mohammad-abazari Goto Github PK

followers: 4.0 following: 62.0 repos: 79.0 gists: 0.0

Name: Mohammad Abazari

Type: User

Bio: Structural engineer interested in finite element analysis, structural dynamics. experienced with ABAQUS, MATLAB, Python, latex ...

Twitter: nomad_8586

👋 Hi, I’m @Mohammad-Abazari
👀 I’m interested in Python, ABAQUS, MATLAB, latex
🌱 I’m currently learning french, fortran, ...
💞️ I’m looking to collaborate on FEM, FEA, Steel design, RC design,
📫 [email protected]

Mohammad Abazari's Projects

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.

2dorthogonal_polynomial_decomposition

OPD implementation with application to a IR sequence of a simulated CFRP sample and two real CFRP and GFRP samples with teflon insertions. Recreates the results presented in Paper "Characterization of defects of pulsed thermography inspections by orthogonal polynomial decomposition"

abaqus-version-cdpm2

This repository contains the user-material (VUMAT) of the concrete damage-plasticity model 2 (CDPM2) for use in ABAQUS

abaqus-vumat-elastic_damage

Abaqus VUMAT subroutine for the linear elastic materials with damage based on von mises stress for explicit analysis.

abaqus-vumat-gurson_gtn

Numerical impementation of the Gurson model with GTN modification.

abaqus-vumat-johnson-cook

Abaqus-VUMAT-Johnson-Cook

abaqus_pdalac

Development of the Failure Criteria for Composites using ABAQUS Subroutines (UMAT/VUMAT)

abaqusvumat

VUMAT for the Abaqus Explicit software

algorithm

Differential evolution; Particle swarm optimization; Simulated annealing; Jaya

answer-to-history-mohammad-reza-pahlavi-1979

"Answer to History" is the last book M.R. Pahlavi published just before his passing by STEIN AND DAY Publishers New York.

blade-damage-tutorial

Tutorial on how to create an use a surrogate model for fatigue damage and extreme events in wind turbine blade optimization.

blade_fatigue_test_manager

A program to process the data in the fatigue tests of full-scale wind turbine blades.

cfrp_damage_location_detection

CFRP_damage_location_detection

cfrpredict

Used to extract, transform and load data to train predictive model. Also includes generated R model and associated prediction algorithm.

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.

code-truss-optimization

Optimizing truss structures using vanishing constraints

composite_cdm_ap_ply

Continuum damage mechanics framework for AP-PLY composites implemented as an Abaqus VUMAT subroutine.

composite_cdm_tan

3D continuum damage mechanics model for composite materials implemented in Fortran (Abaqus Explicit VUMAT).

cpfem-vumat

Crystal plasticity finite element code, VUMAT file for Abaqus

design-and-development-of-hybrid-optimization-enabled-deep-q-learning-model-for-covid-19-detection-

The problem of respiratory sound classification has received good attention from the clinical scientists and medical researcher’s community in the last year to the diagnosis of COVID-19 disease. In this paper, the input audio samples are fed into the pre-processing module in which median filtering is done to remove the noise and artifacts from the audio samples. The feature extraction is carried out by considering features, like spectral contrast, Mel frequency cepstral coefficients (MFCC), Empirical Mode Decomposition (EMD) algorithm, spectral flux, Fast Fourier Transform (FFT), spectral roll-off, spectral centroid, Root mean square energy, zero-crossing rate, spectral bandwidth, spectral flatness, power spectral density, mobility complexity, fluctuation index and relative amplitude. Moreover, the deep Q network is applied for Covid-19 classification phase wherein the training of deep Q network is done using the proposed optimization algorithm, named Snake Jaya Honey Badger Optimization (SJHBO) algorithm. The proposed SJHBO algorithm is the hybridization of Jaya Honey Badger Optimization (JHBO) along with Snake optimization. Hence, the developed method achieved the better superior performance based on the accuracy, sensitivity and specificity .

mohammad-abazari Goto Github PK

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