Computational Predictions for Predicting the Performance of Structure
This package focuses on developing and applying predictive models for the structural analysis of steel and concrete components subjected to fire and subsequent earthquake loading. To accurately simulate the complex behavior of these structures, finite element analysis (FEA) using ABAQUS is employed. The Taguchi method optimizes the number of samples needed for FE analysis, and this method is used with SPSS after explanation its concept. However, due to the computational demands of FEA, various machine learning techniques, including regression models, Gene Expression Programming (GEP), Adaptive Network-Based Fuzzy Inference Systems (ANFIS), and ensemble methods, are explored as surrogate models. These models are trained on large datasets of FEA results to predict structural responses efficiently. The performance of these models is evaluated using statistical metrics such as RMSE, NMSE, and coefficient of determination.
Damage Prediction in Reinforced Concrete Tunnels under Internal Water Pressure
This tutorial package equips you with the knowledge and tools to simulate the behavior of reinforced concrete tunnels (RCTs) subjected to internal water pressure. It combines the power of finite element (FE) modeling with artificial intelligence (AI) for efficient and accurate analysis. The Taguchi method optimizes the number of samples needed for FE analysis, and this method is used with SPSS after explanation its concept.
By leveraging Artificial Intelligence (AI) techniques such as regression, GEP, ML, DL, hybrid, and ensemble models, we significantly reduce computational costs and time while achieving high accuracy in predicting structural responses and optimizing designs.
Computational Modeling of Steel Plate Shear Wall (SPSW) Behavior
This course equips engineers with the tools to design and analyze Steel Plate Shear Wall (SPSW) and Reinforced Concrete Shear Walls (RCSW) subjected to explosive loads. Traditional Finite Element (FE) simulation is time-consuming and requires numerous samples for accurate results. This package offers a more efficient approach using Artificial Intelligence (AI) models trained on FEA data. You'll learn to develop FE models of SPSW and RCSW in ABAQUS software, considering material properties, interactions, and boundary conditions. The Taguchi method optimizes the number of samples needed for FE analysis, and this method is used with SPSS after explanation its concept.
We then delve into AI modeling using MATLAB. Explore various methods like regression, Machine Learning (ML), Deep Learning (DL), and ensemble models to predict the behavior of SPSW and RCSW under blast loads. Statistical analysis helps compare model accuracy. By combining FE analysis with AI models, you'll gain a powerful tool for designing blast-resistant structures while saving time and resources.
In this tutorial, we explore the hygrothermal degradation composites using ABAQUS, a powerful tool for parallel finite element analysis. Industries like aerospace, marine, and automotive heavily rely on these composites due to their high strength-to-weight ratio and versatility. However, long-term exposure to moisture and temperature can degrade their mechanical properties, making an analysis of hygrothermal effects on composite materials essential for ensuring durability.
ABAQUS allows precise modeling of these environmental conditions through Python scripts and Fortran subroutines. This combination enables efficient simulations across multiple processors, offering insights into key elastic properties, such as Young’s modulus and shear modulus, under varying conditions. By leveraging the ABAQUS Python Scripting Micro Modeling (APSMM) algorithm and custom subroutines, engineers can predict the long-term performance of fiber-reinforced composites, optimizing design and enhancing material performance in critical sectors like aerospace and marine.
In the present Abaqus tutorial for parallel finite element analysis, we have presented the software skills that a person needs when he wants to perform a parallel finite element analysis such as a micro-macro scale analysis. The Abaqus tutorial for parallel finite element analysis covers all you need to write a python scripting code for noGUI environment and also Fortran code for the subroutine environment of Abaqus to execute a parallel finite element analysis via Abaqus software. You can download the syllabus of this package here.
Additive Manufacturing (AM), a revolutionary layer-by-layer fabrication technology, is transforming how products are designed and manufactured. This comprehensive tutorial package focuses on the Inherent Strain (IS) method, a highly efficient numerical approach for simulating the Laser Powder Bed Fusion (LPBF) process in metal additive manufacturing. The detailed thermo-mechanical simulation of the Laser Powder Bed Fusion (LPBF) for complex geometric parts requires a large number of time steps to estimate residual stress and distortion, which is not computationally cost-effective. Furthermore, based on the large thermal gradient near the heat source, the mesh size must be sufficiently small to accurately predict the induced residual stress and distortion of the deposited layers in the heat-affected zone. Therefore, applying a coupled thermo-mechanical analysis for multiple laser scans with a fine mesh model to macro-scale simulation would incur excessively large computational costs.
Additionally, the large number of degrees of freedom for each element in the mechanical analysis leads to higher complexity as well as a longer amount of processing time. Detailed thermo-mechanical analysis for an industrial component is almost impractical since it would demand hundreds of terabytes of memory and years to calculate. Therefore, to overcome the huge computational burden associated with the numerical simulation of the LPBF caused by the infinitesimal laser spot size and thousands of thin layers with a thickness at the micron level, the Inherent Strain Method in additive manufacturing has been widely used in research and commercial software.
In this tutorial, the Inherent Strain Method additive manufacturing approach is presented both theoretically and practically in Abaqus. An agglomeration approach will be considered to transfer an equivalent inherent strain from both micro-scale and macro-scale modeling strategies. The implementation of this approach is explained step by step, accompanied by various workshops in micro-scale and macro-scale models for different geometries. This training package enables you to write your subroutine codes and Python scripting, as well as have more control over the LPBF process simulation.
Abaqus shaft slip ring simulation | Using Python scripts for parametric analysis
Creep is one of the most significant failure modes in many components where the working temperature and stresses are high for a prolonged period of time. Existing creep models in commercial analysis software like Abaqus are not adequate to model all stages of creep namely – primary, secondary, and tertiary stages. Theta projection method is a convenient method proven to predict all stages of creep, especially the tertiary stage where strain rates are high leading to internal damage and fracture. The aim of the project is to develop a user subroutine for Abaqus to model creep in components using the Theta projection method. The constitutive model for the Theta projection method based on the accumulation of internal state variables such as hardening, recovery, and damage developed by (R.W.Evans, 1984) is adopted to compile a Fortran code for the user subroutine. The user subroutine is validated through test cases and comparing the results with experimental creep data. Creep analysis of a sample gas turbine blade (Turbine Blade Creep) is then performed in Abaqus through the user subroutine and the results are interpreted.
Results of test cases validate the accuracy of the Theta Projection Method in predicting all primary, secondary, and tertiary stages of creep than existing creep models in Abaqus (Creep Failure in Turbine Blades). Results at interpolated & extrapolated stress & temperature conditions with robust weighted least square regression material constants show the convenience in creep modeling with less input data than existing models. The results of creep analysis not only predicted the creep life but also indicated the internal damage accumulation. Thus, creep modeling of components through the user subroutine at different load conditions could lead us to more reliable creep life predictions and also indicate the regions of high creep strain for improvements in the early stages of design.
Simulation of Pitting Corrosion Mechanism with Scripting in Abaqus
Simulation of shape control by piezoelectric in Abaqus
Composite Pressure Vessel simulation in ABAQUS
Script to transfer load from CFD to structural model in Abaqus
Python Scripting in Abaqus Full Tutorial
Additive manufacturing simulation with Abaqus subroutine & python | 3D printing Python
Python scripting in ABAQUS Part 2
Python scripting in ABAQUS-(FREE Version)
Python scripting in ABAQUS Part1
Additive Manufacturing or 3D Printing Abaqus simulation