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Husqvarna Construction is one of the leading construction machinery manufacturers in the world. To stay in the forefront, investing in novel methods to model, test & and optimize machinery is crucial. The most important part of development and testing is to bridge the gap between desired and actual results. Model-based Simulation in testing plays a superior role in visualizing possibilities while cutting down the usage of resources. Floor Grinders are common in industrial and commercial settings to achieve desired floor results. Like every machinery, optimization towards achieving better results is a necessity. The purpose of this thesis is to develop a methodology to optimize Husqvarna Constructions’s floor-grinding machine through its grinding pattern and further study & gather data about the key indicators for an optimum grinding pattern. This is done by setting up a grinding pattern simulation of the PG 690 floor grinder on SIMGRIND (Husqvarna Construction’s own simulation application). A metric was developed to determine whether a grinding pattern is good, and by utilizing the metric as an optimization goal, the impact of different machine parameters on the grinding pattern was established. The grinding & travel speeds were viewed as ratios and it was observed that optimized patterns were attained at particular ratios. Another crucial factor that was studied was the impact of oscillations. Further, the impact of grinding head size on the grinding pattern was also studied. The investigation was limited to a simulation study since physical validation opened up several uncertainties beyond the scope of this work. At the end of this work, a few recommendations for developing physical validation setups are made, to test the results of the simulation.

The United States Navy is committed to developing technology for an All-Electric Ship (AES) that promises to improve the affordability and capability of its next-generation warships. With the addition of power-intensive 21st century electrical systems, future thermal loads are projected to exceed current heat removal capacity. Furthermore, rising fuel costs necessitate a careful approach to total-ship energy management. Accordingly, the aim of this research is to develop computer tools for early-stage design of shipboard energy distribution systems. A system-level model is developed that enables ship designers to assess the effects of thermal and electrical system configurations on fuel efficiency and survivability. System-level optimization and design exploration, based on these energy system models, is challenging because the models are sometimes computationally expensive and characterized by discrete design variables and discontinuous responses. To address this challenge, a classifier-guided sampling (CGS) method is developed that uses a Bayesian classifier to pursue solutions with desirable performance characteristics. The CGS method is tested on a set of example problems and applied to the AES energy system model. Results show that the CGS method significantly improves the rate of convergence towards known global optima, on average, when compared to genetic algorithms. / text

Premium- Automobile zu entwickeln, gehört zu den erklärten Unternehmenszielen der AUDI AG. Doch ein herausragendes Design und höchste Qualität in Serie zu bringen – wie funktioniert das eigentlich? [... aus der Einleitung]

Computational tools such as finite element analysis and simulation are commonly used for system performance analysis and validation. It is often impractical to rely exclusively on the high-fidelity simulation model for design activities because of high computational costs. Mathematical models are typically constructed to approximate the simulation model to help with the design activities. Such models are referred to as “metamodel.” The process of constructing a metamodel is called “metamodeling.” Metamodeling, however, faces eminent challenges that arise from high-dimensionality of underlying problems, in addition to the high computational costs and unknown function properties (that is black-box functions) of analysis/simulation. The combination of these three challenges defines the so-called high-dimensional, computationally-expensive, and black-box (HEB) problems. Currently there is a lack of practical methods to deal with HEB problems. This dissertation, by means of surveying existing techniques, has found that the major deficiency of the current metamodeling approaches lies in the separation of the metamodeling from the properties of underlying functions. The survey has also identified two promising approaches - mapping and decomposition - for solving HEB problems. A new analytic methodology, radial basis function–high-dimensional model representation (RBF-HDMR), has been proposed to model the HEB problems. The RBF-HDMR decomposes the effects of variables or variable sets on system outputs. The RBF-HDMR, as compared with other metamodels, has three distinct advantages: 1) fundamentally reduces the number of calls to the expensive simulation in order to build a metamodel, thus breaks/alleviates exponentially-increasing computational difficulty; 2) reveals the functional form of the black-box function; and 3) discloses the intrinsic characteristics (for instance, linearity/nonlinearity) of the black-box function. The RBF-HDMR has been intensively tested with mathematical and practical problems chosen from the literature. This methodology has also successfully applied to the power transfer capability analysis of Manitoba-Ontario Electrical Interconnections with 50 variables. The test results demonstrate that the RBF-HDMR is a powerful tool to model large-scale simulation-based engineering problems. The RBF-HDMR model and its constructing approach, therefore, represent a breakthrough in modeling HEB problems and make it possible to optimize high-dimensional simulation-based design problems.

Computational tools such as finite element analysis and simulation are commonly used for system performance analysis and validation. It is often impractical to rely exclusively on the high-fidelity simulation model for design activities because of high computational costs. Mathematical models are typically constructed to approximate the simulation model to help with the design activities. Such models are referred to as “metamodel.” The process of constructing a metamodel is called “metamodeling.” Metamodeling, however, faces eminent challenges that arise from high-dimensionality of underlying problems, in addition to the high computational costs and unknown function properties (that is black-box functions) of analysis/simulation. The combination of these three challenges defines the so-called high-dimensional, computationally-expensive, and black-box (HEB) problems. Currently there is a lack of practical methods to deal with HEB problems. This dissertation, by means of surveying existing techniques, has found that the major deficiency of the current metamodeling approaches lies in the separation of the metamodeling from the properties of underlying functions. The survey has also identified two promising approaches - mapping and decomposition - for solving HEB problems. A new analytic methodology, radial basis function–high-dimensional model representation (RBF-HDMR), has been proposed to model the HEB problems. The RBF-HDMR decomposes the effects of variables or variable sets on system outputs. The RBF-HDMR, as compared with other metamodels, has three distinct advantages: 1) fundamentally reduces the number of calls to the expensive simulation in order to build a metamodel, thus breaks/alleviates exponentially-increasing computational difficulty; 2) reveals the functional form of the black-box function; and 3) discloses the intrinsic characteristics (for instance, linearity/nonlinearity) of the black-box function. The RBF-HDMR has been intensively tested with mathematical and practical problems chosen from the literature. This methodology has also successfully applied to the power transfer capability analysis of Manitoba-Ontario Electrical Interconnections with 50 variables. The test results demonstrate that the RBF-HDMR is a powerful tool to model large-scale simulation-based engineering problems. The RBF-HDMR model and its constructing approach, therefore, represent a breakthrough in modeling HEB problems and make it possible to optimize high-dimensional simulation-based design problems.

