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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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At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Georgia Institute of Technology research on the Gas Core Actinide Transmutation Reactor (GCATR)

The program reviewed is a study of the feasibility, design, and optimization of the GCATR. The program is designed to take advantage of initial results and to continue work carried out on the Gas Core Breeder Reactor. The program complements NASA's program of developing UF6 fueled cavity reactors for power, nuclear pumped lasers, and other advanced technology applications. The program comprises: (1) General Studies--Parametric survey calculations performed to examine the effects of reactor spectrum and flux level on the actinide transmutation for GCATR conditions. The sensitivity of the results to neutron cross sections are to be assessed. Specifically, the parametric calculations of the actinide transmutation are to include the mass, isotope composition, fission and capture rates, reactivity effects, and neutron activity of recycled actinides. (2) GCATR Design Studies--This task is a major thrust of the proposed research program. Several subtasks are considered: optimization criteria studies of the blanket and fuel reprocessing, the actinide insertion and recirculation system, and the system integration. A brief review of the background of the GCATR and ongoing research is presented.

Clement, J. D.↗
NASA NTRS1986

Weight estimation of unconventional structures by structural optimization

Automated techniques are presented that are used in structural optimization technology, with emphasis on modifications of finite element models to obtain an optimal material distribution for minimum weight while satisfying the prescribed design requirements. It is anticipated that the future development of computer aided engineering (CAE) system will provide environments where structural analysis, a design optimization, and weight evaluation modules are integrated, sharing a common data base. Structural optimization capabilities obtained by integrating a finite element structural analysis program and a numerical optimization code are developed and applied to two illustrative examples: marine gear housing structural weight minimization and joined wing structures.

Miura, Hirokazu↗
NASA NTRS1973

Fabrication of aluminum-carbon composites

A screening, optimization, and evaluation program is reported of unidirectional carbon-aluminum composites. During the screening phase both large diameter monofilament and small diameter multifilament reinforcements were utilized to determine optimum precursor tape making and consolidation techniques. Difficulty was encountered in impregnating and consolidating the multifiber reinforcements. Large diameter monofilament reinforcement was found easier to fabricate into composites and was selected to carry into the optimization phase in which the hot pressing parameters were refined and the size of the fabricated panels was scaled up. After process optimization the mechanical properties of the carbon-aluminum composites were characterized in tension, stress-rupture and creep, mechanical fatigue, thermal fatigue, thermal aging, thermal expansion, and impact.

Novak, R. C.↗
DOE OSTI2020

Optimal planning of the joint placement of photovoltaic panels and green roofs under climate change uncertainty

Photovoltaic (PV) panels directly convert sunlight into electricity; but, sunlight also heats the panels, negatively impacting their efficiency. Green roofs are vegetative layers grown on rooftops, mainly to provide added insulation on the roof to save energy. Green roofs also cool near-surface air temperature. Hence, the joint installation of PV panels and green roofs may potentially lead to higher efficiency of PV panels in certain climates. We develop a two-stage stochastic programming model to optimally place PV panels and green roofs under climate change uncertainty to maximize the overall profit from energy generated and saved. We calibrate the model using the literature, industry reports, and the data from different, at times conflicting, climate projections. We then conduct a case study for a mid-size city in the U.S., perform extensive sensitivity and robustness analyses and provide insights.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Near-optimal routing of noisy quantum states

Placement, routing, and scheduling are essential tasks for near-optimal performance of programs for noisy quantum processors. Reliable execution of an arbitrary quantum circuit on current devices requires routing methods that overcome connectivity limitations while meeting data locality requirements. However, current devices also express highly variable noise levels in both the quantum gates and quantum registers. This requires any routing algorithm to be adaptive to both the circuit and the operating conditions. We demonstrate near-optimal routing methods of noisy quantum states that minimize the overall error of data movement while also limiting the computational complexity of routing decisions. We evaluate our methods against the noise characteristics of a 20-qubit superconducting quantum processor.

