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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
DOE OSTI2015

Optimization of the magnetic horn for the nuSTORM non-conventional neutrino beam using the genetic algorithm

This paper describes the strategy for optimizing the magnetic horn for the neutrinos from STORed Muons (nuSTORM) facility. The nuSTORM magnetic horn is the primary collection device for the secondary particles generated by bombarding a solid target with 120 GeV protons. As a consequence of the non-conventional beamline designed for nuSTORM, the requirements on the horn are different from those for a conventional neutrino beamline. At nuSTORM, muons decay while circulating in the storage ring, and the detectors are placed downstream of the production straight so as to be exposed to the neutrinos from muon decay. nuSTORM aims at precisely measuring the neutrino cross sections, and providing a definitive statement about the existence of sterile neutrinos. The nuSTORM horn aims at focusing the pions into a certain phase space so that more muons from pion decay can be accepted by the decay ring. The paper demonstrates a numerical method that was developed to optimize the horn design to gain higher neutrino flux from the circulating muons. A Genetic Algorithm (GA) was applied to the simultaneous optimization of the two objectives in this study. In conclusion, the application of the technique discussed in this paper is not limited to either the nuSTORM facility or muon based facilities, but can be used for other neutrino facilities that use magnetic horns as collection devices.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Artificial intelligence based analysis of nanoindentation load–displacement data using a genetic algorithm

In this work, we developed an automated tool, Nanoindentation Neo package for the analysis of nanoindentation load–displacement curves using a Genetic Algorithm (GA) applied to the Oliver-Pharr method (Oliver et al.,1992). For some materials, such as polycrystalline isotropic graphites, Least Squares Fitting (LSF) of the unload curve can produce unrealistic fit parameters. These graphites exhibit sharply peaked unloading curves not easily fit using the LSF, which tends to overestimate the indenter tip geometry parameter. To tackle this problem, we extended our general materials characterization tool Neo for EXAFS analysis (Terry et al., 2021) to fit nanoindentation data. Nanoindentation Neo automatically processes and analyzes nanoindentation data with minimal user input while producing meaningful fit parameters. GA, a robust metaheuristic method, begins with a population of temporary solutions using model parameters called chromosomes; from these we evaluate a fitness value for each solution, and select the best solutions to mix with random solutions producing the next generation. A mutation operator then modifies existing solutions by random perturbations, and the optimal solution is selected. We tested the GA method using Silica and Al reference standards. We fit samples of graphite and a high entropy alloy (HEA) consisting of BCC and FCC phases.

42 ENGINEERING↗
DOE OSTI2018

Application of Genetic Algorithm (GA) Assisted Partial Least Square (PLS) Analysis on Trilinear and Non-trilinear Fluorescence Data Sets to Quantify the Fluorophores in Multifluorophoric Mixtures: Improving Quantification Accuracy of Fluorimetric Estimations of Dilute Aqueous Mixtures

Excitation-emission matrix fluorescence (EEMF) and total synchronous fluorescence spectroscopy (TSFS) are the 2 fluorescence techniques that are commonly used for the analysis of multifluorophoric mixtures. These 2 fluorescence techniques are conceptually different and provide certain advantages over each other. The manual analysis of such highly correlated large volume of EEMF and TSFS towards developing a calibration model is difficult. Partial least square (PLS) analysis can analyze the large volume of EEMF and TSFS data sets by finding important factors that maximize the correlation between the spectral and concentration information for each fluorophore. However, often the application of PLS analysis on entire data sets does not provide a robust calibration model and requires application of suitable pre-processing step. The present work evaluates the application of genetic algorithm (GA) analysis prior to PLS analysis on EEMF and TSFS data sets towards improving the precision and accuracy of the calibration model. The GA algorithm essentially combines the advantages provided by stochastic methods with those provided by deterministic approaches and can find the set of EEMF and TSFS variables that perfectly correlate well with the concentration of each of the fluorophores present in the multifluorophoric mixtures. The utility of the GA assisted PLS analysis is successfully validated using (i) EEMF data sets acquired for dilute aqueous mixture of four biomolecules and (ii) TSFS data sets acquired for dilute aqueous mixtures of four carcinogenic polycyclic aromatic hydrocarbons (PAHs) mixtures. In the present work, it is shown that by using the GA it is possible to significantly improve the accuracy and precision of the PLS calibration model developed for both EEMF and TSFS data set. Hence, GA must be considered as a useful pre-processing technique while developing an EEMF and TSFS calibration model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Adsorption Hysteresis Under Control: Tuning Host–Guest Interactions via a Genetic Algorithm

