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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Polymorphic Electronic Circuits

Polymorphic electronics is a nascent technological discipline that involves, among other things, designing the same circuit to perform different analog and/or digital functions under different conditions. For example, a circuit can be designed to function as an OR gate or an AND gate, depending on the temperature (see figure). Polymorphic electronics can also be considered a subset of polytronics, which is a broader technological discipline in which optical and possibly other information- processing systems could also be designed to perform multiple functions. Polytronics is an outgrowth of evolvable hardware (EHW). The basic concepts and some specific implementations of EHW were described in a number of previous NASA Tech Briefs articles. To recapitulate: The essence of EHW is to design, construct, and test a sequence of populations of circuits that function as incrementally better solutions of a given design problem through the selective, repetitive connection and/or disconnection of capacitors, transistors, amplifiers, inverters, and/or other circuit building blocks. The evolution is guided by a search-and-optimization algorithm (in particular, a genetic algorithm) that operates in the space of possible circuits to find a circuit that exhibits an acceptably close approximation of the desired functionality. The evolved circuits can be tested by computational simulation (in which case the evolution is said to be extrinsic), tested in real hardware (in which case the evolution is said to be intrinsic), or tested in random sequences of computational simulation and real hardware (in which case the evolution is said to be mixtrinsic).

Stoica, Adrian↗
NASA NTRS1972

Algebraic, geometric, and stochastic aspects of genetic operators

Genetic algorithms for function optimization employ genetic operators patterned after those observed in search strategies employed in natural adaptation. Two of these operators, crossover and inversion, are interpreted in terms of their algebraic and geometric properties. Stochastic models of the operators are developed which are employed in Monte Carlo simulations of their behavior.

Foo, N. Y.↗
NASA NTRS2019

On the Use of Optimization Techniques for Turbulence Model Calibration

This article discusses the use of numerical optimization procedures to aid in the calibration of turbulence model coefficients. Such methods would increase the rigor and repeatability of the calibration procedure by requiring clearly defined and objective optimization metrics, and could be used to identify unique combinations of coefficient values for specific flow problems. The approach is applied to the re-calibration of an explicit algebraic Reynolds stress model for the incompressible planar mixing layer using the Nelder-Mead simplex algorithm and a micro-genetic algorithm with minimally imposed constraints. Three composite fitness functions, each based upon the error in the mixing layer growth rate and the normal and shear components of the Reynolds stresses, are investigated. The results demonstrate a significant improvement in the target objectives through the adjustment of three pressure-strain coefficients. Adjustments of additional coefficients provide little further benefit. Issues regarding the effectiveness of the fitness functions and the efficiency of the optimization algorithms are also discussed.

Yoder, Dennis A.↗
NASA NTRS2004

An Evolved Antenna for Deployment on NASA's Space Technology 5 Mission

We present an evolved X - band antenna design and flight prototype currently on schedule to be deployed on NASA's Space Technology 5(ST5) spacecraft. Current methods of designing and optimizing antennas by hand are time and labor intensive, limit complexity and require significant expertise and experience. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions that would ordinarily not be found. The ST5 antenna was evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly-polarized wave and wide bandwidth.Two evolutionary algorithms were used: one used a genetic algorithm style representation that did not allow branching in the antenna arms: the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested and both yielded similar performance. Both were comparable in performance to a hand-designed antenna produced by a contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. One of the evolved antenna prototypes is undergoing flight qualification testing.

Lohn, Jason D.↗
NASA NTRS1996

Optimization Methodology for Unconventional Rocket Nozzle Design

Several current rocket engine concepts such as the bell-annular tripropellant engine, and the linear aerospike being proposed for the X-33, require unconventional three-dimensional rocket nozzles which must conform to rectangular or sector-shaped envelopes to meet integration constraints. These types of nozzles exist outside the current experience database, therefore, development of efficient design methods for these propulsion concepts is critical to the success of launch vehicle programs. Several approaches for optimizing rocket nozzles, including streamline tracing techniques, and the coupling of CFD analysis to optimization algorithms are described. The relative strengths and weaknesses of four classes of optimization algorithms are discussed: Gradient based methods, genetic algorithms, simplex methods, and surface response methods. Additionally, a streamline tracing technique, which provides a very computationally efficient means of defining a three-dimensional contour, is discussed. The performance of the various optimization methods on thrust optimization problems for tripropellant and aerospike concepts is assessed and recommendations are made for future development efforts.

