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Spray cooling technology offers high levels of uniform heat removal with very low fluid volumes and has found recent application in relatively small-scale use cases. Since it is a complex process, research can enable spray cooling to be applied more widely and at larger scales, such as in HVAC, as a means to operate more efficiently. Weather conditions are one of the main parameters that directly affect the effectiveness of spray cooling. This study investigates the spray cooling performance for temperature and humidity conditions in six Australian cities. ANSYS Fluent (2021 R1) software is applied for the numerical simulation. The numerical model is first validated with the available literature before a numerical simulation is conducted to assess each city throughout the year. These include Adelaide, Brisbane, Darwin, Melbourne, Perth, and Sydney. The spray cooling pattern, temperature, and humidity distribution, as well as the evaporation effect on different regions in Australia, is simulated and analysed based on the CFD technique. The results from this study indicate that weather conditions influence spray cooling for all cities, especially in summer. Along the wind tunnel, the temperature significantly drops at the spray cooling area, while the humidity increases. Due to the effect of spray cooling inside the wind tunnel, the temperature at the outlet is still lower than the inlet for all cases. However, the humidity at the outlet is higher than the inlet for all cases.

Spray cooling technology offers high levels of uniform heat removal with very low fluid volumes and has found recent application in relatively small-scale use cases. Since it is a complex process, research can enable spray cooling to be applied more widely and at larger scales, such as in HVAC, as a means to operate more efficiently. Weather conditions are one of the main parameters that directly affect the effectiveness of spray cooling. This study investigates the spray cooling performance for temperature and humidity conditions in six Australian cities. ANSYS Fluent (2021 R1) software is applied for the numerical simulation. The numerical model is first validated with the available literature before a numerical simulation is conducted to assess each city throughout the year. These include Adelaide, Brisbane, Darwin, Melbourne, Perth, and Sydney. The spray cooling pattern, temperature, and humidity distribution, as well as the evaporation effect on different regions in Australia, is simulated and analysed based on the CFD technique. The results from this study indicate that weather conditions influence spray cooling for all cities, especially in summer. Along the wind tunnel, the temperature significantly drops at the spray cooling area, while the humidity increases. Due to the effect of spray cooling inside the wind tunnel, the temperature at the outlet is still lower than the inlet for all cases. However, the humidity at the outlet is higher than the inlet for all cases. [ABSTRACT FROM AUTHOR]

Caching at the network edge can significantly reduce users’ perceived latency and relieve backhaul pressure, hence invigorating a new set of innovations toward latency-sensitive applications. Nevertheless, the efficacy of caching policies relies on the users’ content preference to be 1) known a priori and 2) highly homogeneous, which is not always the case in the real world. In this article, we explore how artificial intelligence (AI) techniques and recommendation can be leveraged to address those core issues and reap the potentials of cache-enabled wireless networks. Specifically, we present the hierarchical, cache-enabled wireless network architecture, in which AI techniques and recommendation are utilized, respectively, to estimate users’ content requests in real time using historical data and to reshape users’ content preference. Through case studies, we further demonstrate the effectiveness of an AI-based predictor in estimating users’ content requests as well as the superiority of joint recommendation and caching policies over conventional caching policies without recommendation. [ABSTRACT FROM AUTHOR]

This work exploits the advantages of two prominent techniques in future communication networks, namely caching and non-orthogonal multiple access (NOMA). Particularly, a system with Rayleigh fading channels and cache-enabled users is analyzed. It is shown that the caching-NOMA combination provides a new opportunity of cache hit which enhances the cache utility as well as the effectiveness of NOMA. Importantly, this comes without requiring users’ collaboration, and thus, avoids many complicated issues such as users’ privacy and security, selfishness, etc. In order to optimize users’ quality of service and, concurrently, ensure the fairness among users, the probability that all users can decode the desired signals is maximized. In NOMA, a combination of multiple messages are sent to users, and the defined objective is approached by finding an appropriate power allocation for message signals. To address the power allocation problem, two novel methods are proposed. The first one is a divide-and-conquer-based method for which closed-form expressions for the optimal resource allocation policy are derived making this method simple and flexible to the system context. The second one is based on deep reinforcement learning method that allows all users to share the full bandwidth. Finally, simulation results are provided to demonstrate the effectiveness of the proposed methods and to compare their performance. [ABSTRACT FROM AUTHOR]

Proactive caching is considered a cost-effective method to address the backhaul bottleneck problem in cellular network. In this paper, we propose a novel popularity predicting–caching procedure that takes raw video data as an input to determine an optimal cache placement policy, which deals with both published and unpublished videos. To anticipate the popularity of unpublished videos of which the statistical information is not available, we apply the content-based approach by extracting and condensing video features into a high-dimensional vector. Subsequently, we form $G$ clusters of features representing the potential video categories (VCs) and map the feature vector into a $G$ -dimensional space, where each element indicates the percentage to which the video contains the features of the corresponding VC. Finally, we train a prediction model to foresee the popularity, where the set of published videos is used as training data. Last, the prediction with expert advice method is used to update the training set, and to gain insight into how the predictor output will deviate from the best expert prediction, we address the concept of expected cumulative loss and derive the analytical expression for its upper bound. Extensive simulation results are shown to gain insight into our proposed system subject to different factors, such as network size, cache capacity, and user’s preference profile. In summary, we show that applying intelligence-based content-aware proactive caching is an efficient approach to significantly improving the operation of cellular networks in the future. [ABSTRACT FROM PUBLISHER]

To date, determining with high accuracy the optical parameters (extinction coefficient k and refractive index n) of a slab from the sole transmittance data requires an inverse method based on numerical iteration procedures. In this paper, we propose a new inverse analytical method of extracting (k, n) without numerical iterative processes. The high accuracy of this new inverse method is assessed, and as an application example, the optical parameters of CaF2 and Si substrates are determined in the IR spectral range of 4–8 µm. Graphical Abstract [ABSTRACT FROM AUTHOR]