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Adjoint-Based UQ And Optimization Under Uncertainty For Satellite Shield Designs.
Abstract not provided.
Adjoint-Based UQ and Optimization Under Uncertainty for Satellite Shield Designs.
Abstract not provided.
PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling
PyApprox is a Python-based one-stop-shop for probabilistic analysis of numerical models such as those used in the earth, environmental and engineering sciences. Easy to use and extendable tools are provided for constructing surrogates, sensitivity analysis, Bayesian inference, experimental design, and forward uncertainty quantification. The algorithms implemented represent a wide range of methods for model analysis developed over the past two decades, including recent advances in multi-fidelity approaches that use multiple model discretizations and/or simplified physics to significantly reduce the computational cost of various types of analyses. An extensive set of Benchmarks from the literature is also provided to facilitate the easy comparison of new or existing algorithms for a wide range of model analyses. Here, this paper introduces PyApprox and its various features, and presents results demonstrating the utility of PyApprox on a benchmark problem modeling the advection of a tracer in groundwater.
Decomposition-Based Decision Making for Aerospace Vehicle Design
Most practical engineering systems design problems have multiple and conflicting objectives. Furthermore, the satisfactory attainment level for each objective ( requirement ) is likely uncertain early in the design process. Systems with long design cycle times will exhibit more of this uncertainty throughout the design process. This is further complicated if the system is expected to perform for a relatively long period of time, as now it will need to grow as new requirements are identified and new technologies are introduced. These points identify a need for a systems design technique that enables decision making amongst multiple objectives in the presence of uncertainty. Traditional design techniques deal with a single objective or a small number of objectives that are often aggregates of the overarching goals sought through the generation of a new system. Other requirements, although uncertain, are viewed as static constraints to this single or multiple objective optimization problem. With either of these formulations, enabling tradeoffs between the requirements, objectives, or combinations thereof is a slow, serial process that becomes increasingly complex as more criteria are added. This research proposal outlines a technique that attempts to address these and other idiosyncrasies associated with modern aerospace systems design. The proposed formulation first recasts systems design into a multiple criteria decision making problem. The now multiple objectives are decomposed to discover the critical characteristics of the objective space. Tradeoffs between the objectives are considered amongst these critical characteristics by comparison to a probabilistic ideal tradeoff solution. The proposed formulation represents a radical departure from traditional methods. A pitfall of this technique is in the validation of the solution: in a multi-objective sense, how can a decision maker justify a choice between non-dominated alternatives? A series of examples help the reader to observe how this technique can be applied to aerospace systems design and compare the results of this so-called Decomposition-Based Decision Making to more traditional design approaches.
Overview of Recent EAST Testing, Modeling & Analysis
Shock Layer Radiation at NASA Ames - Background: Complex aerothermal and thermochemical phenomena of planetary entry define convective and radiative heating. A spacecraft's TPS (Thermal Protective System) mitigates heat transfer to substructure. Successful TPS design relies on verifiable characterization of these phenomena in the anticipated flight environment. - Approach: EAST (Electric Arc Shock Tube) [facility at Ames] simulates high-enthalpy, real-gas phenomena encountered by hypersonic vehicles entering planetary atmospheres by spectrally imaging the flow behind a moving shock wave. - Goal: Validate aerothermal models (DPLR (Data Parallel Line Relaxation Code) and NEQAIR (Nonequilibrium Radiative Transport and Spectra Program)), inform model improvements, reduce uncertainty and quantify design uncertainties. - Recent Relevant Projects: MSL (Mars Science Laboratory) & Mars 2020, InSight, OSIRIS-REx (Origins Spectral Interpretation Resource Identification Security Regolith Explorer), Orion EFT-1 (Exploration Flight-1) and EM-1 (Exploration Mission-1) and New Frontiers.
Design Considerations for a Launch Vehicle Development Flight Instrumentation System
When embarking into the design of a new launch vehicle, engineering models of expected vehicle performance are always generated. While many models are well established and understood, some models contain design features that are only marginally known. Unfortunately, these analytical models produce uncertainties in design margins. The best way to answer these analytical issues is with vehicle level testing. The National Aeronautics and Space Administration respond to these uncertainties by using a vehicle level system called the Development Flight Instrumentation, or DFI. This DFI system can be simple to implement, with only a few measurements, or it may be a sophisticated system with hundreds of measurement and video, without a recording capability. From experience with DFI systems, DFI never goes away. The system is renamed and allowed to continue, in most cases. Proper system design can aid the transition to future data requirements. This paper will discuss design features that need to be considered when developing a DFI system for a launch vehicle. It will briefly review the data acquisition units, sensors, multiplexers and recorders, telemetry components and harnessing. It will present a reasonable set of requirements which should be implemented in the beginning of the program in order to start the design. It will discuss a simplistic DFI architecture that could be the basis for the next NASA launch vehicle. This will be followed by a discussion of the "experiences gained" from a past DFI system implementation, such as the very successful Ares I-X test flight. Application of these design considerations may not work for every situation, but they may direct a path toward success or at least make one pause and ask the right questions.
