Quantitative Assessment of Risk for Modeling Radiation Impact on System Functions
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Risk Management↗
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Quantitative Assessment of Risk for Modeling Radiation Impact on System Functions
Explore the source record for details and available documents.
probabilistic risk assessment↗
Clinical Decision Support For Exploration Space Flight: Software Augmentation To Enhance Progressively Earth-Independent Medical Operations
This panel identifies the challenges of supporting medical events in deep space using integrated systems software to augment existing capabilities during increasingly Earth-independent missions where an asynchronous communication environment becomes routine. An interdisciplinary team of software designers, physicians, human factors engineers, and computational modelers applied their respective expertise to develop a roadmap for supporting the crew making medical decisions in more autonomous fashion with time-delayed ground support. The first presentation describes predictive modeling implemented to assist medical system design and address risk reduction using robust clinical decision support software. The second presentation details how operational and environmental challenges guide assumptions and, in turn, by what means requirements for medical decision-making in deep space are derived. The third presentation describes and defines the skills and capabilities an exploration spaceflight medical officer will need to perform effectively during extended-duration spaceflight missions. The fourth presentation covers the potential models and function of the software element for supporting clinical decisions. The final presentation covers how these elements may work together with ground support to provide comprehensive care despite deep space travel's extreme challenges and limitations.
Dana Levin↗
Kamodo’s Satellite Constellation Mission Planning Tool
Kamodo provides a functional model-agnostic interface to a growing collection of Heliophysics model outputs. The CCMC, in collaboration with the Geospace Dynamics Constellation Science Team, has recently developed Kamodo’s satellite constellation mission planning tool to perform reconstructions in any pair of dimensions, including time. The ‘reconstruction’ tool enables users to fly any 4-dimensional grid of satellites through a given model data set, reconstructing what the given constellation would observe during the mission. This capability facilitates determination of what satellite configuration is best for a given science question, even allowing comparison across multiple models. This tool, written in Python, is built upon Kamodo’s flythrough tool, which in turn depends on a growing network of model-specific interfaces. Since each model interface is designed with model-agnostic syntax, the flythrough tool and the satellite constellation mission planning tool also feature model-agnostic syntax. In this work, we will describe the basic analysis choices available in the tool and provide a variety of sample workflows. The tool is freely available at https://github.com/nasa/Kamodo for the public. We invite the community to use the reconstruction tool and adapt the provided workflows for their mission planning, and to contribute their own workflows to share with others.
software↗
SM25C-2002: Kamodo’s Satellite Constellation Mission Planning Tool
Kamodo provides a functional model-agnostic interface to a growing collection of Heliophysics model outputs. The CCMC, in collaboration with the Geospace Dynamics Constellation Science Team, has recently developed Kamodo’s satellite constellation mission planning tool to perform reconstructions in any pair of dimensions, including time. The ‘reconstruction’ tool enables users to fly any 4-dimensional grid of satellites through a given model data set, reconstructing what the given constellation would observe during the mission. This capability facilitates determination of what satellite configuration is best for a given science question, even allowing comparison across multiple models. This tool, written in Python, is built upon Kamodo’s flythrough tool, which in turn depends on a growing network of model-specific interfaces. Since each model interface is designed with model-agnostic syntax, the flythrough tool and the satellite constellation mission planning tool also feature model-agnostic syntax. In this work, we will describe the basic analysis choices available in the tool and provide a variety of sample workflows. The tool is freely available at https://github.com/nasa/Kamodo for the public. We invite the community to use the reconstruction tool and adapt the provided workflows for their mission planning, and to contribute their own workflows to share with others.
python↗
POIWG #53 Vehicle Configuration Overview
The Vehicle Configuration Team is composed of the MAGIK, CAMMP, and IPiP groups who function to model and analyze the internal and external configurations of the ISS, plug-in power allocations, as well as the movement of the robotic arms around the station.
