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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to demonstrate the use of design and analysis of computer experiments (DACE) methods in Sandia’s DAKOTA software package for surrogate modeling and optimization. These methods were applied to a flow- path fueled with an interdigitated flushwall injector suitable for scramjet applications at hyper- velocity conditions and ascending along a constant dynamic pressure flight trajectory. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. Because the RAS of this case are computationally expensive, surrogate models are used for optimization. To build a surrogate model a RAS database is created. The sequence of the design variables comprising the database were generated using a Latin hypercube sampling (LHS) method. A methodology was also developed to automatically build geometries and generate structured grids for each design point. The ensuing RAS analysis generated the simulation database from which the two objective functions were computed using a one-dimensionalization (1D) of the three-dimensional simulation data. The data were fitted using four surrogate models: an artificial neural network (ANN), a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model predicted an optimal solution set that exhibited high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts may be required to lower the surrogate model errors and perform more accurate surrogate-model-based optimization.

Shenoy, Rajiv R.↗
NASA NTRS2019

Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to find optimal designs for an interdigitated flushwall injector suitable for scramjet applications at hypervelocity conditions. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. A Latin hypercube sampling design-of-experiments method was used to select design points for RAS. A methodology was developed that automated building geometries and generating grids for each design. The ensuing RAS analysis generated the performance database from which the two objective functions of interest were computed using a one-dimensional performance utility. The data were fitted using four surrogate models: an artificial neural network (ANN) model, a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model obtained an optimal solution set that predicted high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts are required in order to lower the errors and perform more accurate surrogate-based optimization. sed optimization.

Shenoy, Rajiv R.↗
NASA NTRS2004

Aerodynamic Optimization of Rocket Control Surface Geometry Using Cartesian Methods and CAD Geometry

Aerodynamic design is an iterative process involving geometry manipulation and complex computational analysis subject to physical constraints and aerodynamic objectives. A design cycle consists of first establishing the performance of a baseline design, which is usually created with low-fidelity engineering tools, and then progressively optimizing the design to maximize its performance. Optimization techniques have evolved from relying exclusively on designer intuition and insight in traditional trial and error methods, to sophisticated local and global search methods. Recent attempts at automating the search through a large design space with formal optimization methods include both database driven and direct evaluation schemes. Databases are being used in conjunction with surrogate and neural network models as a basis on which to run optimization algorithms. Optimization algorithms are also being driven by the direct evaluation of objectives and constraints using high-fidelity simulations. Surrogate methods use data points obtained from simulations, and possibly gradients evaluated at the data points, to create mathematical approximations of a database. Neural network models work in a similar fashion, using a number of high-fidelity database calculations as training iterations to create a database model. Optimal designs are obtained by coupling an optimization algorithm to the database model. Evaluation of the current best design then gives either a new local optima and/or increases the fidelity of the approximation model for the next iteration. Surrogate methods have also been developed that iterate on the selection of data points to decrease the uncertainty of the approximation model prior to searching for an optimal design. The database approximation models for each of these cases, however, become computationally expensive with increase in dimensionality. Thus the method of using optimization algorithms to search a database model becomes problematic as the number of design variables is increased.

Nelson, Andrea↗
NASA NTRS2021

Optimal Experimental Design With Fast Neural Network Surrogate Models

Designing optimal experiments minimizes the uncertainty of results and maximizes the efficient use of resources. Herein, machine learning surrogate models and the approximate coordinate exchange (ACE) algorithm are used to determine optimum experimental designs over large or arbitrarily restrictive design spaces. Optimal experimental design is particularly salient in materials science where experiments are expensive and material properties must often be inferred indirectly. The proposed framework is demonstrated by finding optimal experiments with which the hidden constituent properties of composite materials can be most efficiently inferred from observable experimental outcomes. The optimum experimental design is given by an information-theoretic criteria, which maximizes the conditional mutual information between the hidden properties and the expected experimental outcomes. To perform tractable optimization a neural network is trained as a surrogate model to mimic a physics based simulation, which can calculate the expected experimental outcome based on a candidate experimental design and sampled constituent properties. The ACE algorithm is used to optimize over large design spaces with many tests and controlled parameters where an exhaustive search would be intractable even with the surrogate model. Using this approach, optimal experimental designs that are consistent with those produced by heuristic knowledge and established best practices are found; then optimal designs in larger design spaces where heuristic knowledge is unavailable are examined.

