The marine environment may change the force on the fluid and inevitably influence bubble behavior and the two-phase flow in the reactor core, which are vital to the safety margin of a nuclear reactor. To explore the effect of the marine motion on the flow and heat transfer features of subcooled flow boiling in the reactor core, the volume of fluid (VOF) method is employed to reveal the interaction between the interface structure and two-phase flow in the subchannel under rolling motion. The variations of several physical parameters are obtained, including the transverse flow, the vapor volume fraction, the vapor adhesion ratio, and the phase distribution of boiling two-phase flow with time. Sensitivity analyses of the amplitude and the period of the rolling motion were performed to demonstrate the mechanisms of the influence of the rolling motion. We found that the transverse flow in the subchannel was mainly affected by the Euler force under the rolling motion. In contrast to the two-... [more]

The estimation of non-stationary random medium parameters of petrophysical parameters is the key to the application of random medium theory in fine seismic exploration. We proposed a method for estimating non-stationary random medium parameters of petrophysical parameters using seismic data. Based on the linear petrophysical model, the relationship between seismic data and porosity, clay volume, and water saturation in the random medium was described, and the principle and method of estimating the autocorrelation parameters of the petrophysical parameter random medium were introduced in this study. Subsequently, the specific steps of applying the power spectrum method, for parameter estimation in non-stationary random media with petrophysical parameters, were explained. The feasibility and correctness of the method were verified through the estimation test of the two-dimensional theoretical model. Eventually, the estimation test of non-stationary random medium parameters of petrophysic... [more]

A cyberattack detection model based on supervised deep neural network is proposed to identify random false data injection (FDI) on the tank’s level measurements of a water distribution system. The architecture of the neural network, as well as various hyper-parameters, is modified and tuned to acquire the highest detection performance using the smallest size of training data set. The efficacy of the proposed detection model against various activation functions including sigmoid, rectified linear unit, and softmax is examined. Regularization and momentum techniques are applied to update the weights and prohibit overfitting. Moreover, statistical metrics are presented to evaluate the performance and effectiveness of the proposed model in the presence of a range of measurement noise levels. The proposed model is tested for three attack scenarios composed for the battle of the attack detection algorithms. Results confirm that the size of the data sets required to train the neural network (... [more]

Renewable energy communities (REC) are bound to play a crucial role in the energy transition, as their role, activities, and legal forms become clearer, and their dissemination becomes larger. Even though their mass grid integration, is regarded with high expectations, their diffusion, however, has not been an easy task. Its legal form and success, entail responsibilities, prospects, trust, and synergies to be explored between its members, whose collective dynamics should aim for optimal operation. In this regard, the pairing methodology of potential participants ahead of asset dimensioning seems to have been overlooked. This article presents a methodology for pairing consumers, based on their georeferenced load consumptions. A case study in an area of Porto (Asprela) was used to test the methodology. QGIS is used as a geo-representation tool and its PlanHeat plugin for district characterization support. A supervised statistical learning approach is used to identify the feature importa... [more]

Hybrid vehicles are gaining increasing global prominence, especially in the military, where unexpected breakdowns or even power deficits are not only associated with greater expense but can also cost the lives of military personnel. In some cases, it is extremely important that all battery cells and modules deliver the specified amount of capacity. Therefore, it is recommended to introduce a new measurement line of rapid diagnostics before deployment, in addition to the usual procedures. Using the results of rapid testing, we recommend the introduction of a hierarchical three-step diagnostics and assessment procedure. In this procedure, the key factor is the building up of a hierarchical tree-structured fuzzy signature that expresses the partial interdependence or redundancy of the uncertain descriptors obtained from the rapid tests. The fuzzy signature structure has two main important components: the tree structure itself, and the aggregations assigned to the internal nodes. The fuzzy... [more]

Solar photovoltaic (PV) power generation is prone to drastic changes due to cloud cover. The power is easily affected within a very short period of time. Thus, the accuracy of grasping cloud distribution is important for PV power forecasting. This study proposes a novel sky image method to obtain the cloud coverage rate used for short-term PV power forecasting. The authors developed an image analysis algorithm from the sky images obtained by an on-site whole sky imager (WSI). To verify the effectiveness of cloud coverage rate as the parameter for PV power forecast, four different combinations of weather features were used to compare the accuracy of short-term PV power forecasting. In addition to the artificial neural network (ANN) model, long short-term memory (LSTM) and the gated recurrent unit (GRU) were also introduced to compare their applicability conditions. After a comprehensive analysis, the coverage rate is the key weather feature, which can improve the accuracy by about 2% co... [more]

One of the bottlenecks of autonomous systems is to identify and/or design models and tools that are not too resource demanding. This paper presents the concept and design process of a moving platform structure−electric vehicle. The objective is to use artificial intelligence methods to control the model’s operation in a resource scarce computation environment. Neural approaches are used for data analysis, path planning, speed control and implementation of the vision system for road sign recognition. For this purpose, multilayer perceptron neural networks and deep learning models are used. In addition to the neural algorithms and several applications, the hardware implementation is described. Simulation results of systems are gathered, data gathered from real platform tests are analyzed. Experimental results show that low-cost hardware may be used to develop an effective working platform capable of autonomous operation in defined conditions.

