Numerical Assessment on Fin Design Parameters Employed for Augmentation of Natural Convection and Fluid Flow in a Horizontal Latent Heat Thermal Energy Storage Unit

The present work focus on the thermal performance of a horizontal concentric heat exchanger, which is numerically investigated to evaluate the heat transfer enhancement process by adding fins with different configurations. As a part of this investigation, the melting process is simulated from the onset of phase change to the offset involving physics of natural convection in PCM fluid pool. The investigation is carried out by ANSYS Fluent code, which is an efficient numerical analysis tool for investigating fluid flow and convective heat transfer phenomena during PCM melting process. The attention is mainly focused on the extension of contact area between the PCM body and cylindrical capsule to enhance heat transfer rates to PCM bodies during the melting process by employing longitudinal fins in the enclosed capsule. Two commercial PCMs: RT50 and C58, are introduced in a 2D cylindrical pipe with their thermo-physical properties as input for modelling. The selected modelling approach is validated against experimental result with respect to the total enthalpy changes that qualify our model to run in the proceeding calculation. It is ensured that an isothermal boundary condition (373 K) is applied to the inner pipe throughout the series of simulation cases and the corresponding Rayleigh number (Ra) ranges from 104 - 105 and Prandtl number (Pr) 0.05 - 0.07. Finally, parametric study is carried out to evaluate the effect of length, thickness and number of longitudinal fins on the thermal performance of PCM-LHTES (Latent Heat Thermal Energy Storage) system associated with the physics of natural convection process during PCM melting.

Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation

Existing solutions do not work well when multi-targets coexist in a sentence.The reason is that the existing solution is usually to separate multiple targets and process them separately.If the original sentence has N target,the original sentence will be repeated for N times,and only one target will be processed each time.To some extent,this approach degenerates the fine-grained sentiment classification task into the sentence-level sentiment classification task,and the research method of processing the target separately ignores the internal relation and interaction between the targets.Based on the above considerations,we proposes to use Graph Convolutional Network(GCN)to model and process multi-targets appearing in sentences at the same time based on the positional relationship,and then to construct a graph of the sentiment relationship between targets based on the difference of the sentiment polarity between target words.In addition to the standard target-dependent sentiment classification task,an auxiliary node relation classification task is constructed.Experiments demonstrate that our model achieves new comparable performance on the benchmark datasets:SemEval-2014 Task 4,i.e.,reviews for restaurants and laptops.Furthermore,the method of dividing the target words into isolated individuals has disadvantages,and the multi-task learning model is beneficial to enhance the feature extraction ability and expression ability of the model.