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NASA NTRSDate not supplied

AERACEPT (AErosol Rapid Analysis Combined Entry Probe/sonde Technology) for the Nephele Venus Cloud Mission Concept

Nephele is a small atmospheric probe mission concept to determine whether the Venus clouds contain organic matter. Nephele complements larger missions that seek to conduct Venus atmospheric analyses, such as DAVINCI, Venera-D, and the Morning Star missions, by directly sampling and analyzing cloud and haze particles. AERACEPT is an aerosol sampling technology designed to minimize the mass, volume, and complexity required for in situ planetary atmosphere characterization. A single aeroshell functions as a combined entry vehicle, descent probe, and instrumented sonde, combining recent advances in thermal protection materials with heritage technology from planetary and airborne science. The velocity of descent drives aerosol through inlets embedded in the aeroshell’s thermal protection material; inertial separation isolates the particles from the gas stream; and the particle capture surface doubles as the analysis substrate. This method can work without heat shield separation, descent control, or active sample handling; these advantages trade against a smaller sample volume, higher risk of sample heating, and shorter time for analysis and data return. AERACEPT further benefits from modeling of the internal and external flow to understand the particle capture efficiency and bias for size and concentration distributions. AERACEPT is well suited for a Venus mission such as Nephele, where a passive descent trajectory is both subsonic over the altitudes of interest and provides sufficient velocity and pressure differential for the particle sizes of interest. A thermal and flow toolchain developed in-house (Traj→ DPLR→ PATO→ openFOAM) was used to model an 80-cm aeroshell with science operations 63–39 km. These models indicate that AERACEPT will provide ~24 µL accumulated sample of particles ≥ 0.2 µm, substantially above the 2 µL threshold requirement. The worst-case particle heating will be ~14 K above ambient, for the smallest particles at the lowest altitude; most particles will experience ≤ 1 K rise, at which almost all analytes of interest will be unaffected. Plasma jet testing of a small-scale aeroshell-material inlet was recently conducted to validate the thermal model parameters; wind tunnel testing to validate the flow model parameters is underway.

AERACEPT↗

NASA NTRS2026

Development, Characterization, and Validation of Elevated-Temperature Constitutive Models: Deformation and Damage

This report provides a brief review of experimentally observed hereditary and nonhereditary material behavior along with background information on standard as well as advanced internal state variable constitutive modeling at elevated temperature. A description of exploratory, characterization, and validation testing is presented along with a detailed outline of what constitutes “sufficient” data content (i.e., quality and quantity) for developing or enhancing, characterizing, and validating a particular sophisticated nonlinear time- and history-dependent (hereditary) class of constitutive models known as GVIPS (generalized viscoplasticity with potential structure). The tests described are necessary to reveal a material’s behavior in the reversible (or viscoelastic) and irreversible (or viscoplastic) regimes, for the identification of both deformation and damage model parameters. Results presented are primarily for metallic materials. In all cases, both uniaxial and multiaxial tests are described, and the linkage between the specific tests and the parameters within the model that can be characterized from the results of these tests are also discussed. Discussion is also provided relative to the role information management must play relative to material data collection, analysis, maintenance, and dissemination. The need for such an information system is particularly important as both analyst and designer move toward utilizations of sophisticated, nonlinear time- and history dependent (hereditary) constitutive models. Lastly, the concept of state space and its utility in understanding and establishing constitutive models is addressed throughout. The intent behind this document is to help both the modeler and experimentalist understand each other’s specific points of view and provide guidance for both model development and characterization. Emphasis has been placed on providing the mechanician with information regarding how tests are performed and what issues to be aware of when interpreting results. It is hoped that experimentalists will take away a new perspective on the types of information that modelers and analysts are looking for from them.

