The overall objective of the study is to determine the influence of various factors on the tightness of frame-based buildings. The study presents airtightness tests—Blow Doors Tests of single-family residential buildings made in the prefabricated wood frame technology. Primarily, the impact of selected quantitative and qualitative parameters on the determined quantity n50 was defined. For that purpose, correlation analyses were performed and the statistical hypothesis stating that there is no statistically significant linear relationship between n50 (a multiplication factor of air exchange in the building effected by pressure difference of 50 Pa) and the specified qualitative and quantitative parameters was verified. The hypothesis was verified using the F and χ2 statistics. The studies demonstrated that there are no grounds to reject the research hypothesis. The obtained results formulate a comprehensive conclusion that allows to test the tightness of buildings made in the prefabricat... [more]

The paper presents a novel twofold use of the photographic technique for flow boiling heat transfer investigation in the horizontal mini-channel. The dedicated measurement system was constructed to record basic thermal and flow parameters, i.e., boiling liquid inlet and outlet temperatures and pressures, and also temperatures inside the heating block to calculate heat flux going into the liquid. A high-speed video camera equipped with synchronous movement system was applied to combine the recording of two-phase flow images with simultaneous local void fraction measurements both based on the same photographic data set. The data were collected, managed, and refined with the scripts developed in the MathWorks Matlab 2019b environment. The synchronous use of two intelligent techniques in the scripts, i.e., the background subtraction technique and the statistical analysis of individual pictures allowed obtaining reliable experimental results. The proposed method of the void fraction determi... [more]

The adoption of electric vehicles promises numerous benefits for modern society. At the same time, there remain significant hurdles to their wide distribution, primarily related to battery-based energy sources. This review concerns the systematization of knowledge in one of the areas of the electric vehicle control, namely, the energy management issues when using braking controllers. The braking process optimization is summarized from two aspects. First, the advantageous solutions are presented that were identified in the field of gradual and urgent braking. Second, several findings discovered in adjacent fields of automation are debated as prospects for their possible application in braking control. Following the specific classification of braking methods, a generalized braking system composition is offered, and all publications are evaluated primarily in terms of their energy recovery abilities as a global target. Then, conventional and intelligent classes of braking controllers are... [more]

In the current state of maturity of severe accident codes, the time has come to foster the systematic application of Best Estimate Plus Uncertainties (BEPU) in this domain. The overall objective of the HORIZON-2020 project on “Management and Uncertainties of Severe Accidents (MUSA)” is to quantify the uncertainties of severe accident codes (e.g., ASTEC, MAAP, MELCOR, and AC2) when modeling reactor and spent fuel pools accident scenarios of Gen II and Gen III reactor designs for the prediction of the radiological source term. To do so, different Uncertainty Quantification (UQ) methodologies are to be used for the uncertainty and sensitivity analysis. Innovative AM measures will be considered in performing these UQ analyses, in addition to initial/boundary conditions and model parameters, to assess their impact on the source term prediction. This paper synthesizes the major pillars and the overall structure of the MUSA project, as well as the expectations and the progress made over the f... [more]

This study uses deep learning to model the discharge characteristic curve of the lithium-ion battery. The battery measurement instrument was used to charge and discharge the battery to establish the discharge characteristic curve. The parameter method tries to find the discharge characteristic curve and was improved by MLP (multilayer perceptron), RNN (recurrent neural network), LSTM (long short-term memory), and GRU (gated recurrent unit). The results obtained by these methods were graphs. We used genetic algorithm (GA) to obtain the parameters of the discharge characteristic curve equation.

Science seeks strategies to mitigate global warming and reduce the negative impacts of the long-term use of fossil fuels for power generation. In this sense, implementing and promoting renewable energy in different ways becomes one of the most effective solutions. The inaccuracy in the prediction of power generation from photovoltaic (PV) systems is a significant concern for the planning and operational stages of interconnected electric networks and the promotion of large-scale PV installations. This study proposes the use of Machine Learning techniques to model the photovoltaic power production for a system in Medellín, Colombia. Four forecasting models were generated from techniques compatible with Machine Learning and Artificial Intelligence methods: K-Nearest Neighbors (KNN), Linear Regression (LR), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The results obtained indicate that the four methods produced adequate estimations of photovoltaic energy generation.... [more]

