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NASA NTRS1993

Conservative force model performance for TOPEX/Poseidon precision orbit determination

The TOPEX/Poseidon spacecraft was launched on August 10, 1992 to study the Earth's oceans. To achieve maximum benefit from the altimetric data collected, mission requirements dictate that TOPEX/Poseidon's orbit must be computed at an unprecedented level of accuracy. In order to satisfy these requirements, a model which accounts for the satellite's complex geometry, attitude, and surface properties has been developed. This `box-wing' representation treats the spacecraft as the combination of flat plates arranged in the shape of a box and a connecetd solar array. The nonconservative forces acting on each of the eight surfaces are computed independently, yielding vector accelerations which are summed to compute the total aggregate effect on the satellite center-of-mass. Parameters associated with each flat plate were derived from a finite element analysis of the spacecraft. Certain parameters can be inferred from tracking data and have been adjusted to obtain a better representation of the satellite acceleration history. Changes in the nominal mission profile and the presence of an `anomalistic' force have complicated this tuning process. Model performance, parameter sensitivities, and the `anomalistic' force will be discussed.

Marshall, J. Andrew↗

NASA NTRS1991

A dynamically adaptive multigrid algorithm for the incompressible Navier-Stokes equations: Validation and model problems

An algorithm is described for the solution of the laminar, incompressible Navier-Stokes equations. The basic algorithm is a multigrid based on a robust, box-based smoothing step. Its most important feature is the incorporation of automatic, dynamic mesh refinement. This algorithm supports generalized simple domains. The program is based on a standard staggered-grid formulation of the Navier-Stokes equations for robustness and efficiency. Special grid transfer operators were introduced at grid interfaces in the multigrid algorithm to ensure discrete mass conservation. Results are presented for three models: the driven-cavity, a backward-facing step, and a sudden expansion/contraction.

Thompson, C. P.↗

DOE OSTI2016

MODELING THE Ly α FOREST IN COLLISIONLESS SIMULATIONS

Cosmological hydrodynamic simulations can accurately predict the properties of the intergalactic medium (IGM), but only under the condition of retaining the high spatial resolution necessary to resolve density fluctuations in the IGM. This resolution constraint prohibits simulating large volumes, such as those probed by BOSS and future surveys, like DESI and 4MOST. To overcome this limitation, we present “Iteratively Matched Statistics” (IMS), a novel method to accurately model the Ly α forest with collisionless N -body simulations, where the relevant density fluctuations are unresolved. We use a small-box, high-resolution hydrodynamic simulation to obtain the probability distribution function (PDF) and the power spectrum of the real-space Ly α forest flux. These two statistics are iteratively mapped onto a pseudo-flux field of an N -body simulation, which we construct from the matter density. We demonstrate that our method can reproduce the PDF, line of sight and 3D power spectra of the Ly α forest with good accuracy (7%, 4%, and 7% respectively). We quantify the performance of the commonly used Gaussian smoothing technique and show that it has significantly lower accuracy (20%–80%), especially for N -body simulations with achievable mean inter-particle separations in large-volume simulations. In addition, we show that IMS produces reasonable and smooth spectra, making it a powerful tool for modeling the IGM in large cosmological volumes and for producing realistic “mock” skies for Ly α forest surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Explainable Artificial Intelligence in Endocrinological Medical Research

Artificial Intelligence (AI) has been a part of the medical community for decades in the form of Clinical Decision Support Systems (CDSS) to aide physicians in diagnosis and categorization of patients (1). Recent years have seen a shift from expert-derived models to the integration of machine learning (ML) algorithms to drive the output of these AI systems due to the ability of ML models to more accurately make predictions by exploiting higher dimensional and often complex data. In many cases ML models gain their advantage in accuracy by capture complex and often non-linear relationships between features being used to make the prediction. However, the hype and excitement around these methods are tempered by the clinical utility of often black-box solutions driven by skepticism of results that are difficult for practitioners to not only interpret but explain to their patients (1-3). This skepticism is not unfounded as multiple examples of black-box solutions identifying incidental correlates as the key predictors have highlighted the potential bias in a training set, or reward system, that a ML model may exploit; for example a model discerning wolves from huskies based on snow in the background rather than features of the dogs (1, 4, 5).

