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At least 865 records · Page 48

The KATE shell: An implementation of model-based control, monitor and diagnosis

The conventional control and monitor software currently used by the Space Center for Space Shuttle processing has many limitations such as high maintenance costs, limited diagnostic capabilities and simulation support. These limitations have caused the development of a knowledge based (or model based) shell to generically control and monitor electro-mechanical systems. The knowledge base describes the system's structure and function and is used by a software shell to do real time constraints checking, low level control of components, diagnosis of detected faults, sensor validation, automatic generation of schematic diagrams and automatic recovery from failures. This approach is more versatile and more powerful than the conventional hard coded approach and offers many advantages over it, although, for systems which require high speed reaction times or aren't well understood, knowledge based control and monitor systems may not be appropriate.

Cornell, Matthew↗

Gray-Box Modeling for Distribution Systems With Inverter-Based Resources: Integrating Physics-Based and Data-Driven Approaches

Here, in this paper, we develop a novel gray-box modeling approach for distribution systems with inverter-based resources (IBRs). The proposed gray-box modeling method aims to improve estimation accuracy by taking advantages of both physics-based (white-box) and data-driven (black-box) modeling approaches. To this end, we utilize partial physical knowledge of the system, including the inverters’ structures and control diagrams, as well as the equivalent network model simplified through Kron reduction. The white-box model containing unknown parameters is then constructed with mathematical equations and an optimization-based method is subsequently employed to identify these unknown parameters within the white-box model. Next, the graybox modeling framework is then constructed by embedding the output variables of the white-box model into the input vector of a black-box model (represented using a neural network). Finally, the black-box section is trained using the collected input-output datasets and the gray-box model is then obtained. Furthermore, case studies demonstrate that our gray-box modeling approach effectively improves estimation accuracy compared to purely physics-based or data-driven methods.

42 ENGINEERING↗

Controlling Microbial Byproducts using Model-Based Substrate Monitoring and Control Strategies

We have developed a computer-controlled bioreactor system to study various aspects of microbially-mediated nitrogen cycling. The system has been used to investigate methods for controlling microbial denitrification (the dissimilatory reduction of nitrate to N2O and N2) in hydroponic plant growth chambers. Such chambers are key elements of advanced life support systems being designed for use on long duration space missions, but nitrogen use efficiency in them is reduced by denitrification. Control software architecture was designed which permits the heterogeneous control of system hardware using traditional feedback control, and quantitative and qualitative models of various system features. Model-based feed forward control entails prediction of future systems in states and automated regulation of system parameters to achieve desired and avoid undesirable system states. A bacterial growth rate model based on the classic Monod model of saturation kinetics was used to evaluate the response of several individual denitrifying species to varying environmental conditions. The system and models are now being applied to mixed microbial communities harvested from the root zone of a hydroponic growth chamber. The use of a modified Monod organism interaction model was evaluated as a means of achieving more accurate description of the dynamic behavior of the communities. A minimum variance parameter estimation routine was also' used to calibrate the constant parameters in the model by iterative evaluation of substrate (nitrate) uptake and growth kinetics. This representation of processes and interactions aids in the formulation of control laws. The feed forward control strategy being developed will increase system autonomy, reduce crew intervention and limit the accumulation of undesirable waste products (NOx).

Smernoff, David T.↗

Development and Characterization of a Rate-Dependent Three-Dimensional Macroscopic Plasticity Model Suitable for Use in Composite Impact Problems

