Outdoor digital displays have become increasingly popular and common for smart city applications, and more recently provides a concealment solution and integration point for outdoor communications devices meant to be attached to buildings, streetlamps, or traffic poles. Given the larger energy requirements for powering next generation 5G cellular networks, these devices create unique difficulties in developing and evaluating thermal management solutions. The present study develops and validates the extreme condition transient (ECT) climate model using a CFD/HT numerical model, to evaluate diurnal thermal responses from a representative 5G small cell devices. The model is validated for local conditions present in Atlanta, GA for two unique days. The thermal response from the ECT climate model is presented alongside three real case study locations, Miami, FL, New York City, NY, and Phoenix, AZ.
This review focuses on progress and emerging challenges in experimentally validated modeling of microscale urban thermal environments over the last two decades. In the last few decades, there has been a surge in urban energy contribution resulting in elevated urban day/night-time air temperatures. While there is no single solution to urban heat, mitigation strategies can be implemented to minimize the harmful effects of urban heat both on humans and the environment. To study the effects of urban heat, numerical modeling of urban thermal environments has seen a rise in usage of several application specific atmospheric modeling software packages, and multiple studies and reviews have already covered the prolific engineering use cases. However, there are inherent and unintentional biases introduced by each modeling software package, that inhibit validity and accuracy for general engineering use. This review critically analyzes the limitations of current state-of-the-art (SOA) microscale atmospheric modeling approaches and identify necessary areas for improvement. Urban thermal environment models must be validated with measurements to gain confidence in the predictive capabilities. This review will additionally examine the next generation of measurement techniques that leverage advances in computing and communications to create distributed meteorological sensor networks for improved spatial and temporal resolution, that can provide a rich platform for model validation. High fidelity and accurate simulations of urban thermal environments improve confidence in the study of urban heat, its mitigation, and impact on urban engineering applications in building energy usage and sustainability.
An internet-of-things (IoT)-based low-cost sensor network can be used to collect the data necessary to study both Urban Heat Island (UHI) and air pollution. There are several key challenges associated with an IoT-based solution to environmental data monitoring, including packaging and deployment. This study explores these challenges by looking at effects the packaging has on the deployed environmental sensors. Several packaging designs are numerically studied using a computation fluid dynamics (CFD) model. Two sensor designs are chosen using results obtained from CFD modeling and then experimentally deployed. The findings conclude that the IoT sensors chosen for this study are not significantly affected by flow velocities or require advanced packaging designs when paired with street-side outdoor digital displays.
As the rate of urbanization increases, local vegetation is being replaced with man-made materials, causing increasingly adverse impacts on the surface-atmosphere energy balance. These negative effects can be simulated by modeling the urban landscapes in question; however, the main challenges of modeling urban thermal environments are the scale and resolution at which to perform such tasks. Current modeling of urban thermal environments is typically limited to either mesoscale (1 km to 2,000 km) or microscale (< 1 km) phenomena. In the present work, an open-source framework for one-way upstream coupled multiscale urban thermal environment simulations is examined and validated. This coupled simulation can provide valuable insights about the flow behavior and energy transport between mesoscale and microscale interactions. The mesoscale to microscale boundary conditions are coupled together using simulated data from the Advanced Research Weather Research and Forecasting Model (WRF-ARW), a mesoscale numerical weather prediction software, and assimilating it into Parallelized Large-eddy Simulation Model (PALM), a computational fluid dynamics style (CFD-style) software designed for microscale atmospheric flows. The multiscale simulations are tested for grid sensitivity to variations in model input and control parameters, and then experimentally validated against distributed sensor measurements at the Georgia Institute of Technology (Georgia Tech) in Atlanta, GA. Validated microscale atmospheric models with heterogeneous domains can be used to project the thermal benefits of urban heat mitigation strategies and advise building energy usage modeling and policies.
Cities are experiencing a number of negative effects caused by increasing urbanization. For decades, the effects of pollution have been recognized and studied and steps have been taken attempting to control this problem. Many urban environments are also experiencing the effect of the Urban Heat Island (UHI). UHIs are metropolitan areas that have measurably warmer average air temperatures during several periods during the year, than their surrounding rural areas. There is a great interest in studying UHI and pollution and its effects on the environment as well as urban residents. However, in order to study these phenomena, we need more information than we currently have. Thus, an IoT based low cost sensor network can be used to collect the data necessary to study UHI and pollution. There are several key challenges associated with an IoT based solution to environmental data monitoring. This study explores these challenges by looking at what effect the packaging has on the deployed environmental sensors, and how and where to deploy sensor modules. Sensor data collected over a few months’ timeframe are analyzed and presented.
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