Search NASA⌕ Search

SEARCH · Search NASA

Results for “Genetic Algorithm”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bridging material models across scales: An integrated approach to equation of state and molecular dynamics modeling of copper

New uncertainty-aware equation of state (EOS) and electrical conductivity (EC) models for copper have been developed. The multiphase EOS/EC models are fit to experimental solid/liquid EC isobar measurements as well as density-functional theory molecular dynamics (DFT-MD) EC calculations in both expanded and compressed regimes (0.1–16 g/ cm 3 ⁠). The liquid and solid EOS phases were fit to available experimental data along with additional DFT-MD data over the same range as the EC. Leveraging the DFT-MD data, a corresponding machine-learned interatomic potential (MLIAP) for copper was trained using genetic-algorithm optimization. The copper MLIAP was constrained by EOS shock points at high compressions. The final EOS bounded MLIAP proves to be stable over a large density range (approximately 0.1–20 g/ cm 3 ) with good agreement to an isothermal compression curve, shock Hugoniot, and liquid speed of sound measurements at high pressures (100s of GPa).

Acoustic measurements and instrumentation↗
DOE OSTI2022

Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback

Magnetic materials are essential for energy generation and information devices, and they play an important role in advanced technologies and green energy economies. Currently, the most widely used magnets contain rare earth (RE) elements. An outstanding challenge of notable scientific interest is the discovery and synthesis of novel magnetic materials without RE elements that meet the performance and cost goals for advanced electromagnetic devices. Here, we report our discovery and synthesis of an RE-free magnetic compound, Fe 3 CoB 2 , through an efficient feedback framework by integrating machine learning (ML), an adaptive genetic algorithm, first-principles calculations, and experimental synthesis. Magnetic measurements show that Fe 3 CoB 2 exhibits a high magnetic anisotropy ( K 1 = 1.2 MJ/m 3 ) and saturation magnetic polarization ( J s = 1.39 T), which is suitable for RE-free permanent-magnet applications. Our ML-guided approach presents a promising paradigm for efficient materials design and discovery and can also be applied to the search for other functional materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Technoeconomic Design Optimization for Fast Reactors. Part I: Workflow Development and Case Study for Small LFR District Energy Application

The nuclear industry is developing small reactor designs that can target a variety of deployment locations and energy products. Smaller nuclear designs have traditionally struggled to handle the steep trade-offs between size and cost that have historically incentivized large reactors. This motivates computational optimization of small reactors to minimize costs and quantify the trade-off between size and cost. In this paper, the cost/size trade-off for a small fast reactor is derived using a multi-objective genetic algorithm optimization, with steady-state, transient, and cost analysis of the fast reactor being performed. Specifically, the method is demonstrated on a small 10- to 120-MW(thermal) U-Pu-Zr–fueled lead-cooled fast reactor with a 10-year core life for district energy applications, which can have a thermal load compatible with this range. The results reinforced that fast reactor cores at the lower end of this power range suffer cost penalties due to critical mass considerations. It was found that high power density cores with strong reactivity swings and many control rods were favored over designing to minimize reactivity swing. Furthermore, this contrasts with some traditional configurations designed using engineering judgment and demonstrates that optimizers can find nontraditional but realistic solutions, along with demonstrating the value of incorporating cost functions into whole-reactor design optimization.

Fast reactor↗
DOE OSTI2018

New structures of Fe3S for rare-earth-free permanent magnets

We applied adaptive genetic algorithm (AGA) to search for low-energy crystal structures of Fe 3 S. A number of structures with energies lower than that of the experimentally reported Pnma and I-4 structures have been obtained from our AGA searches. These low-energy structures can be classified as layer-motif and column-motif structures. In the column-motif structures, Fe atoms self-assemble into rods with bcc type of underlying lattice, which are separated by the holes terminated by S atoms. In the layer-motif structures, the bulk Fe is broken into slabs of several layers passivated by S atoms. Magnetic properties calculations showed that the column-motif structures exhibit reasonably high uniaxial magnetic anisotropy. In addition, we examined the effect of Co doping to Fe 3 S and found magnetic anisotropy can be enhanced through Co doping.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Generation and optimization of cross-sections for electron-C 4 F 7 N collisions