Follett, W.↗
NASA NTRSDate not supplied

Implementation of Combinatorial Optimization Techniques for Automated Fiber Placement Through Thickness Defect Stack-Up Minimization

The Computer Aided Process Planning (CAPP) module was developed to facilitate and accelerate the process planning workflow for Automated Fiber Placement (AFP). CAPP assists process planners in identifying optimal starting point locations and layup strategies for each ply of a laminate. Ply optimization operates on measurement and scoring of geometry-based defects such as gaps, overlaps, angle deviation, and steering. This paper expands on the established framework for analyzing defect stack-up through thickness of a laminate. Four different combinatorial optimization algorithms are implemented and evaluated: (1) genetic algorithm, (2) differential evolution, (3) particle swarm, and (4) greedy search. The algorithms identify the optimal combination of ply-level layup strategies, by scoring potential laminates on defect stacking, using two different objective functions. A final optimization approach is also presented which trades some performance for a large gain in efficiency. These approaches are compared to a randomized combination using a complex tool surface in a virtual case study. The result is a streamlined methodology for comparing different laminate-level manufacturing strategies and minimizing the through thickness defect stack up.

CAPP↗
NASA NTRS2005

Trade Studies for a Manned High-Power Nuclear Electric Propulsion Vehicle

Nuclear electric propulsion (NEP) vehicles will be needed for future manned missions to Mars and beyond. Candidate vehicles must be identified through trade studies for further detailed design from a large array of possibilities. Genetic algorithms have proven their utility in conceptual design studies by effectively searching a large design space to pinpoint unique optimal designs. This research combines analysis codes for NEP subsystems with genetic algorithm-based optimization. Trade studies for a NEP reference mission to the asteroids were conducted to identify important trends, and to determine the effects of various technologies and subsystems on vehicle performance. It was found that the electric thruster type and thruster performance have a major impact on the achievable system performance, and that significant effort in thruster research and development is merited.

SanSoucie, Michael↗
NASA NTRS2010

Particle Swarm Optimization Toolbox

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.

Grant, Michael J.↗
NASA NTRS2016

Route Optimization for Offloading Congested Meter Fixes

The Optimized Route Capability (ORC) concept proposed by the FAA facilitates traffic managers to identify and resolve arrival flight delays caused by bottlenecks formed at arrival meter fixes when there exists imbalance between arrival fixes and runways. ORC makes use of the prediction capability of existing automation tools, monitors the traffic delays based on these predictions, and searches the best reroutes upstream of the meter fixes based on the predictions and estimated arrival schedules when delays are over a predefined threshold. Initial implementation and evaluation of the ORC concept considered only reroutes available at the time arrival congestion was first predicted. This work extends previous work by introducing an additional dimension in reroute options such that ORC can find the best time to reroute and overcome the 'firstcome- first-reroute' phenomenon. To deal with the enlarged reroute solution space, a genetic algorithm was developed to solve this problem. Experiments were conducted using the same traffic scenario used in previous work, when an arrival rush was created for one of the four arrival meter fixes at George Bush Intercontinental Houston Airport. Results showed the new approach further improved delay savings. The suggested route changes from the new approach were on average 30 minutes later than those using other approaches, and fewer numbers of reroutes were required. Fewer numbers of reroutes reduce operational complexity and later reroutes help decision makers deal with uncertain situations.

