil tapes. Fuel line/heater thermal modeling and subsequent thermal vacuum chamber testing simulated heater thermal performance during all worst-case Shuttle/Centaur thermal environmental conditions. Fuel line temperatures were maintained between the 4C to 71C limits during all analytical and test cases. Finally, the thermal model predictions were correlated with the test data, thereby ensuring that the model would provide satisfactory predictions for future missions and/or vehicles.

Unkrich, David B.↗

NASA NTRS1984

Chemoviscosity modeling for thermosetting resins - I

A new analytical model for chemoviscosity variation during cure of thermosetting resins was developed. This model is derived by modifying the widely used WLF (Williams-Landel-Ferry) Theory in polymer rheology. Major assumptions involved are that the rate of reaction is diffusion controlled and is linearly inversely proportional to the viscosity of the medium over the entire cure cycle. The resultant first order nonlinear differential equation is solved numerically, and the model predictions compare favorably with experimental data of EPON 828/Agent U obtained on a Rheometrics System 4 Rheometer. The model describes chemoviscosity up to a range of six orders of magnitude under isothermal curing conditions. The extremely non-linear chemoviscosity profile for a dynamic heating cure cycle is predicted as well. The model is also shown to predict changes of glass transition temperature for the thermosetting resin during cure. The physical significance of this prediction is unclear at the present time, however, and further research is required. From the chemoviscosity simulation point of view, the technique of establishing an analytical model as described here is easily applied to any thermosetting resin. The model thus obtained is used in real-time process controls for fabricating composite materials.

Hou, T. H.↗

NASA NTRS1979

Postseismic viscoelastic deformation and stress. Part 2: Stress theory and computation; dependence of displacement, strain, and stress on fault parameters

A viscoelastic model for deformation and stress associated with earthquakes is reported. The model consists of a rectangular dislocation (strike slip fault) in a viscoelastic layer (lithosphere) lying over a viscoelastic half space (asthenosphere). The time dependent surface stresses are analyzed. The model predicts that near the fault a significant fraction of the stress that was reduced during the earthquake is recovered by viscoelastic softening of the lithosphere. By contrast, the strain shows very little change near the fault. The model also predicts that the stress changes associated with asthenospheric flow extend over a broader region than those associated with lithospheric relaxation even though the peak value is less. The dependence of the displacements, stresses on fault parameters studied. Peak values of strain and stress drop increase with increasing fault height and decrease with fault depth. Under many circumstances postseismic strains and stresses show an increase with decreasing depth to the lithosphere-asthenosphere boundary. Values of the strain and stress at distant points from the fault increase with fault area but are relatively insensitive to fault depth.

Cohen, S. C.↗

NASA NTRS1994

Strong gravitational lensing statistics as a test of cosmogonic scenarios

Gravitational lensing statistics can provide a direct and powerful test of cosmic structure formation theories. Since lensing tests, directly, the magnitude of the nonlinear mass density fluctuations on lines of sight to distant objects, no issues of 'bias' (of mass fluctuations with respect to galaxy density fluctuations) exist here, although lensing observations provide their own ambiguities of interpretation. We develop numerical techniques for generating model density distributions with the very large spatial dynamic range required by lensing considerations and for identifying regions of the simulations capable of multiple image lensing in a conservative and computationally efficient way that should be accurate for splittings significantly larger than 3 seconds. Applying these techniques to existing standard Cold dark matter (CDM) (Omega = 1) and Primeval Baryon Isocurvature (PBI) (Omega = 0.2) simulations (normalized to the Cosmic Background Explorer Satellite (COBE) amplitude), we find that the CDM model predicts large splitting (greater than 8 seconds) lensing events roughly an order-of-magnitude more frequently than the PBI model. Under the reasonable but idealized assumption that lensing structrues can be modeled as singular isothermal spheres (SIS), the predictions can be directly compared to observations of lensing events in quasar samples. Several large splitting (Delta Theta is greater than 8 seconds) cases are predicted in the standard CDM model (the exact number being dependent on the treatment of amplification bias), whereas none is observed. In a formal sense, the comparison excludes the CDM model at high confidence (essentially for the same reason that CDM predicts excessive small-scale cosmic velocity dispersions.) A very rough assessment of low-density but flat CDM model (Omega = 0.3, Lambda/3H(sup 2 sub 0) = 0.7) indicates a far lower and probably acceptable level of lensing. The PBI model is consistent with, but not strongly tested by, the available lensing data, and other open models would presumably do as well as PBI. These preliminary conclusions and the assumptions on which they are based can be tested and the analysis can be applied to other cosmogonic models by straightforward extension of the work presented here.

Cen, Renyue↗

NASA NTRS1986

A Constitutive Model for the Inelastic Multiaxial Cyclic Response of a Nickel Base Superalloy Rene 80

The objective was to develop unified constitutive equations which can model a variety of nonlinear material phenomena observed in Rene 80 at elevated temperatures. A constitutive model was developed based on back stress and drag stress. The tensorial back stress was used to model directional effects; whereas, the scalar drag stress was used to model isotropic effects and cyclic hardening or softening. A flow equation and evolution equations for the state variables were developed in multiaxial form. Procedures were developed to generate the material parameters. The model predicted very well the monotonic tensile, cyclic, creep, and stress relaxation behavior of Rene 80 at 982 C. The model was then extended to 871, 760, and 538 C. It was shown that strain rate dependent behavior at high temperatures and strain rate independent behavior at the lower temperatures could be predicted very well. A large number of monotonic tensile, creep, stress relation, and cyclic experiments were predicted. The multiaxial capabilities of the model were verified extensively for combined tension/torsion experiments. The prediction of the model agreed very well for proportional, nonproportional, and pure shear cyclic loading conditions at 982 and 871 C.

Ramaswamy, V. G.↗

NASA NTRS1974

Application of an invariant second-order closure model to compressible turbulent shear layers

A second-order closure model for two-dimensional, compressible shear flows is investigated using the invariant modeling technique developed by Donaldson. The invariant model parameters were originally selected by comparison of model predictions with critical experimental data on basic incompressible flows. Additional modeling for compressible flows has been introduced. A number of different shear flows such as the free shear layer, flat plate boundary layer and a simulation of the mixing region of a chemical laser are computed. The model is consistent with first-order closure turbulence models for equilibrium flows and is further capable of predicting nonequilibrium flows that cannot be correctly solved by eddy viscosity models. The results are in generally good agreement with experimental measurements but suggest the need for inclusion of a Mach number dependent model of the pressure diffusion terms in order to adequately represent high speed compressible flows.

Varma, A. K.↗