The cracks and holes in underground engineering are the critical factors that cause the instability of the surrounding rock. It is helpful to control the stability of surrounding rock to study the samples with combined defects of cracks and holes. In this study, PFC 2D is used to analyze the numerical models. Seven combined models of single circular hole and double cracks with different angles are established, and the fracture angle varies from 0° to 90° with an interval of 15°. First, uniaxial compression experiments and numerical simulations are carried out in the 0° defect combination model, and the microscopic parameters of PFC 2D are determined. Then, the numerical simulations of seven defect models under uniaxial compression are carried out, and the crack development law and acoustic emission characteristics of different defect combination models are studied. The failure modes, mechanical behavior, and stress states are studied. The displacement vector distributions of different... [more]

The growth in renewable energy integration over the past few years, primarily fueled by the drop in capital cost, has revealed the requirement for more sustainable methods of integration. This paper presents a collocated hybrid plant consisting of solar photovoltaic (PV) and Ternary pumped-storage hydro (TPSH) and designs controls that integrate the PV plant such that the behavior and the controllability of the hybrid plant are similar to those of a conventional plant within operational constraints. The PV array control and hybrid plant control implement a neural−network-based framework to coordinate the response, de-loading, and curtailment of multiple arrays with the response of the TPSH. With the help of the designed controls, a symbiotic relationship is developed between the two energy resources, where the PV compensates for the TPSH nonlinearities and provides required speed of response, while the TPSH firms the PV system and allows the PV to be integrated using its existing infra... [more]

Natural ventilation is gaining more attention from architects and engineers as an alternative way of cooling and ventilating indoor spaces. Based on building types, it could save between 13 and 40% of the building cooling energy use. However, this needs to be implemented and operated with a well-designed and integrated control system to avoid triggering discomfort for occupants. This paper seeks to review, discuss, and contribute to existing knowledge on the application of control systems and optimisation theories of naturally ventilated buildings to produce the best performance. The study finally presents an outstanding theoretical context and practical implementation for researchers seeking to explore the use of intelligent controls for optimal output in the pursuit to help solve intricate control problems in the building industry and suggests advanced control systems such as fuzzy logic control as an effective control strategy for an integrated control of ventilation, heating and co... [more]

In the extremely competitive environment of shipping, minimizing shipping cost is the key factor for the survival and growth of shipping companies. However, stricter rules and regulations that aim at the reduction of greenhouse gas emissions published by the International Maritime Organization, force shipping companies to increase the operational efficiency of their fleet. The prediction of a ship speed in actual seas with a given power by its engine is the most important performance indicator and thus makes it the “holy grail” in pursuing better efficiency. Traditionally, tank model tests and semi-empirical formulas were the preferred solution for the aforementioned prediction and are still widely applied. However, currently, with the increased computational power that is widely available, novel and more sophisticated methods taking into consideration computational fluid dynamics (CFD) and machine learning (ML) algorithms are emerging. In this paper, we briefly present the different a... [more]

The structural anisotropy of open-cell foam leads to the anisotropy of effective thermal conductivity (ETC). To quantitatively analyze the effect of structural anisotropy on the anisotropy of ETC, a new predicting model for the ETC of anisotropic open-cell foam was proposed based on an anisotropy tetrakaidecahedron cell (ATC). Feret diameters in three orthogonal directions obtained by morphological analysis of real foam structures were used to characterize the anisotropy of ATC. To validate our proposed anisotropic model, the ETCs of real foam structures in three orthogonal directions predicted by it were compared with the numerical results, for which the structures of numerical models are reconstructed by X-ray computed tomography (X-CT). Using the present anisotropic model, the influences of the thermal conductivity ratio (TCR) and porosity of the foams on the anisotropic ratios of ETCs are also investigated. Results show that there is good consistency between the ETCs obtained by th... [more]

High-speed vehicles traveling in a tube with pressures similar to those experienced by aircraft at their maximum altitude are presented. Although the concept resembles Hyperloop, the pressure level investigated here is much higher and safer than that suggested by Hyperloop, and, therefore, the system design is markedly different. Calculating a vehicle’s aerodynamic performance in the initial design stages requires low-budget computational tools to enable iterative design processes. This study presents an algorithm for rapid flow-field prediction based on a one-dimensional Reimann solution, including viscosity and heat transfer effects. The flow-field is divided into near- and far-fields, where the near-field represents the solution directly around the vehicle. The far-field demonstrates the impact of the vehicle’s motion on the vehicle’s flow-field upstream and downstream. Two-dimensional URANS models are compared to the current numerical scheme. The developed algorithm analyzes the fl... [more]