Several key capabilities have been identified by the aerospace community as lacking in the material/models for composite materials currently available within commercial transient dynamic finite element codes such as LS-DYNA. Some of the specific desired features that have been identified include the incorporation of both plasticity and damage within the material model, the capability of using the material model to analyze the response of both three-dimensional solid elements and two dimensional shell elements, and the ability to simulate the response of composites composed with a variety of composite architectures, including laminates, weaves and braids. In addition, a need has been expressed to have a material model that utilizes tabulated experimentally based input to define the evolution of plasticity and damage as opposed to utilizing discrete input parameters (such as modulus and strength) and analytical functions based on curve fitting. To begin to address these needs, an orthotropic macroscopic plasticity based model suitable for implementation within LS-DYNA has been developed. Specifically, the Tsai-Wu composite failure model has been generalized and extended to a strain-hardening based orthotropic plasticity model with a non-associative flow rule. The coefficients in the yield function are determined based on tabulated stress-strain curves in the various normal and shear directions, along with selected off-axis curves. Incorporating rate dependence into the yield function is achieved by using a series of tabluated input curves, each at a different constant strain rate. The non-associative flow-rule is used to compute the evolution of the effective plastic strain. Systematic procedures have been developed to determine the values of the various coefficients in the yield function and the flow rule based on the tabulated input data. An algorithm based on the radial return method has been developed to facilitate the numerical implementation of the material model. The presented paper will present in detail the development of the orthotropic plasticity model and the procedures used to obtain the required material parameters. Methods in which a combination of actual testing and selective numerical testing can be combined to yield the appropriate input data for the model will be described. A specific laminated polymer matrix composite will be examined to demonstrate the application of the model.

Impact↗

ASTER Digital Elevation Models

This document is based on information and ideas developed by members of the ASTER Digital Elevation Model Working Group (ADEMWG).

ASTER digital elevation model geometric D-13751↗

Development and Verification of a Finite-Difference Model of the Sorbent-Based Atmosphere Revitalization System for the Crew Exploration

A vacuum-swing adsorption (VSA) process to remove metabolic water, metabolic carbon dioxide, and metabolic and equipment generated trace contaminant gases from the Crew Exploration Vehicle (CEV) atmosphere is being designed. For the CEV, the approach is taken that all metabolic water must be removed by the Sorbent-Based Atmosphere Revitalization System (SBAR), a technology approach that has not been used in previous spacecraft life support systems. To support the design of the SBAR, a Finite Difference computer model has been developed. In addition, examination of the competitive adsorption aspects of water and carbon dioxide on various sorbents is being investigated in order to both refine the computer simulation and gain understanding of the VSA process

Ritter, James↗

Multiobjective Multidisciplinary Optimization of Low-Boom Supersonic Transports Using Multifidelity Models

A multidisciplinary optimization (MDO) method has been developed to design a computational fluid dynamics (CFD) based low-boom configuration that can be obtained from a Pareto solution of a low-fidelity multiobjective MDO problem with mission constraints. This paper refines the developed MDO method using multifidelity models for CFD-based multiobjective MDO. The refined MDO method can generate a low-boom configuration that satisfies the mission requirements, has the lowest takeoff gross weight and the longest range for the low-boom mission, trims the low-boom cruise flight with fuel redistributions, and has a reversed equivalent area distribution closely matching a low-boom target with ground noise level below 70 PLdB. The validity of the refined MDO method is demonstrated by a design study of a low-boom supersonic transport that carries 40 passengers, flies a low-boom mission with cruise Mach of 1.7 and range of 3500 nm, and cruises overwater at Mach 1.8 with range of 3882 nm. Moreover, the refined MDO method eliminates the difference between the assumed cruise weight for CFD-based low-boom inverse design optimization and the estimated cruise weight of the optimal inverse design solution with respect to the mission requirements.

multidisciplinary optimization↗

CIMI Simulations with Newly Developed Multiparameter Chorus and Plasmaspheric Hiss Wave Models

Numerical simulation studies of the Earth's radiation belts are important to understand the acceleration and loss of energetic electrons. The Comprehensive Inner Magnetosphere-Ionosphere (CIMI) model considers the effects of the ring current and plasmasphere on the radiation belts to obtain plausible results. The CIMI model incorporates pitch angle, energy, and cross diffusion of electrons, due to chorus and plasmaspheric hiss waves. These parameters are calculated using statistical wave distribution models of chorus and plasmaspheric hiss amplitudes. However, currently, these wave distribution models are based only on a single-parameter, geomagnetic index (AE) and could potentially underestimate the wave amplitudes. Here we incorporate recently developed multiparameter chorus and plasmaspheric hiss wave models based on geomagnetic index and solar wind parameters. We then perform CIMI simulations for two geomagnetic storms and compare the flux enhancement of megavolt electrons with data from the Van Allen Probes and Akebono satellites. We show that the relativistic electron fluxes calculated with multiparameter wave models resemble the observations more accurately than the relativistic electron fluxes calculated with single-parameter wave models. This indicates that wave models based on a combination of geomagnetic index and solar wind parameters are more effective as inputs to radiation belt models.

