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All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Implementation of a Very Large Atmospheric Correction Lookup Table for ASTER Using a Relational Database Management System

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)is designed to provide a high resolution map of the Earth in both visible, near-infrared, and thermal spectral regions of the electromagnetic spectrum. The ASTER Science Team has developed several standard data product algorithms, but the most complex aod computing-intensive of these is the estimation of surface radiance and reflectance values, which is done by modeling and correcting for the effects of the atmosphere. The algorithm for atmospheric correction in the visible bands sensed by ASTER calls for use of a very large Atmospheric Correction Look Up Table (ACLUT).

ASTER↗
NASA NTRSDate not supplied

Agent-based modeling of microbes in space

Space is tough on organisms. Microorganisms traveling to space experience stress from environmental features such as ionizing radiation and lack of normal microgravity; however, much remains unknown about the mechanisms by which those environmental features affect microbial physiology. Microbes experience changes in gravity not directly but rather through changes in their fluid environment, and deep-space particle radiation causes cell damage that is complex but rare. Computational modeling at the single-cell level (agent-based modeling) can allow us to probe the spatially heterogeneous processes that characterize space stresses, to gain insight into the relationships of microbial cells with their environments and with each other. Here we present two software packages for simulating microbial population dynamics in space conditions: CAMDLES and AMMPER. Microbes growing in liquid culture medium in the microgravity of an orbital space station experience a quiescent, poorly-mixed fluid environment. CAMDLES (CFD-DEM Artificial Microgravity Developments for Living Ecosystem Simulation) simultaneously simulates biological, chemical, and mechanical processes to predict microbial ecological dynamics in microgravity, and in the rotating culture vessels used to create an artificial microgravity environment in the lab. Initial results demonstrate that the growth of a cross-feeding microbial consortium, dependent on the exchange of soluble metabolites, is sensitive to the initial spatial distribution of cells, and grows differently in real versus artificial microgravity. Microbial populations exposed to deep-space radiation experience spatially and temporally heterogeneous damage from the traversal of high-energy particles. AMMPER (Agent-Based Model for Microbial Populations Exposed to Radiation) pairs a 3d model of energy deposition along a radiation particle track with a microbial population growth and damage model to predict the effects of localized radiation damage on population-level responses. It includes a user-friendly graphical interface. AMMPER results agree with experimental data indicating that indirect effects of radiation (reactive oxygen species generation, metabolic impairment) have a greater impact on microorganisms than direct effects (DNA damage).

Jessica A Lee↗
NASA NTRS2022

Agent-Based Modeling of Microbes in Space

Space is tough on organisms. Microorganisms traveling to space experience stress from environmental features such as ionizing radiation and lack of normal gravity, and much remains unknown about the mechanisms by which those environmental features affect microbial physiology. Microbes experience changes in gravity not directly but rather through changes in their fluid environment, and deep-space particle radiation causes cell damage that is complex but rare. Computational modeling at the single-cell level (agent-based modeling) can allow us to probe the spatially heterogeneous processes that characterize space stresses, to gain insight into the relationships of microbial cells with their environments and with each other. Here we present two software packages for simulating microbial population dynamics in space conditions: CAMDLES and AMMPER. Microbes growing in liquid culture medium in the microgravity of an orbital space station experience a quiescent, poorly-mixed fluid environment. CAMDLES (CFD-DEM Artificial Microgravity Developments for Living Ecosystem Simulation) simultaneously simulates biological, chemical, and mechanical processes to predict microbial ecological dynamics in microgravity, and in the rotating culture vessels used to create an artificial microgravity environment in the lab. Initial results demonstrate that the growth of a cross-feeding microbial consortium, dependent on the exchange of soluble metabolites, is sensitive to the initial spatial distribution of cells, and grows differently in real versus artificial microgravity. Microbial populations exposed to deep-space radiation experience spatially and temporally heterogeneous damage from the traversal of high-energy particles. AMMPER (Agent-Based Model for Microbial Populations Exposed to Radiation) pairs a 3d model of energy deposition along a radiation particle track with a microbial population growth and damage model to predict the effects of localized radiation damage on population-level responses. It includes a user-friendly graphical interface. AMMPER growth curves recapitulate experimental results, and allow comparison between direct effects (DNA damage) and indirect effects (reactive oxygen species generation, metabolic impairment) of radiation.

