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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.↗
NASA NTRS2005

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.↗
NASA NTRS2008

Genetic Algorithm Optimizes Q-LAW Control Parameters

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Lee, Seungwon↗
NASA NTRS2010

Reducing the Volume of NASA Earth-Science Data

A computer program reduces data generated by NASA Earth-science missions into representative clusters characterized by centroids and membership information, thereby reducing the large volume of data to a level more amenable to analysis. The program effects an autonomous data-reduction/clustering process to produce a representative distribution and joint relationships of the data, without assuming a specific type of distribution and relationship and without resorting to domain-specific knowledge about the data. The program implements a combination of a data-reduction algorithm known as the entropy-constrained vector quantization (ECVQ) and an optimization algorithm known as the differential evolution (DE). The combination of algorithms generates the Pareto front of clustering solutions that presents the compromise between the quality of the reduced data and the degree of reduction. Similar prior data-reduction computer programs utilize only a clustering algorithm, the parameters of which are tuned manually by users. In the present program, autonomous optimization of the parameters by means of the DE supplants the manual tuning of the parameters. Thus, the program determines the best set of clustering solutions without human intervention.

Lee, Seungwon↗
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 NTRS2014

A Comparison of Metallic, Composite and Nanocomposite Optimal Transonic Transport Wings

Current and future composite material technologies have the potential to greatly improve the performance of large transport aircraft. However, the coupling between aerodynamics and structures makes it challenging to design optimal flexible wings, and the transonic flight regime requires high fidelity computational models. We address these challenges by solving a series of high-fidelity aerostructural optimization problems that explore the design space for the wing of a large transport aircraft. We consider three different materials: aluminum, carbon-fiber reinforced composites and an hypothetical composite based on carbon nanotubes. The design variables consist of both aerodynamic shape (including span), structural sizing, and ply angle fractions in the case of composites. Pareto fronts with respect to structural weight and fuel burn are generated. The wing performance in each case is optimized subject to stress and buckling constraints. We found that composite wings consistently resulted in lower fuel burn and lower structural weight, and that the carbon nanotube composite did not yield the increase in performance one would expect from a material with such outstanding properties. This indicates that there might be diminishing returns when it comes to the application of advanced materials to wing design, requiring further investigation.

Kennedy, Graeme J.↗
NASA NTRS2014

Material and Thickness Grading for Aeroelastic Tailoring of the Common Research Model Wing Box

This work quantifies the potential aeroelastic benefits of tailoring a full-scale wing box structure using tailored thickness distributions, material distributions, or both simultaneously. These tailoring schemes are considered for the wing skins, the spars, and the ribs. Material grading utilizes a spatially-continuous blend of two metals: Al and Al+SiC. Thicknesses and material fraction variables are specified at the 4 corners of the wing box, and a bilinear interpolation is used to compute these parameters for the interior of the planform. Pareto fronts detailing the conflict between static aeroelastic stresses and dynamic flutter boundaries are computed with a genetic algorithm. In some cases, a true material grading is found to be superior to a single-material structure.

Stanford, Bret K.↗
NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters will be added to the decision space of the GA, and specify the range of desired values for each optimization parameter. For performance objectives, the user is presented with a list of available coverage and revisit performance based calculation options from which two metrics are chosen to serve as the objective functions that the GA will use to evaluate solutions during the optimization process. Finally, the definition of the GA parameters provides user control over the number of generations (number of optimization iterations), the population size (number of candidate constellations created in each generation), and the adaptive mutation and crossover threshold values (control parameters for how frequently each process occurs during the optimization). GenETOC has been extensively tested to verify the individual components of the optimization process. The GA has been tested against a suite of GA test problems to confirm convergence to the known two and three-dimensional Pareto fronts. The coverage and revisit performance metrics obtained in GenETOC are compared with STK desktop scenarios, confirming the constellations are being appropriately modeled within GenETOC simulations. A walkthrough of a simple, example problem is provided to illustrate the workings of GenETOC and to demonstrate the output available to the mission designer.

mission design↗
NASA NTRSDate not supplied

Efficient Global Optimization with Gradient Finish for Design Under Uncertainty

The Efficient Global Optimization (EGO) algorithm is extended to include a gradient-descent-based finish upon reaching a threshold value of the expected improvement function. Emphasis is placed on efficient evaluation of local gradients using Kriging models during the gradient-based finish to enable application to design under uncertainty (DUU) problems. The modified algorithm is applied to both the well-known Rosenbrock function and a more challenging hypersonic inlet design under uncertainty problem. Results demonstrate improvement in locating the global optimum compared to the classical implementation of EGO, as well as a reduced number of true function evaluations compared to pure gradient-based algorithms. For the Rosenbrock function, a global optimum is returned using an average of 12% fewer function calls than a gradient-based optimizer with comparable tolerances. For the design under uncertainty problem, a global optimum is found using an average of 75% fewer function calls than a gradient-based optimizer. Global Pareto fronts of multiobjective DUU problems are obtained at little additional cost after a single optimization is complete.

