This paper applies CFD modelling and numerical calculations to predict the thermal performance of a cross flow heat pipe based heat exchanger. The heat exchanger under study transfers heat from air to water and it is equipped with six water-charged wickless heat pipes, with a single-pass flow pattern on the air side (evaporator) and two flow passes on the water side (condenser). For the purpose of CFD modelling, the heat pipes were considered as solid devices of a known thermal conductivity which was estimated by experiments conducted on the exact same heat pipe configuration under an entire testing range. The CFD results were compared with the experimental and the numerical results and it was found that the modelling predictions are within 10% of the experimental results.
Keywordsheat pipe; heat exchanger; CFD modelling; heat recovery. Nomenclature π Pi ρ Density σ Surface tension Subscripts act Actual b Boiling c Condenser cd/cond Condensation e Evaporator h Heat transfer coefficient i Inside in inner k Conduction l Liquid phase min Minimum n Number of pipes o Outside s Surface sat Saturation t Total ts thermosyphon
Fuzzy cognitive maps are signed directed graphs used to model the evolution of scenarios with time. FCMs can be useful in decision support for predicting future states given an initial state. Genetic algorithms (GA) are well-established tools for optimization. This paper concerns the use of FCMs in goal-directed analysis of scenarios for aiding decision making. A methodology for GA-based goal-directed analysis is presented. The search for the initial stimulus state, that over time leads to a target state of interest, is optimized using GA. This initial state found can be used to answer the question – what course of events leads to a certain state in a given scenario?
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