Robust Control Design for Systems With Probabilistic Uncertainty
This paper presents a reliability- and robustness-based formulation for robust control synthesis for systems with probabilistic uncertainty. In a reliability-based formulation, the probability of violating design requirements prescribed by inequality constraints is minimized. In a robustness-based formulation, a metric which measures the tendency of a random variable/process to cluster close to a target scalar/function is minimized. A multi-objective optimization procedure, which combines stability and performance requirements in time and frequency domains, is used to search for robustly optimal compensators. Some of the fundamental differences between the proposed strategy and conventional robust control methods are: (i) unnecessary conservatism is eliminated since there is not need for convex supports, (ii) the most likely plants are favored during synthesis allowing for probabilistic robust optimality, (iii) the tradeoff between robust stability and robust performance can be explored numerically, (iv) the uncertainty set is closely related to parameters with clear physical meaning, and (v) compensators with improved robust characteristics for a given control structure can be synthesized.
Robust Control of Wave Energy Converters Using Unstructured Uncertainty
In the design of ocean wave energy converters, proper control design is essential to the maximization of the power generation performance for the device. However, in realistic applications, this control design must be undertaken in the presence of model uncertainty. This paper considers the use of robust control theory to optimize the nominal performance for a wave energy converter in stochastic waves, subject to the constraint that the controller be stability-robust to unstructured uncertainties. We formulate the problem as a multi-objective optimal control problem, in which the primary objective is the maximization of power generation for the nominal system, and the competing objective is the norm of the uncertainty input/output channel. This optimal control problem is nonconvex, and we therefore propose an iterative algorithm that can be used to arrive at a local optimal solution. This iterative approach employs the concept of Iterative Convex Overbounding, in the context of the classical Method of Centers. Here, the methodology is demonstrated on a model of a single, buoy- type wave energy converter.
Data-driven prediction of scaling and ignition of inertial confinement fusion experiments
Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.
Probabilistic Aeroelastic Analysis Developed for Turbomachinery Components
Aeroelastic analyses for advanced turbomachines are being developed for use at the NASA Glenn Research Center and industry. However, these analyses at present are used for turbomachinery design with uncertainties accounted for by using safety factors. This approach may lead to overly conservative designs, thereby reducing the potential of designing higher efficiency engines. An integration of the deterministic aeroelastic analysis methods with probabilistic analysis methods offers the potential to design efficient engines with fewer aeroelastic problems and to make a quantum leap toward designing safe reliable engines. In this research, probabilistic analysis is integrated with aeroelastic analysis: (1) to determine the parameters that most affect the aeroelastic characteristics (forced response and stability) of a turbomachine component such as a fan, compressor, or turbine and (2) to give the acceptable standard deviation on the design parameters for an aeroelastically stable system. The approach taken is to combine the aeroelastic analysis of the MISER (MIStuned Engine Response) code with the FPI (fast probability integration) code. The role of MISER is to provide the functional relationships that tie the structural and aerodynamic parameters (the primitive variables) to the forced response amplitudes and stability eigenvalues (the response properties). The role of FPI is to perform probabilistic analyses by utilizing the response properties generated by MISER. The results are a probability density function for the response properties. The probabilistic sensitivities of the response variables to uncertainty in primitive variables are obtained as a byproduct of the FPI technique. The combined analysis of aeroelastic and probabilistic analysis is applied to a 12-bladed cascade vibrating in bending and torsion. Out of the total 11 design parameters, 6 are considered as having probabilistic variation. The six parameters are space-to-chord ratio (SBYC), stagger angle (GAMA), elastic axis (ELAXS), Mach number (MACH), mass ratio (MASSR), and frequency ratio (WHWB). The cascade is considered to be in subsonic flow with Mach 0.7. The results of the probabilistic aeroelastic analysis are the probability density function of predicted aerodynamic damping and frequency for flutter and the response amplitudes for forced response.
Pervaporative Dehydration of 2,3-Butanediol by Dense Poly(vinylidene fluoride) Hollow Fiber Membranes: Parameter Estimation, Process Design, and Technoeconomic Evaluation under Uncertainty
Pervaporation, combined with other separation processes, can effectively remove water from fermentation product streams, making it highly suitable for purifying alcohols like 2,3-butanediol (BDO). In this study, a dense poly(vinylidene fluoride) (PVDF) hollow fiber membrane module prototype was fabricated for BDO dehydration, achieving >0.2 LMH total flux and >95% BDO rejection. With a Markov chain Monte Carlo (MCMC) approach, Bayesian inference was used to quantify the uncertainty of the permeance parameters. A membrane cascade model was developed to scale up a process that purifies a preconcentrated BDO feed (70 wt %) to high purity (90 wt %). Through propagation of the uncertainty of the parameters and sensitivity analyses of the process variables, a cascade design was recommended. Despite data and model limitations, the framework enabled a reliable system analysis and economic evaluation, validated through tight confidence intervals in key process metrics, establishing the foundation for future applications of Bayesian methods in membrane-based processes.