microbiology↗
NASA NTRS2000

Perspectives on the Future of CFD

This viewgraph presentation gives an overview of the future of computational fluid dynamics (CFD), which in the past has pioneered the field of flow simulation. Over time CFD has progressed as computing power. Numerical methods have been advanced as CPU and memory capacity increases. Complex configurations are routinely computed now and direct numerical simulations (DNS) and large eddy simulations (LES) are used to study turbulence. As the computing resources changed to parallel and distributed platforms, computer science aspects such as scalability (algorithmic and implementation) and portability and transparent codings have advanced. Examples of potential future (or current) challenges include risk assessment, limitations of the heuristic model, and the development of CFD and information technology (IT) tools.

Kwak, Dochan↗
NASA NTRS2013

An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition

Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.

Structural Decomposition↗
NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben D Phillips↗
NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben Phillips↗
NASA NTRS1971

Table look-up approach to pattern recognition.

The table look-up approach is based on prestoring in fast, random-access, core memory the desired answer (e.g., crop-type) for all combinations of multispectral scanner outputs from selected channels. Specifically, each set of measurements from a given point on the ground is interpreted as that address in core memory where the answer can be retrieved. Substituting the simple retrieval operation for the length y computations required by the conventional approach offers two advantages: (1) the processing time is reduced by more than an order of magnitude; (2) the multispectral scanner data can be processed by computers having minimal sophistication, complexity, and cost. These two advantages may make it possible to use an onboard computer to perform the classification function in flight.

Eppler, W. G.↗
NASA NTRS2018

Applications of Phase-Based Motion Processing

Image pyramids provide useful information in determining structural response at low cost using commercially available cameras. The current effort applies previous work on the complex steerable pyramid to analyze and identify imperceptible linear motions in video. Instead of implicitly computing motion spectra through phase analysis of the complex steerable pyramid and magnifying the associated motions, instead present a visual technique and the necessary software to display the phase changes of high frequency signals within video. The present technique quickly identifies regions of largest motion within a video with a single phase visualization and without the artifacts of motion magnification, but requires use of the computationally intensive Fourier transform. While Riesz pyramids present an alternative to the computationally intensive complex steerable pyramid for motion magnification, the Riesz formulation contains significant noise, and motion magnification still presents large amounts of data that cannot be quickly assessed by the human eye. Thus, user-friendly software is presented for quickly identifying structural response through optical flow and phase visualization in both Python and MATLAB.

Branch, Nicholas A.↗
NASA NTRS1991

Computer aided reliability, availability, and safety modeling for fault-tolerant computer systems with commentary on the HARP program

Many of the most challenging reliability problems of our present decade involve complex distributed systems such as interconnected telephone switching computers, air traffic control centers, aircraft and space vehicles, and local area and wide area computer networks. In addition to the challenge of complexity, modern fault-tolerant computer systems require very high levels of reliability, e.g., avionic computers with MTTF goals of one billion hours. Most analysts find that it is too difficult to model such complex systems without computer aided design programs. In response to this need, NASA has developed a suite of computer aided reliability modeling programs beginning with CARE 3 and including a group of new programs such as: HARP, HARP-PC, Reliability Analysts Workbench (Combination of model solvers SURE, STEM, PAWS, and common front-end model ASSIST), and the Fault Tree Compiler. The HARP program is studied and how well the user can model systems using this program is investigated. One of the important objectives will be to study how user friendly this program is, e.g., how easy it is to model the system, provide the input information, and interpret the results. The experiences of the author and his graduate students who used HARP in two graduate courses are described. Some brief comparisons were made with the ARIES program which the students also used. Theoretical studies of the modeling techniques used in HARP are also included. Of course no answer can be any more accurate than the fidelity of the model, thus an Appendix is included which discusses modeling accuracy. A broad viewpoint is taken and all problems which occurred in the use of HARP are discussed. Such problems include: computer system problems, installation manual problems, user manual problems, program inconsistencies, program limitations, confusing notation, long run times, accuracy problems, etc.