Optimization↗
NASA NTRSDate not supplied

Multi-Objective Optimal Control of High Aspect Ratio Wing Wind Tunnel Model

This paper presents the design of a multi-objective optimal controller for the 13.5 aspect ratio Common Research Model (CRM) wind tunnel model. A Pareto front analysis is performed to provide the optimal tradeoff between the competing objectives - gust load alleviation, maneuver load alleviation, and drag minimization - so that the weighting matrices for the control law are selected optimally. Also presented are two formulations of the controller: a state feedback with Kalman filter design, and an output feedback form where sensors are used directly for feedback control. The controller demonstrates a reduced peak-to-peak strain of 45.78% while also reducing drag and mean bending moment. An output feedback formulation for gust load alleviation is formulated and produces a 53% reduction in the strain response due to a gust input.

Gust Load Alleviation↗
NASA NTRSDate not supplied

Conclusions of a Cryogenic Motor Configuration Study for Aircraft Propulsion

Cryogenic electric machines are a promising technology for multi megawatt electric aircraft drivetrains. Superconducting stators that make use of low AC loss superconducting wires are considered to be a key component for these cryogenic machines. This paper shares the lessons learned from a design study that evaluated several configurations of a 5 MW cryogenic motor for this application. The lessons are drawn from a qualitative study of 16 configurations and a quantitative study of 8 down selected configurations. The quantitative study involved using analytical calculations (electromagnetic, thermofluid, mechanical, and motor sizing) to determine each configuration’s Pareto front of total efficiency and total specific power. The conclusions from a preliminary assessment of different stator conductors (superconductors and cryogenic metals) are also presented. One motor configuration is down selected for future detailed design and demonstration at the multi megawatt level.

Electrified aircraft propulsion↗
NASA NTRSDate not supplied

Motor Configuration Selection for A New Technical Challenge to Develop A 5 MW Cryogenic Motor and Drive

Due to aviation’s appreciable and growing share of humanity’s impact on our environment and estimates that CO2emissions only account for 34% of aviation’s total effective radiative forcing [1], there is a need to reach beyond climate goals that focus only on CO2emissions, such as the US Aviation Climate Action Plan’s [3] goal to reach net-zero carbon emissions by 2050.There is motivation to develop technology that pushes toward future large transport aircraft with net zero climate impact that are highly electrified (i.e., have higher power electrical propulsion system components). This paper describes a new, 6-year technical challenge to address this need by developing a 5 MW superconducting motor and cryogenic drive. Section 1 will detail the motivation for this work. Section 2 will describe the technical challenge and the selected specifications for the motor. Section 3 will present the results of a motor configuration trade study and the down selection of one configuration to develop a detailed design for. The technical challenge focuses on the design of a5 MW superconducting motor and cryogenic drive and demonstration of it at a 2+ MW scale to achieve TRL 3. Both fully superconducting (superconducting stator and rotor) and fully cryogenic (superconducting rotor and cryogenic stator) machine configurations will be explored. An emphasis will be placed on addressing the key tall poles for high power superconducting machines. Further details will be included in the full paper. The requirements and goals of the motor will be detailed. The rated speed (2,000 to 3,000 rpm) is defined to be appropriate for directly driving multi-MW fans or propellers. A range of rated speed is permitted because the motor is not designed for a specific aircraft and to provide design flexibility if AC losses in the stator winding are found to be a significant constraint (i.e., a lower speed can be selected to reduce electrical frequency). Relatively conservative requirements for efficiency (99%) and specific power (20 kW/kg) are defined, because TRL advancement and pushing toward flight readiness is emphasized over performance optimization. However, more aggressive efficiency and specific power goals are specified (99.9% and 40 kW/kg). The 3rd section will present the results of a motor configuration trade study. The study started with a qualitative assessment of sixteen motor configurations based on geometric, mechanical, thermal, and electromagnetic criteria. This assessment has been completed with three evaluators scoring all nine criteria. A configuration down select was made by prioritizing the sixteen configurations into four tiers based on each configuration’s total score and consideration of manufacturability, complexity, and support hardware (e.g., rotary vacuum seals, bearings). Configurations in priority A and B will be further evaluated through quantitative assessments, whereas those in priority C will only be further evaluated if time permits and priority D will not be further evaluated. Eight of the sixteen configurations were down selected for quantitative assessment, which will include analytical calculations and low-to moderate-fidelity finite element analysis to produce a preliminary Pareto front of efficiency versus specific power for each configuration. This assessment emphasizes the calculation of AC losses in the stator winding and an exploration of thermal management approaches to remove that heat and maintain cryogenic temperature. The final paper will include a description of each motor configuration that was considered. The quantitative assessments are underway, and an assessment of one configuration is complete for multiple stator conductor options. The remaining assessments are scheduled to be completed by late March so that the final down select to one configuration can be included in this paper.

