In Algorithmic Synthesis Laboratory (ASL), we conduct research in the area of computational design synthesis, namely the development of the algorithms and software tools for assisting (or sometimes eliminating) human designers, which are applicable to a certain class of products as oppsed to specific individual products. In some occcations, we work on designing specific producs, either as projects on their own or as application examples for generic algorithms and tools. Below are samples of research topics we currently investigate. Click here to see some of our recent past work.
Design Optimization of Photovoltaic-Powered Reverse Osmosis Desalination Systems
Combining solar photovoltaic (PV) energy source and reverse osmosis (RO) desalination has been considered as one of the most promising water generating systems for remote areas where water source is scarce while solar irradiation is abundant. Due to the time-dependent nature of PV electrical power, this desalination system have been equipped with batteries in order for RO desalination system to continuously operate at constant feed pressure and feed flow. On the other hand, PV-RO systems have been designed without batteries since the use of batteries is associated with high capital cost. We are going to investigate on the design optimization of PV-RO desalination systems both without and with batteries, and compare the performances of the optimized systems. This work is in collaboration with Prof. Sayed Metwalli at Cairo U and Profs. El Morsi and Nassef at American U of Cario.
Data-Driven Manufacturing Constraint Modeling
This project presents a data-driven synthesis for manufacturing constraint modeling (MCM), through massive process simulations and data mining. For given geometric representation of the component, the output of the constraint model is the manufacturability estimation (either binary prediction with confidence calibration, or continuous quantitative prediction). The training data is generated by finite element manufacturing process simulations, and the predictive model is trained by classification/regression statistical learning methods. The resulting manufacturing constraint model will be useful to enhance the component manufacturability during manual design iterations as well as computer-based design optimization.
Multi-Material Topology Optimization of Vehicle Body Structures
The aim of this project is to develop a two-step method for multi-material topology optimization of vehicle body structures and demonstrating the developed method with a simplified body structure model of a mini-size electric vehicle. The first step ( (a)-(b) ) selects the overall structural topology and distribution of materials and plate thicknesses considering global bending and torsions, where the conventional SIMP method is extended to handle multiple materials and thicknesses. The second step ( (b)-(c) ) refines the selections of materials and thicknesses, and also joint configurations considering NVH and crashworthiness, where active learning based optimization is adopted to reduce computational time. The successful completion of the project will, for the first time, demonstrate the multi-material structural topology optimization of 3D structures with a complexity comparable to vehicle bodies. The developed method will contribute to the further weight reduction of vehicle body structures and to the improvement energy efficiency of future vehicles. This project is a part of US-China Clean Energy Reseach Center for Clean Vehicles (CERC-CV). Click here for a related ME news article.
Production System and Multi-source Energy Supplies Integration
While wind and solar are commonly considered alternative energy sources for sustainable manufacturing, the fluctuation and uncertainty of natural phenomena pose unique challenges on a production system with the concern of energy shortage during peak demand periods. This work develops an optimal planning framework for a production system utilizing multi-source distributed energy supplies, in order to improve its energy efficiency with lower grid energy dependency and outage risk, without the risk of sacrificing customer satisfaction. The optimization allows the peak load of production system to be synchronized with the varied energy supply through production planning and scheduling
Top-Down Structural Assembly Synthesis
Topology optimization is the process of allocating structural material within a design domain (subject to certain loading and boundary conditions) in order to optimally achieve a desired functionality. An extension of topology optimization, this project aims to develop a top-down structural assembly synthesis. Unlike regular topology optimization, which generally deals with a monolithic structure, optimal assembly synthesis faces the unique challenge of coupled material allocation within a design domain, as well as allocation of separators (joints). It is established that the optimal material allocation depends on the joint allocations, and in turn, the optimal joints allocation depends on the material allocation , which means that to reach a global optimal, both problems must be solved simultaneously. This research also considers multi-objective performance measures of the synthesized structures such as structural performance (mass and stiffness), assemblability and manufacturability
Traffic Flow Modeling for Information-Efficient Traffic Management
Real-time prediction of traffic state is fundamental to improve efficiency, sustainability and safety of the transportation system, and GPS-equiped vehicles and smart phones has become a new approach to collect probe data from the traffic. The objective of this research is to develop and validate a multiphase macroscopic traffic flow model and an adaptive strategy to assimilate data from probe vehicles for real-time update of the model, so that traffic information can be exchanged between drivers and the transportation system efficiently to improve transportation system utilization. This work is in collaboration with Prof. Romesh Saigal in Industrial and Operations Engineering , University of Michigan.
ChemReader : Automated Annotation of Chemical Structure Database
The goal of this project is to develop an automated system annotating chemical database, ChemReader for recognizing chemical structure diagrams in research articles and linking them with entries in the database. ChemReader system consists of mainly four steps: 1) Document segmentation building logical relationships of objects and elements in the input literature and natural language processing of text components to extract the contextual scientific knowledge, 2) Classification of chemical structure diagrams from documents objects or elements, 3) Recognition step converting classified chemical structure diagrams into standard, searchable chemical file formats, and 4) Annotation of chemical database entries which match to extracted structures of step 3) with obtained knowledge at the step 1). By annotating each molecule in the database with one or more relevant links to the scientific literature, the database would be a more useful resource to bio/chemical research scientists. ChemReader project has been worked in collaboration with Gus Rosania in College of Pharmacy, University of Michigan. Click here for a related ME news article.
Rotamer-Dependent Atomic Statistical Potential for Assessment and Prediction of Protein Structures
Statistical potential energy functions are usually derived from known protein structures in the Protein Data Bank, and widely used in protein structure modeling and quality assessment. Since atomic interactions in protein structures can be more accurately described by multi-body potentials than typical distance-dependent energy potentials, it is desirable to take local environmental features such as secondary structure, solvent accessibility or side-chain orientation into account when developing potential energy functions. On the other hand, amino acid side-chains prefer to adopt only a limited number of conformations, known as rotamers. Thus, given a limited number of structures in PDB, the micro-environment of protein atoms can be effectively discretized by rotameric states of residues. Here, we have developed a novel rotamer-dependent statistical potential, which can improve the prediction accuracy of side-chain conformations as well as overall folded structures. Click here for a related ME news article.
Biomechanically-Guided Deformable Image Registration
As outcomes of current radiotherapy techniques are often degraded by anatomical variations during the course of treatments (5-7 weeks), deteriorating quality of patient’s life after the treatment, the investigation on adapting radiotherapy planning in response to the variations has gathered strong interest. Developing accurate deformable image registration (DIR) algorithms is of critical importance since DIR calculates geometric mapping between patient scans so that the variations can be integrated into planning or delivery stages. The objective of this study is to introduce biomechanical guidance to existing DIR algorithms either by developing tissue-specific mechanical penalties (e.g. muscle) or by associating with finite element method. We have been collaborating with Prof. James M. Balter and Prof. Martha M. Matuszak in Department of Radiation Oncology. Click here for a related ME news article.