Mesoporous adsorbent materials offer a large volumetric capacity; however, cyclic adsorption/desorption processes in these systems often suffer from hysteresis and may require a significant pressure swing to access this capacity. To mitigate hysteresis, a proposed strategy is to include nucleation sites on the walls of the mesoporous material to facilitate droplet and bubble formation, lowering the free energy barriers to the respective phase transitions. It is unclear, however, what combination of adsorbate− adsorbent interactions and spatial patterning would be beneficial for a given application, considering that improvements to some sorption properties may come at the expense of other attributes. To understand these interconnected observables, we examine two model systems, planar-slit and cylindrical pores with tunable interaction sites, using GPU-accelerated transition matrix Monte Carlo simulations. The simulations provide a free energy map of the pressure−adsorption space in a matter of minutes, which we use to track adsorption isotherm characteristics as a function of adsorbent properties. We then leverage the rapid acquisition of simulation data to construct a genetic algorithm to iteratively modify interaction sites of the slit-pore wall to minimize the hysteresis of this system without sacrificing uptake. We find that the adsorption branch of the isotherm is easily modulated via the average host−guest interaction strength, but desorption is only adjustable if there is a suitable bubble nucleation site. Within the context of a slit-pore system, we identify relative interaction strengths and patch sizes required to gain control over both branches of the hysteresis loop.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Broadband infrared binary-pattern metasurface absorbers with micro-genetic algorithm optimization

Broadband binary-pattern metasurface absorbers are designed and demonstrated in the mid-infrared wavelength range through the micro-genetic algorithm. The tungsten-based metasurface absorbers with the optimized binary-pattern nanostructures exhibit broadband near-perfect absorption due to the multiple plasmonic resonances supported within the unit cell. Furthermore, the influence of minor pixel modifications in the optimized binary-pattern nanostructures on the absorption performance is investigated in the experiment. This Letter presents a promising approach to design and optimize complex optical nanostructures with the desired properties for metamaterial and metasurface applications.

42 ENGINEERING↗
DOE OSTI2024

New Calabi–Yau manifolds from genetic algorithms

Calabi–Yau manifolds can be obtained as hypersurfaces in toric varieties built from reflexive polytopes. We generate reflexive polytopes in various dimensions using a genetic algorithm. As a proof of principle, we demonstrate that our algorithm reproduces the full set of reflexive polytopes in two and three dimensions, and in four dimensions with a small number of vertices and points. Motivated by this result, we construct five-dimensional reflexive polytopes with the lowest number of vertices and points. By calculating the normal form of the polytopes, we establish that many of these are not in existing datasets and therefore give rise to new Calabi–Yau four-folds. In some instances, the Hodge numbers we compute are new as well.

Astronomy & Astrophysics↗
DOE OSTI2021

Per-Phase and 3-Phase Optimal Coordination of Directional Overcurrent Relays Using Genetic Algorithm

Penetration of the power grid by renewable energy sources, distributed storage, and distributed generators is becoming increasingly common. Increased utilization of these distributed energy resources (DERs) has given rise to additional protection coordination concerns, particularly where they are utilized in an unbalanced manner or where loading among phases is unbalanced. Digital relays such as the SEL-751 (produced by Schweitzer Engineering Laboratories, Pullman, WA, USA) series have the capability of being set on a per-phase basis. This capability is underutilized in common practice. Additionally, in optimization algorithms for determining relay settings, the time-overcurrent characteristics (TOCs) of relays are generally not treated as variables and are assigned before running the optimization algorithm. In this paper, TOC options themselves are treated as discrete variables to be considered in the optimization algorithm. A mixed integer nonlinear programming problem (MINLP) is set up where the goal is to minimize relay operating times. A genetic algorithm (GA) approach is implemented in MATLAB where two cases are considered. In the first case, the TOC and Time dial setting (TDS) of each relay is set on a three-phase basis. In the second case, per-phase settings are considered. Relay TDSs and TOCs are both considered as simultaneous discrete control variables. Despite the stochastic nature of using per-phase settings for unbalanced systems is found to generally allow for shorter operating times. However, for relatively balanced systems, it is best to use three-phase settings if computation time is of importance.

