Computational Analysis - Overview

Introduction

• Computational analysis is essential for understanding different state-problems in collaborative and distributed design processes.
• It helps discern dynamics in agent organization and interactions, using mathematical and computational methods.
• Both theoretical and empirical models offer insights into organizational activities.
• Design processes are viewed as computational, transforming information and knowledge-driven activities.

Computational Design/Analysis Process

• Initial step involves creating a 2D or 3D shape using 3D CAD software.
• Basic analysis includes stress, deflection, buckling, heat transfer, natural frequency, and durability.
• Advanced simulations can include fluid flow, dynamic analysis, and mechanisms analysis.

Computational Analysis in Catalysis

• Vital for understanding catalytic systems and enhancing catalysis design.
• Involves parameter estimation and optimization within constraints.
• Uses kinetic models to improve chemical process understanding and design.
• Aspen Plus simulator is a tool for process simulation involving complex conditions.

Computational Analysis in Social Sciences

• Augments traditional methods with computational text analysis.
• Enhances transparency, reproducibility, and validation in research.
• Focuses on methodological improvements in data collection and model validation.
• Suggests need for codes of practice in computational text analysis.

Multiscale Modeling in Biomechanics

• Models organs and tissues at different scales using computational analysis.
• Enables testing of biomechanical theories and development of patient-specific therapies.

Computational Science

• Replaces costly experiments with computational models in complex scenarios.
• Adds a third discipline to scientific inquiry alongside theory and experimentation.
• Useful in weather prediction, crash simulations, and airflow studies.

Advances in Computational Analysis

• Finite element analysis originates from solving complex structural problems.
• Mesh generation plays a key role in analyzing forces, displacements, and temperatures.
• Involves solving partial differential equations and creating subdomains for calculations.
• Finer meshes require more computing power.

SERS Spectra and Machine Learning

• Analyzes Raman spectral signals using machine learning for feature extraction.
• PCA, K-Means, and SVM are common algorithms used in spectral data analysis.
• Deep learning models like CNN enhance prediction accuracy and model robustness.

Highlights

• Computational analysis is critical in geothermal energy research and technological impact assessments.
• Algorithmic theme identification aids in understanding domain evolution and setting future research agendas.
• Provides insights into subdomain status, limits, and emerging research fronts.