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1

Thermodynamic model of steam injection pipeline considering the effect of time and phase change

Zhao Xuan, Niu Chengcheng, Wang Jun, Zhu Xiuxing

Journal of Power Technologies

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2019

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Vol. 99, nr 2

123--130

EN

Thermodynamic parameters in heavy oil thermal recovery wells form the basis for evaluating the thermal efficiency of steam injection. However, various factors in wellbores affect the variation law of thermodynamic parameters, hindering attempts to make an accurate description of them. A thermodynamic model of wellbores is proposed in this study which factors in the effects of time and phase change with a view to: (i) improving the accuracy of thermodynamic parameter analysis, and (ii) identifying the main factors and rules that govern thermal efficiency. With the time factor considered, the transient conduction function of a coupled wellbore-formation was established, and the heat loss during steam injection was analyzed. Meanwhile, a wellbore pressure gradient equation was established using the Beggs-Brill model with consideration of the influence of phase transformation in wellbore. Steam pressure, which varies with flow pattern, was also analyzed. The accuracy of the proposed model was verified by comparing the results of the analysis with the test data. Taking this approach, the influence of steam injection parameters on thermal efficiency was studied. The results demonstrate that the relative error of the pressure analysis result of proposed model is 1.06% and the relative error of temperature is 0.24%. The main factor affecting thermal efficiency is water in the annulus of the wellbore, followed by the steam injection rate. The thermal efficiency of the wellbore is about 80% when the water depth in the annulus is 300 m. An increase in the injection rate or extension of the injection time can improve thermal efficiency, whereas an increase in steam injection pressure reduces thermal efficiency. The proposed method provides good prospects for optimizing high efficiency steam injection parameters of heavy oil thermal recovery wells.

2

Dynamic analysis of embedded chains in mooring line for fish cage system

Hou H. M., Dong G. H., Xu T. J., Zhao Y. P., Bi C. W.

Polish Maritime Research

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2018

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nr 4

83--97

EN

Investigation of the embedded chains in soil starts to play an important role in understanding the structural performance of mooring system, when the embedded anchors will be employed to sustain large loads with the gradually growth of installation depth of offshore aquaculture farm. The aim of this study is to investigate the dynamic response of mooring line considering the influence of embedded chains in clay soil for net cage system. Lumped-mass method is used to establish the numerical model for evaluating the performance of mooring line with embedded chains. To validate the numerical model, comparisons of numerical results with the analytical formulas and the experimental data are conducted. A good agreement of the profile and the tension response is obtained. Then, the effect of embedded chains on the static and dynamic response of mooring line is evaluated, and the dynamic behavior of mooring system considering embedded chains for net cage system is investigated. The results indicate that the soil resistance on embedded chains should be included to predict the mooring line development and the load on the embedded anchors in the numerical simulations. An appropriate safety factor should be included if employing the simplified model Case C at the initial design phase. And the effect of embedded chains on the holding capacity of embedded anchors in single-point mooring system for single net cage cannot be negligible during the design and operation phases. Consequently, it is profound to take into account the interaction of embedded chains and soil for accurately predicting the reliability of mooring system for fish cage.

3

Parametric sensitivity through evolutionary optimization under uncertainty approach

Mitra K.

Computer Methods in Materials Science

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2013

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Vol. 13, No. 1

107--112

EN

While mimicking a physical phenomenon in a computational framework, there are tuning parameters quite often present in a computational model. These parameters are generally tuned with the experimental data to capture the process behavior as close as possible. Any optimization study based on this model assumes the values of these tuning parameters as constant. However, it is known that these parameters are subjected to inherent source of uncertainties such as errors in measurement or model tuning etc. for which they are not tuned for. Assuming these parameters constant for rest of the optimization is, therefore, not realistic and one should ideally check the sensitivity of these parameters on the final results. In this study, we are going to use approach based on the paradigm of optimization under uncertainty that allows a decision maker to carry out such an analysis. Additionally, this study captures the tradeoff between solution quality and solution reliability that is captured here using non-dominated genetic algorithm II. The generic concept has been applied on a grinding process model and can be extended to any other process model

PL

Modelowanie zjawisk fizycznych metodami numerycznymi często wymaga określenia wartości parametrów charakteryzujących modelowany proces w taki sposób, tak aby jak najdokładniej uchwycić przebieg zjawiska fizycznego. Proces optymalizacji wykorzystujący tak zdefiniowany model przyjmuje wartości dopasowanych parametrów jako stałe. Jednocześnie wiadomo, że parametry te zależą od źródeł niepewności związanych z błędami pomiaru lub samą regulacją modelu, i dla innych danych pomiarowych model z wcześniej dopasowanymi parametrami może nie dawać wystarczająco dokładnej odpowiedzi. Przyjęcie stałych wartość tych parametrów w optymalizacji jest zatem nierzeczywiste i należałoby sprawdzić wrażliwość odpowiedzi modelu względem tych parametrów. W niniejszej pracy zastosowano metodę opartą na optymalizacji z wykorzystaniem analizy niepewności pomiarów, która umożliwia przeprowadzenie tego typu analizy wrażliwości. Ponadto w optymalizacji za istotne uznano utrzymanie równowagi pomiędzy jakością rozwiązania i jego wiarygodnością, co było możliwe dzięki zastosowaniu niezdominowanego algorytmu genetycznego II (ang. NSGA II). Ogólną koncepcję rozwiązania zastosowano w modelu procesu szlifowania, ale może ona być rozszerzona na każdy inny rodzaj modelu procesu.