Heat pipes and two‐phase thermosyphon systems are passive heat transfer systems that employ a two‐phase cycle of a working fluid within a completely sealed system. Consequently, heat exchangers based on heat pipes have low thermal resistance and high effective thermal conductivity, which can reach up to the order of (105 W/(m K)). In energy recovery systems where the two streams should be unmixed, such as airconditioning systems of biological laboratories and operating rooms in hospitals, heat pipe heat exchangers (HPHEs) are recommended. In this study, an experimental and theoretical study was carried out on the thermal performance of an air‐to‐air HPHE filled with two refrigerants as working fluids, R22 and R407c. The heat pipe heat exchanger used was composed of two rows of copper heat pipes in a staggered manner, with 11 pipes per row. Tests were conducted at different airflow rates of 0.14, 0.18, and 0.22m3/h, evaporator inlet‐air temperatures of 40, 44, and 50°C, filling ratios of 45%, 70%, and 100%, and ratios of heat capacity rate of the evaporator to condenser sections (Ce/Cc) of 1 and 1.5. For HPHE's steady‐state operation, a mathematical model for heat‐transfer performance was set and solved using MATLAB. Results illustrated that the heat transfer rate was in direct proportion with the evaporator inlet‐air temperature and flow rate. The highest HPHE's effectiveness was obtained at a 100% filling ratio and (Ce/Cc) of 1.5. The predicted and experimental values of condenser outletair temperature were in good agreement, with a maximum difference of 3%. HPHE's effectiveness was found to increase with the increase in evaporator inletair temperature and number of transfer units (NTU) and with the decrease in airflow rate, up to 33% and 20% for refrigerants R22 and R407c, respectively. Refrigerant R22 was the superior of the two refrigerants investigated.
This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper,
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In this paper, fatigue damage accumulation were studied using many methods i.e.Corton-Dalon (CD),Corton-Dalon-Marsh(CDM), new non-linear model and experimental method. The prediction of fatigue lifetimes based on the two classical methods, Corton-Dalon (CD)andCorton-Dalon-Marsh (CDM), are uneconomic and non-conservative respectively. However satisfactory predictions were obtained by applying the proposed non-linear model (present model) for medium carbon steel compared with experimental work. Many shortcomings of the two classical methods are related to their inability to take into account the surface treatment effect as shot peening. It is clear that the new model shows that a much better and cons
... Show MoreMost of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B
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