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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Bell-Curve Based Evolutionary Strategies for Structural Optimization

Evolutionary methods are exceedingly popular with practitioners of many fields; more so than perhaps any optimization tool in existence. Historically Genetic Algorithms (GAs) led the way in practitioner popularity (Reeves 1997). However, in the last ten years Evolutionary Strategies (ESs) and Evolutionary Programs (EPS) have gained a significant foothold (Glover 1998). One partial explanation for this shift is the interest in using GAs to solve continuous optimization problems. The typical GA relies upon a cumber-some binary representation of the design variables. An ES or EP, however, works directly with the real-valued design variables. For detailed references on evolutionary methods in general and ES or EP in specific see Back (1996) and Dasgupta and Michalesicz (1997). We call our evolutionary algorithm BCB (bell curve based) since it is based upon two normal distributions.

Kincaid, Rex K.↗
DOE OSTI2018

Exploring the posterior surface of the large scale structure reconstruction

The large scale structure (LSS) of the universe is generated by the linear density gaussian modes, which are evolved into the observed nonlinear LSS. Given a set of data the posterior surface of the modes is convex in the linear regime, leading to a unique global maximum (MAP), but this is no longer guaranteed in the nonlinear regime. In this paper we investigate the nature of posterior surface using the recently developed MAP reconstruction method, with a simplified but realistic N-body simulation as the forward model. The reconstruction method uses optimization with analytic gradients from back-propagation through the simulation. For low noise cases we recover the initial conditions well into the nonlinear regime (k~ 1 h/Mpc) nearly perfectly. We show that the large scale modes can be recovered more precisely than the linear expectation, which we argue is a consequence of nonlinear mode coupling. For noise levels achievable with current and planned LSS surveys the reconstruction cannot recover very small scales due to noise. We see some evidence of non-convexity, specially for smaller scales where the non-injective nature of the mappings: several very different initial conditions leading to the same near perfect final data reconstruction. We investigate the nature of these phenomena further using a 1-d toy gravity model, where many well separated local maximas are found to have identical data likelihood but differ in the prior. We also show that in 1-d the prior favors some solutions over the true solution, though no clear evidence of these in 3-d. Our main conclusion is that on very small scales and for a very low noise the posterior surface is multi-modal and the global maximum may be unreachable with standard methods, while for realistic noise levels in the context of the current and next generation LSS surveys MAP optimization method is likely to be nearly optimal.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Resilient Operations of Networked Microgrids (RONM)

Objective: Improve the resiliency of power systems with optimization-based methods that leverage advanced microgrid technologies to reduce system recovery times after extreme event induced outages. Outcome: First-of-kind, high-fidelity physics-based optimization method for modeling networked microgrids which includes key engineering constraints associated with system recovery after extreme events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2016

A Robust Method to Integrate End-to-End Mission Architecture Optimization Tools

End-to-end mission simulations include multiple phases of flight. For example, an end-to-end Mars mission simulation may include launch from Earth, interplanetary transit to Mars and entry, descent and landing. Each phase of flight is optimized to meet specified constraints and often depend on and impact subsequent phases. The design and optimization tools and methodologies used to combine different aspects of end-to-end framework and their impact on mission planning are presented. This work focuses on a robust implementation of a Multidisciplinary Design Analysis and Optimization (MDAO) method that offers the flexibility to quickly adapt to changing mission design requirements. Different simulations tailored to the liftoff, ascent, and atmospheric entry phases of a trajectory are integrated and optimized in the MDAO program Isight, which provides the user a graphical interface to link simulation inputs and outputs. This approach provides many advantages to mission planners, as it is easily adapted to different mission scenarios and can improve the understanding of the integrated system performance within a particular mission configuration. A Mars direct entry mission using the Space Launch System (SLS) is presented as a generic end-to-end case study. For the given launch period, the SLS launch performance is traded for improved orbit geometry alignment, resulting in an optimized a net payload that is comparable to that in the SLS Mission Planner's Guide.