Optimized Route Capability↗
NASA NTRS1974

Study of genetic direct search algorithms for function optimization

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

Zeigler, B. P.↗
NASA NTRS2012

Optimal Integration of Departure and Arrivals in Terminal Airspace

Coordination of operations with spatially and temporally shared resources such as route segments, fixes, and runways improves the efficiency of terminal airspace management. Problems in this category include scheduling and routing, thus they are normally difficult to solve compared with pure scheduling problems. In order to reduce the computational time, a fast time algorithm formulation using a non-dominated sorting genetic algorithm (NSGA) was introduced in this work and applied to a test case based on existing literature. The experiment showed that new method can solve the whole problem in fast time instead of solving sub-problems sequentially with a window technique. The results showed a 60% or 406 second delay reduction was achieved by sharing departure fixes (more details on the comparison with MILP results will be presented in the final paper). Furthermore, the NSGA algorithm was applied to a problem in LAX terminal airspace, where interactions between 28% of LAX arrivals and 10% of LAX departures are resolved by spatial segregation, which may introduce unnecessary delays. In this work, spatial segregation, temporal segregation, and hybrid segregation were formulated using the new algorithm. Results showed that spatial and temporal segregation approaches achieved similar delay. Hybrid segregation introduced much less delay than the other two approaches. For a total of 9 interacting departures and arrivals, delay reduction varied from 4 minutes to 6.4 minutes corresponding flight time uncertainty from 0 to 60 seconds. Considering the amount of flights that could be affected, total annual savings with hybrid segregation would be significant.

high density terminal↗
NASA NTRS2013

Optimal Integration of Departures and Arrivals in Terminal Airspace

Coordination of operations with spatially and temporally shared resources, such as route segments, fixes, and runways, improves the efficiency of terminal airspace management. Problems in this category are, in general, computationally difficult compared to conventional scheduling problems. This paper presents a fast time algorithm formulation using a non-dominated sorting genetic algorithm (NSGA). It was first applied to a test problem introduced in existing literature. An experiment with a test problem showed that new methods can solve the 20 aircraft problem in fast time with a 65% or 440 second delay reduction using shared departure fixes. In order to test its application in a more realistic and complicated problem, the NSGA algorithm was applied to a problem in LAX terminal airspace, where interactions between 28% of LAX arrivals and 10% of LAX departures are resolved by spatial separation in current operations, which may introduce unnecessary delays. In this work, three types of separations - spatial, temporal, and hybrid separations - were formulated using the new algorithm. The hybrid separation combines both temporal and spatial separations. Results showed that although temporal separation achieved less delay than spatial separation with a small uncertainty buffer, spatial separation outperformed temporal separation when the uncertainty buffer was increased. Hybrid separation introduced much less delay than both spatial and temporal approaches. For a total of 15 interacting departures and arrivals, when compared to spatial separation, the delay reduction of hybrid separation varied between 11% or 3.1 minutes and 64% or 10.7 minutes corresponding to an uncertainty buffer from 0 to 60 seconds. Furthermore, as a comparison with the NSGA algorithm, a First-Come-First-Serve based heuristic method was implemented for the hybrid separation. Experiments showed that the results from the NSGA algorithm have 9% to 42% less delay than the heuristic method with varied uncertainty buffer sizes.

terminal airspace↗
NASA NTRS2015

Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

Spacecraft Systems↗
NASA NTRS1999

Co-evolution for Problem Simplification

This paper explores a co-evolutionary approach applicable to difficult problems with limited failure/success performance feedback. Like familiar "predator-prey" frameworks this algorithm evolves two populations of individuals - the solutions (predators) and the problems (prey). The approach extends previous work by rewarding only the problems that match their difficulty to the level of solut,ion competence. In complex problem domains with limited feedback, this "tractability constraint" helps provide an adaptive fitness gradient that, effectively differentiates the candidate solutions. The algorithm generates selective pressure toward the evolution of increasingly competent solutions by rewarding solution generality and uniqueness and problem tractability and difficulty. Relative (inverse-fitness) and absolute (static objective function) approaches to evaluating problem difficulty are explored and discussed. On a simple control task, this co-evolutionary algorithm was found to have significant advantages over a genetic algorithm with either a static fitness function or a fitness function that changes on a hand-tuned schedule.

Haith, Gary L.↗
NASA NTRS2004

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

Source record↗