The two dimensional transient heat conduction surrounding ground heat exchangers in clay soils is investigated analytically and numerically using finite element methods. The study is directed towards the application of the ground heat exchangers in a central solar heating plant with seasonal storage (CSHPSS). The analytical solution to the transient heat conduction for both the borehole and the U-tube configurations are presented. The analytical solution to the transient heat conduction in the hollow cylinder geometry is derived in cylindrical coordinates using the separation of variables method. The results of this solution are used to validate the finite element models of the borehole geometry. The procedures for developing the finite element models are included. The finite element modeling was extended to the geometry of the U-tube ground heat exchanger. Results are presented for the transient heat exchange performance of the U-tube for variations in geometry, material properties, and boundary conditions. The solution of the heat conduction in a circular region with two internal point sources of energy generation is derived in cylindrical coordinates to approximate the heat transfer for the U-tube geometry. The temperature profiles of the analytical solution compared closely to the finite element model of the U-tube geometry despite the inherent differences of the two models. The finite element models were further used to demonstrate that the geometry of the borehole can be used to approximate the transient thermal performance of a U-tube ground heat exchanger. The heat exchange performance of the two configurations compared closely over time beyond a short initial transient period.

This thesis proposes a novel approach, called Analogy-X to extend and improve the classical data-intensive analogy approach for software effort estimation. The Analogy-X approach combines the notions of distance matrix correlation found in ecology literature and statistic analysis techniques to provide useful inferential statistics to support analogy-based systems. Data-intensive analogy for software effort estimation has been proposed as a viable alternative to other prediction methods such as linear regression. In many cases, researchers found analogy outperformed algorithmic methods. However, the overall performance of analogy depends on the dataset quality or relevance of project cases to the target project, and the feature subset selected in the analogy-based model. Unfortunately, there is no mechanism to assess its appropriateness for a specific dataset, in most of the cases analogy will continue to execute regardless of the dataset quality. The Analogy-X approach is a set of procedures that utilize the principles of Mantel randomization test to provide inferential statistics to Analogy. Inspired by the Mantel correlation randomization test commonly used in ecology and psychology, Analogy-X uses the strength of correlation between the distance matrix of project features and the distance matrix of known effort values of the dataset to assess the suitability of the dataset for analogy, to identify the most appropriate feature subset, and to remove any atypical project cases from the dataset. The empirical studies show that Analogy-X is capable of: -- Detect extremely outlying project cases that will ultimately distort prediction outcomes using a sensitivity analysis strategy. -- Detect relevant project features that are useful to identify potential source analogues in a stepwise fashion similar to that of stepwise regression. -- Identifying whether analogy-based approach is appropriate for the dataset Analogy-X, thus is a robust solution, provides a sound statistical basis for analogy. It removes the need of using any forms of heuristic search and greatly improves its algorithmic performance. The studies also show that the Analogy-X approach is capable of removing the bottlenecks of performance in data-intensive analogy. The overall results obtained also suggest that a fully automated data-intensive analogy for software effort estimation can be implemented using the Analogy-X approach, and it is indeed an effective front end to analogy-based systems. The contribution of this work is significant since it provides an approach that will have major impact on the evolution of data-intensive analogy-based and case-based reasoning systems.

University of Macau / Faculty of Science and Technology / Department of Mathematics

published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy

No description available.

No description available.

No description available.

No description available.

No description available.

This thesis proposes a novel approach, called Analogy-X to extend and improve the classical data-intensive analogy approach for software effort estimation. The Analogy-X approach combines the notions of distance matrix correlation found in ecology literature and statistic analysis techniques to provide useful inferential statistics to support analogy-based systems. Data-intensive analogy for software effort estimation has been proposed as a viable alternative to other prediction methods such as linear regression. In many cases, researchers found analogy outperformed algorithmic methods. However, the overall performance of analogy depends on the dataset quality or relevance of project cases to the target project, and the feature subset selected in the analogy-based model. Unfortunately, there is no mechanism to assess its appropriateness for a specific dataset, in most of the cases analogy will continue to execute regardless of the dataset quality. The Analogy-X approach is a set of procedures that utilize the principles of Mantel randomization test to provide inferential statistics to Analogy. Inspired by the Mantel correlation randomization test commonly used in ecology and psychology, Analogy-X uses the strength of correlation between the distance matrix of project features and the distance matrix of known effort values of the dataset to assess the suitability of the dataset for analogy, to identify the most appropriate feature subset, and to remove any atypical project cases from the dataset. The empirical studies show that Analogy-X is capable of: -- Detect extremely outlying project cases that will ultimately distort prediction outcomes using a sensitivity analysis strategy. -- Detect relevant project features that are useful to identify potential source analogues in a stepwise fashion similar to that of stepwise regression. -- Identifying whether analogy-based approach is appropriate for the dataset Analogy-X, thus is a robust solution, provides a sound statistical basis for analogy. It removes the need of using any forms of heuristic search and greatly improves its algorithmic performance. The studies also show that the Analogy-X approach is capable of removing the bottlenecks of performance in data-intensive analogy. The overall results obtained also suggest that a fully automated data-intensive analogy for software effort estimation can be implemented using the Analogy-X approach, and it is indeed an effective front end to analogy-based systems. The contribution of this work is significant since it provides an approach that will have major impact on the evolution of data-intensive analogy-based and case-based reasoning systems.