Lugo, Rafael↗
DOE OSTI2013

Proton beam scattering system optimization for clinical and research applications

Purpose: To develop and test a method for optimizing and constructing a dual scattering system in passively scattered proton therapy. Methods: A beam optics optimization algorithm was developed to optimize the thickness of the first scatterer (S1) and the profile (of both the high-Z material and Lexan) of the second scatterer (S2) to deliver a proton beam matching a given set of parameters, including field diameter, fluence, flatness, and symmetry. A new manufacturing process was also tested that allows the contoured second scattering foil to be created much more economically and quickly using Cerrobend casting. Two application-specific scattering systems were developed and tested using both experimental and Monte Carlo techniques to validate the optimization process described. Results: A scattering system was optimized and constructed to deliver large uniform irradiations of radiobiology samples at low dose rates. This system was successfully built and tested using film and ionization chambers. The system delivered a uniform radiation field of 50 cm diameter (to a dose of {+-}7% of the central axis) while the depth dose profile could be tuned to match the specifications of the particular investigator using modulator wheels and range shifters. A second scattering system for intermediate field size (4 cm < diameter < 10 cm) stereotactic radiosurgery and radiation therapy (SRS and SRT) treatments was also developed and tested using GEANT4. This system improved beam efficiency by over 70% compared with existing scattering systems while maintaining field flatness and depth dose profile. In both cases the proton range uniformity across the radiation field was maintained, further indicating the accuracy of the energy loss formalism in the optimization algorithm. Conclusions: The methods described allow for rapid prototyping of scattering foils to meet the demands of both research and clinical beam delivery applications in proton therapy.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1972

Applications of the theory of optimal control of distributed-parameter systems to structural optimization

An extension of classical methods of optimal control theory for systems described by ordinary differential equations to distributed-parameter systems described by partial differential equations is presented. An application is given involving the minimum-mass design of a simply-supported shear plate with a fixed fundamental frequency of vibration. An optimal plate thickness distribution in analytical form is found. The case of a minimum-mass design of an elastic sandwich plate whose fundamental frequency of free vibration is fixed. Under the most general conditions, the optimization problem reduces to the solution of two simultaneous partial differential equations involving the optimal thickness distribution and the modal displacement. One equation is the uniform energy distribution expression which was found by Ashley and McIntosh for the optimal design of one-dimensional structures with frequency constraints, and by Prager and Taylor for various design criteria in one and two dimensions. The second equation requires dynamic equilibrium at the preassigned vibration frequency.

Armand, J. P.↗
NASA NTRS1983

Optimization of dish solar collectors

Methods for optimizing parabolic dish solar collectors and the consequent effects of various optical, thermal, mechanical, and cost variables are examined. The most important performance optimization is adjusting the receiver aperture to maximize collector efficiency. Other parameters that can be adjusted to optimize efficiency include focal length, and, if a heat engine is used, the receiver temperature. The efficiency maxima associated with focal length and receiver temperature are relatively broad; it may, accordingly, be desirable to design somewhat away from the maxima. Performance optimization is sensitive to the slope and specularity errors of the concentrator. Other optical and thermal variables affecting optimization are the reflectance and blocking factor of the concentrator, the absorptance and losses of the receiver, and, if a heat engine is used, the shape of the engine efficiency versus temperature curve. Performance may sometimes be improved by use of an additional optical element (a secondary concentrator) or a receiver window if the errors of the primary concentrator are large or the receiver temperature is high. Previously announced in STAR as N83-19224

Jaffe, L. D.↗
NASA NTRS1990

Differential equation based method for accurate approximations in optimization

This paper describes a method to efficiently and accurately approximate the effect of design changes on structural response. The key to this new method is to interpret sensitivity equations as differential equations that may be solved explicitly for closed form approximations, hence, the method is denoted the Differential Equation Based (DEB) method. Approximations were developed for vibration frequencies, mode shapes and static displacements. The DEB approximation method was applied to a cantilever beam and results compared with the commonly-used linear Taylor series approximations and exact solutions. The test calculations involved perturbing the height, width, cross-sectional area, tip mass, and bending inertia of the beam. The DEB method proved to be very accurate, and in msot cases, was more accurate than the linear Taylor series approximation. The method is applicable to simultaneous perturbation of several design variables. Also, the approximations may be used to calculate other system response quantities. For example, the approximations for displacement are used to approximate bending stresses.