Real-time and accurate state-of-charge estimation performs an important role in the smooth operation of various electric vehicle battery management systems. Neural network theory represents one of the most effective and commonly used methods of SOC prediction. However, traditional neural network methods are disadvantaged by such issues as the limited range of application, limited generalization ability, and low accuracy, which makes it difficult to meet the increasing safety requirements on electric vehicles. In view of these problems, an ensemble learning algorithm based on the AdaBoost.Rt is proposed in this paper. AdaBoost.Rt recurrent neural network model is purposed to ensure the accurate prediction of lithium battery SOC. Relying on a chain-connected recurrent neural network model, this method enables the correlation adaptability of sample data in the spatio-temporal dimension. The ensemble learning method was adopted to devise a method of multi-RNN model integration, with the RN... [more]

Seismic data are widely used in oil, gas, and other kinds of mineral exploration and development. However, due to low artificial interpretation accuracy and small sample sizes, seismic data may not meet the needs of convolutional neural network training. There are major differences between optical image and seismic data, making it difficult for a model to learn seismic data characteristics. Therefore, a style transfer network is necessary to make the styles of optical image and seismic data more similar. Since the stylization effect of a seismic section is similar to that of most art styles, based on an in-depth study of image style transfer, this paper compared the effects of various style transfer models, and selected a Laplacian pyramid network to carry out a study of seismic section stylization. It transmits low-resolution global style patterns through a drafting network, revises high-resolution local details through correction networks, and aggregates all pyramid layers to output... [more]

In the last few years, deep reinforcement learning has been proposed as a method to perform online learning in energy-efficiency scenarios such as HVAC control, electric car energy management, or building energy management, just to mention a few. On the other hand, quantum machine learning was born during the last decade to extend classic machine learning to a quantum level. In this work, we propose to study the benefits and limitations of quantum reinforcement learning to solve energy-efficiency scenarios. As a testbed, we use existing energy-efficiency-based reinforcement learning simulators and compare classic algorithms with the quantum proposal. Results in HVAC control, electric vehicle fuel consumption, and profit optimization of electrical charging stations applications suggest that quantum neural networks are able to solve problems in reinforcement learning scenarios with better accuracy than their classical counterpart, obtaining a better cumulative reward with fewer parameter... [more]

Even though the milder climate scenario and constant evolution of thermal building regulation are in light of European initiatives, in Portugal, there are few houses where occupants can remain all the time in perfectly comfortable temperature conditions without resorting to heating or cooling. According to the Long Term Strategy for the Renewal of Buildings (ELPRE), this results from the combination of several factors, namely, low energy use for air conditioning compared to energy needs and aged building stock with poor energy performance. In fact, around 70% of the dwellings currently certified have low energy efficiency (C or less). The purpose of this review article was to analyse and discuss the factors affecting indoor thermal comfort, the inability to keep the home adequately warm in winter and cool in summer, and the risk of poverty or social exclusion in the European context, namely in Portugal. It fills the gap in the literature researching and analysing the motivations for th... [more]

The production of high-performing thermoelectrics is limited by the high computational energy and time required by the current finite element method solvers that are used to analyze these devices. This paper introduces a new concentrating solar thermoelectric generator made of segmented materials that have non-uniform leg geometry to provide high efficiency. After this, the optimum performance of the device is obtained using the finite element method conducted using ANSYS software. Finally, to solve the high energy and time requirements of the conventional finite element method, the data generated by finite elements are used to train a regressive artificial neural network with 10 neurons in the hidden layer. Results are that the power and efficiency obtained from the optimized device design are 3× and 2× higher than the original unoptimized device design. Furthermore, the developed neural network has a high accuracy of 99.95% in learning the finite element data. Finally, the neural net... [more]

An incoming canonical spatially developing turbulent boundary layer (SDTBL) over a 2-D curved hill is numerically investigated via the Reynolds-averaged Navier−Stokes (RANS) equations plus two eddy-viscosity models: the K−ω SST (henceforth SST) and the Spalart−Allmaras (henceforth SA) turbulence models. A spatially evolving thermal boundary layer has also been included, assuming temperature as a passive scalar (Pr = 0.71) and a turbulent Prandtl number, Prt, of 0.90 for wall-normal turbulent heat flux modeling. The complex flow with a combined strong adverse/favorable streamline curvature-driven pressure gradient caused by concave/convex surface curvatures has been replicated from wind-tunnel experiments from the literature, and the measured velocity and pressure fields have been used for validation purposes (the thermal field was not experimentally measured). Furthermore, direct numerical simulation (DNS) databases from the literature were also employed for the incoming turbulent flow... [more]