Endocrinological, artificial intelligence, diabete↗

DOE OSTI2025

Grey-Box System Identification of Grid-Forming Inverters

This paper demonstrates the use of grey-box system identification methods for simplifying and understanding the nonlinear power dynamics of grid-forming inverters (GFMs). The power and frequency outputs of complex high-order GFM models are fed into system identification software in order to fit them to a predetermined LTI system and learn system parameters such as (synthetic) inertia and droop constants. The same process is then run for a high-order synchronous generator model, and the outputs are fit to the same set of LTI equations. Simulation of a network of GFM inverters with diverse control architecture is also performed for the same process. The intent is threefold: first, to demonstrate the appropriateness of unified LTI models for describing the power and frequency dynamics of individual resources and connected networks, in order to facilitate analysis of larger heterogeneous networked systems; second, to discover the relationship between internal control parameters of GFMs and their externally observed values; and third, to validate that grey-box data-driven system identification techniques can be a valuable tool to discover the values of important parameters in the absence of explicit vendor models.

analytical models↗

NASA NTRS1983

HOST liner cyclic facilities

The HOST Liner Cyclic Program is utilizing two types of test apparatus, rectangular box rigs and a full annular rig. To date two quartz lamp cyclic box rigs have been tested and a third is to begin testing in late October 1983. The box rigs are used to evaluate 5x8 inch rectangular linear samples. A 21 inch diameter outer liner simulator is also being built up for testing beginning in April 1984. All rigs are atmospheric rigs. The first box rig, a three 6-kVA lamp installation, was operated under adverse conditions to determine feasibility of using quartz lamps for cyclic testing. This work was done in December 1981 and looked promising. The second box rig, again using three 6-kVA lamps, was operated to obtain instrumentation durability information and initial data input to a Finite Element Model. This limited test program was conducted in August 1983. Five test plates were run. Instrumentation consisted of strain gages, thermocouples and thermal paint. The strain gages were found to fail at 1200 F as expected though plates were heated to 1700 F. The third box rig, containing four 6-kVA lamps, is in build up for testing to begin in late October 1983. In addition to 33 percent greater power input, this rig has provision for 400 F backside line cooling air and a viewing port suitable for IR camera viewing. The casing is also water cooled for extended durability.

Schultz, D.↗

DOE OSTI2019

Reconstruction of larval origins based on genetic relatedness and biophysical modeling

The assessment of the mechanisms and patterns of larval connectivity between geographically separated populations leads to a better understanding of benthic marine population dynamics, especially in commercially valuable species. This study investigated for the first time the fine-scale temporal genetic variability of new settlers and their origins in a benthic marine organism with one of the longest pelagic larval phases, the Caribbean spiny lobster (Panulirus argus). We genotyped newly settled postlarvae in the Florida Keys and adults of spiny lobster from the Florida Keys and throughout the Caribbean Sea. We identified strong larval connectivity between Dominican Republic, Belize, Nicaragua, the Florida Keys, and West-Florida. the larval dispersal modeling suggests that Florida’s lobster population could receive recruits from within and from other areas outside its state and national maritime boundaries. The genetic analyses refine the oceanographic model indicating that the connectivity patterns described could also result from unknown parental populations sourcing adults and postlarvae in different spawning seasons to the Florida Keys. We discuss the importance of small temporal scales to identify patterns in larval export. Our findings are significant on two levels. From the larval dispersal perspective, genetic results and biophysical modeling identify patterns of gene flow enhancing persistence of local populations. From an economic and fishery perspective, P. argus is the most important commercial species in the Caribbean and our results inform how considering larval source and sink dynamics across international boundaries could improve management plans at local, national, and regional levels.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2017

Monte Carlo Computational Modeling of Atomic Oxygen Interactions

Computational modeling of the erosion of polymers caused by atomic oxygen in low Earth orbit (LEO) is useful for determining areas of concern for spacecraft environment durability. Successful modeling requires that the characteristics of the environment such as atomic oxygen energy distribution, flux, and angular distribution be properly represented in the model. Thus whether the atomic oxygen is arriving normal to or inclined to a surface and whether it arrives in a consistent direction or is sweeping across the surface such as in the case of polymeric solar array blankets is important to determine durability. When atomic oxygen impacts a polymer surface it can react removing a certain volume per incident atom (called the erosion yield), recombine, or be ejected as an active oxygen atom to potentially either react with other polymer atoms or exit into space. Scattered atoms can also have a lower energy as a result of partial or total thermal accommodation. Many solutions to polymer durability in LEO involve protective thin films of metal oxides such as SiO2 to prevent atomic oxygen erosion. Such protective films also have their own interaction characteristics. A Monte Carlo computational model has been developed which takes into account the various types of atomic oxygen arrival and how it reacts with a representative polymer (polyimide Kapton H) and how it reacts at defect sites in an oxide protective coating, such as SiO2 on that polymer. Although this model was initially intended to determine atomic oxygen erosion behavior at defect sites for the International Space Station solar arrays, it has been used to predict atomic oxygen erosion or oxidation behavior on many other spacecraft components including erosion of polymeric joints, durability of solar array blanket box covers, and scattering of atomic oxygen into telescopes and microwave cavities where oxidation of critical component surfaces can take place. The computational model is a two dimensional model which has the capability to tune the interactions of how the atomic oxygen reacts, scatters, or recombines on polymer or nonreactive surfaces. In addition to the specification of atomic oxygen arrival details, a total of 15 atomic oxygen interaction parameters have been identified as necessary to properly simulate observed interactions and resulting polymer erosion that have been observed in LEO. The tuning of the Monte Carlo model has been accomplished by adjusting interaction parameters so the erosion patterns produced by the model match those from several actual LEO space experiments. Surface texturing in LEO can also be predicted by the model. Such comparison of space tests with ground laboratory experiments have enabled confidence in ground laboratory lifetime prediction of protected polymers. Results of Monte Carlo tuning, examples of surface texturing and undercutting erosion prediction, and several examples of how the model can be used to predict other LEO and Mars orbital space results are presented.