Abstract A complete and consistent set of electron-neutral collision cross-sections for the novel insulating gas C 4 F 7 N is reported. The set is composed of a combination of cross-sections previously reported in literature, optimized via a genetic algorithm in conjunction with a multi-term Boltzmann equation solver, and calculatedab initiousing the R-matrix code Quantemol-EC. The finalized set accurately reproduces reported macroscopic rate and transport coefficients as well as Townsend coefficients and critical electric field strengths in C 4 F 7 N and its mixtures with nitrogen, carbon dioxide, and argon.

Physics↗
DOE OSTI2023

Main belt asteroids taxonomical information from dark energy survey data

While proper orbital elements are currently available for more than 1 million asteroids, taxonomical information is still lagging behind. Surveys like SDSS-MOC4 provided preliminary information for more than 100 000 objects, but many asteroids still lack even a basic taxonomy. In this study, we use Dark Energy Survey (DES) data to provide new information on asteroid physical properties. By cross-correlating the new DES data base with other data bases, we investigate how asteroid taxonomy is reflected in DES data. While the resolution of DES data is not sufficient to distinguish between different asteroid taxonomies within the complexes, except for V-type objects, it can provide information on whether an asteroid belongs to the C- or S-complex. Here, machine learning methods optimized through the use of genetic algorithms were used to predict the labels of more than 68 000 asteroids with no prior taxonomic information. Using a high-quality, limited set of asteroids with data on gri slopes and i − z colours, we detected 409 new possible V-type asteroids. Their orbital distribution is highly consistent with that of other known V-type objects.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

tonalli : an asexual genetic code to characterize APOGEE-2 stellar spectra. I. Validation with synthetic and solar spectra

ABSTRACT We present tonalli, a spectroscopic analysis python code that efficiently predicts effective temperature, stellar surface gravity, metallicity, $\alpha$-element abundance, and rotational and radial velocities for stars with effective temperatures between 3200 and 6250 K, observed with the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2). tonalli implements an asexual genetic algorithm to optimize the finding of the best comparison between a target spectrum and the continuum-normalized synthetic spectra library from the Model Atmospheres with a Radiative and Convective Scheme (MARCS), which is interpolated in each generation. Using simulated observed spectra and the APOGEE-2 solar spectrum of Vesta, we study the performance, limitations, accuracy, and precision of our tool. Finally, a Monte Carlo realization was implemented to estimate the uncertainties of each derived stellar parameter.

Adame, Lucía (ORCID:0000000263286099)↗
DOE OSTI2019

Laser wakefield acceleration with active feedback at 5 Hz

We describe the use of a genetic algorithm to apply active feedback to a laser wakefield accelerator at a higher power (10 TW) and a lower repetition rate (5 Hz) than previous work. The temporal shape of the drive laser pulse was adjusted automatically to optimize the properties of the electron beam. By changing the software configuration, different properties could be improved. This included the total accelerated charge per bunch, which was doubled, and the average electron energy, which was increased from 22 to 27 MeV. Using experimental measurements directly to provide feedback allows the system to work even when the underlying acceleration mechanisms are not fully understood, and, in fact, studying the optimized pulse shape might reveal new insights into the physical processes responsible. Our work suggests that this technique, which has already been applied with low-power lasers, can be extended to work with petawatt-class laser systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Compensating slice emittance growth in high brightness photoinjectors using sacrificial charge

