The microchannel heat exchanger is one of the most compact and effective heat exchangers used for cooling devices in building air conditioning system, while application of nanofluids in microchannel further enhance its thermal performance due to its much higher thermal conductivity. Considering the continuous rapid increase in energy consumption in the building sector, especially in air conditioning systems, the heat transfer performance of a microchannel with nanofluids should be further enhanced to realize energy savings. This study analyzes the influence of combining nanofluid and flow disturbance structure on the heat transfer enhancement of a microchannel, which is also the noted novelty. A rectangular grooved microchannel (RGMC) is proposed, and its thermal performance using Al2O3/water nanofluids is investigated using the CFD method, with the mixture model to simulate the Al2O3/water nanofluids considering the slip velocity between the base fluid and nanoparticles. The results show that at 1.5 m/s, Nu of RGMC with 2 vol% nanofluids is 38.5% larger than that of smooth microchannel (SMC) with the same nanofluids, and 36.7% larger than that of RGMC with pure water, indicating the much better heat transfer performance of the novel designed RGMC structure. The maximum temperature for RGMC is 5 K lower than SMC with 2 vol% Al2O3/water nanofluid at inlet velocity of 1.5 m/s. Further analysis on the integrated effect between fluid flow and heat transfer shows that the synergy angle β near the center line of RGMC is much lower than that of SMC, representing that the better thermal performance is caused by the flow structured induced by the grooves. Moreover, at 1.5 m/s, βα of SMC with 2 vol% nanofluid is 89.4 Deg, which is 1.66 Deg higher than the βα value of RGMC, while at 0.25 m/s, the βα of two types of microchannel are close to each other. This indicates that the groove structure shows greater enhancement at higher inlet velocity. It is concluded that combining nanofluid and groove structure can significantly enhance heat transfer of the microchannel. The nanofluid enhances heat transfer at lower inlet velocity, while the groove structure enhances it at higher inlet velocity. This study will be helpful for the design of a high-efficiency microchannel heat exchanger that promotes building energy savings.

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High-level synthesis (HLS) tools have gained great attention in recent years because it emancipates engineers from the complicated and heavy hardware description language writing and facilitates the implementations of modern applications (e.g., deep learning models) on Field-programmable Gate Array (FPGA) , by using high-level languages and HLS directives. However, finding good HLS directives is challenging, due to the time-consuming design processes, the balances among different design objectives, and the diverse fidelities (accuracies of data) of the performance values between the consecutive FPGA design stages. To find good HLS directives, a novel automatic optimization algorithm is proposed to explore the Pareto designs of the multiple objectives while making full use of the data with different fidelities from different FPGA design stages. Firstly, a non-linear Gaussian process (GP) is proposed to model the relationships among the different FPGA design stages. Secondly, for the first time, the GP model is enhanced as correlated GP (CGP) by considering the correlations between the multiple design objectives, to find better Pareto designs. Furthermore, we extend our model to be a deep version deep CGP (DCGP) by using the deep neural network to improve the kernel functions in Gaussian process models, to improve the characterization capability of the models, and learn better feature representations. We test our design method on some public benchmarks (including general matrix multiplication and sparse matrix-vector multiplication) and deep learning-based object detection model iSmart2 on FPGA. Experimental results show that our methods outperform the baselines significantly and facilitate the deep learning designs on FPGA.

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