Monte Carlo Modeling↗

NASA NTRS2017

Monte Carlo Computational Modeling of Atomic Oxygen Interactions

Atomic Oxygen↗

NASA NTRS2019

Method for Tracking and Communicating Aggregate Risk Through the Use of Model-Based Systems Engineering (MBSE) Tools

Large, complex projects can identify a significant number and variety of risks, throughout the project life cycle. These risks are analyzed, mitigated, closed or accepted as independent uncertainties. Once closed or accepted, it is easy for projects to lose awareness of their impact. In reality, each of these risks contributes some amount to the overall risk posture of the project. The ability to track and effectively communicate this aggregate risk has represented a challenge to project management. There have been previous attempts to create a schema to communicate the aggregate effect of risks, without notable success. Most of these attempts have centered on some additive metric derived from the scoring of likelihood and consequence values. This, in and of itself, is a logical approach, but all too often the scores were then aggregated to a level where all context was lost. One weakness has been a lack of attempt to create linkages or logical groups of the risks upon which useful aggregation could then occur. The overall move to model-based (systems) engineering (MBSE) has opened up a vast frontier of opportunities to better integrate all project data. MBSE provides an underlying layer that links data items to each other. Objectives link to requirements, which then link to functions, functions to physical architecture items, and so on, as far down as projects want to model. While it started with a focus on modeling requirements based on things like use cases, efforts are now underway to integrate safety and mission assurance (S&MA) information and analyses, such as risks. This effort, called Model Based Mission Assurance (MBMA), is yielding models that are more useful and are a more accurate representations of the systems. MBSE models, with this ability to link related items, provide a new means of tracking and communicating aggregate risks. In the proposed method, risks are added into the models as distinct items, having attributes that communicate a scoring derived from the likelihood and consequence values as charted on the standard NASA 5x5 risk matrix. Like earlier efforts, each box in the 5x5 has an associated scoring, which may include both a current score and potential post-mitigation/control score. The risk items are then linked to elements of the model, such as system objectives/goals, requirements, functions, or physical architecture items, with "Risk to" relationships. These risks will then be communicated by use of reports generated from the model, detailing all risks and/or hazards linked to model elements. These reports can include aggregate impacts, including a current scoring and potential future state scoring based on the planned mitigations and/or controls. These reports will show all risks, open, accepted, and closed, linked to project objectives or requirements. When run as part of an upcoming risk acceptance discussion, these reports will serve to remind the team of all previous risks that relate to the effected portion of the system. When included as part of periodic program or project reviews, risk reviews, and safety reviews, this method can improve the overall understanding of the system's true risk posture. This proposed method takes full advantage of the advances that modern modeling techniques provide, with a minimal investment of additional time. Utilizing the model environment also enables a near constant access to current state of aggregate risks.

model based mission assurance↗

DOE OSTI2020

On the vulnerability of data-driven structural health monitoring models to adversarial attack

Many approaches at the forefront of structural health monitoring rely on cutting-edge techniques from the field of machine learning. Recently, much interest has been directed towards the study of so-called adversarial examples; deliberate input perturbations that deceive machine learning models while remaining semantically identical. This article demonstrates that data-driven approaches to structural health monitoring are vulnerable to attacks of this kind. In the perfect information or ‘white-box’ scenario, a transformation is found that maps every example in the Los Alamos National Laboratory three-storey structure dataset to an adversarial example. Also presented is an adversarial threat model specific to structural health monitoring. The threat model is proposed with a view to motivate discussion into ways in which structural health monitoring approaches might be made more robust to the threat of adversarial attack.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Relative Importance of Vital Rates to Population Dynamics of Wood Ducks