This paper presents the results of a comparative analysis of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) models created for the prediction of the extent and intensity of damage caused to multi-storey reinforced concrete (RC) buildings. The research was conducted on a group of residential buildings, which were subjected to mining impacts in the form of surface deformations and rock mass tremors during their technical life cycle. Damage to buildings poses a significant threat to the safety of the structure and the serviceability of the buildings. They are often the cause of breaks in thermal insulation, which leads to excessive consumption of thermal energy used for space heating, which in turn contributes to over-emissions of CO2 into the atmosphere. Therefore, this problem is important, not only from a technical dimension, but also includes social, economic, and environmental aspects, which allows it to be classified as an issue of sustainable development in the... [more]

An important element of numerical modeling for specific mining issues is the selection of model parameters. The incorrect determination of geomechanical parameters can result in significant calculation errors carried throughout the entire problem. This paper presents a method for determining effective geomechanical parameters for technological and residual pillars through the use of numerical modeling, specifically, back-calculation. This is based on the results of numerical simulations, measurement data (e.g., excavation convergence measurements), and statistical methods (a non-linear regression model with “dummy” variables). The result is that appropriate parameters of pillars are set out iteratively so that the displacements of selected points in the numerical model correspond (with some approximation) to the results of mine measurements. The procedure of determining pillar parameters is presented using a case study of one mining field in an underground copper mine, where the deposi... [more]

To better understand the mechanism influencing the periodic lattice structures in gas turbine blade cooling, these numerical simulations present a systematic comparison of the effects in cases involving pin-fin, Kagome, and BCC lattice arrays on film-cooling effectiveness under three blowing ratios (i.e., M = 0.5, 1.0, and 1.5). The results indicate that the introduction of lattice array structures improves film-cooling effectiveness within the whole streamwise range, especially downstream of the film hole. With an increase in the blowing ratio, the superiority of lattice array structures relative to those without a lattice becomes increasingly evident. The local film-cooling effectiveness can be increased, to a maximum of about 100%, under a blowing ratio of 1.5. The secondary flow induced by the lattice array structure at the internal flow channel increases the TKE and accelerates the development of vortices in the film cooling hole. Using the lattice array model, the improvement of... [more]

An index on the gas market commonly refers to the average price of a certain trading product, e.g., over the period of one month. Index-based sourcing is a widely-used habit in modern gas business. Risks are reduced by averaging prices over the purchasing period. Due to the significant volume, there have been many attempts to ”beat the index”, i.e., to design a strategy that, over time, offers cheaper prices than the index. Here, we use neural networks to identify n, n∈N, optimal shopping points. Both classification- and forecasting-based strategies are tested to decide on each trading day if gas should be purchased or not. Thereby, we use the Front Month index based on prices from the Dutch Title Transfer Facility as an example. Regarding cumulative performance, all but a very simple myopic algorithm are able to outperform the index. However, each strategy has its flaws and some positive results are due to the price increase during 2021. If one opts for an active sourcing strategy, th... [more]

This paper presents an advanced methodology for restoration of the electric power transmission system after its partial or complete failure. This load-optimized restoration is dependent on sectioning of the transmission system based on artificial neural networks. The proposed methodology and the underlying algorithm consider the transmission system operation state just before the fallout and, based on this state, calculate the power grid parameters and suggest the methodology for system restoration for each individual interconnection area. The novel methodology proposes an optimization objective function as a maximum load recovery under a set of constraints. The grid is analyzed using a large amount of data, which results in an adequate number of training data for artificial neural networks. Once the artificial neural network is trained, it provides an almost instantaneous network recovery plan scheme by defining the direct switching order.

