The Digital Astronaut Project (DAP) Applying Computational Models and Simulations to Inform Life Science Research
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Meyers, Jerry G., Jr.↗
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Integrated Model of the Eye/Optic Nerve Head Biomechanical Environment
Visual Impairment and Intracranial Pressure (VIIP) syndrome is a concern for long-duration space flight. Previously, it has been suggested that ocular changes observed in VIIP syndrome are related to the cephalad fluid shift that results in altered fluid pressures [1]. We are investigating the impact of changes in intracranial pressure (ICP) using a combination of numerical models, which simulate the effects of various environment conditions, including finite element (FE) models of the posterior eye. The specific interest is to understand how altered pressures due to gravitational changes affect the biomechanical environment of tissues of the posterior eye and optic nerve sheath. METHODS: Additional description of the numerical modeling is provided in the IWS abstract by Nelson et al. In brief, to simulate the effects of a cephalad fluid shift on the cardiovascular and ocular systems, we utilized a lumped-parameter compartment model of these systems. The outputs of this lumped-parameter model then inform boundary conditions (pressures) for a finite element model of the optic nerve head (Figure 1). As an example, we show here a simulation of postural change from supine to 15 degree head-down tilt (HDT), with primary outcomes being the predicted change in strains at the optic nerve head (ONH) region, specifically in the lamina cribrosa (LC), retrolaminar optic nerve, and prelaminar neural tissue (PLNT). The strain field can be decomposed into three orthogonal components, denoted as the first, second and third principal strains. We compare the peak tensile (first principal) and compressive (third principal) strains, since elevated strain alters cell phenotype and induces tissue remodeling. RESULTS AND CONCLUSIONS: Our lumped-parameter model predicted an IOP increase of c. 7 mmHg after 21 minutes of 15 degree HDT, which agreed with previous reports of IOP in HDT [1]. The corresponding FEM simulations predicted a relative increase in the magnitudes of the peak tensile and compressive strains in the lamina cribrosa of 42 and 43, respectively (Fig. 2). The corresponding changes in the optic nerve strains were 17 and 39, while in the PLNT they were 47 and 43. These magnitudes of relative elevations in peak strains may induce a phenotypic response in resident mechano-responsive resident cells [2]. This approach may be expanded to investigate other environmental changes (e.g. parabolic flight). Through our VIIP SCHOLAR project, we will validate and improve these integrated models by measuring patient-specific changes in optic nerve sheath geometry in patients with idiopathic intracranial hypertension before and after lumbar puncture and CSF removal.
biomechanics↗
Release of the NASA Parallel Electric-Gas Architecture with Synergistic Utilization Scheme (PEGASUS) OpenVSP Model
The Open VSP model represents the geometry of PEGASUS as is currently modeled. PEGASUS is a concept vehicle that will not be physically produced. As such, the baseline model is subject to change without notice or requirement that updates are provided external to NASA. The current baseline is derived from the outer mold line (OML) of the ATR 42-500. As such, planform areas for the wing, fuselage, vertical tail, and horizontal tail mimic those published in public ATR 42 literature. NASA Aeronautics Systems Analysis Branch (ASAB) does not contain any proprietary data on the ATR 42, thus the PEGASUS model does not contain any proprietary or sensitive information. Further, model details that are required for an actual aircraft such as airfoils, twist distributions and propeller geometries are unknown at this time, and generic stand-ins are used within the OpenVSP model. The intent is to provide this model to Georgia Tech ASDL to allow the ASDL team to model the impact of the propulsion system at the wingtip’s impact on structural characteristics. The ASDL will be using the model to generate estimated aerodynamic load and structural members for analysis.
PEGASUS↗
Physics-Informed Deep Learning for Modeling & Simulation Under Uncertainty
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Patrick Leser↗
Physics-Informed Data-driven Modeling of Supersonic Retropropulsion
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Supersonic Retropropulsion↗
Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution
Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of complete prior knowledge of a dynamical system's physical characteristics, determining the optimal structure and optimization strategy for these models can be difficult. In this work, we explore methods for discovering neural state space dynamics models for system identification. Starting with a design space of block-oriented state space models and structured linear maps with strong physical priors, we encode these components into a model genome alongside network structure, penalty constraints, and optimization hyperparameters. Demonstrating the overall utility of the design space, we employ an asynchronous genetic search algorithm that alternates between model selection and optimization and obtains accurate physically consistent models of three physical systems: an aerodynamics body, a continuous stirred tank reactor, and a two tank interacting system.