Evaluation of beryllium for space shuttle components
Application of beryllium to specific full-scale space shuttle structural components and assemblies was studied. Material evaluations were conducted to check the mechanical properties of as-received material to gain design information on characteristics needed for the material in the space shuttle environment, and to obtain data needed for evaluating component and panel tests. Four beryllium structural assemblies were analyzed and designed. Selected components of these assemblies, representing areas of critical loading or design/process uncertainty, were designed and tested, and two panel assemblies were fabricated. Trends in cost and weight factors were determined by progressive estimation at key points of preliminary design, final design, and fabrication to aid in a cost/weight evaluation of the use of beryllium.
Robust H(infinity) control design for the Space Station with structured parameter uncertainty
A robust H(infinity) control design methodology and its application to a Space Station attitude and momentum control problem are presented. This new approach incorporates nonlinear multiparameter variations in the state-space formulation of H(infinity) control theory. An application of this robust control synthesis technique tothe Space Station control problem yields a remarkable result in stability robustness with respect to the moments-of-inertia variation of about 73 percent in one of the structured uncertainty directions. The performance and stability of this new robust H(infinity) controller for the Space Station are compared to those of other controllers designed using a standard linear-quadratic-regulator synthesis technique.
Refinement of an 'alternate' method for measuring heating rates in hypersonic wind tunnels
Recently obtained hypersonic wind tunnel results are presented which resolve the principal uncertainty associated with the use of thin film gages on substrates of the glass ceramic substance designated MACOR.THe uncertainty is with respect to both MACOR's thermal properties and the variation of these properties with temperature. The relatively large influence of shock strength on stagnation point heating at low Reynolds numbers is illustrated.
Intrusive Uncertainty Quantification and Optimal Experiment Design in the Open-Source Pyomo Ecosystem
This contribution describes ParmEst and Pyomo.DoE, two pillars of the open-source Python-based Pyomo ecosystem for computational optimization with (partial differential) algebraic equation mathematical models. Specifically, ParmEst facilitates intrusive frequentist parameter estimation (PE) and uncertainty quantification (UQ) through built-in features, such as covariance matrix estimation, bootstrapping, and likelihood ratio tests. Complementary, Pyomo.DoE enables optimal experiment design by maximizing various metrics of the Fisher information matrix, such as A-optimality (trace), D-optimality (determinant), E-optimality (minimum eigenvalue), and ME-optimality (condition number). ParmEst and Pyomo.DoE can solve high-dimensional optimization problems by leveraging the model structure and exact derivative information. Finally, we will discuss future opportunities to integrate PE and UQ capabilities with optimization under uncertainty, including robust optimization with non-convex models via PyROS.
Using High Resolution Design Spaces for Aerodynamic Shape Optimization Under Uncertainty
This paper explains why high resolution design spaces encourage traditional airfoil optimization algorithms to generate noisy shape modifications, which lead to inaccurate linear predictions of aerodynamic coefficients and potential failure of descent methods. By using auxiliary drag constraints for a simultaneous drag reduction at all design points and the least shape distortion to achieve the targeted drag reduction, an improved algorithm generates relatively smooth optimal airfoils with no severe off-design performance degradation over a range of flight conditions, in high resolution design spaces parameterized by cubic B-spline functions. Simulation results using FUN2D in Euler flows are included to show the capability of the robust aerodynamic shape optimization method over a range of flight conditions.
Trajectory Dispersed Vehicle Process for Space Launch System
The Space Launch System (SLS) vehicle is part of NASA's deep space exploration plans that includes manned missions to Mars. Manufacturing uncertainties in design parameters are key considerations throughout SLS development as they have significant effects on focus parameters such as lift-off-thrust-to-weight, vehicle payload, maximum dynamic pressure, and compression loads. This presentation discusses how the SLS program captures these uncertainties by utilizing a 3 degree of freedom (DOF) process called Trajectory Dispersed (TD) analysis. This analysis biases nominal trajectories to identify extremes in the design parameters for various potential SLS configurations and missions. This process utilizes a Design of Experiments (DOE) and response surface methodologies (RSM) to statistically sample uncertainties, and develop resulting vehicles using a Maximum Likelihood Estimate (MLE) process for targeting uncertainties bias. These vehicles represent various missions and configurations which are used as key inputs into a variety of analyses in the SLS design process, including 6 DOF dispersions, separation clearances, and engine out failure studies.