The technical focus of drilling operations is changing to oil and gas reservoirs with higher difficulty factors such as low permeability and fracture. During the drilling process, drilling operations in deep complex formations are prone to overflow and leakage complications. Leakage and overflow problems will change the performance of the drilling fluid in the wellbore, impacting the wellbore pressure, and causing complex accidents such as stuck drilling and collapse. In order to improve the level of control over the risk of wellbore overflow and leakage, it is necessary to predict the mud overflow and leakage situation and to arrange and control the risk of leakage and overflow that may occur in advance to ensure the safety of drilling. By using a genetic algorithm to optimize the multi-layer feedforward neural network, this paper establishes a GA-BP Neural Network Drilling overflow and leakage prediction model based on multi-parameter fusion. Through the optimization training of 14 p... [more]

The use of electric vehicles and their adoption have become imperative due to the policies and goals to reduce harmful emissions emitted by fossil fuel vehicles. The adoption and acceptance of electric vehicles by the public may be determined by several factors, such as the price of the vehicles, the environmental and political costs of vehicle production, infrastructure availability, etc. However, given the diverse context of each country, different factors may have a different impact on customer attitudes. According to that fact, the purpose of this research is to identify which factors influence attitudes towards the purchase of electric vehicles in the Republic of Croatia. In order to identify those factors, this paper is based on a survey in which 578 randomly selected respondents took part. The research was conducted between 1 October 2021 and 1 May 2022. The obtained research results were analyzed using linear regression, i.e., moderation analysis. The results indicated that the... [more]

This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as R2, MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical... [more]

Kick is one of the most important drilling problems, and because its occurrence makes drilling engineering extremely complex, it is essential to predict the possibility of kick as soon as possible. In this study, k-means clustering was combined with four artificial neural networks: regularized RBFNN, generalized RBFNN, GRNN, and PNN, to estimate the kick risk. To reduce data redundancy and normalize the drilling data, which contain kick conditions, k-means clustering was introduced. The output layer weights were then determined using a brute-force search with different Gaussian function widths, resulting in a series of artificial neural networks composed of different clustering samples and different Gaussian function widths. The results showed that the prediction accuracy of regularized RBFNN + k-means model was the highest, that of the GRNN + k-means model was the lowest. The kick prediction accuracy for regularized RBFNN, generalized RBFNN, GRNN, and PNN were 75.90%, 65.20%, 51.70%,... [more]

Pipeline transportation of crude oil has great advantages over traditional oil transmission methods, in terms of economic and environmental protection. The main costs in the oilfield surface system are the fuel costs for heating the crude oil during transportation and the electricity costs for the pumping units. In the northeast of China, where winter temperatures are extremely low and the oil has a high freezing point and high viscosity, higher temperatures, and pressures are required to transport crude oil. With machine learning widely used in many industries and achieving better results, the digital management of oil pipelines has stored a large amount of production and operation data, which has laid the foundation for the research of oil pipeline process calculation using machine learning methods. In this paper, a crude oil pressure drop calculation of an oil pipeline in Northeast China is carried out based on a neural network. For pipeline pressure drop calculation, the back propa... [more]

Supervisory control and data acquisition (SCADA) systems are critical for wind power grid integration and wind farm operation and maintenance. However, wind turbines are affected by regulation, severe weather factors, and mechanical failures, resulting in abnormal SCADA data that seriously affect the usage of SCADA systems. Thus, strict and effective data quality control of the SCADA data are crucial. The traditional anomaly detection methods based on either “power curve” or statistical evaluation cannot comprehensively detect abnormal data. In this study, a multi-approach based abnormal data detection method for SCADA wind speed data quality control is developed. It is mainly composed of the EEMD (Ensemble Empirical Mode Decomposition)-BiLSTM network model, wind speed correlation between adjacent wind turbines, and the deviation detection model based on dynamic power curve fitting. The proposed abnormal data detection method is tested on SCADA data from a real wind farm, and statistic... [more]

Maintaining stability and control over the electric system requires increasing information about the consumers’ profiling due to changes in the form of electricity generation and consumption. To overcome this trouble, short-term load forecasting (STLF) on individual consumers gained importance in the last years. Nonetheless, predicting the profile of an individual consumer is a difficult task. The main challenge lies in the uncertainty related to the individual consumption profile, which increases forecasting errors. Thus, this paper aims to implement a load predictive model focused on individual consumers taking into account its randomness. For this purpose, a methodology is proposed to determine and select predictive features for individual STLF. The load forecasting of an individual consumer is simulated based on the four main machine learning techniques used in the literature. A 2.73% reduction in the forecast error is obtained after the correct selection of the predictive features... [more]