genetic algorithms, neural architecture search, ne↗
Phase-field simulations to inform nuclear fuel performance modeling
Software tools to simulate nuclear fuel performance at the engineering scale, such as Idaho National Laboratory (INL)’s BISON code, are increasingly relied upon in regulatory and economic decision-making. However, accurate results from these tools depends on the availability of materials parameters that are used as input. In recent years, atomistic and mesoscale simulation methods have emerged as a cost-effective, expedient means to obtain such input parameters. Phase-field simulations using INL’s Marmot application have been used to obtain microstructure-level parameters and to improve material models for fuel performance modeling using BISON. In this talk, recent examples of this process are given, including applications in UO2, U3Si2, and UZr fuel.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Natural Language Processing to Inform Agent-Based Modeling: With Application to Modeling Adoption of Medium-Duty Electric Vehicles
Agent-based socio-technical modeling of medium- and heavy-duty (MDHD) electric vehicle (EV) adoption has the potential to provide analysis, prediction, and gui. This paper describes new applications of text analysis developed through machine learning (ML) to build and understand relevant topics and their saliency in the published discourse on adoption of MDHD EVs. This work contributes to the state of the art in topic mining models by defining a new metric of topic ranking (START) that quantifies the importance of predefined topics within the corpus using weighted results for predefined topics from two topic modeling approaches: Latent Dirichlet Allocation (LDA) and BERTopic. The START metric is then demonstrated in practice to model how academia and industry view the EV adoption process based on the respective texts published by these groups. Results show that academic literature places more emphasis on categories of interests such as norms/attitudes and adopter knowledge, while trade journals tend to emphasize long-term cost more than academia. The two bodies of literature agree on the importance of policy and incentives in MDHD EV adoption. Together these results illustrate the potential to use ML-based text analysis to populate the characteristics of agent-based socio-technical models.
Electric vehicle adoption, fleet electrification, ↗
Integrated Modeling and Simulation Verification, Validation, and Accreditation Strategy for Exploration Systems Mission Directorate
Models and simulations (M&S) are critical resources in the exploration of space. They support program management, systems engineering, integration, analysis, test, and operations and provide critical information and data supporting key analyses and decisions (technical, cost and schedule). Consequently, there is a clear need to establish a solid understanding of M&S strengths and weaknesses, and the bounds within which they can credibly support decision-making. Their usage requires the implementation of a rigorous approach to verification, validation and accreditation (W&A) and establishment of formal process and practices associated with their application. To ensure decision-making is suitably supported by information (data, models, test beds) from activities (studies, exercises) from M&S applications that are understood and characterized, ESMD is establishing formal, tailored W&A processes and practices. In addition, to ensure the successful application of M&S within ESMD, a formal process for the certification of analysts that use M&S is being implemented. This presentation will highlight NASA's Exploration Systems Mission Directorate (ESMD) management approach for M&S W&A to ensure decision-makers receive timely information on the model's fidelity, credibility, and quality.
Hale, Joseph P.↗
Semantic Property Graph for Scalable Knowledge Graph Analytics
Graphs are a natural and fundamental representation to describe entities, relationships, activities, and evolution of complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Resource Description Framework (RDF) and Labeled Property Graph (LPG) are two of the most used data models to encode information in a graph. Both models are similar in terms of using basic graph elements such as nodes and edges but differ in terms of the modeling approach, expressibility, serialization, and target applications. RDF is a flexible data exchange model for expressing information about entities but it tends to a have high memory footprint and inefficient storage, which does not make it a natural choice to perform scalable graph analytics. In contrast, LPG has gained traction as a reliable model to perform scalable graph analytic tasks such as sub-graph matching, network alignment, and real-time knowledge graph query. It provides efficient storage, fast traversal, and flexibility to model various real-world domains. At the same time, the LPG lacks the support of a formal knowledge representation such as an ontology to provide automated knowledge inference. We propose Semantic Property Graph (SPG) as a logical projection of reified RDF into the LPG model. SPG continues to use RDF ontology to define the type hierarchy of the projected graph and validate it against a given ontology. We present a framework to convert reified RDF graphs into SPG using two different computing environments. We also present cloud-based graph migration capabilities using Amazon Web Services.
Purohit, Sumit↗
Binocular device for displaying numerical information in field of view
An apparatus is described for superimposing numerical information on the field of view of binoculars. The invention has application in the flying of radio-controlled model airplanes. Information such as airspeed and angle of attack are sensed on a model airplane and transmitted back to earth where this information is changed into numerical form. Optical means are attached to the binoculars that a pilot is using to track the model air plane for displaying the numerical information in the field of view of the binoculars. The device includes means for focusing the numerical information at infinity whereby the user of the binoculars can see both the field of view and the numerical information without refocusing his eyes.
Fuller, H. V.↗
Multiscale Modeling of Thermoplastics Using Atomistic-informed Micromechanics
A multiscale repeating unit cell model of a single spherulite containing four disparate length scales was developed to predict the thermoelastic behavior of semicrystalline thermoplastic materials for composite aerospace applications. The continuum level scales were fully coupled and modeled using the generalized method of cells and the high fidelity generalized method of cells micromechanics theories. Data from molecular dynamics simulations were used as inputs for the amorphous and crystalline constituents in the multiscale continuum models. Effective Young’s modulus, shear modulus, Poisson’s ratio, coefficient of thermal expansion, and thermal conductivity were predicted for polyether ether ketone and polyether ketone ketone, showing good agreement with the available experimental data from the open literature. Moreover, it is shown that predicted properties are fairly insensitive to the fidelity of the micromechanics model used at the highest continuum scale or the assumed shape of the spherulite.