Aryan, Homayon↗

Investigations of Clouds and Aerosols on Mars, Venus and Titan

For Titan our interest during the past few years was to explain the observed asymmetry in the albedo. We suggested earlier, from one-dimensional modeling studies, that vertical transport rates were comparable to particle fall speeds. Since heating of the upper atmosphere, which drives dynamical motions, is largely due to the aerosols, a non- linear interaction between dynamics, radiative heating and particle microphysics is possible. We pursued this interaction in a two-dimensional model. We showed that the observed variations in the albedo between the two hemispheres and over an orbital cycle, could be due to dynamical motions suspending particles so that particle sizes and optical depths vary across the planet. In the Cassini time frame, future studies of this interaction between dynamics, radiation and microphysics may be worthwhile using the strong modeling base that others, and we have developed. In our recently approved proposal, however, we plan to extend our modeling to hydrocarbon clouds that lie at lower levels. We know very little about such clouds, and numerical models for their properties are non-existent. These clouds may be observed by the Huygen's Probe, and by the Cassini orbiter, so predictions of their properties should help in the analysis of Cassini data. We have also developed a sophisticated model for the lower, condensational, clouds on Venus. In this model we explored the water vapor budget on Venus, and the properties of the clouds such as particle size distribution. Most researchers have investigated the upper clouds on Venus, which are essentially a photochemical aerosol with a long lifetime. The lower clouds, however, are similar to stratus clouds on Earth. These clouds have short lifetimes, and are tightly coupled to the dynamics at the base of the Venus cloud deck. We believe that the holes in these clouds seen at near infrared wavelengths by Galileo are related to some interaction between dynamics and cloud physics. One goal of our recently approved proposed work is to better understand this interaction, and to better understand how these clouds may vary if the climate of Venus were to vary. During the past few years we have applied our model of the water ice clouds on Mars to new data sets from Pathfinder, and Mars Global Surveyor. We have compared our predictions of cloud properties with those seen by Pathfinder, and found reasonable agreement. More recently we have improved our model by including a radiative transfer algorithm and a boundary layer transport scheme . The goal of this work is to understand the thermal inversions seen in Mars Global Surveyor data. We find that the inversions are created by clouds radiatively cooling the atmospheric layers in which the clouds form. We have also initiated laboratory work on the physics of carbon dioxide particle formation. In our recently approved work we propose to use these laboratory data, and to extend our modeling to carbon dioxide clouds in the Martian atmosphere.

Toon, Owen B.↗

Fourier functional analysis for unsteady aerodynamic modeling

A method based on Fourier analysis is developed to analyze the force and moment data obtained in large amplitude forced oscillation tests at high angles of attack. The aerodynamic models for normal force, lift, drag, and pitching moment coefficients are built up from a set of aerodynamic responses to harmonic motions at different frequencies. Based on the aerodynamic models of harmonic data, the indicial responses are formed. The final expressions for the models involve time integrals of the indicial type advocated by Tobak and Schiff. Results from linear two- and three-dimensional unsteady aerodynamic theories as well as test data for a 70-degree delta wing are used to verify the models. It is shown that the present modeling method is accurate in producing the aerodynamic responses to harmonic motions and the ramp type motions. The model also produces correct trend for a 70-degree delta wing in harmonic motion with different mean angles-of-attack. However, the current model cannot be used to extrapolate data to higher angles-of-attack than that of the harmonic motions which form the aerodynamic model. For linear ramp motions, a special method is used to calculate the corresponding frequency and phase angle at a given time. The calculated results from modeling show a higher lift peak for linear ramp motion than for harmonic ramp motion. The current model also shows reasonably good results for the lift responses at different angles of attack.