Conservation through voltage reduction (CVR) aims to reduce the peak load and energy savings in electric power systems and is being deployed at various utilities. The effectiveness of the CVR program depends on the load characteristics, i.e., the sensitivity of the load to voltage variation, and voltage regulation device settings. In the current literature, there is a lack of discussion on the CVR factor calculation using different measurements, and there is a lack of method for active power loss estimation using substation measurements. This paper provides insights into CVR factor calculation based on the measurements captured at the substation and those at the load location. This paper also proposes a new method based on curve fitting and artificial neural network to estimate the active power loss using input active power, input reactive power and input voltage at the substation. The CVR comparison study conducted in this paper helps in understanding the factors affecting CVR factor... [more]

Explainable Artificial Intelligence (XAI) methods demonstrate internal representation of data hidden within neural network trained weights. That information, presented in a form readable to humans, could be remarkably useful during model development and validation. Among others, gradient-based methods such as Grad-CAM are broadly used in an image processing domain. On the other hand, the autonomous vehicle sensor suite consists of auxiliary devices such as radars and LiDARs, for which existing XAI methods do not apply directly. In this article, we present our adaptation approach to utilize Grad-CAM visualization for LiDAR pointcloud specific object detection architectures used in automotive perception systems. We try to solve data and network architecture compatibility problems and answer the question whether Grad-CAM methods could be used with LiDAR sensor data efficiently. We showcase successful results of our method and all the benefits that come with a Grad-CAM XAI application to a... [more]

The artificial neural network method has been widely applied to the performance prediction of fillers and evaporative coolers, but its application to the dew point indirect evaporative coolers is rare. To fill this research gap, a novel performance prediction model for dew point indirect evaporative cooler based on back propagation neural network was established using Matlab2018. Simulation based on the test date in the moderately humid region of Yulin City (Shaanxi Province, China) finds that: the root mean square error of the evaporation efficiency of the back propagation model is 3.1367, and the r2 is 0.9659, which is within the acceptable error range. However, the relative error of individual data (sample 7) is a little bit large, which is close to 10%. In order to improve the accuracy of the back propagation model, an optimized model based on particle swarm optimization was established. The relative error of the optimized model is generally smaller than that of the BP neural netwo... [more]

State of charge (SOC) is the most important parameter in battery management systems (BMSs), but since the SOC is not a directly measurable state quantity, it is particularly important to use advanced strategies for accurate SOC estimation. In this paper, we first propose a bidirectional long short-term memory (BiLSTM) neural network, which enhances the comprehensiveness of information by acquiring both forward and reverse battery information compared to the general one-way recurrent neural network (RNN). Then, the parameters of this network are optimized by introducing a Bayesian optimization algorithm to match the data characteristics of lithium batteries with the network topology. Finally, two sets of lithium battery public data sets are used to carry out experiments under different constant temperature and variable temperature environments. The experimental results show that the proposed model can effectively fit the actual measurement curve. Compared with traditional long short-ter... [more]

The conventional permanent magnet torque motor (PMTM) is slotted on the inner surface of the stator core. When the size of the stator core is small, the winding coils are difficult to embed into the slots. To solve the problem, a novel PMTM is presented, which is slotted on the outer surface of the stator core. As a result, the winding coils can be conveniently embedded into the slots from the outer surface of the stator core. The novel structure of the PMTM with back winding (BWPMTM) is introduced, and the advantage of the novel structure is analyzed. Furthermore, this paper proposes a limit analytical method to solve the optimal parameters of the motor which comprehensively considers four constraints: no-load back electromotive force (EMF), torque, temperature and slot space factor. The optimal parameters of the motor are directly solved to maximize torque density within the constrained range. This method avoids repeated iterative processes and greatly reduces the amount of calculati... [more]

In the course of the documentation works aimed at updating exploitable resources of the groundwater intake, an unexpected change in hydrodynamic field arrangement was observed in a fragment of the area. In relation to the observations made so far, lasting several decades, the groundwater table switched its slope, indicating the flow in the opposite direction than originally. Initially, in the range of the surveyed hydrogeological structure, no object was identified whose draining character could cause such an effect. No significant groundwater extraction or drainage points were documented within a radius of several kilometers. It was only the extended scope of spatial analysis that made it possible to identify the probable cause of a local change in the water table position as a mining pit located in the neighboring hydrogeological structure. The two adjacent hydrogeological structures treated separately so far revealed an experimentally confirmed hydraulic connection. An in-depth anal... [more]

Tower foundations have been used as grounding electrodes to reduce the area of the grounding devices. However, it is difficult to calculate the grounding resistance due to the complex structure of the reinforced concrete foundation. A method for calculating grounding resistance of reinforced concrete foundations is proposed in this paper. The method equates the complex foundation structure into a cylindrical conductor and then calculates the grounding resistance with the help of the method of moments, which simplifies establishment of the simulation model and reduces the extensive computation. In addition, the applicability of the equal cross-sectional area method and the equal cross-sectional perimeter method is analyzed. It shows that both methods are applicable only when the concrete resistivity is close to the soil resistivity. The equal cross-sectional area method is applicable when the concrete resistivity is within twice the soil resistivity, while the equal cross-sectional peri... [more]

