Several previous experiments showed that the leapfrog–hopscotch and the adapted Dufort–Frankel methods are the most efficient among the explicit and stable numerical methods to solve heat transfer problems in building walls. In this paper, we extensively measure the running times of the most successful methods and compare them to the performance of other available solvers, for example, ANSYS transient thermal analysis and the built-in routines of MATLAB, where three different mesh resolutions are used. We show that the running time of our methods changes linearly with mesh size, unlike in the case of other methods. After that, we make a long-term simulation (one full winter month) of two-dimensional space systems to test the two best versions of the methods. The real-life engineering problem we solve is the examination of thermal bridges with different shapes in buildings to increase energy efficiency.

Using efficient methods to calculate heat transfer in building components is an important issue. In the current work, 14 numerical methods are examined to solve the heat transfer problem inside building walls. Not only heat conduction but convection and radiation are considered as well, in addition to heat generation. Five of the used methods are recently invented explicit algorithms, which are unconditionally stable for conduction problems. First, the algorithms are verified in a 1D case by comparing the results of the methods to an analytical solution. Then they are tested on real-life cases in the case of surface area (made of brick) and cross-sectional area (two-layer brick and insulator) walls with and without thermal bridging. Equidistant and non-equidistant grids are used as well. The goal was to determine how the errors depend on the properties of the materials, the mesh type, and the time step size. The results show that the best algorithms are typically the leapfrog-hopscotch and the modified Dufort–Frankel and odd–even hopscotch algorithms since they are quite accurate for larger time step sizes, even for 100 s as well.

This study aimed to estimate the heating load (HL) and the cooling load (CL) of a residential building using neural networks and to simulate the thermal behavior of a four-layered wall with different orientations. The neural network models were developed and tested using Multi-Layer Perceptron (MLP) and Radial Basis (RB) networks with three algorithms, namely the Levenberg-Marquardt (LM), the Scaled Conjugate Gradient (SCG), and the Radial Basis Function (RB). To generate the data, 624 models were used, including six building shapes, four orientations, five glazing areas, and five ways of distributing glazing. The LM model showed the best accuracy compared to the experimental data. The L-shape facing south with windows on the east and south sides and a 20% window area was found to be the best shape for balancing the lighting and ventilation requirements with the heating and cooling loads near the mean value. The heating and cooling loads for this shape were 22.5 kWh and 24.5 kWh, respectively. The simulation part used the LH algorithm coded in MATLAB to analyze the temperature and heat transfer across the wall layers and the effect of solar radiation. The maximum and minimum percentage differences obtained by HAP are 10.7% and 2.7%, respectively. The results showed that the insulation layer and the wall orientation were important factors for optimizing the thermal comfort of a building. This study demonstrated the effectiveness of neural networks and simulation methods for building energy analysis.

Treatment of municipal effluents has long been a challenge for modern technologies combining high effectiveness of degradation of pollutants with low costs of the process. Hydrodynamic cavitation is a promising application in wastewater treatment due to its simple reactor design. In this work, for a system available in the laboratory a hydrodynamic reactor is designed based on literature recommendations. On the designed Venturi tube, two-dimensional numerical simulations were investigated by the means of CFD computations using the commercial software package, Ansys Fluent. The resulting cavitation bubbles were analysed at different inlet pressures.

Roof models with low heat loss and low heating costs for buildings are crucial for reducing energy consumption and greenhouse gas emissions in cold regions. This study aimed to calculate the heat loss for various roof models with low heat loss, which are widely used in houses in Europe and other cold regions. We created 324 models for three types of roofs (light, medium, and heavy) with different materials and thicknesses using the HAP program. We then trained a neural network (ANN) to generate a mathematical function that can be used to calculate the optimal surface using a multi-objective genetic algorithm method. The resulting optimal roof design was simulated using the Ansys program on a January day. We compared the heat loss results for the optimal roof for HAP, ANN, and transient thermal analysis by Ansys, respectively, showing a close agreement among the methods.