machine learning↗
NASA NTRSDate not supplied

Surrogate Optimization for Quantum Circuits

Variational quantum Eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has yet to be realized with few claims of quantum advantage and high resource estimates mainly due to the need for optimization in the presence of noise. Finding algorithms and methods to improve the convergence is essential to accelerate the capabilities of near-term hardware for VQE or more broad applications of hybrid methods in which optimization is required. To this goal we look to use modern approaches recently developed in circuit simulations and stochastic classical optimization that can be combined in a surrogate optimization approach to classical circuits. Using an approximate state vector simulator, we efficiently calculate an approximate Hessian, fed as an input for a detailed quantum circuit simulator. We demonstrate the capabilities of such an approach with and without sampling noise. We also show that this method outperforms Powell in the presence of quantum circuit shot noise by a factor of 2-4

quantum computing↗
NASA NTRS1998

A Rigorous Framework for Optimization of Expensive Functions by Surrogates

The goal of the research reported here is to develop rigorous optimization algorithms to apply to some engineering design problems for which design application of traditional optimization approaches is not practical. This paper presents and analyzes a framework for generating a sequence of approximations to the objective function and managing the use of these approximations as surrogates for optimization. The result is to obtain convergence to a minimizer of an expensive objective function subject to simple constraints. The approach is widely applicable because it does not require, or even explicitly approximate, derivatives of the objective. Numerical results are presented for a 31-variable helicopter rotor blade design example and for a standard optimization test example.

Booker, Andrew J.↗
NASA NTRS1998

Using Approximations to Accelerate Engineering Design Optimization

Optimization problems that arise in engineering design are often characterized by several features that hinder the use of standard nonlinear optimization techniques. Foremost among these features is that the functions used to define the engineering optimization problem often are computationally intensive. Within a standard nonlinear optimization algorithm, the computational expense of evaluating the functions that define the problem would necessarily be incurred for each iteration of the optimization algorithm. Faced with such prohibitive computational costs, an attractive alternative is to make use of surrogates within an optimization context since surrogates can be chosen or constructed so that they are typically much less expensive to compute. For the purposes of this paper, we will focus on the use of algebraic approximations as surrogates for the objective. In this paper we introduce the use of so-called merit functions that explicitly recognize the desirability of improving the current approximation to the objective during the course of the optimization. We define and experiment with the use of merit functions chosen to simultaneously improve both the solution to the optimization problem (the objective) and the quality of the approximation. Our goal is to further improve the effectiveness of our general approach without sacrificing any of its rigor.

Torczon, Virginia↗
NASA NTRS2019

A Mixed Integer Efficient Global Optimization Algorithm with Multiple Infill Strategy - Applied to a Wing Topology Optimization Problem

With the advancement in high performance computing and numerical optimization techniques,engineering design optimization problems are becoming more complex, larger scale,higher fidelity, and computationally more demanding, requiring longer run times than ever before. There exists methodologies and techniques that can address some of these challenges but very few can address all, and most are limited in the extent that these concerns can be addressed. With the goal of addressing such challenging engineering problems, we developed anew optimization framework, named AMIEGO, that combines concepts from surrogate-based optimization approaches, gradient-based numerical methods, Partial Least Squares, evolutionary algorithms, and Branch-and-Bound, providing newer capabilities that were not previouslyperceived. However, the original version of this framework, in the process of adaptive samplingto explore and exploit the design space, finds only a single sample point per iteration. The efforthere builds upon this previously developed optimization framework to include multiple infillsampling capability that combines the concept of generalized expected improvement function,unsupervised learning, and multi-objective evolutionary technique. To demonstrate, AMIEGOwith the multiple infill capability (called AMIEGO-MIMOS) solves a series of increasingly difficultengineering design optimization problems. The results reveal the performance of the newapproach is problem dependent. When applied to a ten-bar truss problem, the newly proposedmultiple infill strategy consistently leads to a better design solutions when compared to theexisting CPTV method (implemented with the context of the AMIEGO framework). On theother hand, when applied to a mixed-integer high fidelity wing topology optimization problem- MIMOS, despite showing a steeper convergence at the start, eventually leads to an inferiorsolution as compared to CPTV approach. These results also reveal that a small number ofstarting points, in general, are sufficient to lead to a good overall solution.

Mixed-integer optimization↗
NASA NTRS2005

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm

This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.