The quality of the electrical energy distribution service has a significant impact on consumer satisfaction and the guarantee of the right of concession for the distribution companies. For the utility that is the object of the case study, the main continuity of service indicators was at levels below the regulatory limits. Still, due to budget constraints, the forecast of the benefit that improvement or expansion projects bring to continuity indicators must be assertive for a proper direction of investments and decision making. In this work, a methodology for evaluating projects to improve the quality of service was proposed, with the realization of the estimated benefit associated with the reduction in continuity indicators (DEC and FEC), using concepts of artificial neural networks and evolutionary algorithms. The results were obtained from a three-year history of execution of the utility’s projects. Based on the correlation analysis, a variable selection procedure was developed, wher... [more]

This work applies emergent self-organizing map (ESOM) techniques, a form of machine learning, in the multidimensional interpretation and prediction of rare earth element (REE) abundance in produced and geothermal waters in the United States. Visualization of the variables in the ESOM trained using the input data shows that each REE, with the exception of Eu, follows the same distribution patterns and that no single parameter appears to control their distribution. Cross-validation, using a random subsample of the starting data and only using major ions, shows that predictions are generally accurate to within an order of magnitude. Using the same approach, an abridged version of the U.S. Geological Survey Produced Waters Database, Version 2.3 (which includes both data from produced and geothermal waters) was mapped to the ESOM and predicted values were generated for samples that contained enough variables to be effectively mapped. Results show that in general, produced and geothermal wat... [more]

The environmental repercussions of extensive carbon dioxide (CO2) emissions on the environment are crucial for policymakers and scholars. The repercussions of and connection between economic growth (ECG), tourism (TOUR), and foreign direct investment (FDI) on CO2 emission mitigation have been measured and argued from empirical and theoretical perspectives by scholars. Notwithstanding, the extant body of knowledge has failed to incorporate and investigate the function of governance in decarbonizing tourism activities and FDI from CO2 emissions to attain a healthy and quality environment in Africa. Hence, this current research investigates governance’s role in the reduction processes of CO2 emissions grounded in environmental Kuznets curve (EKC) conceptual assumptions for panel data spanning 2000 through 2020 for 27 African countries. This research utilized the Westerlund panel cointegration approach for the investigation of the cointegration of the selected variables. This study applied... [more]

The goal of today’s technological era is to make every item smart. Internet of Things (IoT) is a model shift that gives a whole new dimension to the common items and things. Wireless sensor networks, particularly Low-Power and Lossy Networks (LLNs), are essential components of IoT that has a significant influence on daily living. Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard protocol for IoT and LLNs. It is not only used widely but also researched by various groups of people. The extensive use of RPL and its customization has led to demanding research and improvements. There are certain issues in the current RPL mechanism, such as an energy hole, which is a huge issue in the context of IoT. By the initiation of Grid formation across the sensor nodes, which can simplify the cluster formation, the Cluster Head (CH) selection is accomplished using fish swarm optimization (FSO). The performance of the Graph-Grid-based Convolution clustered neural network w... [more]

“Short-term load forecasting (STLF)” is increasingly significant because of the extensive use of distributed energy resources, the incorporation of intermitted RES, and the implementation of DSM. This paper provides a novel ensemble forecasting model with wavelet transform for the STLF depending on the decomposition principle of load profiles. The model can effectively capture the portion of daily load profiles caused by seasonal variations. The results indicate that it is possible to improve STLF accuracy with the proposed method. The proposed approach is tested with the data taken from Ontario’s electricity market in Canada. The results show that the proposed technique performs well in-terms of prediction when compared to existing traditional and cutting-edge methods. The performance of the model was validated with different datasets. Moreover, this approach can provide accurate load forecasting using ensemble models. Therefore, utilities and smart grid operators can use this approac... [more]

Due to failures or even the absence of an electricity grid, microgrid systems are becoming popular solutions for electrifying African rural communities. However, they are heavily stressed and complex to control due to their intermittency and demand growth. Demand side management (DSM) serves as an option to increase the level of flexibility on the demand side by scheduling users’ consumption patterns profiles in response to supply. This paper proposes a demand-side management strategy based on load shifting and peak clipping. The proposed approach was modelled in a MATLAB/Simulink R2021a environment and was optimized using the artificial neural network (ANN) algorithm. Simulations were carried out to test the model’s efficacy in a stand-alone PV-battery microgrid in East Africa. The proposed algorithm reduces the peak demand, smoothing the load profile to the desired level, and improves the system’s peak to average ratio (PAR). The presence of deferrable loads has been considered to br... [more]