Net Zero↗
NASA NTRSDate not supplied

Lessons Learned From A Cryogenic Motor Configuration Study for Aircraft Propulsion

Cryogenic electric machines are a promising technology for multi megawatt electric aircraft drivetrains. Superconducting stators that make use of low AC loss superconducting wires are considered to be a key component for these cryogenic machines. NASA has undertaken 6-year technical challenge to address this need by designing a 5 MW superconducting motor and cryogenic drive and developing multi-MW prototypes. This poster shares the lessons learned from a completed design study that evaluated several configurations of a 5 MW cryogenic motor for this application. The lessons are drawn from a qualitative study of 16 configurations and a quantitative study of 8 down selected configurations. The quantitative study involved using a motor design code based on analytical calculations (electromagnetic, thermofluid, mechanical, and motor sizing) to determine each configuration’s Pareto front of total efficiency and total specific power. This poster builds upon preliminary results that were previously presented by (1) reporting final results based on a revised design code for each configuration, including a considerable increase in fidelity for a novel radial flux motor and (2) making a down selection of the configuration for future detailed design and demonstration and documenting the rationale for the selection.

Superconductors↗
DOE OSTI2021

Design and Optimization of a Gas-Cooled, Airfoil Fin Microchannel Heat Exchanger

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗
DOE OSTI2022

Multi-Objective Cycle Optimization of an Integrally Geared Waste Heat Recovery Unit for a Combined Cycle Power System

This paper has presented the cycle design and optimization details for a sCO2-based WHRS targeting the Solar Turbines Titan 130. The PreheatSR cycle layout was chosen to effectively address the issue of acid dew point corrosion and ensure high system performance is not significantly impacted by use of alternative fuels. The optimization process discussed uses a multi-objective optimization to discover a series of optimal cycle configurations given ambient temperature variability for a chosen site location while considering the initial capital cost of the cycle components. Cycle models built that incorporated off-design methods for the heat exchangers and turbomachinery allowed for the investigation of cycle operation that maximizes power output for individual cycle conditions. The resulting Pareto front serves as a guide for how to configure the WHRS cycle for the highest yearly energy extracted for a given investment.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2002

Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization

We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.

Lohn, Jason D.↗
DOE OSTI2026

Scaling considerations for supercritical carbon dioxide cycles including turbomachinery loss models

A modeling framework for the supercritical carbon dioxide recompressed closed Brayton cycle was developed. Unlike typical models, this effort incorporated generalized empirical turbomachinery loss models. Aerodynamic, windage, and leakage losses were considered in order to address the limitations of conventional constant-efficiency turbomachinery assumptions without relying on machine-specific or computationally expensive simulations. The model enables system-level exploration of optimal cycle design across a range of power scales, including smaller scales that are relevant to microreactors and extraterrestrial power applications. Parametric studies and multi-objective optimizations are used to evaluate the trade-offs between thermal efficiency and system compactness based on an analytical heat exchanger scaling model, yielding Pareto-optimal fronts across a range of operating pressures. Results reveal that at small power scales, the Pareto-optimal compressor inlet pressure becomes subcritical due to the increasing influence of density-dependent turbomachinery losses. Here, the relative contributions of each loss mechanism are quantified, and design recommendations are provided for key parameters such as recompression split ratio and generator cavity pressure across varying power scales.

Multi-objective optimization↗
DOE OSTI2022

Pareto Optimization of Oligomer Polarizability and Dipole Moment Using a Genetic Algorithm

High-performance electronic components are highly sought after in order to produce increasingly smaller and cheaper electronic devices. Drawing inspiration from inorganic dielectric materials, in which both polarizability and polarization contribute, organic materials can also maximize both. For a large set of small molecules drawn from PubChem, a Pareto-like front appears between the polarizability and dipole moment, indicating the presence of an apparent trade-off between these two properties. We tested this balance in π-conjugated materials by searching for novel conjugated hexamers with simultaneously large polar- izabilities and dipole moments with potential use for dielectric materials. Using a genetic algorithm (GA) screening technique in conjunction with an approximate density functional tight-binding method for property calculations, we were able to efficiently search chemical space for optimal hexamers. Given the scope of chemical space, using the GA technique saves considerable time and resources by speeding up molecular searches compared to a systematic search. Here, we also explored the underlying structure–function relationships, including sequence and monomer properties, that characterize large polarizability and dipole moment regimes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