Matthews, Ronald C.↗
DOE OSTI2018

Structures and magnetic properties of iron silicide from adaptive genetic algorithm and first-principles calculations

We performed a systematic search for low-energy structures of binary iron silicide over a wide range of compositions using the crystal structure prediction method based on adaptive genetic algorithm. 36 structures with formation energies within 50 meV/atom (11 of them are within 20 meV) above the convex hull formed by experimentally known stable structures are predicted. Magnetic properties of these low-energy structures are investigated. Some of these structures can be promising candidates for rare-earth-free permanent magnet.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Genetic Algorithms for Rapid Molecular Materials Screening

This project developed efficient methods for rapid computational screening of conjugated polymers and oligomers with optimal or near-optimal photovoltaic, dielectric, spintronic, and other unique electronic structure properties using genetic algorithms (GA). A key promise of organic materials is that using the incredible tailorability of synthetic organic chemistry enables experimental optimization of performance across multiple relevant properties (e.g., organic photovoltaics, organic lasers). The fundamental challenge is inverse design: how does one find the molecular or polymer structures with desired properties in the face of a near-infinite (e.g., >10 20 - 10 60 ) number of possible molecular structures, combined with time-consuming experiments or electronic structure calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

sp3-hybridized framework structure of group-14 elements discovered by genetic algorithm

Group-14 elements, including C, Si, Ge, and Sn, can form various stable and metastable structures. Finding new metastable structures of group-14 elements with desirable physical properties for new technological applications has attracted a lot of interest. Using a genetic algorithm, we discovered a new low-energy metastable distorted sp3-hybridized framework structure of the group-14 elements. It has P42/mnm symmetry with 12 atoms per unit cell. The void volume of this structure is as large as 139.7Å3 for Si P42/mnm, and it can be used for gas or metal-atom encapsulation. Band-structure calculations show that P42/mnm structures of Si and Ge are semiconducting with energy band gaps close to the optimal values for optoelectronic or photovoltaic applications. With metal-atom encapsulation, the P42/mnm structure would also be a candidate for rattling-mediated superconducting or used as thermoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Phase retrieval and reconstruction of coherent synthesis by genetic algorithm

Abstract In the context of diffractive optics, phase retrieval is a heavily investigated process of recreating an entire complex electric field from partial amplitude-only information through iterative algorithms. However, existing methods can fall into local minima during reconstructions or struggle to recover unusual and novel electric field distributions. We present a numerical method based on a global-optimization genetic algorithm that reconstructs non-trivial electric field distributions from single diffracted intensity distributions. Diffraction and propagation of the optical fields over arbitrary distances is modeled through implementation of the angular spectrum technique. Additionally, a coherently-locked laser array system is used as an experimental case-study demonstrating 0.09 π phase reconstruction accuracy of initial laser parameters from single intensity images.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Optimization of an antenna array using genetic algorithms

An array of antennas is usually used in long distance communication. The observation of celestial objects necessitates a large array of antennas, such as the Giant Metrewave Radio Telescope (GMRT). Optimizing this kind of array is very important when observing a high performance system. The genetic algorithm (GA) is an optimization solution for these kinds of problems that reconfigures the position of antennas to increase the u-v coverage plane or decrease the sidelobe levels (SLLs). This paper presents how to optimize a correlator antenna array using the GA. A brief explanation about the GA and operators used in this paper (mutation and crossover) is provided. Then, the results of optimization are discussed. The results show that the GA provides efficient and optimum solutions among a pool of candidate solutions in order to achieve the desired array performance for the purposes of radio astronomy. The proposed algorithm is able to distribute the u-v plane more efficiently than GMRT with a more than 95% distribution ratio at snapshot, and to fill the u-v plane from a 20% to more than 68% filling ratio as the number of generations increases in the hour tracking observations. Finally, the algorithm is able to reduce the SLL to –21.75 dB.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Pressurized-Water Reactor Core Design Demonstration with Genetic Algorithm Based Multi-Objective Plant Fuel Reload Optimization Platform