Sadlier III, Ronald↗
DOE OSTI2017

Modeling Road Vulnerability to Snow Using Mixed Integer Optimization

As the number and severity of snowfall events continue to grow, the need to intelligently direct road maintenance during these snowfall events will also grow. In several locations, local governments lack the resources to completely treat all roadways during snow events. Furthermore, some governments utilize only traffic data to determine which roads should be treated. As a result, many schools, businesses, and government offices must be unnecessarily closed, which directly impacts the social, educational, and economic well-being of citizens and institutions. In this work, we propose a mixed integer programming formulation to optimally allocate resources to manage snowfall on roads using meteorological, geographical, and environmental parameters. Additionally, we evaluate the impacts of an increase in budget for winter road maintenance on snow control resources.

Mixed integer optimization↗
NASA NTRS1987

Optimization of payload mass placement in a dual keel space station

In order to keep a Space Station in a stable low-Earth orbit, angular momentum storage and translational attitude control systems will have to be used. In order to minimize the size of these attitude control systems, the induced gravity gradient torque effects will have to be minimized. This can be done by minimizing the cross-products of inertia of the Station through the management of payload placement with the Station geometry. A derived and automated methodology is presented which utilizes mathematical nonlinear programming techniques. An optimal arrangement of a set of five payloads on a Dual Keel Space Station was found that minimized the cross products of inertia and thus the required controllability resources.

Ferebee, Melvin J., Jr.↗
DOE OSTI2026

Maximizing dynamic range and performance of anatase TiO 2 ECRAM through structure and programming

Here, in this study, we investigate the structure-dependent modulation characteristics of all-solid-state three-terminal electrochemical random-access memory (ECRAM) based on an anatase Li x TiO 2 channel. By directly comparing “asymmetric” and “symmetric” ECRAM device architectures, we reveal significant insight into the impact of a non-zero gate-drain open-circuit voltage and its influence on voltage vs. current-controlled gating. We also explore the impact of potentiation/depression write parameters on the symmetry, linearity, and dynamic range of the device response. Together, initial results from optimizing structure and programming approaches yielded unprecedented G max /G min ratios of >1,000 for ECRAM and hundreds of tunable memory states with excellent linearity and symmetry. Simulations based on these ECRAM devices further illustrate the promise of this analog memory technology, achieving near 2% classification error in the MNIST digit recognition benchmark for a range of training parameters compared to a theoretical best of 1.66% and outperforming other device models extracted from the literature.

AIHWKit↗
NASA NTRS2003

Nuclear Electric Vehicle Optimization Toolset (NEVOT): Integrated System Design Using Genetic Algorithms

The Nuclear Electric Vehicle Optimization Toolset (NEVOT) optimizes the design of all major Nuclear Electric Propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool uses a Genetic Algorithm (GA) search technique to combine subsystem designs and evaluate the fitness of the integrated design to fulfill a mission. The fitness of an individual is used within the GA to determine its probability of survival through successive generations in which the designs with low fitness are eliminated and replaced with combinations or mutations of designs with higher fitness. The program can find optimal solutions for different sets of fitness metrics without modification and can create and evaluate vehicle designs that might never be conceived of through traditional design techniques. It is anticipated that the flexible optimization methodology will expand present knowledge of the design trade-offs inherent in designing nuclear powered space vehicles and lead to improved NEP designs.

Tinker, Michael L.↗
NASA NTRS2004

Nuclear Electric Vehicle Optimization Toolset (NEVOT)

The Nuclear Electric Vehicle Optimization Toolset (NEVOT) optimizes the design of all major nuclear electric propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool uses a genetic algorithm (GA) search technique to combine subsystem designs and evaluate the fitness of the integrated design to fulfill a mission. The fitness of an individual is used within the GA to determine its probability of survival through successive generations in which the designs with low fitness are eliminated and replaced with combinations or mutations of designs with higher fitness. The program can find optimal solutions for different sets of fitness metrics without modification and can create and evaluate vehicle designs that might never be considered through traditional design techniques. It is anticipated that the flexible optimization methodology will expand present knowledge of the design trade-offs inherent in designing nuclear powered space vehicles and lead to improved NEP designs.

