Jasmin Jelovica

Marine vessels need to improve in a number of different ways, which could potentially be fulfilled by using new, advanced structural components. Sandwich structures are currently not used in cargo vessels but can potentially offer improvements in terms of several structural features. Metallic sandwich structures are recyclable and could be incorporated within the surrounding structure usually made from metals. This research focuses on metallic sandwich structures with different core topologies whose natural frequency is maximized using topology and sizing optimization techniques. To connect sandwich structure to the surroundings, a joint connection is optimized using stress-based topology optimization (TO) and compared with previously proposed alternatives. Furthermore, metallic sandwich structures have very thin plates which are laser welded. Since the weld is thinner than the plates, this research investigates the consequence of weld geometrical variation on vibration. Homogenization methods including Solid Isotropic Material with Penalization (SIMP) method and Equivalent Single Layer (ESL) method are utilized to build simplified models and to accelerate computation. Based on the performed optimization, X-core and corrugated core sandwich topologies show the highest efficiency. Additionally, TO provides even better topologies at certain mass levels with non-periodic shapes. The proposed new joints by TO feature 2-3 times smaller maximum stress than the previously proposed joint, even at lower structural weight. Furthermore, weld geometry plays a very important role in the response of laser welded web-core sandwich panels. Weld characteristics should be considered in design and production to ensure the desired natural frequency.

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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.

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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.

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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.

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