Vehicle↗
Parametric Optimization of Rigid Wheels for Planetary Surface Mobility Applications
Design-Build-Test approaches for spaceflight hardware are time and cost intensive, which can result in suboptimal mechanism designs. Optimization-based approaches that utilize high-fidelity models and physics simulation could overcome these limitations while simultaneously speeding up the mechanical design process and reducing cost. In this work, we present a toolchain that enables the multi-objective optimization of rigid rover wheels for planetary surface mobility applications. The toolchain uses Chrono’s Continuous Representation Model (CRM) functionality to simulate granular soil and performs multi-objective parametric optimization on candidate rover wheels to meet a desired performance criterion. The resulting wheel design is then evaluated experimentally using a single-wheel testbed. We end with a discussion of how the toolchain can be extended to simultaneously co-optimize other system parameters, such as system power consumption and feedback control gains.
Optimization↗
Loads Generation Process for Multi-Commercial Supplier Space Station
Create a standardized process to accurately and consistently generate loads from evolving/maturing models, forcing functions, and configuration manifests.
Claire Puccini↗
Shape Servoing of Deformable Objects Using Model Estimation and Barrier Lyapunov Function
An adaptive shape servoing control method is presented in this article to manipulate a deformable object into a desired shape in 3-D. A finite-point-based representation of the deformable object is used and the deformation Jacobian matrix is approximated using Fourier series basis functions. The unknown parameters of the deformation Jacobian are learned by using the velocity applied to a control point on the object and corresponding change of positions of the points describing the entire object. An integral concurrent learning (ICL)-based parameter update law is designed along with a constrained controller to satisfy the state constraints on the motion of the control point using Barrier Lyapunov function analysis. ICL-based parameter update law uses data history of velocity and corresponding positions of the points along with their current values. An efficient algorithm to update the history stack using singular value maximization is proposed based on the structure of the regressor matrix. Simulations using a physical simulator and experiments using a robot platform are performed to validate the performance of the proposed controller on two different deformable objects.
Vrithik Raj Guthikonda↗
MOdeling charged defects inside density functional theory band gaps: An improved inside-out perspective.
Abstract not provided.
Schultz, Peter Andrew↗
A Polyhedral Finite-Element Formulation using Harmonic Shape Functions with Applications to the Modeling of Multi-Physics Fracture Processes.
Abstract not provided.
Bishop, Joseph E.↗
Flexible Computer Science Infrastructure (FleCSI)
FleCSI is a compile-time configurable framework designed to support multi-physics application development. As such, FleCSI attempts to provide a very general set of infrastructure design patterns that can be specialized and extended to suit the needs of a broad variety of solver and data requirements. Current support includes multi-dimensional mesh topology, mesh geometry, and mesh adjacency information, n-dimensional hashed-tree data structures, graph partitioning interfaces, and dependency closures. FleCSI also introduces a functional programming model with control, execution, and data abstractions that are consistent with both MPI and state-of-the-art task-based runtimes such as Legion and Charm++. The FleCSI abstraction layer provides the developer with insulation from the underlying runtime, while allowing support for multiple runtime systems, including conventional models like asynchronous MPI. The intent is to give developers a concrete set of user-friendly programming tools that can be used now, while allowing flexibility in choosing runtime implementations and optimizations that can be applied to architectures and runtimes that arise in the future. The control and execution models in FleCSI also provide formal nomenclature for describing poorly understood concepts like kernels and tasks.
Bergen, Ben↗
Multi-technology Investigation of the Atomic Structure of Calcium Silicate Hydrates
In this study, synthetic C-S-H samples were investigated to reveal the feature at atomic scale. Rietveld refinement was applied to high resolution X-ray scattering data, yielding the lattice constants of the pseudocrystal structure, as well as the crystallinity along three axes. Near Edge X-ray Absorption Fine Structure (NEXAFS) spectra was collected at calcium L3,2-edge. Evolution of calcium coordination symmetry were studied by investigating spectra characteristics. Pair Distribution Function (PDF) study yields the statistics of atom pair distribution. Coordination number of Ca and Si were obtained by integrating Radial distribution function. Atomic model based on dimeric structure were discussed and compared with experimental data. Synthetic C-S-H samples with increasing Ca/Si ratio exhibit pseudo-crystal structure, resembling Dreierketten configuration similar to natural tobermorite structure. Along c-axis, the repeated structure could not survives two layers in case of low Ca/Si ratio (0.70, 1.05). But in high Ca/Si ratio (1.42) case, the crystallinity along c-axis is much bigger. The coordination number of Ca decreases with increasing Ca/Si ratio. Octahedrally coordinated Ca are observed in sample with Ca/Si ratio of 1.42. Various dimeric models are compared with experimental data. In case of Ca/Si ratio of 1.42, SiO4 tetrahedron chain needs to be shortened in linkage, most probably by substituting bridging SiO4 tetrahedron with CaO6 octahedron. These octahedrons in interlayer space act like pins to join two adjacent layer structures together. The crystallinity is thus increased along c-axis, and average coordination number is therefore reduced. In case of Ca/Si 1.05, crystallinity is low along c-axis since, indicating that not too many Ca ions exist in interlayer space to hold two layers together. Instead, negative charge of end oxygen could be balanced by proton. Ca/Si 0.70 has long tetrahedron chain linkage within layer while the linkage between adjacent layers are not strong, resulting in low crystallinity along c-axis. Neither Ca/Si ratio 0.70 nor 1.42 sample contains any Ca in octahedral symmetry, as indicated by the weak crystal field splitting of NEXAFS spectra.
