Mohammad Abazari's repositories
ABAQUS_PDALAC
Development of the Failure Criteria for Composites using ABAQUS Subroutines (UMAT/VUMAT)
FJSSP-with-worker-Flexibility
Flexible job shop scheduling with worker flexibility using discrete Jaya Algorithm
Two-bar-truss-optimization
A truss that has a minimum weight, will not yield, will not buckle, and does not deflect "excessively,” the model should calculate the combination of weight, stress, buckling stress and deflection combined in a most optimized way using non linear optimization
Truss-Structure-Analysis
Thruss Structure anlaysis executes a stress and displacement analysis on a spesific structure that is user defined.
GranularFlowModels
The software is a list of Abaqus User MATerial subroutines (VUMAT) for modeling granular flow physics. It includes (1) density dependent Mohr-Coulomb model, (2) density dependent Drager-Prager\Cap model, (3)Gudehus-Bauer hypoplastic model, and (4) critical state-based NorSand model.
gtoa
Group Teaching Optimization Algorithm on 10 bar truss
Truss-optimization
Library implementing Genetic algorithm to find optimal truss structure.
fatigue_cvae
Code for "Conditional Variational Autoencoders for Probabilistic Wind Turbine Blade Fatigue Estimation using SCADA data"
Multiaxial-high-cycle-fatigue-life-prediction-model
Multiaxial-high-cycle-fatigue-life-prediction-model
GA-Truss-Optimization
A genetic algorithm-based truss topology optimization solver programmed in MATLAB
MATH307notes
Course notes for MATH 307 Applied Linear Algebra
CFRP_damage_location_detection
CFRP_damage_location_detection
sga
Simple Genetic Algorithm - Truss optimization
2D_topo_opt_truss_structure
As an academic project, I developed a 2D topology optimization of a truss structure in python, the algorithm took and performed many examples available in the web. The topology optimization was done under the nested formulation, It means that the process is static. Many examples of this problem have been done, however, this is able to analyze structures of any size, and shows a nice visualization with VTK library and Matplotlib. I hope this work could be useful for you.
code-truss-optimization
Optimizing truss structures using vanishing constraints
truss_optimization
truss_optimization++
abaqusVumat
VUMAT for the Abaqus Explicit software
GARCH
GARCH model implementation
SwarmPackagePy
Library of swarm optimization algorithms.
Abaqus-VUMAT-Johnson-Cook
Abaqus-VUMAT-Johnson-Cook
Abaqus-VUMAT-Gurson_GTN
Numerical impementation of the Gurson model with GTN modification.
Optimization-Benchmark-Truss-Problems
MATLAB codes for benchmark truss optimization problems
Teaching-Learning-Based-Optimization
Teaching Learning Based Optimization for Truss Optimization with MATLAB
VUMAT_Fortran_Hashin3D
VUMAT Hashin 3D implementation
Optimization-of-Trusses-Benchmarks
Practice Problems using Metaheuristic algorithms
fatigueblades
Fatigue life estimation of hydrokinetic turbine blades
Pultrusion
Abaqus user subroutines for numerical simulation of cracking during pultrusion of 80 mm diameter GFRP rod
Abaqus-VUMAT-elastic_damage
Abaqus VUMAT subroutine for the linear elastic materials with damage based on von mises stress for explicit analysis.
Chaotic-GSA-for-Engineering-Design-Problems
All nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers are now using chaotic maps. The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's gravity principle and laws of motion. It uses 10 chaotic maps for global search and fast convergence speed. Basically, in GSA gravitational constant (G) is utilized for adaptive learning of the agents. For increasing the learning speed of the agents, chaotic maps are added to gravitational constant. The practical applicability of CGSA has been accessed through by applying it to nine Mechanical and Civil engineering design problems which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss (TBT), Stepped Cantilever Beam design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with seven state of the art stochastic algorithms particularly Constriction Coefficient based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Standard Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CGSA shows efficient performance as compared to other seven participating algorithms.
ABAQUS-version-CDPM2
This repository contains the user-material (VUMAT) of the concrete damage-plasticity model 2 (CDPM2) for use in ABAQUS