Constitutive Modeling↗

NASA NTRS1985

Polynomic nonlinear dynamical systems - A residual sensitivity method for model reduction

The motivation for using polynomic combinations of system states and inputs to model nonlinear dynamics systems is founded upon the classical theories of analysis and function representation. A feature of such representations is the need to make available all possible monomials in these variables, up to the degree specified, so as to provide for the description of widely varying functions within a broad class. For a particular application, however, certain monomials may be quite superfluous. This paper examines the possibility of removing monomials from the model in accordance with the level of sensitivity displayed by the residuals to their absence. Critical in these studies is the effect of system input excitation, and the effect of discarding monomial terms, upon the model parameter set. Therefore, model reduction is approached iteratively, with inputs redesigned at each iteration to ensure sufficient excitation of remaining monomials for parameter approximation. Examples are reported to illustrate the performance of such model reduction approaches.

Yurkovich, S.↗

NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗

NASA NTRS1994

Determining the accuracy of maximum likelihood parameter estimates with colored residuals

An important part of building high fidelity mathematical models based on measured data is calculating the accuracy associated with statistical estimates of the model parameters. Indeed, without some idea of the accuracy of parameter estimates, the estimates themselves have limited value. In this work, an expression based on theoretical analysis was developed to properly compute parameter accuracy measures for maximum likelihood estimates with colored residuals. This result is important because experience from the analysis of measured data reveals that the residuals from maximum likelihood estimation are almost always colored. The calculations involved can be appended to conventional maximum likelihood estimation algorithms. Simulated data runs were used to show that the parameter accuracy measures computed with this technique accurately reflect the quality of the parameter estimates from maximum likelihood estimation without the need for analysis of the output residuals in the frequency domain or heuristically determined multiplication factors. The result is general, although the application studied here is maximum likelihood estimation of aerodynamic model parameters from flight test data.

Morelli, Eugene A.↗

NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗

NASA NTRS1974

Control system design using frequency domain models and parameter optimization, with application to supersonic inlet controls

A technique is described for designing feedback control systems using frequency domain models, a quadratic cost function, and a parameter optimization computer program. FORTRAN listings for the computer program are included. The approach is applied to the design of shock position controllers for a supersonic inlet. Deterministic or random system disturbances, and the presence of random measurement noise are considered. The cost function minimization is formulated in the time domain, but the problem solution is obtained using a frequency domain system description. A scaled and constrained conjugate gradient algorithm is used for the minimization. The approach to a supersonic inlet included the calculations of the optimal proportional-plus integral (PI) and proportional-plus-integral-plus-derivative controllers. A single-loop PI controller was the most desirable of the designs considered.

Seidel, R. C.↗

NASA NTRS2014

Combined Uncertainty and A-Posteriori Error Bound Estimates for CFD Calculations: Theory and Implementation

Simulation codes often utilize finite-dimensional approximation resulting in numerical error. Some examples include, numerical methods utilizing grids and finite-dimensional basis functions, particle methods using a finite number of particles. These same simulation codes also often contain sources of uncertainty, for example, uncertain parameters and fields associated with the imposition of initial and boundary data,uncertain physical model parameters such as chemical reaction rates, mixture model parameters, material property parameters, etc.

CFD↗

NASA NTRS1989

Noninteractive macroscopic reliability model for ceramic matrix composites with orthotropic material symmetry

A macroscopic noninteractive reliability model for ceramic matrix composites is presented. The model is multiaxial and applicable to composites that can be characterized as orthotropic. Tensorial invariant theory is used to create an integrity basis with invariants that correspond to physical mechanisms related to fracture. This integrity basis is then used to construct a failure function per unit volume (or area) of material. It is assumed that the overall strength of the composite is governed by weakest link theory. This leads to a Weibull type model similar in nature to the principle of independent action (PIA) model for isotropic monolithic ceramics. An experimental program to obtain model parameters is briefly discussed. In addition, qualitative features of the model are illustrated by presenting reliability surfaces for various model parameters.

