Orthogonal decomposition as a design tool: With application to a mixing impeller

Digital manufacturing eliminates the expense and time required to develop custom products. By utilizing this technology, designers can quickly create a customized product specifically for their performance needs. But the timescale and expense from the engineering design workflows used to develop these customized products have not been adapted from the workflows used in mass production. In many cases these customized designs build upon already successful mass-produced products that were developed using conventional engineering design workflows. Many times as part of this conventional design process significant time is spent creating and validating high fidelity models that accurately predict the performance of the final design. These existing validated high fidelity models used for the mass-produced design can be reused for analysis and design of unknown products. This thesis explores the integration of reduced order modeling and detailed analysis into the engineering design workflow developing a customized design using digital manufacturing. Specifically, detailed analysis is coupled with proper orthogonal decomposition to enable the exploration of the design space while simultaneously shaping the model representing the design. This revised workflow is examined using the design of a laboratory scale overhead mixer impeller. The case study presented here is compared with the design of the Kar Dynamic Mixer impeller developed by The Dow Chemical Company. The result of which is a customized design for a refined set of operating conditions with improved performance

SENSITIVITY AND PRECISION ANALYSIS OF THE GRAPH COMPLEXITY CONNECTIVITY METHOD

In the Graph Complexity Connectivity Method (GCCM), twenty nine complexity metrics applied against engineering design graphs are used to create surrogate prediction models of engineering design representations (assembly models and function structures) for given product performance values (assembly time and market value). The performance of these prediction models has been previously assessed solely based on accuracy. In this thesis, the predictive precision of the surrogate models is evaluated in order to assess the GCCM\u27s ability to generate consistent results under the same conditions. The Assembly Model - Assembly Time (AM-AT) prediction model performed the best in terms of both accuracy and precision. This demonstrates that when given assembly models, one can consistently predict accurate assembly times. Further, a sensitivity analysis is conducted to identify the significant complexity metrics in the estimation of the performance values, assembly time and market value. The results of the analysis suggest that for each prediction model, there exists at least one metric from each complexity class (size, interconnection, centrality, and decomposition) which is identified as a significant predictor. Two of the twenty nine complexity metrics are found to be significant for all four prediction models: number of elements and density of the in-core numbers. The significant complexity metrics were used to create simplified surrogate models to predict the product performance values. The test results indicate that the precision of the prediction models increases but the accuracy decreases when the unique significant metric sets are used. Finally, three experiments are conducted in order to investigate the effect of manipulation of the significant complexity metrics in predicting the performance values. The results suggest that the significant metric sets perform better in predicting the product performance values as compared to the manipulated metric sets of either union or intersection of metrics

A real-time assessment of the ship design complexity

The paper introduces an innovative complexity metric for passenger ships taking into account the shape complexity of steel parts, the assembly complexity and the material complexity. The goal is to provide the designer with such information throughout the design process so that an efficient design is obtained at the first design run. Real-time assessment of complexity and quality measurements is rather imperative to ensure efficient and effective optimality search, and to allow real-time adjustment of requirements during the design. Application and validation on a real passenger ship show that the new method is effective in giving a complementary aid to decision process for ship designers