Achieving maximum electron beam brightness in photoinjectors requires detailed control of the 3D bunch shape and precise tuning of the beam focusing. Even in state-of-the-art designs, slice emittance growth due to nonlinear space charge forces and partial nonlaminarity often remains non-negligible. In this work, we introduce a new means to linearize the transverse slice phase space: a sacrificial portion of the bunch’s own charge distribution, formed into a wavebroken shock front by highly nonlinear space charge forces within the gun, whose downstream purpose is to dynamically linearize the desired bunch core. We show that linearization of an appropriately prepared bunch can be achieved via strongly nonlaminar focusing of the sacrificial shock front, while the inner core focuses laminarly. This leads to a natural spatial separation of the two distributions: a dense core surrounded by a diffuse halo of sacrificial charge that can be collimated. Multiobjective genetic algorithm optimizations of the ultracompact x-ray free electron laser injector employ this concept, and we interpret it with an analytic model that agrees well with the simulations. In simulation, we demonstrate a final bunch charge of 100 pC, peak current ∼ 30 A, and a sacrificial charge of 150 pC (250 pC total emitted from cathode) with normalized emittance growth of < 20 nm rad due to space charge. This implies a maximum achievable brightness approximately an order of magnitude greater than existing free electron laser injector designs. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds

Interatomic potentials based on neural-network machine learning (ML) approach to address the long-standing challenge of accuracy versus efficiency in molecular-dynamics simulations have recently attracted a great deal of interest. In this work, utilizing Pd-Si system as a prototype, we extend the development of neural-network ML potentials to compounds exhibiting various types of bonding characteristics. The ML potential is trained by fitting to the energies and forces of both liquid and crystal structures first-principles calculations based on density-functional theory (DFT). We show that the generated ML potential captures the structural features and motifs in Pd 82 Si 18 and Pd 75 Si 25 liquids more accurately than the existing interatomic potential based on embedded-atom method (EAM). The ML potential also describes the solid-liquid interface of these systems very well. Moreover, while the existing EAM potential fails to describe the relative energies of various crystalline structures and predict wrong ground-state structures at Pd 3 Si and Pd 9 Si 2 composition, the developed ML potential predicts correctly the ground-state structures from genetic algorithm search. The efficient ML potential with DFT accuracy from our study will provide a promising scheme for accurate atomistic simulations of structures and dynamics of complex Pd-Si system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Structural hierarchy as a key to complex phase selection in Al-Sm

Investigating the unknown structure of the complex cubic phase, previously observed to crystallize from melt-spun amorphous Al–10 at.% Sm alloy, we determine the structure in full site-occupancy detail, highlighting several critical structural features that govern the far-from-equilibrium phase selection pathway. Using an efficient genetic algorithm combining molecular dynamics, density functional theory, and x-ray diffraction, the structure is clearly identified as body-centered cubic Im¯3m (No. 229) with ~140 atoms per cubic unit cell and a lattice parameter of 1.4 nm. The complex structure is further refined to elucidate the detailed site occupancy, revealing full Sm occupancy on 6b sites and split Sm/Al occupancy on 16f sites. Based on the refined site occupancy associated with the experimentally observed phase, we term this phase ε–Al 60 Sm 11 (bcc), corresponding to the limiting situation when all 16f sites are occupied by Sm. However, it should be recognized that the range of solubility enabled by split occupancy at Sm sites is an important feature in phase selection under experimental conditions, permitting an avenue for transition with little or no chemical partitioning. Our analysis shows that the ε–Al 60 Sm 11 (bcc) exhibits a “3-6-6-1” first-shell packing around Sm centers on 16f sites, the same dominant motif exhibited by the undercooled liquid. Here, the coincident motif supports the notion that liquid/glass ordering at high undercooling may give rise to topological invariants between the noncrystalline and crystalline states that provide kinetic pathways to metastable phases that are not accessible during near-equilibrium processing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning-accelerated discovery of iron cobalt phosphides as rare-earth-free magnets