Source record↗
NASA NTRSDate not supplied

Real-Time Drag Optimization of Aspect Ratio 13.5 Common Research Model with Distributed Flap System

This paper presents a real-time drag optimization study of the aspect ratio 13.5 Common Research Model (CRM) with a distributed mini-plain flap system. A surrogate aerodynamic model of the aspect ratio 13.5 CRM is developed based on an aerodynamic database computed by a transonic panel method to capture the aerodynamic coefficients as functions of the angle of attack, flap deflections, and Mach number. A recursive least-squares parameter estimation algorithm is designed to estimate the aerodynamic parameters of the surrogate model. The estimated surrogate model is then used in an on-line drag optimization strategy based on an adjoint method. The results of the real-time drag optimization indicate a drag reduction of 2.46% for Mach 0.80, 3.37% for Mach 0.85, and 1.95% for Mach 0.88. The results show the convergence of all parameter estimates.

Drag Optimization↗
NASA NTRS2006

Central East Pacific Flight Routing

With the introduction of the Federal Aviation Administration s Advanced Technology and Oceanic Procedures system at the Oakland Oceanic Center, a level of automation now exists in the oceanic environment to potentially begin accommodating increased user preferred routing requests. This paper presents the results of an initial feasibility assessment which examines the potential benefits of transitioning from the fixed Central East Pacific routes to user preferred routes. As a surrogate for the actual user-provided routing requests, a minimum-travel-time, wind-optimal dynamic programming algorithm was developed and utilized in this paper. After first describing the characteristics (e.g., origin airport, destination airport, vertical distribution and temporal distribution) of the westbound flights utilizing the Central East Pacific routes on Dec. 14-16 and 19-20, the results of both a flight-plan-based simulation and a wind-optimal-based simulation are presented. Whereas the lateral and longitudinal distribution of the aircraft trajectories in these two simulations varied dramatically, the number of simulated first-loss-of-separation events remained relatively constant. One area of concern that was uncovered in this initial analysis was a potential workload issue associated with the redistribution of traffic in the oceanic sectors due to thc prevailing wind patterns.

Grabbe, Shon↗
NASA NTRS2018

Sizing and Layout Design of an Aeroelastic Wingbox Through Nested Optimization

The goals of this work are to 1) develop an optimization algorithm that can simultaneously handle a large number of sizing variables and topological layout variables for an aeroelastic wingbox optimization problem and 2) utilize this algorithm to ascertain the benefits of curvilinear wingbox components. The algorithm used here is a nested optimization, where the outer level optimizes the rib and skin stiffener layouts with a surrogate-based optimizer, and the inner level sizes all of the components via gradient-based optimization. Two optimizations are performed: one restricted to straight rib and stiffener components only, the other allowing curved members. A moderate 1.18% structural mass reduction is obtained through the use of curvilinear members.

Stanford, Bret K.↗
NASA NTRSDate not supplied

Understanding Resilience Optimization Architectures With an Optimization Problem Repository

Optimizing a system’s resilience can be challenging, especially when it involves considering both the inherent resilience of a robust design and the active resilience of a health management system to a set of computationally-expensive hazard simulations. While prior work has developed specialized architectures to effectively and efficiently solve combined design and resilience optimization problems, the comparison of these architectures has been limited to a single case study. To further study resilience optimization formulations, this work develops a problem repository which includes previously-developed resilience optimization problems and additional problems presented in this work: a notional system resilience model, a pandemic response model, and a cooling tank hazard prevention model. This work then uses models in the repository at large to understand the characteristics of resilience optimization problems and study the applicability of optimization architectures and decomposition strategies. Based on the comparisons in the repository, applying an optimization architecture effectively requires understanding the alignment and coupling relationships between the design and resilience models, as well as the efficiency characteristics of the algorithms. While alignment determines the necessity of a surrogate of resilience cost in the upper-level design problem, coupling determines the overall applicability of a sequential, alternating, or bilevel structure. Additionally, the application of decomposition strategies is dependent on there being limited interactions between variable sets, which often does not hold when a resilience policy is parameterized in terms of actions to take in hazardous model states rather than specific given scenarios.

