Computer Aided Design (CAD)

Welcome to the esteemed Computer-Aided Design Research Group within the Department of Chemical, Polymer, and Composite Materials Engineering! Our dynamic and pioneering team stands at the forefront of cutting-edge research, employing state-of-the-art technologies to redefine the landscape of chemical engineering. Devoted to advancing the application of computer-aided design techniques in the domain of chemical processes and systems, our group systematically explores innovative solutions to chemical engineering challenges. Fostering interdisciplinary collaboration and demonstrating an unwavering commitment to pushing the boundaries of knowledge, we are dedicated to contributing significantly to the development of sustainable and efficient processes within the chemical industry.

Mission

Our mission is to advance knowledge and foster innovation in the field of chemical engineering. We are committed to shaping the future of the discipline through cutting-edge research, interdisciplinary collaboration, and the development of sustainable solutions.

Goals

  1. Advance Research in Process Systems Engineering.
  2. Explore and advance research in Pore-Scale Modeling.
  3. Develop innovative solutions to complex engineering challenges.
  4. Contribute to the development of sustainable processes within the chemical industry.
  5. Promote interdisciplinary collaboration for comprehensive insights.
  6. Engage in educational outreach and knowledge dissemination.
  7. Actively participate in shaping the future of chemical engineering.

Research Areas

The Computer-Aided Design Research Group within the Department of Chemical, Polymer, and Composite Materials Engineering specializes in two primary and impactful research areas: Process Systems Engineering (PSE) and Pore-Scale Modeling.

Process System Engineering (PSE)

In the realm of Process Systems Engineering, our group focuses on the application of advanced mathematical and computational methodologies to optimize the design, operation, and control of chemical processes. Employing cutting-edge technologies, we aim to enhance the efficiency, reliability, and sustainability of chemical engineering systems. By leveraging sophisticated modeling and simulation techniques, our researchers tackle complex challenges in process optimization, integration, and innovation.

Pore Scale Modelling

Pore scale modeling stands as the gateway to unraveling the world concealed within porous materials. Computer aided design research group in, Department of Chemical, Polymer, and Composite Materials Engineering, research revolves around employing advanced computational techniques to investigate the structural and transport properties of materials at the microscopic level. Our focus is on utilizing computer-aided design, image processing, and machine learning to gain unprecedented insights into the complex architecture of porous media. This interdisciplinary approach allows us to bridge the gap between theory and experimentation, offering a comprehensive understanding of how pore-scale features influence the overall behavior of materials.

Computational Tools and Advanced Techniques: PoreSpy and OpenPNM

In our pursuit of unraveling the secrets held within porous materials, we employ powerful computational tools such as PoreSpy and OpenPNM. These tools serve as indispensable assets in our exploration of porous structures, enabling a detailed analysis of their morphology and connectivity. The integration of image processing techniques further enhances our ability to extract meaningful data from complex tomography images. This combination of computational tools and advanced techniques allows us to go beyond traditional methods, providing a nuanced understanding of the pore-scale characteristics that govern material behavior.

Machine Learning in Pore Scale Modelling: Efficient Algorithms for Structural Extraction

A distinctive aspect of our research lies in the integration of machine learning techniques into pore scale modeling. We have developed efficient algorithms that leverage machine learning to extract intricate porous structures from tomography images. This innovative approach not only accelerates the analysis process but also enhances the accuracy of structural extraction. By harnessing the power of machine learning, we aim to not only characterize porous materials but also predict and optimize their behavior with a precision that was previously unattainable.