Here, the discovery of rare-earth-free permanent magnets has been a goal of scientists for decades. The absence of rare-earth elements will alleviate a pressing concern about the availability of rare-earth elements used in permanent magnets. These magnets are crucial for applications such as wind turbines, electric cars, and memory devices. Rare-earth magnets are special owing to a large magnetic anisotropy energy (K 1 ). In contrast, iron cobalt phosphides hold promise since doping P into cubic FeCo can induce anisotropy, leading to a large coercivity, without introducing rare-earth elements. We present a comprehensive search over the Fe-Co-P ternary space for magnets, utilizing recently developed adaptive machine learning feedback to efficiently screen over 850 000 structures. We focus on machine learning acceleration as a paradigm for materials design. Further adaptive genetic algorithm searches and first-principles calculations aid in the identification of 16 new structures below the known convex hull. Five of them possess high magnetic polarization (J s > 1 T). The structures with desirable magnetic properties center on (Fe,Co) 2⁢ P. This supports conventional wisdom, which focuses on the mixture of the two known end compounds: Fe 2 ⁢P and Co 2 ⁢P. Our work provides guidance for synthesis. We find Fe 7 ⁢CoP 4 shows the most promise (J s = 1.03T and K 1 = 0.83MJ/m 3 ).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning to Select Experiments Driven by Fundamental Science and Applications for Targeted Nuclear Data Improvement

This work describes a blueprint for a process that accelerates progress in science by quantitatively answering the following question: What is the optimal combination of fundamental-science and application-driven experiments to maximally reduce pertinent data uncertainties? Answering this question entails solving a high-dimensional and complex optimization problem that is best solved with advanced statistic techniques often classified as machine learning. We apply this process within the framework of nuclear data with the aim to select an experiment combination that will reduce uncertainties in 239 Pu nuclear data for neutron energies between 1 and 600 keV. In this field, fundamental-physics driven data, called differential, look at one nuclear physics observable at a time. They are contrasted to application-driven, integral, data where one or few resulting values inform a broad set of nuclear data across several nuclides and energies. The candidates for integral experiments are criticality measurements that were refined by a genetic algorithm to be maximally sensitive to 239 Pu fission cross sections in the desired energy range. Twenty-three candidate differential experiments were investigated and span multiple nuclear physics observables (e.g., total, capture cross sections) for isotopes appearing in the integral experiments. The optimal combination among these candidate experiments was investigated via generalized least squares fitting, augmented with Gaussian processes to ameliorate statistical irregularities in data, and the D-optimality criterion. The latter evaluates for each pair of candidates the joint reduction in uncertainties of all 12200 nuclear data appearing in the integral experiments compared to the knowledge we have from 168 past experiments, theory, and nuclear data. We chose as differential measurements those that investigate 63 Cu and 239 Pu total cross sections, based on D-optimality rank and feasibility constraints. Two integral (criticality) experiments were selected: An experiment with Al 2 ⁢O 3 and graphite interleaved with Pu and a thick Cu reflector explores 1–30 keV, while we target the 30–600 keV range with an experiment that swaps boron in place of graphite with a different geometry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Single-Shot Reconstruction of Electron Beam Longitudinal Phase Space in a Laser Wakefield Accelerator

We report on a single-shot longitudinal phase-space reconstruction diagnostic for electron beams in a laser wakefield accelerator via the experimental observation of distinct periodic modulations in the angularly resolved spectra. Such modulated angular spectra arise as a result of the direct interaction between the ultrarelativistic electron beam and the laser driver in the presence of the wakefield. A constrained theoretical model for the coupled oscillator, assisted by a genetic algorithm, can recreate the experimental electron spectra and, thus, fully reconstructs the longitudinal phase-space distribution of the electron beam with a temporal resolution of approximately 1.3 fs. In particular, it reveals the slice energy spread of the electron beam, which is important to measure for applications such as x-ray free electron lasers. In our experiment, the root-mean-square energy spread retrieved is bounded at 9.9 MeV, corresponding to a 0.9%–3.0% relative spread, despite the overall GeV energy beam having approximately 100% relative energy spread.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

