A global/local framework for multidisciplinary optimization of generalized aircraft wing structure has been developed. The concept of curvilinear stiffening members (spars, ribs and stiffeners) has been applied in the optimization of a wing structure. A global wing optimization framework EBF3WingOpt, which integrates the static aeroelastic, flutter and buckling analysis, has been implemented for exploiting the optimal design at the wing level. The wing internal structure is optimized using curvilinear spars and ribs (SpaRibs). A two-step optimization approach, which consists of topology optimization with shape design variables and size optimization with thickness design variables, is implemented in EBF3WingOpt. A local panel optimization EBF3PanelOpt, which includes stress and buckling evaluation criteria, is performed to optimize the local panels bordered by spars and ribs for further structural weight saving. The local panel model is extracted from the global finite element model. The boundary conditions are defined on the edges of local panels using the displacement fields obtained from the global model analysis. The local panels are optimized to satisfy stress and buckling constraints. Stiffened panel with curvilinear stiffeners is implemented in EBF3PanelOpt to improve the buckling resistance of the local panels. The optimization of stiffened panels has been studied and integrated in the local panel optimization. EBF3WingOpt has been applied for the optimization of the wing structure of the Boeing N+2 supersonic transport wing and NASA common research model (CRM). The optimization results have shown the advantage of curvilinear spars and ribs concept. The local panel optimization EBF3PanelOpt is performed for the NASA CRM wing. The global-local optimization framework EBF3GLWingOpt, which incorporates global wing optimization module EBF3WingOpt and local panel optimization module EBF3PanelOpt, is developed using MATLAB and Python programming to integrate several commercial software: MSC.PATRAN for pre and post processing, MSC.NASTRAN for finite element analysis. An approximate optimization method is developed for the stiffened panel optimization so as to reduce the computational cost. The integrated global-local optimization approach has been applied to subsonic NASA common research model (CRM) wing which proves the methodology's application scaling with medium fidelity FEM analysis. Both the global wing design variables and local panel design variables are optimized to minimize the wing weight at an acceptable computational cost. / Ph. D.

This dissertation deals with the solution of three inverse problems in structural mechanics. The first one is load updating for finite element models (FEMs). A least squares fitting is used to identify the load parameters. The basic studies are made for geometrically linear and nonlinear FEMs of beams or frames by using a four-noded curved beam element, which, for a given precision, may significantly solve the ill-posed problem by reducing the overall number of degrees of freedom (DOF) of the system, especially the number of the unknown variables to obtain an overdetermined system. For the basic studies, the unknown applied load within an element is represented by a linear combination of integrated Legendre polynomials, the coefficients of which are the parameters to be extracted using measured displacements or strains. The optimizer L-BFGS-B is used to solve the least squares problem. The second problem is the placement optimization of a distributed sensing fiber optic sensor for a smart bed using Genetic Algorithms (GA), where the sensor performance is maximized. The sensing fiber optic cable is represented by a Non-uniform Rational B-Splines (NURBS) curve, which changes the placement of a set of infinite number of the infinitesimal sensors to the placement of a set of finite number of the control points. The sensor performance is simplified as the integration of the absolute curvature change of the fiber optic cable with respect to a perturbation due to the body movement of a patient. The smart bed is modeled as an elastic mattress core, which supports a fiber optic sensor cable. The initial and deformed geometries of the bed due to the body weight of the patient are calculated using MSC/NASTRAN for a given body pressure. The deformation of the fiber optic cable can be extracted from the deformation of the mattress. The performance of the fiber optic sensor for any given placement is further calculated for any given perturbation. The third application is stiffened panel optimization, including the size and placement optimization for the blade stiffeners, subject to buckling and stress constraints. The present work uses NURBS for the panel and stiffener representation. The mesh for the panel is generated using DistMesh, a triangulation algorithm in MATLAB. A NASTRAN/MATLAB interface is developed to automatically transfer the data between the analysis and optimization processes respectively. The optimization consists of minimizing the weight of the stiffened panel with design variables being the thickness of the plate and height and width of the stiffener as well as the placement of the stiffeners subjected to buckling and stress constraints under in-plane normal/shear and out-plane pressure loading conditions. / Ph. D.

Climate change has already had major impacts on our planet. Loss of sea ice, accelerated sea level rise and longer, more intense heat waves, are a few of these. Many scientists believe that a continued climate change will have even more severe impact on our planet. To tackle the climate change, a fast transition towards renewable energy sources is necessary. One of the most promising sources of renewable energy is solar energy. To achieve the goal of making the world more reliable on solar energy, various actors try to improve the technology and the financial basis regarding this way of extracting energy. In this transition, calculations have to be as accurate as possible, in order to benefit from them when installing solar panels. This bachelor thesis intends to create an investment model for solar plants and an optimization of the plant’s size based on net present value. The model is built in Microsoft Excel, and factors such as electricity prices, electricity production/consumption and several others are taken into account. Based on the comparison with a case study and the calculations made by the model, the results suggest a reliable model. On behalf of Herrljunga Elektriska AB, the model is created to help them generate reliable and fast investment calculations, which will hopefully bring value to their business.