Resilience↗
NASA NTRS1995

Computer-Simulation Surrogates for Optimization: Application to Trapezoidal Ducts and Axisymmetric Bodies

Engineering design and optimization efforts using computational systems rapidly become resource intensive. The goal of the surrogate-based approach is to perform a complete optimization with limited resources. In this paper we present a Bayesian-validated approach that informs the designer as to how well the surrogate performs; in particular, our surrogate framework provides precise (albeit probabilistic) bounds on the errors incurred in the surrogate-for-simulation substitution. The theory and algorithms of our computer{simulation surrogate framework are first described. The utility of the framework is then demonstrated through two illustrative examples: maximization of the flowrate of fully developed ow in trapezoidal ducts; and design of an axisymmetric body that achieves a target Stokes drag.

Otto, John C.↗
NASA NTRS2009

The Integrated Medical Model - Optimizing In-flight Space Medical Systems to Reduce Crew Health Risk and Mission Impacts

The Integrated Medical Model (IMM) is a decision support tool used by medical system planners and designers as they prepare for exploration planning activities of the Constellation program (CxP). IMM provides an evidence-based approach to help optimize the allocation of in-flight medical resources for a specified level of risk within spacecraft operational constraints. Eighty medical conditions and associated resources are represented in IMM. Nine conditions are due to Space Adaptation Syndrome. The IMM helps answer fundamental medical mission planning questions such as What medical conditions can be expected? What type and quantity of medical resources are most likely to be used?", and "What is the probability of crew death or evacuation due to medical events?" For a specified mission and crew profile, the IMM effectively characterizes the sequence of events that could potentially occur should a medical condition happen. The mathematical relationships among mission and crew attributes, medical conditions and incidence data, in-flight medical resources, potential clinical and crew health end states are established to generate end state probabilities. A Monte Carlo computational method is used to determine the probable outcomes and requires up to 25,000 mission trials to reach convergence. For each mission trial, the pharmaceuticals and supplies required to diagnose and treat prevalent medical conditions are tracked and decremented. The uncertainty of patient response to treatment is bounded via a best-case, worst-case, untreated case algorithm. A Crew Health Index (CHI) metric, developed to account for functional impairment due to a medical condition, provides a quantified measure of risk and enables risk comparisons across mission scenarios. The use of historical in-flight medical data, terrestrial surrogate data as appropriate, and space medicine subject matter expertise has enabled the development of a probabilistic, stochastic decision support tool capable of optimizing in-flight medical systems based on crew and mission parameters. This presentation will illustrate how to apply quantitative risk assessment methods to optimize the mass and volume of space-based medical systems for a space flight mission given the level of crew health and mission risk.

Kerstman, Eric↗
NASA NTRS1998

On Managing the Use of Surrogates in General Nonlinear Optimization and MDO

This paper is concerned with a trust region approximation management framework (AMF) for solving the nonlinear programming problem in general and multidisciplinary optimization problems in particular The intent of the AMF methodology is to facilitate the solution of optimization problems with high-fidelity models. While such models are designed to approximate the physical phenomena they describe to a high degree of accuracy, their use in a repetitive procedure, for example, iterations of an optimization or a search algorithm, make such use prohibitively expensive. An improvement in design with lower-fidelity, cheaper models, however, does not guarantee a corresponding improvement for the higher-fidelity problem. The AMF methodology proposed here is based on a class of multilevel methods for constrained optimization and is designed to manage the use of variable-fidelity approximations or models in a systematic way that assures convergence to critical points of the original high-fidelity problem.

Alexandrov, Natalia M.↗
NASA NTRS2012

OpenMDAO: Framework for Flexible Multidisciplinary Design, Analysis and Optimization Methods

The OpenMDAO project is underway at NASA to develop a framework which simplifies the implementation of state-of-the-art tools and methods for multidisciplinary design, analysis and optimization. Foremost, OpenMDAO has been designed to handle variable problem formulations, encourage reconfigurability, and promote model reuse. This work demonstrates the concept of iteration hierarchies in OpenMDAO to achieve a flexible environment for supporting advanced optimization methods which include adaptive sampling and surrogate modeling techniques. In this effort, two efficient global optimization methods were applied to solve a constrained, single-objective and constrained, multiobjective version of a joint aircraft/engine sizing problem. The aircraft model, NASA's nextgeneration advanced single-aisle civil transport, is being studied as part of the Subsonic Fixed Wing project to help meet simultaneous program goals for reduced fuel burn, emissions, and noise. This analysis serves as a realistic test problem to demonstrate the flexibility and reconfigurability offered by OpenMDAO.

Kriging↗
NASA NTRS2020

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.

Corbetta, Matteo↗