Relevant Degree Programs
Affiliations to Research Centres, Institutes & Clusters
1) New finite element analysis techniques for lightweight structures;
2) Development of optimization algorithms for structural optimization;
3) Machine learning methodology for reduced order modeling of mechanical systems.
Several courses on numerical analysis techniques in solid mechanics, mechanics of materials, structural vibrations, optimization and machine learning.
Experience using programming languages (Python, Matlab) for numerical analysis and/or optimization
Research experience on the above topics, possibily with journal papers published
Complete these steps before you reach out to a faculty member!
- Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
- Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Admission Information & Requirements" - "Prepare Application" - "Supervision" or on the program website.
- Identify specific faculty members who are conducting research in your specific area of interest.
- Establish that your research interests align with the faculty member’s research interests.
- Read up on the faculty members in the program and the research being conducted in the department.
- Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.
- Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
- Do not send non-specific, mass emails to everyone in the department hoping for a match.
- Address the faculty members by name. Your contact should be genuine rather than generic.
- Include a brief outline of your academic background, why you are interested in working with the faculty member, and what experience you could bring to the department. The supervision enquiry form guides you with targeted questions. Ensure to craft compelling answers to these questions.
- Highlight your achievements and why you are a top student. Faculty members receive dozens of requests from prospective students and you may have less than 30 seconds to pique someone’s interest.
- Demonstrate that you are familiar with their research:
- Convey the specific ways you are a good fit for the program.
- Convey the specific ways the program/lab/faculty member is a good fit for the research you are interested in/already conducting.
- Be enthusiastic, but don’t overdo it.
G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.
ADVICE AND INSIGHTS FROM UBC FACULTY ON REACHING OUT TO SUPERVISORS
These videos contain some general advice from faculty across UBC on finding and reaching out to a supervisor.
Graduate Student Supervision
Doctoral Student Supervision
Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.
Metal foam structures (MFS) show promising static and dynamic performances with some initial real-life applications. However, the dynamic behavior of MFS is not thoroughly addressed yet, which is one of the reasons that limits their use. This thesis studies their nonlinear vibration behavior for better understanding and further development of this structural material.Theoretical models are developed in this thesis to study nonlinear free and forced vibrational behavior of beams with bidirectional porosities, where the voids are non-uniformly distributed through the length and thickness of the beam. Moreover, effect of porosities on nonlinear natural frequencies of cylindrical panels is studied as well. In addition, a few novel porosity distributions are proposed to improve the vibrational response of the metal foam structures. The dynamic governing equations are derived based on the third-order shear deformation theory, Hamilton’s principle and von Kármán geometrical nonlinearity. The governing equations are solved using numerical and analytical methods (method of multiple scales and harmonic balance method) based on the problem type. Generalized differential quadrature method (GDQM) is one of the utilized numerical methods due to its simplicity and low computational cost. However, the GDQM has some limitations in implementing boundary conditions, thus to overcome this issue, a novel and simple approach is proposed in this study.Elastic properties of the composite metal foams (CMFs) are evaluated using homogenization technique and finite element simulation. CMFs are a new class of closed-cell porous materials that can be produced by distributing metallic spheres in a metallic matrix. The CMFs have better mechanical performance than the regular metal foams. Studying the effect of microporosities in spheres’ wall and matrix of the CMFs revealed that the matrix microporosity has higher effect on the CMFs elastic modulus than the spheres’ microporosity. Furthermore, results revealed thatincreasing the packing factor and spheres wall thickness increases the CMFs elastic modulus. Finally, it is shown that the CMF structures could have better vibrational response in comparison to the regular metal foam structures. This allows widespread use of CMFs as a new and attractive type of metal foam.
Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
Structural design typically involves nonconvex criteria that need effective optimization algorithms which can find the global optimum or Pareto optima. Constraints create complex hyperspaces that are difficult to navigate, and traditional constraint handling techniques (CHTs) might not be capable of steering the search. Repair techniques are one type of CHTs that can be very effective but have a few limitations that restrict their use. We here present a new repair-based CHT that addresses these issues by being: (i) adaptive to the share of infeasible solutions in a population and (ii) free of problem-specific heuristic for repair that a user typically needs to provide. Only the best performing infeasible solutions are repaired, to balance the normal operating procedure of the optimization algorithm with CHT, i.e., minimizing objectives and satisfying constraints. A procedure is proposed to apply artificial neural network (ANN) to automate the definition of problem-specific knowledge by identifying and ranking the most significant variables that influence each constraint. The proposed CHT approach is implemented in single-objective swarm algorithm PSO and multi-objective evolutionary algorithms NSGA-II and MOEA/D. The following test cases are considered: mathematical benchmark problem, truss optimization and structural optimization of a chemical tanker’s main frame. Trained ANN is used as surrogate model in the latter case. In comparison to the original algorithms, a few state-of-the-art algorithms and CHTs, all modified algorithms show significantly better performance.
Liquefied natural gas (LNG) is increasingly being used as fuel in ships to decrease air pollution. Although materials intended to be in contact with this cryogenic fluid (-163 °C) can safely bear the thermal stresses, the surrounding structure is made of structural steel. It is known that structural steels undergo ductile to brittle transition at low temperatures, and cracks could form in case of large LNG spills. However, there is a significant gap in scientific knowledge on the effect of small spills of LNG on the shipbuilding steels, where the material returns to room temperature after each spill; the focus of this investigation is, therefore, placed upon potential cases with repetitive small and localized LNG spills. In this study, liquid nitrogen (LN₂) was used instead of LNG since its release is not harmful to the environment and has a lower boiling temperature than LNG. A series of experimental investigations were carried out on E36 shipbuilding steel, a material commonly used for building ships that may be deployed in cold regions of temperatures reaching as low as -40 °C. Specimens were exposed to up to 150 cryocycles, after which tensile and hardness tests were performed, in addition to X-ray diffraction (XRD) and metallography investigations. Moreover, finite element analysis was performed with Abaqus on four different plate geometries with a thickness of 3, 6, and 18 mm. Non-linear material properties were used in the analyses. Out of a few models or varying complexity, the axisymmetric model was found to have good ratio between accuracy and computational cost. It was shown that material could experience high stresses, close to the tensile strength of the material; however, the experiments did not show any change in the microstructure of the material, and no crack formation was observed. XRD analysis showed the existence of residual stress in the material after cryocyclic tests; however, the trend of stress buildup was inconclusive. The results generally show that E36 steel without pre-existing cracks is safe when subjected to moderate number of LNG spills, but that needs to be re-evaluated for larger number of spills or for more complex situations.
Evolutionary algorithms are popular tools for optimization of both theoretical and real-world problems due to their ability to perform global search and to deal with non-convex, multi-objective problems. Handling the constraints is a major concern in optimization that can prolong the search or prevent the algorithm from convergence. Common approaches for constraint handling usually discard or devalue infeasible solutions, losing the valuable information they carry. Alternatively, common repair methods for constraint handling are limited to specific problem types. This study focuses on the development of a repair method for constraint handling in multi-objective optimization.A generic approach is proposed for improving the constraint handling. The method identifies infeasible solutions with high-quality objective values or small constraint violations. These solutions are modified to make them feasible while preserving their good position in the objective space. The repair is performed based on the relationship between constraints and variables in the problem. Variables causing infeasibility are replaced with values from other solutions. The number of repaired solutions varies during optimization. The remaining part of the solution set is created by usual operators to preserve the diversity and normal procedure of the algorithm.The proposed repair method is applied to NSGA-II as one of the most commonly used multi-objective algorithms. The algorithm is tested on an optimization benchmark test case and an engineering optimization problem involving the structural design of a product tanker. The performance of the proposed approach is compared to the original algorithm and a few other constraint handling methods. Also, a competitive evolutionary algorithm, MOEA/D, is used for validation of the results. The proposed method showed faster convergence to the Pareto frontier and better diversity, covering the highly constrained regions of the design space. Additionally, the proposed algorithm was successful in reaching feasible solutions much faster, which is important in the case of computationally expensive problems, a common situation in engineering.
If this is your researcher profile you can log in to the Faculty & Staff portal to update your details and provide recruitment preferences.