Tinker, Michael L.↗
NASA NTRS2011

Reusable Solid Rocket Motor - Accomplishments, Lessons, and a Culture of Success

The Reusable Solid Rocket Motor represents the largest solid rocket motor ever flown and the only human rated solid motor. Each Reusable Solid Rocket Motor (RSRM) provides approximately 3-million lb of thrust to lift the integrated Space Shuttle vehicle from the launch pad. The motors burn out approximately 2 minutes later, separate from the vehicle and are recovered and refurbished. The size of the motor and the need for high reliability were challenges. Thrust shaping, via shaping of the propellant grain, was needed to limit structural loads during ascent. The motor design evolved through several block upgrades to increase performance and to increase safety and reliability. A major redesign occurred after STS-51L with the Redesigned Solid Rocket Motor. Significant improvements in the joint sealing systems were added. Design improvements continued throughout the Program via block changes with a number of innovations including development of low temperature o-ring materials and incorporation of a unique carbon fiber rope thermal barrier material. Recovery of the motors and post flight inspection improved understanding of hardware performance, and led to key design improvements. Because of the multidecade program duration material obsolescence was addressed, and requalification of materials and vendors was sometimes needed. Thermal protection systems and ablatives were used to protect the motor cases and nozzle structures. Significant understanding of design and manufacturing features of the ablatives was developed during the program resulting in optimization of design features and processing parameters. The project advanced technology in eliminating ozone-depleting materials in manufacturing processes and the development of an asbestos-free case insulation. Manufacturing processes for the large motor components were unique and safety in the manufacturing environment was a special concern. Transportation and handling approaches were also needed for the large hardware segments. The reusable solid rocket motor achieved significant reliability via process control, ground test programs, and postflight assessment. Process control is mandatory for a solid rocket motor as an acceptance test of the delivered product is not feasible. Process control included process failure modes and effects analysis, statistical process control, witness panels, and process product integrity audits. Material controls and inspections were maintained throughout the sub tier vendors. Material fingerprinting was employed to assess any drift in delivered material properties. The RSRM maintained both full scale and sub-scale test articles. These enabled continuous improvement of design and evaluation of process control and material behavior. Additionally RSRM reliability was achieved through attention to detail in post flight assessment to observe any shift in performance. The postflight analysis and inspections provided invaluable reliability data as it enables observation of actual flight performance, most of which would not be available if the motors were not recovered. These unique challenges, features of the reusable solid rocket motor, materials and manufacturing issues, and design improvements will be discussed in the paper.

Moore, Dennis R.↗
DOE OSTI2019

Controlling risk and demand ambiguity in newsvendor models

We use distributionally robust optimization (DRO) to model a general class of newsvendor problems with unknown demand distribution. The goal is to find an order quantity that minimizes the worst-case expected cost among an ambiguity set of distributions. The ambiguity set consists of those distributions that are not far—in the sense of the total variation distance—from a nominal distribution. The maximum distance allowed in the ambiguity set (called level of robustness) places the DRO between the risk-neutral stochastic programming and robust optimization models. An important problem a decision maker faces is how to determine the level of robustness—or, equivalently, how to find an appropriate level of risk-aversion. We answer this question in two ways. Our first approach relates the level of robustness and risk to the regions of demand that are critical (in a precise sense we introduce) to the optimal cost. Our second approach establishes new quantitative relationships between the DRO model and the corresponding risk-neutral and classical robust optimization models. To achieve these goals, we first focus on a single-product setting and derive explicit formulas and properties of the optimal solution as a function of the level of robustness. Then, we demonstrate the practical and managerial relevance of our results by applying our findings to a healthcare problem to reserve operating room time for cardiovascular surgeries. Finally, we extend some of our results to the multi-product setting and illustrate them numerically.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Automated property optimization via ab initio O(N) elongation method: Application to (hyper-)polarizability in DNA