Product visualization in AR/VR applications requires a largely manual process of data preparation. Previous publications focus on error-free triangulation or transformation of product structure data and display attributes for AR/VR applications. This paper focuses on the preparation of the required geometry data. In this context, a significant reduction in effort can be achieved through automation. The steps of geometry preparation are identified and examined concerning their automation potential. In addition, possible couplings of sub-steps are discussed. Based on these explanations, a structure for the geometry preparation process is proposed. With this structured preparation process, it becomes possible to consider the available computing power of the target platform during the geometry preparation. The number of objects to be rendered, the tessellation quality, and the level of detail (LOD) can be controlled by the automated choice of transformation parameters. Through this approach, tedious preparation tasks and iterative performance optimization can be avoided in the future, which also simplifies the integration of AR/VR applications into product development and use. A software tool is presented in which partial steps of the automatic preparation are already implemented. After an analysis of the product structure of a CAD file, the transformation is executed for each component. Functions implemented so far allow, for example, the selection of assemblies and parts based on filter options, the transformation of geometries in batch mode, the removal of certain details, and the creation of UV maps. Flexibility, transformation quality, and timesavings are described and discussed.

<ul> <li>Energy Optimization Stategies</li> <li>Hydraulic Models for Water Distribution Systems</li> <li>Heuristic Multi-objective Optimization Algorithms</li> <li>Multi-objective Optimization Problems</li> <li>System Constraints</li> <li>Encoding Techniques</li> <li>Optimal Pumping Operations</li> <li>Sovling Real-World Optimization Problems </li> </ul> / <p>The water supply industry is a very important element of a modern economy; it represents a key element of urban infrastructure and is an integral part of our modern civilization. Billions of dollars per annum are spent internationally in pumping operations in rural water distribution systems to treat and reliably transport water from source to consumers.</p> <p>In this dissertation, a new multi-objective optimization approach referred to as energy optimization strategy is proposed for minimizing electrical energy consumption for pumping, the cost, pumps maintenance cost, and the cost of maximum power peak, while optimizing water quality and operational reliability in rural water distribution systems. Minimizing the energy cost problem considers the electrical energy consumed for regular operation and the cost of maximum power peak. Optimizing operational reliability is based on the ability of the network to provide service in case of abnormal events (e.g., network failure or fire) by considering and managing reservoir levels. Minimizing pumping costs also involves consideration of network and pump maintenance cost that is imputed by the number of pump switches. Water quality optimization is achieved through the consideration of chlorine residual during water transportation.</p> <p>An Adaptive Parallel Clustering-based Multi-objective Particle Swarm Optimization (APC-MOPSO) algorithm that combines the existing and new concept of Pareto-front, operating-mode specification, selecting-best-efficiency-point technique, searching-for-gaps method, and modified K-Means clustering has been proposed. APC-MOPSO is employed to optimize the above-mentioned set of multiple objectives in operating rural water distribution systems.</p> <p>Saskatoon West is, a rural water distribution system, owned and operated by Sask-Water (i.e., is a statutory Crown Corporation providing water, wastewater and related services to municipal, industrial, government, and domestic customers in the province of Saskatchewan). It is used to provide water to the city of Saskatoon and surrounding communities. The system has six main components: (1) the pumping stations, namely Queen Elizabeth and Aurora; (2) The raw water pipeline from QE to Agrium area; (3) the treatment plant located within the Village of Vanscoy; (4) the raw water pipeline serving four major consumers, including PCS Cogen, PCS Cory, Corman Park, and Agrium; (5) the treated water pipeline serving a domestic community of Village of Vanscoy; and (6) the large Agrium community storage reservoir.</p> <p>In this dissertation, the Saskatoon West WDS is chosen to implement the proposed energy optimization strategy. Given the data supplied by Sask-Warer, the scope of this application has resulted in savings of approximately 7 to 14% in energy costs without adversely affecting the infrastructure of the system as well as maintaining the same level of service provided to the Sask-Water’s clients.</p> <p>The implementation of the energy optimization strategy on the Saskatoon West WDS over 168 hour (i.e., one-week optimization period of time) resulted in savings of approximately 10% in electrical energy cost and 4% in the cost of maximum power peak. Moreover, the results showed that the pumping reliability is improved by 3.5% (i.e., improving its efficiency, head pressure, and flow rate). A case study is used to demonstrate the effectiveness of the multi-objective formulations and the solution methodologies, including the formulation of the system-operational optimization problem as five objective functions. Beside the reduction in the energy costs, water quality, network reliability, and pumping characterization are all concurrently enhanced as shown in the collected results. The benefits of using the proposed energy optimization strategy as replacement for many existing optimization methods are also demonstrated.</p> / Doctor of Science (PhD)