Duffy, Stephen F.↗

NASA NTRS1989

Noninteractive macroscopic reliability model for ceramic matrix composites with orthotropic material symmetry

Duffy, Stephen F.↗

NASA NTRS2018

Europa Clipper Preliminary Design Review Propellant Slosh Analysis

Propellant slosh was analyzed for both the oxidizer and the fuel for the Europa Clipper propulsion system. Slosh was examined for various fill fractions for cases where acceleration was on the order of magnitude of 10(exp -2) m/sq. s using the computational fluid dynamics software package STAR-CCM+ and at various fill fractions for cases where acceleration was on the order of magnitude of 10(exp -5) m/sq. s using Surface Evolver. Equivalent mechanical model parameters were derived from the CFD data using MATLAB for both the higher and the lower acceleration slosh cases. These parameters were plotted and can be used to interpolate mechanical model parameters at fill fractions not analyzed by CFD or Surface Evolver.

CFD↗

NASA NTRS1993

Wind field model-based estimation of Seasat scatterometer winds

A model-based approach to estimating near-surface wind fields over the ocean from Seasat scatterometer (SASS) measurements is presented. The approach is a direct assimilation technique in which wind field model parameters are estimated directly from the scatterometer measurements of the radar backscatter of the ocean's surface using maximum likelihood principles. The wind field estimate is then computed from the estimated model parameters. The wind field model used in this approach is based on geostrophic approximation and on simplistic assumptions about the wind field vorticity and divergence but includes ageostrophic winds. Nine days of SASS data were processed to obtain unique wind estimates. Comparisons in performance to the traditional two-step (point-wise wind retrieval followed by ambiguity removal) wind estimate method and the model-based method are provided using both simulated radar backscatter measurements and actual SASS measurements. In the latter case the results are compared to wind fields determined using subjective ambiguity removal. While the traditional approach results in missing measurements and reduced effective swath width due to fore/aft beam cell coregistration problems, the model-based approach uses all available measurements to increase the effective swath width and to reduce data gaps. The results reveal that the model-based wind estimates have accuracy comparable to traditionally estimated winds with less 'noise' in the directional estimates, particularly at low wind speeds.

Long, David G.↗

NASA NTRS2023

Reduced-Order Modeling and Parameter Identification of Wind Tunnel Measurement Systems

We present a method to develop a physics-based, reduced-order model of a wind tunnel measurement system (including a sting, strain gage force balance, and test article) that can be used to predict the dynamics of the system. This reduced-order model is combined with a simple finite element beam model of a sting to estimate the dynamics of the full assembly. We make comparisons between a full finite element model and the hybrid reduced-order model to show that this hybrid reduced-order model is capable of predicting the first six natural frequencies to within 10% error. This technique could be used to identify reduced-order parameters for a large number of balances and stings, which could then be used to estimate the dynamics of different measurement assemblies.

Reduced-order Modeling↗

NASA NTRSDate not supplied

Reduced-Order Modeling and Parameter Identification of Wind Tunnel Measurement Systems

Reduced-order Modeling↗

NASA NTRS1972

Nonlinear and Digital Man-machine Control Systems Modeling

An adaptive modeling technique is examined by which controllers can be synthesized to provide corrective dynamics to a human operator's mathematical model in closed loop control systems. The technique utilizes a class of Liapunov functions formulated for this purpose, Liapunov's stability criterion and a model-reference system configuration. The Liapunov function is formulated to posses variable characteristics to take into consideration the identification dynamics. The time derivative of the Liapunov function generate the identification and control laws for the mathematical model system. These laws permit the realization of a controller which updates the human operator's mathematical model parameters so that model and human operator produce the same response when subjected to the same stimulus. A very useful feature is the development of a digital computer program which is easily implemented and modified concurrent with experimentation. The program permits the modeling process to interact with the experimentation process in a mutually beneficial way.