An automated property optimization method was developed based on the ab initio O(N) elongation (ELG) method and applied to the optimization of nonlinear optical (NLO) properties in DNA as a first test. The ELG method mimics a polymerization reaction on a computer, and the reaction terminal of a starting cluster is attacked by monomers sequentially to elongate the electronic structure of the system by solving in each step a limited space including the terminal (localized molecular orbitals at the terminal) and monomer. The ELG-finite field (ELG-FF) method for calculating (hyper-)polarizabilities was used as the engine program of the optimization method, and it was found to show linear scaling efficiency while maintaining high computational accuracy for a random sequenced DNA model. Furthermore, the self-consistent field convergence was significantly improved by using the ELG-FF method compared with a conventional method, and it can lead to more feasible NLO property values in the FF treatment. The automated optimization method successfully chose an appropriate base pair from four base pairs (A, T, G, and C) for each elongation step according to an evaluation function. From test optimizations for the first order hyper-polarizability (β) in DNA, a substantial difference was observed depending on optimization conditions between “choose-maximum” (choose a base pair giving the maximum β for each step) and “choose-minimum” (choose a base pair giving the minimum β). In contrast, there was an ambiguous difference between these conditions for optimizing the second order hyper-polarizability (γ) because of the small absolute value of γ and the limitation of numerical differential calculations in the FF method. It can be concluded that the ab initio level property optimization method introduced here can be an effective step towards an advanced computer aided material design method as long as the numerical limitation of the FF method is taken into account.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Performance optimization of helicopter rotor blades

As part of a center-wide activity at NASA Langley Research Center to develop multidisciplinary design procedures by accounting for discipline interactions, a performance design optimization procedure is developed. The procedure optimizes the aerodynamic performance of rotor blades by selecting the point of taper initiation, root chord, taper ratio, and maximum twist which minimize hover horsepower while not degrading forward flight performance. The procedure uses HOVT (a strip theory momentum analysis) to compute the horse power required for hover and the comprehensive helicopter analysis program CAMRAD to compute the horsepower required for forward flight and maneuver. The optimization algorithm consists of the general purpose optimization program CONMIN and approximate analyses. Sensitivity analyses consisting of derivatives of the objective function and constraints are carried out by forward finite differences. The procedure is applied to a test problem which is an analytical model of a wind tunnel model of a utility rotor blade.

Walsh, Joanne L.↗
DOE OSTI2016

Discrete Time McKean–Vlasov Control Problem: A Dynamic Programming Approach

We consider the stochastic optimal control problem of nonlinear mean-field systems in discrete time. We reformulate the problem into a deterministic control problem with marginal distribution as controlled state variable, and prove that dynamic programming principle holds in its general form. We apply our method for solving explicitly the mean-variance portfolio selection and the multivariate linear-quadratic McKean–Vlasov control problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Adiabatic quantum optimization for associative memory recall

Hopfield networks are a variant of associative memory that recall patterns stored in the couplings of an Ising model. Stored memories are conventionally accessed as fixed points in the network dynamics that correspond to energetic minima of the spin state. We show that memories stored in a Hopfield network may also be recalled by energy minimization using adiabatic quantum optimization (AQO). Numerical simulations of the underlying quantum dynamics allow us to quantify AQO recall accuracy with respect to the number of stored memories and noise in the input key. We investigate AQO performance with respect to how memories are stored in the Ising model according to different learning rules. Our results demonstrate that AQO recall accuracy varies strongly with learning rule, a behavior that is attributed to differences in energy landscapes. Consequently, learning rules offer a family of methods for programming adiabatic quantum optimization that we expect to be useful for characterizing AQO performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Data-Driven Generic Turbines for Distributed Wind Modeling, Optimization, and Economic Studies

As distributed energy resources (DER) become less expensive and more popular, utilities, project developers, and customers have an increasing need to model the performance of existing and proposed DER systems. Distributed wind has been shown to have widespread economic potential but is often represented by a simplified model in - or excluded from - DER modeling tools and studies. There is often no economic imperative to extend models and studies to give full consideration to distributed wind. We present a set of data-driven generic turbines derived from 16 years of annual distributed wind market survey data. The proposed methodology can be used to derive generic turbines from separate or updated data sets. Finally, a mixed-integer linear programming approach to optimal distributed wind project sizing is used to demonstrate the generic turbine models. Combined, these models and methods can reduce barriers to considering distributed wind in modeling tools and studies.

Reiman, Andrew P.↗