Search

Abstract: A scientific workflow can be viewed as formal model of the flow of data between processing components. It often involves a combination of data integration, computation, analysis, and visualization steps. An emerging use case involves determining some input parameters that minimize (or maximize) the output of a computation. Kepler is a good framework for specifying such optimizations because arbitrary computations can be composed into a pipeline, which is then repeated until an optimal set of inputs is found. Genetic Algorithms are generic optimization algorithms based on the principles of genetics and natural selection, and are well suited for models with discontinuous objective functions. This paper discusses an implementation of a Genetic Algorithm in Kepler, building on the Nimrod/OK framework. The resulting tool is generic and flexible enough to support a variety of experimental domains. The paper reports a number of experiments that demonstrate the performance with a set of benchmarking functions. [Copyright &y& Elsevier]

This paper presents an automated system for parameter identification of inelastic constitutive models. The system can find good approximate parameters for various identification problems under a user-friendly environment. In order to identify parameters efficiently and in a robust manner, an optimisation method is first proposed. The paper then describes the generalisations applied of modelling, simulation and identification for its various identification uses. Finally, a system, which is developed in conjunction with the generalisations, is described. The performances of the proposed optimisation method and the developed system were investigated with actual material data, and their effectiveness was consequently confirmed. [Copyright &y& Elsevier]

This paper presents a novel, simple method for reducing external operating condition datasets to be used in multi-generation system optimization models. The method, called the Characteristic Operating Pattern (CHOP) method, is a visually-based aggregation method that clusters reference data based on parameter values rather than time of occurrence, thereby preserving important information on short-term relations between the relevant operating parameters. This is opposed to commonly used methods where data are averaged over chronological periods (months or years), and extreme conditions are hidden in the averaged values. The CHOP method is tested in a case study where the operation of a fictive Danish combined heat and power plant is optimized over a historical 5-year period. The optimization model is solved using the full external operating condition dataset, a reduced dataset obtained using the CHOP method, a monthly-averaged dataset, a yearly-averaged dataset, and a seasonal peak/off-peak averaged dataset. The economic result obtained using the CHOP-reduced dataset is significantly more accurate than that obtained using any of the other reduced datasets, while the calculation time is similar to those obtained using the monthly averaged and seasonal peak/off-peak averaged datasets. The outcomes of the study suggest that the CHOP method is advantageous compared to chronology-averaging methods in reducing external operating condition datasets to be used in the design optimization models of flexible multi-generation systems. [ABSTRACT FROM AUTHOR]

Average surface roughness value ( R a ) is an important measure of the quality of a machined work piece. Lower the R a value, the higher is the work piece quality and vice versa. It is therefore desirable to develop mathematical models that can predict the minimal R a value and the associated machining conditions that can lead to this value. In this paper, real experimental data from an end milling process is used to develop models for predicating minimum R a value. Two techniques, model tree and sequential minimal optimization based support vector machine, which have not been used before to model surface roughness, were applied to the training data to build prediction models. The developed models were then applied to the test data to determine minimum R a value. Results indicate that both techniques reduced the minimum R a value of experimental data by 4.2% and 2.1% respectively. Model trees are found to be better than other approaches in predicting minimum R a value. [ABSTRACT FROM AUTHOR]

Abstract: An integrated part of modern design practice in both engineering and industry is simulation and optimization. Significant challenges still exist, though huge progress has been made in the last few decades. This 5th workshop on Computational Optimization, Modelling and Simulation (COMS 2014) at ICCS 2014 will summarize the latest developments of optimization and modelling and their applications in science, engineering and industry. This paper reviews the past developments, the state-of-the-art present and the future trends, while highlighting some challenging issues in these areas. It can be expected that future research should focus on the data intensive applications, approximations for computationally expensive methods, combinatorial optimization, and large-scale applications. [Copyright &y& Elsevier]

Abstract: This paper describes two new strategies for controlling air economizers. The model-based control strategy uses a model to estimate the load on the cooling coil for 100% outdoor air and minimum outdoor air and transitions dampers between these two positions to minimize the load. The optimization-based control strategy uses a cooling coil model and a one-dimensional optimization routine to determine the fraction of outdoor air that minimizes the coil load. A coil model based on the contact–mixture analogy was adopted for this study and validated with experimental data. Simulations were used to compare the new economizer control strategies with traditional strategies. Simulations evaluated the sensitivity of the annual and peak cooling loads to outdoor air and return air sensor errors and climatic conditions. Incorrect control decisions stemming from sensor errors reduce the achievable energy savings and lead to higher peak hourly loads. For a scenario representative of poorly maintained sensors, the lowest annual coil loads of the economizer strategies investigated are 3.0–40.3% higher than those achieved for the ideal scenario of optimization-based control and ideal sensors (i.e., no sensor errors), and the lowest peak hourly loads for those cases are 6.8–84.1% higher than those for the ideal scenario. [Copyright &y& Elsevier]