Algorithmic Synthesis Laboratory (ASL) investigates algorithmic and computational synthesis of for mechanical, industrial and biomedical systems. We develop advanced synthesis methods tailored for application domains utilizing techniques such as finite element analysis, geometric and kinematic reasoning, image and pattern recognition, and planning and optimization. The recent application domains include micro and nano-systems, biomedical imaging, structural and automotive systems, and production and energy systems. Below are samples of research topics we currently investigate. Click here to see some of our recent past work.
- Click here to learn about US-China Clean Energy Reseach Center for Clean Vehicles (CERC-CV)
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.
Information-Efficient Sustainable Traffic Management
The objective of this research is to reduce vehicle emission by an innovative intelligent transportation system which integrates probe vehicle management, traffic flow prediction, and efficient traffic information exchange between drivers and the transportation system. This work would develop strategy for probe vehicle deployment for traffic data collection, and then a stochastic traffic flow model can use traffic information from transportation system and probe vehicles for traffic prediction. Traffic information would be provided to the drivers considering emission pollution and transportation system utilization. This work is in collaboration with Prof. Romesh Saigal in Industrial and Operations Engineering , University of Michigan.
Product-Process Co-Design for Selective Material Separation in E-Waste
The project aims at developing a product design methodology to enable energy-efficient selective material separation in e-waste. Current separation processes rely on physical properties of materials such as density, magnetivity, and conductivity to separate recyclable materials in shredded e-waste. These processes are, however, often energy intensive, low-yield, and require pre-shredding manual disassembly due to inherent uncertainties in the particle separation processes. We will explore the concepts of design for distractive disassembly combined with hammer milling processes, in order to enable electrical and electronics products that facilitate efficient material recovery and the associated material separation systems for extracting target materials.
Production System and Multi-source Energy Supplies Integration
The objective of this research is to develop a mode-based optimal design method for production system when using multi-source distributed energy supplies in order to reduce the dependency of grid energy and minimize exceed energy supply. This work involves the mathematical modeling of the energy consumption profiles of assembly plants and the supply capacity of distributed alternative energy sources such as solar and wind power. The optimization of the plant-energy supply systems would be focused on synchronizing the peak load of the plant with the varied energy supply through process improvement, process planning and scheduling.
University of Michigan Solar Car Team
Well, this is not exactly a research topic, but I am the Engineering Faculty Advisor for the University of Michigan Solar Car Team. Our recent accomplishments include the 3rd place in 2009 World Solar Challenge and the 1st place in 2010 American Solar Challenge. Click here to learn more about the team's activities. Photo courtesy of UM Solar Car Team. Copyright The University of Michigan Solar Cart Team, all rights reserved.
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
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.
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.
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.
Locally-compressed automated ultrasound scanning system for breast cancer screening
Breast cancer is the second leading cause of cancer deaths in women today. About 1.3 million women are diagnosed annually worldwide and about 465,000 will die from the disease. Automated ultrasound (AUS) imaging is a very promising breast cancer screening technology that can detect cancers in dense breasts, particularly with extreme compounding. However, AUS has been less successful than expected since the ultrasound imaging is limited in resolution and contrast due to increased shadow artifacts from the tissues compared with what is achieved with hand control of the transducer array. Recent study has shown that this difficulty in AUS could be significantly reduced in very light mammographic compression by a flexible mesh in a frame, along with additional localized compression of the breast as the transducer is scanned in contact with the mesh and the breast. The project aims at designing, testing, and charactersizing the automated system for locally compressed ultrasound scanning capable of yielding images in the same geometry as conventional and 3D mammography. This project is worked in collaboration with Prof. Paul Carson in Department of Radiology in Medical School.