Abstract: Porous pavement is a well-documented, low-impact stormwater management technique. When it comes to design of the top layer, the amount of void space (porosity) is often of interest as it influences both infiltration and strength of the pavement. Laboratory equipment can be used to measure the porosity of core samples, but when more detail is required, other equipment or methods must be used. One such method is to scan the entire sample using a computer tomography (CT) machine and then perform some image processing techniques on the scanned data to reconstruct the sample digitally. While the workflow of scanning and processing to produce the 3D digital twin of porous pavement is not new and can be in fact done by open-source or commercial software, there are still some parts of the process that deserve a deeper investigation, for example binarization and segmentation algorithms applied to the solid-and-void space and void space, respectively. This is difficult to do with commercial software which operates like a black-box, and there needs to be more open-source codes that are user-friendly, extendable, and competitive to what commercial software can do. This work presents a MATLAB-based code that allows for a deeper investigation of how one can accurately and efficiently quantify the effective (or connected) void space of a porous pavement sample from a 3D digital model. We demonstrate the effect of dataset coarsening, which can be used to reduce the computational intensity of the algorithm while preserving accuracy. The code is publicly available online to allow for reproducible research and the possibility of extensions for increased functionality and complexity.
Permalink: https://doi.org/10.3384/ecp200049

Abstract not available

Abstract: Many studies have provided methods for predicting building energy consumption and indoor air quality using deep learning approaches. However, the predicted and actual energy and indoor air quality rates show discrepancies due to actual occupancy and local environmental conditions that could significantly impact on building energy and indoor air quality. Building energy consumption is influenced by the building envelop, lighting, heating, cooling air ventilation and the occupants’ electricity demand. And indoor air quality is also affected by local environmental condition, air ventilation performance and occupant behaviors. This research investigates the prediction of how significantly local environment changes and occupant behaviors impact building energy consumption and indoor air quality with different environmental conditions adapting climate change and global warming. For energy consumption and air quality prediction modeling in buildings, various mathematical algorithms have been proposed and developed such as fuzzy mathematics, wavelet analysis, support vector machine, artificial neural network (ANN), grey system method, and linear regression analysis. ANNs mimic the function and structure of the human brain by performing nonlinear processing. In addition, it has large-scale parallel structure computing with a distributed large storage capacity. To adapt to variable environments, the neural network system approaches with self-learning dealing with various information types. Thus, they have been widely used in pattern recognition for prediction, decision-making, process control, and other tasks. Recently, utilizing of the sensitivity of prediction of building energy consumption model is developed because the sensitivity is related to the robustness and performance of the prediction model.

Abstract: Heat pumps are an attractive heating system in residential buildings. They operate based on the vapor compression cycle used in refrigeration systems. Design questions surrounding heat pumps can be investigated and answered using modelling tools that incorporate the necessary thermodynamics, fluid mechanics, and machinery component efficiency. Several modelling tools are available, however there is a need for more open-source, script-based programs that are competitive to those already available. This work presents a Python-based code for modeling the thermodynamics of the vapor compression cycle (VCC) in typical heat pumps. The main contribution of this work is an openly available online code, complete with a few examples to show its functionality, that provides the basic thermodynamic model of a heat pump for researchers or development engineers to use, modify, and extend. Its current features include choice of refrigerant, heat exchanger size and characteristics, compressor, and other design parameters such as heating load, and fluid temperatures in and out of the heat exchangers. Simulation outputs include the P-h and T-s diagrams and coefficient of performance (COP). The code is flexible and suggestions for future code development are given.
Permalink: https://doi.org/10.3384/ecp200022

Abstract: This study explores a systematic review of a decentralized ventilation (DV) system compared with conventional centralized ventilation systems adapting Nordic climates. Air ventilation strategies can be classified into three main categories: natural ventilation, mechanical ventilation, and hybrid ventilation. Decentralized ventilation (DV) offers several advantages. For instance, it can simplify individual zoning control in spaces because outdoor air supply volume can be easily controlled by fans in compact decentralized ventilation units. These systems are also less affected by outdoor environmental conditions such as high stack or wind pressure compared to natural ventilation systems, which means they can consistently supply air into a room. This review introduces the systematic performance of a compact mechanical air ventilator designed for decentralized ventilation as a replacement for natural ventilation and conventional centralized ventilation (CV) system in urban areas. The study compares the DV system to conventional CV systems for adapting to Nordic climates. To evaluate acceptable operating conditions for fan-assisted natural ventilation systems as a hybrid ventilation system, the study uses selected Nordic weather conditions and analyzes the cooling and heating loads of the decentralized ventilation system. The research calculates the entire fan and pump loads of the DV system using published data. Compared to conventional centralized ventilation systems, the DV system has shorter air transport distances, resulting in lower fan pressure losses. Additionally, the fan speed and airflow rate of the DV system can be adjusted easily and effectively according to indoor thermal conditions. For example, natural ventilation and centralized mechanical ventilation systems face certain challenges when it comes to adjusting the supply airflow rate effectively based on factors such as indoor occupant rates, air quality, and thermal comfort. The study demonstrates that a radiant panel with a decentralized ventilation system (RPDV) consumes the least amount of heating, ventilation, and air conditioning (HVAC) energy. Especially Norwegian regulations for building constructions does not allow indoor air recirculation, therefore, all-air system has not used in Norway. The radiator, radiant panel or air convection coils for heating and cooling with air ventilation system is quite common in Scandinavian countries. The RPDV minimizes supply and exhaust air pressure losses and can also function as a fan-assisted natural ventilation system during periods when outdoor air can beused without additional thermal loads. This systematic review includes a new analysis of fan and pump energy consumption in the decentralized ventilation system compared to centralized ventilation systems, based on both numerically calculated and measured data. The study provides insights into fan assisted DV systems in Nordic climates, taking into account outdoor weather conditions.