Pritchard, Jocelyn I.↗
DOE OSTI2021

Explicit structural topology optimization using boundary element method‐based moving morphable void approach

Abstract This article presents an explicit topology optimization approach using boundary element method based moving morphable void (MMV). Structural analysis in the conventional MMV approach is performed on a fixed mesh over the entire design domain. However, in the proposed method structural boundaries are described using B‐splines and discretized with boundary elements making explicit representation of the boundaries possible. In this regard, the proposed approach has following merits: a) the use of weak material, which is often adopted to mimic voids can be completely avoided; b) exact and explicit description of voids in MMV allows the capture of tiny structural features in a robust and effective way; c) numerical instabilities stemming from the use of fixed meshes can be naturally avoided. Several numerical examples are provided to demonstrate the effectiveness of the proposed approach.

Zhang, Weisheng↗
DOE OSTI2016

Penalty-free method for multi-constrained optimization problems based on parallel simulated annealing

A method is presented for addressing constraints within the context of large-scale combinatorial optimization problems. The method, based on constraint annealing, eliminates the use of traditional constraint penalty factors by treating each constraint as separate and concurrently solved minimization problems within a global optimization search framework. Results are demonstrated for a core loading pattern optimization problem. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Toward a scalable robust security-constrained optimal power flow using a proximal projection bundle method

Robust security-constrained optimal power flow (rSCOPF) aims to find the worst-case contingencies of alternating current optimal power flow (ACOPF) in power systems. With the rise of GPU architectures on the upcoming supercomputer architectures, optimization algorithms that rely on sparse linear algebra and indefinite linear systems are becoming increasingly hard to solve efficiently (e.g. interior-point method). To address this we revisit a maximin optimization formulation of the rSCOPF and the single-level mixed-integer semidefinite programming (MISDP) reformulation, which is obtained by taking the Lagrangian relaxation of the inner minimization ACOPF problem. In this paper, we focus on the development of a proximal projection bundle method (PPBM) for solving continuous relaxation node subproblems of the MISDP problem, based primarily on the well-known alternating direction method of multipliers. Cutting planes reminiscent of bundle method ideas are also applied in coordination with updates of the proximal parameter. The cutting-plane method can generate a large number of linear inequalities, leading to a large scale but decomposable quadratic programming (QP) subproblem that is amenable to GPUs. We present the numerical results on the IEEE 30, 57, 118, and 300-bus systems by using our PBMM method. We discuss the main computational bottleneck of our method, which is the time taken to solve each iteration of a QP subproblem instance of the PPBM, and how GPU architectures can accelerate this solution process.

bundle method↗
DOE OSTI2024

Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles

Off-road vehicles, such as wheel loaders, excavators, and harvesters, are extensively utilized across a wide range of industries, including construction, agriculture, and mining. These machines have become indispensable in supporting the day-to-day operational needs of a nation, playing a critical role in various sectors' infrastructure and productivity. However, despite their utility, off-road vehicles are significant consumers of fossil fuels, resulting in substantial emissions that contribute to environmental degradation. This highlights the pressing need for research and technological advancements aimed at improving their energy efficiency and reducing their carbon footprint. There are, however, two primary challenges that must be addressed to achieve these goals. First, off-road vehicles typically perform both driving and working tasks simultaneously, which introduces a high level of complexity into their overall dynamic systems. Analysis the interactions between these functions is challenging. Second, research into off-road vehicles is inherently interdisciplinary, demanding expertise across several domains such as fluid power systems, vehicle dynamics, control theory, optimization techniques, and real-world implementation. Recognizing these challenges, we proposed the project titled "Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles" as a comprehensive solution to enhance fuel efficiency while simultaneously improving productivity. This project specifically focuses on autonomous off-road vehicles, with particular attention to wheel loaders, and seeks to develop novel methods to optimize energy consumption without sacrificing operational performance. The project integrates real-time control algorithms, vehicle dynamics modeling, and co-optimization of powertrain system and vehicle system to achieve these goals. Our optimization strategy dynamically co-optimizes critical parameters at both the powertrain and vehicle levels, including vehicle speed, working tool movements, powertrain dynamics, and engine operations in real-time. To streamline this optimization process, we developed a vehicle model that captures the key dynamics while significantly enhancing computational efficiency. This allows the system to intelligently minimize fuel consumption, all while maintaining or even improving productivity through real-time calculations during various off-road operations. To validate the effectiveness of this energy optimization method, we introduced a state-of-the-art Hardware-in-the-Loop (HIL) testbed. This reconfigurable testbed seamlessly integrates the actual engine with virtual models of the wheel loader's subsystems, allowing for accurate emulation of real-world operational loads and environments. By simulating these conditions, the HIL testbed enables us to evaluate the wheel loader’s performance under diverse working scenarios, ensuring the developed solution is applicable in real-world operations. This testbed proved to be instrumental in validating the optimization algorithms and demonstrating the system's practical effectiveness. During the evaluation and testing phase, we employed the HIL testbed to rigorously assess the energy savings and productivity improvements generated by the optimized system. The results were highly encouraging, revealing that the automated wheel loader achieved over 30% fuel savings compared to traditional, human-operated cycles, with comparable or even enhanced levels of productivity. The insights gained from this HIL-based testing provided critical validation of our approach and highlighted the potential for deploying these optimized autonomous technologies in real-world off-road vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1993