Lan, C. Edward↗

Fourier functional analysis for unsteady aerodynamic modeling

A method based on Fourier analysis is developed to analyze the force and moment data obtained in large amplitude forced oscillation tests at high angles of attack. The aerodynamic models for normal force, lift, drag, and pitching moment coefficients are built up from a set of aerodynamic responses to harmonic motions at different frequencies. Based on the aerodynamic models of harmonic data, the indicial responses are formed. The final expressions for the models involve time integrals of the indicial type advocated by Tobak and Schiff. Results from linear two- and three-dimensional unsteady aerodynamic theories as well as test data for a 70-degree delta wing are used to verify the models. It is shown that the present modeling method is accurate in producing the aerodynamic responses to harmonic motions and the ramp type motions. The model also produces correct trend for a 70-degree delta wing in harmonic motion with different mean angles-of-attack. However, the current model cannot be used to extrapolate data to higher angles-of-attack than that of the harmonic motions which form the aerodynamic model. For linear ramp motions, a special method is used to calculate the corresponding frequency and phase angle at a given time. The calculated results from modeling show a higher lift peak for linear ramp motion than for harmonic ramp motion. The current model also shows reasonably good results for the lift responses at different angles of attack.

Chin, Suei↗

AMMPER: a user-friendly agent-based model that recapitulates simple metabolic responses of yeast to deep-space radiation

For humans venturing to deep space, radiation exposure poses a major health risk. Fundamental research into the biological effects of space radiation are essential for enabling exploration, and the first experimental organisms we send to deep space will be microbial. Yet there are many ways in which microorganisms are likely to experience the effects of high-energy particle radiation (such as Galactic Cosmic Rays) differently from multicellular animals, partly due to the simple fact that microbes are small and unicellular-- less likely to get hit in the first place, and less likely to communicate damage between cells. Computational modeling can aid in designing experiments and predicting the biological effects of radiation, but thus far particle radiation models have not focused on microbes. Here we present the latest developments in AMMPER, the Agent-based Model for Microbial Populations Exposed to Radiation. Originally written in 2021, AMMPER is a Python-based model that incorporates radiation track data from NASA's RITRACKS software and simulates the growth, damage, and death of yeast cells in 3D. It is now freely available as an open-source package on NASA's GitHub repository. Recent improvements include the ability to simulate the dynamics of alamarBlue, a color-changing redox dye commonly used to track metabolic activity in microbial spaceflight experiments. We demonstrate that a simple blue-pink-clear transition model is able to recapitulate key features observed in empirical data from ground studies. AMMPER also includes a new graphical user interface and introductory tutorial to facilitate ease of use by a wider audience. AMMPER can help us to understand how spatially heterogeneous particle radiation damage at the single-cell level can translate to growth differences at the population level, ultimately allowing us to better interpret experiments using microbes as model organisms and how well their results apply to humans.

yeast↗

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Image Processing; Pattern Recognition↗

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Image Processing:Pattern Recognition↗

Machine-learning based model reduction for partial differential equations

We develop a novel synergistic approach between model reduction and machine learning. The specific goal of this project is to aid in the construction of reduced order models for basis functions that are custom-made to represent the solution of partial differential equations. Partial differential equations (PDEs) are one of the main mathematical tools for describing physical phenomena. However, due to either efficiency or necessity, for many real-world problems, we are interested in constructing reduced order models (ROMs) which focus only on the explicit computation of subsets of the active spatio-temporal scales in the problem, while treating the interaction with the rest of the scales approximately. The task of accurate representation of such interactions (usually called memory terms) constitutes a vast area of research known as model reduction. PI Stinis has significant expertise in the construction of ROMs for complex systems. In addition, in recent work with the project key participant Qadeer, they have utilized machine learning to acquire custom-made basis functions (CBFs) to expand the solutions of PDEs. In the proposed work, we will merge the two concepts by constructing ROMs for subsets of the CBFs needed to represent the solution of a PDE. Specifically, we will use the Mori-Zwanzig model reduction formalism to construct ROMs for subsets of CBFs for nonlinear PDEs of various complexity, as well as investigate the usage of CBFs in the spectral vanishing viscosity method for problems that can form shocks in finite time. The outcome of the research is aimed to be proof-of-concept about a novel synergistic approach between model reduction and machine learning, thus advancing the field of scientific machine learning. Such a capability will benefit the efficient modeling of physical systems appearing in various areas of interest to the DOE.