Shooman, Martin L.↗
NASA NTRS1978

Architectures for fault-tolerant spacecraft computers

This paper summarizes the results of a long-term research program in fault-tolerant computing for spacecraft on-board processing. In response to changing device technology this program has progressed from the design of a fault-tolerant uniprocessor to the development of fault-tolerant distributed computer systems. The unusual requirements of spacecraft computing are described along with the resulting real-time computer architectures. The following aspects of these designs are discussed: (1) architectural features to minimize complexity in the distributed computer system, (2) fault-detection and recovery, (3) techniques to enhance reliability and testability, and (4) design approaches for LSI implementation.

Rennels, D. A.↗
NASA NTRS2000

On the Computational Capabilities of Physical Systems: The Impossibility of Infallible Computation - Part 1

In this first of two papers, strong limits on the accuracy of physical computation are established. First it is proven that there cannot be a physical computer C to which one can pose any and all computational tasks concerning the physical universe. Next it is proven that no physical computer C can correctly carry out any computational task in the subset of such tasks that can be posed to C. This result holds whether the computational tasks concern a system that is physically isolated from C, or instead concern a system that is coupled to C. As a particular example, this result means that there cannot be a physical computer that can, for any physical system external to that computer, take the specification of that external system's state as input and then correctly predict its future state before that future state actually occurs; one cannot build a physical computer that can be assured of correctly 'processing information faster than the universe does'. The results also mean that there cannot exist an infallible, general-purpose observation apparatus, and that there cannot be an infallible, general-purpose control apparatus. These results do not rely on systems that are infinite, and/or non-classical, and/or obey chaotic dynamics. They also hold even if one uses an infinitely fast, infinitely dense computer, with computational powers greater than that of a Turing Machine. This generality is a direct consequence of the fact that a novel definition of computation - a definition of 'physical computation' - is needed to address the issues considered in these papers. While this definition does not fit into the traditional Chomsky hierarchy, the mathematical structure and impossibility results associated with it have parallels in the mathematics of the Chomsky hierarchy. The second in this pair of papers presents a preliminary exploration of some of this mathematical structure, including in particular that of prediction complexity, which is a 'physical computation analogue' of algorithmic information complexity. It is proven in that second paper that either the Hamiltonian of our universe proscribes a certain type of computation, or prediction complexity is unique (unlike algorithmic information complexity), in that there is one and only version of it that can be applicable throughout our universe.

Wolpert, David H.↗
NASA NTRS1997

Computer-Based Tools for Evaluating Graphical User Interfaces

The user interface is the component of a software system that connects two very complex system: humans and computers. Each of these two systems impose certain requirements on the final product. The user is the judge of the usability and utility of the system; the computer software and hardware are the tools with which the interface is constructed. Mistakes are sometimes made in designing and developing user interfaces because the designers and developers have limited knowledge about human performance (e.g., problem solving, decision making, planning, and reasoning). Even those trained in user interface design make mistakes because they are unable to address all of the known requirements and constraints on design. Evaluation of the user inter-face is therefore a critical phase of the user interface development process. Evaluation should not be considered the final phase of design; but it should be part of an iterative design cycle with the output of evaluation being feed back into design. The goal of this research was to develop a set of computer-based tools for objectively evaluating graphical user interfaces. The research was organized into three phases. The first phase resulted in the development of an embedded evaluation tool which evaluates the usability of a graphical user interface based on a user's performance. An expert system to assist in the design and evaluation of user interfaces based upon rules and guidelines was developed during the second phase. During the final phase of the research an automatic layout tool to be used in the initial design of graphical inter- faces was developed. The research was coordinated with NASA Marshall Space Flight Center's Mission Operations Laboratory's efforts in developing onboard payload display specifications for the Space Station.

Moore, Loretta A.↗
NASA NTRS1991

GRID3D-v2: An updated version of the GRID2D/3D computer program for generating grid systems in complex-shaped three-dimensional spatial domains

In order to generate good quality systems for complicated three-dimensional spatial domains, the grid-generation method used must be able to exert rather precise controls over grid-point distributions. Several techniques are presented that enhance control of grid-point distribution for a class of algebraic grid-generation methods known as the two-, four-, and six-boundary methods. These techniques include variable stretching functions from bilinear interpolation, interpolating functions based on tension splines, and normalized K-factors. The techniques developed in this study were incorporated into a new version of GRID3D called GRID3D-v2. The usefulness of GRID3D-v2 was demonstrated by using it to generate a three-dimensional grid system in the coolent passage of a radial turbine blade with serpentine channels and pin fins.