thermoplastics↗
A comparison of surrogate constitutive models for viscoplastic creep simulation of HT-9 steel
Mechanistic microstructure-informed constitutive models for the mechanical response of polycrystals are a cornerstone of computational materials science. However, as these models become increasingly more complex – often involving coupled differential equations describing the effect of specific deformation modes – their associated computational costs can become prohibitive, particularly in optimization or uncertainty quantification tasks that require numerous model evaluations. To address this challenge, surrogate constitutive models that balance accuracy and computational efficiency are highly desirable. Data-driven surrogate models, that learn the constitutive relation directly from data, have emerged as a promising solution. In this work, we develop two local surrogate models for the viscoplastic response of a steel: a piecewise response surface method and a mixture of experts model. These surrogates are designed to adapt to complex material behavior, which may vary with material parameters or operating conditions. The surrogate constitutive models are applied to creep simulations of HT-9 steel, an alloy of considerable interest to the nuclear energy sector due to its high tolerance to radiation damage, using training data generated from viscoplastic self-consistent (VPSC) simulations. In conclusion, we define a set of test metrics to numerically assess the accuracy of our surrogate models for predicting viscoplastic material behavior, and show that the mixture of experts model outperforms the piecewise response surface method in terms of accuracy.
36 MATERIALS SCIENCE↗
Thermal-dynamic modeling study
Study provides basic information for designing models and conducting thermal-dynamic structural tests. Factors considered are development and interpretation of thermal-dynamic structural scaling laws; identification of major problem areas; and presentation of model fabrication, instrumentation, and test procedures.
Ojalvo, I. U.↗
(Project 18-15502) Reducing Uncertainty in Radionuclide Transport Prediction Using Multiple Environmental Tracers (Final Report)
In order to successfully site and design a nuclear waste disposal facility, DOE scientists are required to show safe containment of the radioactive waste for up to 1 million years. A significant hurdle to accurately and convincingly demonstrating disposal safety is predicting the fate of radioactive elements once they enter the groundwater system surrounding the repository. These predictions are often made with computer models, which contain accurate physics of groundwater movement and chemical reactions that occur during groundwater flow. As computer power increases, the physics and chemistry of these computer simulators can become more and more realistic and the physically based error decreases. However, the ability of these computer models to provide accurate predictions in a specific place, over long time periods, requires the scientists and engineers to know the subsurface properties of the Earth that control groundwater movement. In particular, groundwater scientists and engineers need to know the groundwater fluid velocity, which can change over time and strongly vary with location within the groundwater system. Because the Earth’s subsurface cannot be directly seen, and can only be sampled at drilling locations, the properties of the groundwater system are never known completely, and computer models of groundwater transport will always have some amount of uncertainty. This project’s principal goal was to use chemicals and isotope “tracers”, which have been introduced to the groundwater system by natural processes over long time periods, to help inform computer models of the groundwater velocity and subsurface properties. The goal was to calculate how much better predictions of groundwater transport were when these “tracers” were used to inform the computer models.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Energy storage solutions to decarbonize electricity through enhanced capacity expansion modelling
To meet ambitious global decarbonization goals, electricity system planning and operations will change fundamentally. With increasing reliance on variable renewable energy resources, energy storage is likely to play a critical accompanying role to help balance generation and consumption patterns. As grid planners, non-profit organizations, non-governmental organizations, policy makers, regulators and other key stakeholders commonly use capacity expansion modelling to inform energy policy and investment decisions, it is crucial that these processes capture the value of energy storage in energy-system decarbonization. Here we conduct an extensive review of literature on the representation of energy storage in capacity expansion modelling. We identify challenges related to enhancing modelling capabilities to inform decarbonization policies and electricity system investments, and to improve societal outcomes throughout the clean energy transition. Additionally, we further identify corresponding research activities that can help overcome these challenges and conclude by highlighting tangible real-world outcomes that will result from pursuing these research activities. Capacity expansion modelling (CEM) approaches need to account for the value of energy storage in energy-system decarbonization. A new Review considers the representation of energy storage in the CEM literature and identifies approaches to overcome the challenges such approaches face when it comes to better informing policy and investment decisions.
25 ENERGY STORAGE↗
Error rate information in attention allocation pilot models
The Northrop urgency decision pilot model was used in a command tracking task to compare the optimized performance of multiaxis attention allocation pilot models whose urgency functions were (1) based on tracking error alone, and (2) based on both tracking error and error rate. A matrix of system dynamics and command inputs was employed, to create both symmetric and asymmetric two axis compensatory tracking tasks. All tasks were single loop on each axis. Analysis showed that a model that allocates control attention through nonlinear urgency functions using only error information could not achieve performance of the full model whose attention shifting algorithm included both error and error rate terms. Subsequent to this analysis, tracking performance predictions for the full model were verified by piloted flight simulation. Complete model and simulation data are presented.
Faulkner, W. H.↗