97 MATHEMATICS AND COMPUTING↗

Research opportunities in bone demineralization, phase 3

An overview of bone demineralization during space flight, observations in bone demineralization and experiments related to bone loss planned for Spacelab flights, and suggestions for further research are investigated. The observations of the working group focused upon the following topics: (1) pathogenesis of bone demineralization, (2) potential for occurrence of renal stones consequent to prolonged hypercalciuria, (3) development of appropriate ground based and inflight models to study bone demineralization, (4) integration of research efforts, and (5) development of effective countermeasures.

Anderson, S. A.↗

Weather Intelligent Navigation Data and Models for Aviation Planning (WINDMAP)

WINDMAP addresses the emerging needs in the aviation community of providing real-time weather forecasting to improve the safety of low altitude aircraft operations. This is accomplished through the integration of real-time observations from autonomous systems, such as drones and urban air taxis, with numerical weather prediction models and flight management and safety systems. To solve this problem, several technical challenges have been identified. These include (1) developing autonomous UAS capable of conducting observations accurately and reliably; (2) determining the number and frequency of required observations and the sensitivity of these observations in data sparse regions of the lower atmosphere;(3) assimilating dense observational data into models in real-time with sufficient resolution and accuracy; (4) developing novel physics-based reduced order models capable of incorporating diverse data sets; and (5)integrating real-time forecasting into UTM and DAA (detect-and-avoid) architectures for path planning and navigation. The goal of this proposed effort is to demonstrate the value of using small UAS to collect measurements of the dynamic and thermodynamic properties of the lower atmosphere at scales that match or exceed the spatio-temporal resolution of today’s best numerical weather prediction models

Koushik Datta↗

Enabling Assurance in the MBSE Environment

A number of specific benefits that fit within the hallmarks of effective development are realized with implementation of model-based approaches to systems and assurance. Model Based Systems Engineering (MBSE) enabled by standardized modeling languages (e.g., SysML®) is at the core. These benefits in the context of spaceflight system challenges can include [1]: • Improved management of complex development • Reduced risk in the development process • Improved cost management • Improved design decisions With appropriate modeling techniques the assurance community also can improve early oversight and insight into project development. NASA has shown the basic constructs of SysML in an MBSE environment offer several key advantages, within a Model Based Mission Assurance (MBMA) initiative [2, 3]. These include the following: • Model viewpoints that promote rapid and systematic assessment of requirements coverage, hazard tagging and risk management • Embedded safety assessments for launch vehicles • Deployment of model assisted development of reliability products - Failure Modes and Effects Analyses (FMEAs) and Fault Trees • Test Planning • Validation and Verification of complex functions • Support of Assurance Case development for complex systems In addition, while there are benefits to be harvested, there is a realization that these do not come without effort and cost. Enabling model-based approaches requires structure, not only in an organizational context, but in a modeling context as well. There can be a steep learning curve and costs associated to train skilled modelers. But, on the other hand, not all of the assurance community need to be modelers. Models themselves must conform to ontologies that enable assurance. This places constraints upon the models and modelers. Optimums have yet to be developed where resources and constraints on modeling must be traded off in the organization and modeling efforts for projects. A number of barriers need to be overcome, as well, which pose challenges to the developers of the software that supports MBSE/MBMA. Information and data must be made to flow seamlessly through the life cycle. Because there is a wide variety of tools used in the community, to avoid the problems of the past of silos, delays, and diverging interests, information should flow among these tools to support the “single source of truth” paradigm of MBSE. This will greatly facilitate MBMA and advancement of assurance functions.

Evans, John W.↗