Steinthorsson, E.↗
NASA NTRSDate not supplied

Benchmarking Computational Tools for Calling SNPs and Indels in Complex Microbial Populations

The NASA BioNutrients missions seek to understand the suitability of microorganisms for bioproduction during space flight. One topic of interest is the stability of microbial genomes during long-term ambient storage and subsequent rehydration and growth. To address these questions, samples from 8 species were flown to ISS for 5 years of desiccated storage at ambient temperature (Stasis Packs) and 2 species were packaged along with powdered media inside a bioreactor system to allow hydration and growth in microgravity (Production Packs). For both systems, Whole Genome Sequencing (WGS) of the DNA extracted from the returned samples and paired ground controls will be conducted to identify changes in genome stability due to time, storage conditions and growth in space. Across the technical replicates, ground controls, 10 timepoints, and multiple experimental conditions, ~300 samples have been selected for initial analysis with WGS sequencing to 100x coverage. A flexible and resource efficient mutation calling pipeline is needed to process this large dataset and allow for comparisons between species. Many bioinformatics tools for calling Indels and Single Nucleotide Variants (SNVs) are designed for use with pure isolates, where true variations from the reference genome are expected to dominate the reads aligning to the location of mutation. In contrast, DNA from the Stasis Pack (SP) samples was collected directly after recovery from desiccated storage and the Production Pack (PP) samples were collected after fermentation. In this context, reads with mutations are expected to be less frequent than reads that align with the reference genome, as each sample will include multiple lines of cells. Thus, BioNutrients samples are expected to be similar to samples from cancer cell or “pooled” sequencing approaches. In preparation for the analysis of the BioNutrients samples, we have tested three mutation calling tools (GATK for Microbes, BreSeq and DiscoSNP) designed for complex samples. A challenge of validating mutation identification pipelines is a lack of “Ground Truth” datasets, especially for complex samples. To compare these three tools, we sought to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO as part of the Space Algae-1 mission. Here we present a summary of these tools against the analysis originally conducted using the CRISP tool. Critical metrics are compared such as runtime, the number of SNPs, the number and size of Indels, and patterns of transversion and transitions identified by each tool are reported. By sharing these benchmarking results collected in support of the BioNutrients mission, we aim to guide others seeking to identify SNVs in similarly complex microbial samples.

Biology↗
NASA NTRS1987

A new method to real-normalize measured complex modes

A time domain subspace iteration technique is presented to compute a set of normal modes from the measured complex modes. By using the proposed method, a large number of physical coordinates are reduced to a smaller number of model or principal coordinates. Subspace free decay time responses are computed using properly scaled complex modal vectors. Companion matrix for the general case of nonproportional damping is then derived in the selected vector subspace. Subspace normal modes are obtained through eigenvalue solution of the (M sub N) sup -1 (K sub N) matrix and transformed back to the physical coordinates to get a set of normal modes. A numerical example is presented to demonstrate the outlined theory.

Wei, Max L.↗
NASA NTRS1979

Trends in reliability modeling technology for fault tolerant systems

Developments in reliability modeling for large fault tolerant avionic computing systems are presented. Issues of state size and complexity, fault coverage, and practical computation are addressed. A two-fold developmental effort is described based on the structural and fault coverage modeling approaches. A technique which was successfully applied to an 865 state pure death stationary Markov model is presented. Of particular interest is a short computer program which executes very quickly to produce reliability results of a large state space model. This model also incorporates fault coverage states for processor, memory, and bus line replaceable units. A second structural reliability modeling scheme is aimed at solving nonstationary Markov models. This technique provides the tool required for studying the reliability of systems with nonconstant failure rates and includes intermittent/transient faults, electronic hardware which exhibits decreasing failure rates, and hydromechanical devices which typically have wearout failure mechanisms. Several aspects of fault coverage, including modeling and data measurement of intermittent/transient faults and latent faults, are elucidated and illustrated. The CARE II (computer-aided reliability estimation) coverage is presented and shortcomings to be eliminated are discussed.

Bavuso, S. J.↗