The wood duck (Aix sponsa) is a common and important cavity-nesting duck in North America; however, we know very little about how changes in vital rates influence population growth rate (λ). We used estimates of fertility and survival of female wood ducks from our nest-box studies in South Carolina, Alabama, and Georgia, USA, to create a stage-based matrix population model. We conducted perturbation analyses and ranked elasticity values to examine the relative importance of 17 component vital rates to λ. Female survival is influenced by nest success, so we recognized this female heterogeneity in our analyses. Four vital rates showed the greatest importance to λ. Analytic elasticities were greatest for breeding season and nonbreeding season survival of females that nested successfully, followed by nest success and female recruitment to the breeding population. Differences in female quality were important to λ. Next, we used process variation of vital rates and conducted life-stage simulation analyses (LSA) followed by variance decomposition to determine the amount of variation in λ explained by each vital rate. Female recruitment to the breeding population explained 57.7% of the variation in λ followed by nest success (11.4%), and breeding and nonbreeding season survival of females that nested successfully (9.3% and 9.4%, respectively). Together these 4 vital rates explained 88% of the variation in λ. Mean asymptotic population growth rate (λ = 0.80 ± 0.08 [SD]) from LSA revealed a declining population. Recruitment of females hatched from nest boxes was insufficient to sustain the nest-box population. Furthermore, including yearling (SY) females that were produced outside of nest boxes (i.e., immigrants) increased recruitment rates 1.5 to 2 times more than when only SY females recruited from nest boxes were included. Future research that examines how emigration and immigration interact with survival and reproduction to influence local population dynamics of wood ducks will be important for identifying the value of nest-box programs to wood duck conservation and management.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1987

Cloud fluid models of gas dynamics and star formation in galaxies

The large dynamic range of star formation in galaxies, and the apparently complex environmental influences involved in triggering or suppressing star formation, challenges the understanding. The key to this understanding may be the detailed study of simple physical models for the dominant nonlinear interactions in interstellar cloud systems. One such model is described, a generalized Oort model cloud fluid, and two simple applications of it are explored. The first of these is the relaxation of an isolated volume of cloud fluid following a disturbance. Though very idealized, this closed box study suggests a physical mechanism for starbursts, which is based on the approximate commensurability of massive cloud lifetimes and cloud collisional growth times. The second application is to the modeling of colliding ring galaxies. In this case, the driving processes operating on a dynamical timescale interact with the local cloud processes operating on the above timescale. The results is a variety of interesting nonequilibrium behaviors, including spatial variations of star formation that do not depend monotonically on gas density.

Struck-Marcell, Curtis↗

NASA NTRS1999

Computer Modeling of Electrostatic Aggregation of Granular Materials in Planetary and Astrophysical Settings

Electrostatic forces strongly influence the behavior of granular materials in both dispersed (cloud) systems and semi-packed systems. These forces can cause aggregation or dispersion of particles and are important in a variety of astrophysical and planetary settings. There are also many industrial and commercial settings where granular matter and electrostatics become partners for both good and bad. This partnership is important for human exploration on Mars where dust adheres to suits, machines, and habitats. Long-range Coulombic (electrostatic) forces, as opposed to contact-induced dipoles and van der Waals attractions, are generally regarded as resulting from net charge. We have proposed that in addition to net charge interactions, randomly distributed charge carriers on grains will result in a dipole moment regardless of any net charge. If grains are unconfined, or fluidized, they will rotate so that the dipole always induces attraction between grains. Aggregates are readily formed, and Coulombic polarity resulting from the dipole produces end-to-end stacking of grains to form filamentary aggregates. This has been demonstrated in USML experiments on Space Shuttle where microgravity facilitated the unmasking of static forces. It has also been demonstrated in a computer model using grains with charge carriers of both sign. Model results very closely resembled micro-g results with actual sand grains. Further computer modeling of the aggregation process has been conducted to improve our understanding of the aggregation process, and to provide a predictive tool for microgravity experiments slated for Space Station. These experiments will attempt to prove the dipole concept as outlined above. We have considerably enhanced the original computer model: refinements to the algorithm have improved the fidelity of grain behavior during grain contact, special attention has been paid to simulation time steps to enable establishment of a meaningful, quantitative time axis, and calibration of rounding accuracies have been conducted to test cumulative numerical influences in the model. The model has been run for larger grain populations, variable initial cloud densities, and we have introduced random net charging to individual grains, as well as a net charge to the cloud as a whole. The model uses 3 positive and 3 negative charges randomly distributed on each grain, with up to 160 grains contained within various size "boxes" that define the initial number densities in the clouds. Each charge represents localized charged region on a grain, but does not necessarily imply single quantized charge carriers. The Coulomb equations are then allowed to interact for each monopole: dipoles and any higher order charge coupling is a natural product of these "free" interactions over which the modeler exerts no influence. The charges are placed on surfaces of grains at random locations. A series of runs was conducted for neutral grains that had a perfect balance of negative and positive char carriers. Runs were also conducted with grains having additional fractional charges ranging between 0 and 1. By adding fractional charges of one sign, the model created grain populations in which all grains had excess charges the same sign, giving the cloud an overall net charge. This simulates clouds subjected to ionizing radiation (e. protoplanetary debris disk around a protosun), or any other process of charge biasing in a grain population (e.g., volcanic plumes). In another run series, random fractional charges of either sign were added to the grains so th some grains had a slight net positive charge while others had a slight net negative charge. This simulates triboelectrically-charged grain populations in which acquisition of an electron by one surface is at the expense creating a hole elsewhere. This dual sign charging was applied in two ways: in one case the cloud remained neutral by ensuring that all grain excess charges added to zero; in the other case, the cloud was permitted slight net char by not imposing a charge-balance condition. Additional information is contained in the original.