ζ-Glycine: insight into the mechanism of a polymorphic phase transition

Glycine is the simplest and most polymorphic amino acid, with five phases having been structurally characterized at atmospheric or high pressure. A sixth form, the elusive ζ phase, was discovered over a decade ago as a short-lived intermediate which formed as the high-pressure ϵ phase transformed to the γ form on decompression. However, its structure has remained unsolved. We now report the structure of the ζ phase, which was trapped at 100 K enabling neutron powder diffraction data to be obtained. The structure was solved using the results of a crystal structure prediction procedure based on fully ab initio energy calculations combined with a genetic algorithm for searching phase space. We show that the fate of ζ-glycine depends on its thermal history: although at room temperature it transforms back to the γ phase, warming the sample from 100 K to room temperature yielded β-glycine, the least stable of the known ambient-pressure polymorphs.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Comprehensive Evaluation and Application of GIS Insulation Condition Part 1: Selection and Optimization of Insulation Condition Comprehensive Evaluation Index Based on Multi-Source Information Fusion

How to select and optimize the evaluation indicators from many characteristic information to characterize the internal insulation condition of GIS equipment, and establish a scientific technical and economic evaluation model of insulation condition is a huge challenge needs to be addressed urgently. Motived by this purpose, this study simulates the deterioration process of four typical insulation defects in GIS to obtain the information of UHF, ultrasound, and SF 6 decomposition components excited by different degradation stages of each insulating defect simultaneously. Moreover, the operating environment conditions, running time, overhaul times, and the residual value of equipment are taken into account. Using the improved minimum-redundancy-maximum-relevance (mRMR) criterion and genetic algorithm, a comprehensive evaluation index system including the 23 evaluation indicators in three categories is established, which covers the PD hazard, SF 6 insulation performance, equipment running time and environment, and residual value. The research lays a foundation for the later construction and application of multi-source information fusion evaluation model of GIS equipment insulation condition considering economic value.

42 ENGINEERING↗
DOE OSTI2018

50-kW 1kV DC bus air-cooled inverter with 1.7 kV SiC MOSFETs and 3D-printed novel power module packaging structure for grid applications

The traditional heatsink design technologies for forced air-cooling and power semiconductors with low junction temperatures have constrained the converters to be designed with massive heatsinks. The low power losses of WBG device technology and higher junction temperature operation over a wide operating range of power have not been fully utilized with liquid-cooled systems. The other major limitation has also been the traditional power module packaging “stack” approach with baseplate. This paper presents a novel power stage design which involves 1.7 kV silicon carbide (SiC) MOSFETs, a heatsink design with Genetic Algorithm (GA) and built using 3D printing technology, and a novel integrated modular power module for high power density. The air-cooled module assembly has a SiC MOSFET phase leg module with split high-side and low-side switches and a gate driver with cross-talk and short circuit protection functions. The heatsink design was modeled using a co-simulation environment with finite element analysis software and GA in MATLAB and COMSOL. The proposed concepts were verified and validated through experiments at each stage of development. The power stage was evaluated at 800V, 900 V, and 1kV for 20 kHz switching frequency and 50-kW load. The experimental results show that the CEC efficiency is 98.4 %. In addition to the efficiency, a power density of 75 W/in 3 was also achieved.

Chinthavali, Madhu Sudhan↗
DOE OSTI2023

A High-Power Density Segmented Traction Drive Inverter

High power density is one of the requirements for traction drive inverters for meeting increasing demand for higher power and performance electrical vehicles (EV). This paper presents design and preliminary experimental results for a 100 kW high-power density inverter for EV traction drive applications. The inverter design was based on the segmented inverter topology that can significantly reduce the inverter DC filter capacitor and employs low-profile planar double-sided cooled SiC MOSFET-based power modules, compact mini-channel heat sinks with fin-profile optimized using genetic-algorithms, and high-ripple current capacitors. The design produced a compact inverter package with a total volume less than 1 litter, exceeding the power density goal of 100 kW/L. Experimental results are included to demonstrate the cooling and electrical performance.

Su, Gui-Jia↗