Atomic Configuration↗
NONPARAMETRIC EMPIRICAL BAYES ESTIMATION THROUGH DECONVOLUTION IN PROBABILISTIC RISK ANALYSIS
The advent of Markov Chain Monte Carlo (MCMC) simulations and availability of open source software made full hierarchical Bayesian analysis tractable and a popular choice in parameter estimation for Probabilistic Risk Assessment (PRA). However, despite its theoretical attractiveness, the hierarchical Bayesian analysis is not without its problems. Two of the most prominent practical difficulties in applying hierarchical Bayes analysis in practice to analyze source-to-source variability, for example, is its sensitivity to the selection of the first-stage prior and dependence of the rate of convergence on the selection of the first-stage prior. The first-stage prior in hierarchical Bayesian analysis is usually assumed having a parametric form, often conjugate to the likelihood function (aleatory model). To facilitate convergence for hierarchical Bayes models, the first-stage prior is frequently set through empirical Bayes estimate, which can use available historical data to find the parameters of the first-stage prior. The paper discusses a new nonparametric empirical Bayes estimation and compare it to several well-known parametric estimates such as method of moments and maximum likelihood. The new nonparametric technique exploits the prior predictive distribution as integral equation and proceeds to solve it with respect to prior assuming the sampling data distribution and aleatory model are available. The discussion covers topics such as selection of aleatory model for future data, selection of regularization parameter, and density estimation for historical data. For comparison, the paper is using contrived as well as real-world data from utilities. Utilities data represent failure data with Poisson distribution used for aleatory model.
97 MATHEMATICS AND COMPUTING↗
Identifying RNA contacts from SHAPE-MaP by partial correlation analysis
In a recent paper Siegfried et al. published a new sequence-based structural RNA assay that utilizes mutational profiling to detect base pairing (MaP). Output from MaP provides information about both pairing (via reactivities) and contact (via correlations). Reactivities can be coupled to partition function folding models for structural inference, while correlations can reveal pairs of sites that may be in structural proximity. The possibility for inference of 3D contacts via MaP suggests a novel approach to structural prediction for RNA analogous to covariance structural prediction for proteins. We explore this approach and show that partial correlation analysis outperforms na\"ive correlation analysis. Our results should be applicable to a wide range of high-throughput sequencing based RNA structural assays that are under development.
Tambe, Akshay↗
Identifying Cryptographic Functions : from Data Curation to Model Building.
Abstract not provided.
Lee, Jina↗
Arsenic Doping of Polycrystalline CdSeTe Devices for Microsecond Life-times with High Carrier Concentrations
We report seminal advances in fabrication and understanding of group V (As) doped thin-film polycrystalline CdTe-based solar cells. The devices are fabricated using a novel approach, by sublimating layers of CdSeTe and CdSeTe:As. This new method allowed us to achieve minority carrier lifetime of over 1 μs, carrier concentration of more than 5x1015 cc-1 and external radiative efficiency of over 2% in a device configuration. We find an increase in open-circuit voltage when comparing As-doped, Cu-doped and undoped devices. The choice of CdSeTe instead of a CdTe-only absorber has been explained using first-principle density functional theory model. A SCAPS device model is used to analyze the potential causes for lower open-circuit voltage.
14 SOLAR ENERGY↗