Marshall, J.↗

NASA NTRS1988

The GISS global climate-middle atmosphere model. I - Model structure and climatology

A coupled global climate/middle atmosphere model (GCMAM) is developed by extending the Hansen et al. (1983) GISS global climate model to include the middle atmosphere up to 85 km. The model includes numerical solutions of the primitive equations, calculations of the radiative and surface fluxes, and a complete hydrologic cycle with convective and cloud-cover parameterizations. In addition, a parameterized gravity wave drag cycle is incorporated, in which gravity-wave momentum fluxes due to flow over topography, wind shear, and convection are calculated at each grid box, using theoretical relationships between the grid-scale variables and expected source strengths. The results of the GCMAM demonstrate that the model produces a reasonable simulation of the stratosphere. It is shown that the improvements over the previous version were achieved largely through the incorporation of the parameterized gravity wave drag.

Rind, D.↗

DOE OSTI2021

The local structure of 0.5Ba(Zr 0.2 Ti 0.8 )O 3 -0.5(Ba 0.7 Ca 0.3 )TiO 3 from neutron total scattering measurements and multi-edge X-ray absorption analysis

Neutron total scattering measurements and multi-edge X-ray absorption analysis were performed on a 0.5Ba(Zr 0.2 Ti 0.8 )O 3 -0.5(Ba 0.7 Ca 0.3 )TiO 3 powder sample. The TiIII-II edges showed that titanium is distorted in the rhombohedral [111] direction (in a BaTiO 3 -like chemical and bonding environment). Extended X-ray fine structure analysis (EXAFS) was performed on the zirconium K-edge and it was found that zirconium resides in the center of the ZrO 6 octahedra in cubic symmetry. Furthermore, the local structure was initially modelled with the orthorhombic Amm2 space group with unsatisfactory results. To better model the local structure, the results from the EXAFS analysis were incorporated into the small-box pair distribution function (PDF) refinements and the best fit of the observed data was a combination of barium calcium titanate (BCT) and barium calcium zirconate (BCZ) phases.

36 MATERIALS SCIENCE↗

DOE OSTI2024

BiRD (BioReactorDesign) [SWR-24-35]

Numerical simulations can accelerate scale up and optimization of bioreactors. This project provides a toolbox to facilitate such numerical simulations in OpenFOAM. The tool box contains 1) mesh generations tools for several typical bio reactor types in OpenFOAM 2) Inverse modeling tools to calibrate models using available experimental data 3) post processing tools that accelerate and streamline the analysis of the numerical simulations.

Hassanaly, Malik↗

NASA NTRS1980

Statistical Analysis of daily mean temperatures

Data of daily mean temperatures recorded at the Kennedy Center during the period of 1957-1977 were analyzed to forecast daily mean temperatures and their thirty-day moving averages for a period of ten to fifteen days in a given month. Since it is found that the standard deviation is linear in the mean, a logarithmic transformation of the data is used for finding an integrated moving average process IMA by the Box-Jenkins aproach. The first differences of the transformed data seem to fit a moving average model with parameter value 2, MA(2). The consideration of seasonality factor makes the fit worse.

Ross, D. C.↗