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Related Concept Videos

Genetic Variation01:25

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimization of Chaboche Material Parameters with a Genetic Algorithm.

Nejc Dvoršek1, Iztok Stopeinig2, Simon Klančnik1

  • 1Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia.

Materials (Basel, Switzerland)
|March 11, 2023
PubMed
Summary

This study developed a genetic algorithm (GA) to optimize Chaboche material model parameters, improving accuracy by 40% over traditional methods. The Python-based GA offers automation and faster results for industrial applications.

Keywords:
Chaboche material modelfinite element methodgenetic algorithmparameter optimization

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Area of Science:

  • Computational Materials Science
  • Mechanical Engineering
  • Optimization Algorithms

Background:

  • Accurate material modeling is crucial for industrial applications, particularly in predicting material behavior under various stress conditions.
  • The Chaboche model is widely used for plasticity and creep, but parameter optimization can be complex and time-consuming.
  • Traditional methods for parameter tuning often rely on trial and error, lacking efficiency and automation.

Purpose of the Study:

  • To develop and evaluate a genetic algorithm (GA) for optimizing Chaboche material model parameters.
  • To compare the GA's performance against traditional parameter optimization techniques.
  • To implement the GA in Python for cost-effectiveness and future scalability.

Main Methods:

  • A genetic algorithm (GA) was designed to minimize the difference between experimental and simulated data.
  • Experiments included tensile, low-cycle fatigue, and creep tests, with finite element models created in Abaqus.
  • The GA's fitness function utilized a similarity measure, with real-valued genes representing model parameters.

Main Results:

  • The GA successfully identified optimal Chaboche material model parameters, achieving a suitable global minimum.
  • Population size was found to be the most critical parameter for GA performance.
  • The GA demonstrated a 40% improvement in fitness score compared to trial and error methods.
  • Optimal GA configuration: population size of 150, mutation probability of 0.1, and two-point crossover.

Conclusions:

  • The developed genetic algorithm provides an efficient, automated, and cost-effective method for Chaboche material model parameter optimization.
  • The GA significantly outperforms traditional trial and error approaches in terms of accuracy and speed.
  • The Python implementation ensures the algorithm is upgradable and suitable for industrial deployment.