An inverse method for computation of structural stiffness distributions of aeroelastically optimized wings

An inverse method has been developed to compute the structural stiffness properties of wings given a specified wing loading and aeroelastic twist distribution. The method directly solves for the bending and torsional stiffness distribution of the wing using a modal representation of these properties. An aeroelastic design problem involving the use of a computational aerodynamics method to optimize the aeroelastic twist distribution of a tighter wing operating at maneuver flight conditions is used to demonstrate the application of the method. This exercise verifies the ability of the inverse scheme to accurately compute the structural stiffness distribution required to generate a specific aeroelastic twist under a specified aeroelastic load.

Schuster, David M.↗
DOE OSTI2015

A method of surface marker location optimization for tumor motion estimation in lung stereotactic body radiation therapy

Purpose: Accurately localizing lung tumor localization is essential for high-precision radiation therapy techniques such as stereotactic body radiation therapy (SBRT). Since direct monitoring of tumor motion is not always achievable due to the limitation of imaging modalities for treatment guidance, placement of fiducial markers on the patient’s body surface to act as a surrogate for tumor position prediction is a practical alternative for tracking lung tumor motion during SBRT treatments. In this work, the authors propose an innovative and robust model to solve the multimarker position optimization problem. The model is able to overcome the major drawbacks of the sparse optimization approach (SOA) model. Methods: The principle-component-analysis (PCA) method was employed as the framework to build the authors’ statistical prediction model. The method can be divided into two stages. The first stage is to build the surrogate tumor matrix and calculate its eigenvalues and associated eigenvectors. The second stage is to determine the “best represented” columns of the eigenvector matrix obtained from stage one and subsequently acquire the optimal marker positions as well as numbers. Using 4-dimensional CT (4DCT) and breath hold CT imaging data, the PCA method was compared to the SOA method with respect to calculation time, average prediction accuracy, prediction stability, noise resistance, marker position consistency, and marker distribution. Results: The PCA and SOA methods which were both tested were on all 11 patients for a total of 130 cases including 4DCT and breath-hold CT scenarios. The maximum calculation time for the PCA method was less than 1 s with 64 752 surface points, whereas the average calculation time for the SOA method was over 12 min with 400 surface points. Overall, the tumor center position prediction errors were comparable between the two methods, and all were less than 1.5 mm. However, for the extreme scenarios (breath hold), the prediction errors for the PCA method were not only smaller, but were also more stable than for the SOA method. Results obtained by imposing a series of random noises to the surrogates indicated that the PCA method was much more noise resistant than the SOA method. The marker position consistency tests using various combinations of 4DCT phases to construct the surrogates suggested that the marker position predictions of the PCA method were more consistent than those of the SOA method, in spite of surrogate construction. Marker distribution tests indicated that greater than 80% of the calculated marker positions fell into the high cross correlation and high motion magnitude regions for both of the algorithms. Conclusions: The PCA model is an accurate, efficient, robust, and practical model for solving the multimarker position optimization problem to predict lung tumor motion during SBRT treatments. Due to its generality, PCA model can also be applied to other imaging guidance system whichever using surface motion as the surrogates.

60 APPLIED LIFE SCIENCES↗