Mekel, R.↗

NASA NTRS1985

Application of adaptive control to space stations

The space station will be deployed and assembled in low earth orbit with multiple Shuttle trips. Several construction phases will be required involving both ground and in-orbit operations. In this paper, the construction process of a four-panel space station and its control problems are discussed. The applicability of a direct model reference adaptive control technique with plant augmentation is investigated. Control during several key assembly operation periods has been simulated. These include Shuttle docking with initial-phase station, habitat module mating, and Shuttle docking with operational station. High rate of convergence and robust performance have been observed for all the simulated cases even with 40 percent model parameter errors and model truncations and more than 100 percent instant mass property variations. Controller with severe gain saturations is also discussed and results show only slight performance deterioration.

Ih, C.-H. C.↗

NASA NTRS1996

Noninvasive Determination of Bone Mechanical Properties Using Vibration Response: A Refined Model and Validation in vivo

Accurate non-invasive mechanical measurement of long bones is made difficult by the masking effect of surrounding soft tissues. Mechanical response tissue analysis (MRTA) offers a method for separating the effects of the soft tissue and bone; however, a direct validation has been lacking. A theoretical analysis of wave propagation through the compressed tissue revealed a strong mass effect dependent on the relative accelerations of the probe and bone. The previous mathematical model of the bone and overlying tissue system was reconfigured to incorporate the theoretical finding. This newer model (six-parameter) was used to interpret results using MRTA to determine bone cross-sectional bending stiffness, EI(sub MRTA). The relationship between EI(sub MRTA) and theoretical EI values for padded aluminum rods was R(sup 2) = 0.999. A biological validation followed using monkey tibias. Each bone was tested in vivo with the MRTA instrument. Postmortem, the same tibias were excised and tested to failure in three-point bending to determine EI(sub 3-PT) and maximum load. Diaphyseal bone mineral density (BMD) measurements were also made. The relationship between EI(sub 3-PT) and in vivo EI(sub MRTA) using the six-parameter model is strong (R(sup 2) = 0.947) and better than that using the older model (R(sup 2) = 0.645). EI(sub MRTA) and BMD are also highly correlated (R(sup 2) = 0.853). MRTA measurements in vivo and BMD ex vivo are both good predictors of scaled maximum strength (R(sup 2) = 0.915 and R(sup 2) = 0.894, respectively). This is the first biological validation of a non- invasive mechanical measurement of bone by comparison to actual values. The MRTA technique has potential clinical value for assessing long-bone mechanical properties.

Roberts, S. G.↗

NASA NTRS1996

Noninvasive Determination of Bone Mechanical Properties using Vibration Response: A Refined Model and Validation in vivo

Accurate non-invasive mechanical measurement of long bones is made difficult by the masking effect of surrounding soft tissues. Mechanical Response Tissue Analysis (MRTA) offers a method for separating the effects of the soft tissue and bone; however, a direct validation has been lacking. A theoretical analysis of wave propagation through the compressed tissue revealed a strong mass effect dependent on the relative accelerations of the probe and bone. The previous mathematical model of the bone and overlying tissue system was reconfigured to incorporate the theoretical finding. This newer model (six-parameter) was used to interpret results using MRTA to determine bone cross-sectional bending stiffness, EI(sub MRTA). The relationship between EI(MRTA) and theoretical EI values for padded aluminum rods was R(exp 2) = 0.999. A biological validation followed using monkey tibias. Each bone was tested in vivo with the MRTA instrument. Postmortem, the same tibias were excised and tested to failure in three-point bending to determine EI(sub 3-PT) and maximum load. Diaphyseal Bone Mineral Density (BMD) measurements were also made. The relationship between E(sub 3-PT) and in vivo EI(sub MRTA) using the six-parameter model is strong (R(exp 2) = 0.947) and better than that using the older model (R(exp 2) = 0.645). EI(MRTA) and BMD are also highly correlated (R(exp 2) = 0.853). MRTA measurements in vivo and BMD ex vivo are both good predictors of scaled maximum strength (R(exp 2) = 0.915 and R(exp 2) = 0.894, respectively). This is the first biological validation of a non-invasive mechanical measurement of bone by comparison to actual values. The MRTA technique has potential clinical value for assessing long-bone mechanical properties.

Roberts, S. G.↗