LWRS M3 milestone report due September 15, 2023. This report summarizes development and demonstration activities of PRLO optimization platform built in Risk Analysis and Virtual ENviroment (RAVEN), specifically: (1) Improvement of multi-objective non-dominated sorting genetic algorithm II (NSGA-II) to handle large size of objectives and constraints, (2) Demonstration of pressurized water reactor core design with NSGA-II multi-objective optimization platform, and (3) Single-objective optimized core design including system analysis and fuel performance feedback.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Weight optimization of large span steel truss structures with genetic algorithm

The paper presents the weight optimization process of the main steel truss that supports the Slatina Sport Hall roof. The structure was loaded with self-weight, dead loads, live loads, snow, wind and temperature, grouped in eleven load cases. The optimization of the structure was made using genetic algorithms implemented in a Matlab code. A total number of four different cases were taken into consideration when trying to determine the lowest weight of the structure, depending on the types of connections with the concrete structure ( types of supports, bearing modes), and the possibility of the lower truss chord nodes to change their vertical position. A number of restrictions for tension, maximum displacement and buckling were enforced on the elements, and the cross sections are chosen by the program from a user data base. The results in each of the four cases were analyzed in terms of weight, element tension, element section and displacement. The paper presents the optimization process and the conclusions drawn.

42 ENGINEERING↗
DOE OSTI2014

Genetic algorithm applied to the optimization of quantum cascade lasers with second harmonic generation

A computational model for the optimization of the second order optical nonlinearities in GaInAs/AlInAs quantum cascade laser structures is presented. The set of structure parameters that lead to improved device performance was obtained through the implementation of the Genetic Algorithm. In the following step, the linear and second harmonic generation power were calculated by self-consistently solving the system of rate equations for carriers and photons. This rate equation system included both stimulated and simultaneous double photon absorption processes that occur between the levels relevant for second harmonic generation, and material-dependent effective mass, as well as band nonparabolicity, were taken into account. The developed method is general, in the sense that it can be applied to any higher order effect, which requires the photon density equation to be included. Specifically, we have addressed the optimization of the active region of a double quantum well In{sub 0.53}Ga{sub 0.47}As/Al{sub 0.48}In{sub 0.52}As structure and presented its output characteristics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Insights into Constraining Rate Coefficients in Fuel Oxidation Mechanisms Using Genetic Algorithm Optimization

Accurate fuel oxidation mechanisms can enable predictive capabilities that aid in advancing combustion technologies. High-level computational kinetics can yield reasonable rate coefficients with uncertainties, in some cases, below a factor of 2. Computed rate coefficients can be constrained further by optimizing against experimental data. Here, we explore the application of genetic algorithm (GA) optimization to constrain computed rate coefficients in complex fuel oxidation mechanisms in conjunction with temperature-dependent species mole fractions from jet-stirred reactor (JSR) measurements. Cyclohexane is a model candidate for understanding the reactivity of cyclic fuels. In this work, we optimize the rate coefficients of the most recent literature cyclohexane mechanism, which incorporates theoretically computed rate coefficients for the reaction networks stemming from the first and second O 2 addition pathways, against the experimental results of two separate literature JSR studies. Optimization consistency is evaluated by carrying out three GA optimizations: fitting to the temperature-dependent species mole fractions in each JSR experiment separately and simultaneously fitting the species mole fractions in both experiments. Local sensitivity analyses are used to identify five influential low-temperature oxidation reactions for optimization. Although the three optimizations do not yield identical rate coefficients, the direction of change in all five rate coefficients is consistent among the three optimizations. Performance of the models from the three optimizations is assessed against literature ignition delay times with differences in the level of agreement observed among the different optimizations. In conclusion, comparisons are made with our recent optimization work of a cyclopentane oxidation master-equation model against time-resolved species concentrations, and insights and improvements of the strategy for constraining rate coefficients using GA optimization are discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Preliminary Assessment of Elemental Analysis of X-Ray Fluorescence with Genetic Algorithm

X-ray fluorescence (XRF) spectroscopy is a promising approach to elemental analysis of forensic samples. Elements in the mixture can be identified from the XRF data because chemical elements are associated with unique spectra. This report consists of preliminary assessment of automated identification of unknown mixtures of elements from XRF spectra using a genetic algorithm (GA). The XRF spectra are measured at the Advanced Photon Source (APS) at the liquid-liquid interface during solvent extraction process. Possible advantages of using GA for identifying elements at the interface from XRF data are discussed, and a path for implementation of GA is outlined.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