The integration of photovoltaic systems (PVS) in electric vehicles (EV) increases the vehicle’s autonomy by providing an additional energy source other than the battery. However, current solar cell technology generates around 200 W for a 1.4 m2 panel (to be installed on the roof of the EV) at stable irradiance conditions. This limitation in production and the sudden changes in irradiance produced by shadows of clouds, buildings, and other structures make developing a fast and efficient maximum power point tracking (MPPT) technique in this area necessary. This article proposes an artificial neural network (ANN)-based MPPT, called DS-ANN, that uses manufacturer datasheet parameters as inputs to the network to address this problem. The Bayesian backpropagation-regularization performs the training, ensuring that the MPPT technique operates satisfactorily on different PVS without retraining. We simulated the response of 20 commercial modules against actual irradiance data to validate the pr... [more]

Liquid hydrogen (LH2) spills share many of the characteristics of liquefied natural gas (LNG) spills. LNG spills on water sometimes result in localized vapor explosions known as rapid phase transitions (RPTs), and are a concern in the LNG industry. LH2 RPT is not well understood, and its relevance to hydrogen safety is to be determined. Based on established theory from LNG research, we present a theoretical assessment of an accidental spill of a cryogen on water, including models for pool spreading, RPT triggering, and consequence quantification. The triggering model is built upon film-boiling theory, and predicts that the mechanism for RPT is a collapse of the gas film separating the two liquids (cryogen and water). The consequence model is based on thermodynamical analysis of the physical processes following a film-boiling collapse, and is able to predict peak pressure and energy yield. The models are applied both to LNG and LH2, and the results reveal that (i) an LNG pool will be la... [more]

Outages in an overhead power distribution system are caused by multiple environmental factors, such as weather, trees, and animal activity. Since they form a major portion of the outages, the ability to accurately estimate these outages is a significant step towards enhancing the reliability of power distribution systems. Earlier research with statistical models, neural networks, and committee machines to estimate weather-related and animal-related outages has reported some success. In this paper, a deep neural network ensemble model for outage estimation is proposed. The entire input space is partitioned with a distinct neural network in the ensemble performing outage estimate in each partition. A novel algorithm is proposed to train the neural networks in the ensemble, while simultaneously partitioning the input space in a suitable manner. The proposed approach has been compared with the earlier approaches for outage estimation for four U.S. cities. The results suggest that the propo... [more]

Geothermal power is being regarded as depending on techniques derived from hydrocarbon production in worldwide current strategy. However, it has artificially been developed far less than its natural potentials due to technical restrictions. This paper introduces the Enhanced Geothermal System based on Excavation (EGS-E), which is an innovative scheme of geothermal energy extraction. Then, based on cohesion-weakening-friction-strengthening model (CWFS) and literature investigation of granite test at high temperature, the initiation, propagation of excavation damaged zones (EDZs) under unloading and the EDZs scale in EGS-E closed to hydrostatic pressure state is studied. Finally, we have a discussion about the further evolution of surrounding rock stress and EDZs during ventilation is studied by thermal-mechanical coupling. The results show that the influence of high temperature damage on the mechanical parameters of granite should be considered; Lateral pressure coefficient affects the... [more]

The combination of different propulsion and energy storage systems for hybrid vehicles is changing the focus in the field of powertrain calibration. Shorter time-to-market as well as stricter legal requirements regarding the validation of Real Driving Emissions (RDE) require the adaptation of current procedures and the implementation of new technologies in the powertrain development process. In order to achieve highest efficiencies and lowest pollutant emissions at the same time, the layout and calibration of the control strategies for the powertrain and the exhaust gas aftertreatment system must be precisely matched. An optimal operating strategy must take into account possible trade-offs in fuel consumption and emission levels, both under highly dynamic engine operation and under extended environmental operating conditions. To achieve this with a high degree of statistical certainty, the combination of advanced methods and the use of virtual test benches offers significant potential.... [more]

Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also... [more]

In this paper, we introduce a model representing the key characteristics of high frequency variations of solar irradiance and photovoltaic (PV) power production based on Clear Sky Index (CSI) data. The model is suitable for data-driven decision-making in electrical distribution grids, e.g., descriptive/predictive analyses, optimization, and numerical simulation. We concentrate on solar irradiance data since the power production of a PV system strongly correlates with solar irradiance at the site location. The solar irradiance is not constant due to the Earth’s orbit and irradiance absorption/scattering from the clouds. To simulate the operation of a PV system with one-minute resolution for a specific coordinate, we have to use a model based on the CSI of the solar irradiance data, capturing the uncertainties caused by cloud movements. The proposed model is based on clustering the days of each year into groups of days, e.g., (i) cloudy, (ii) intermittent cloudy, and (iii) clear sky. The... [more]

In the present study, flame propagation statistics from turbulent statistically planar premixed flames obtained from simple and detailed chemistry, three-dimensional Direct Numerical Simulations, were evaluated and compared to each other. To this end, a new database was established encompassing five different conditions on the turbulent premixed combustion regime diagram, using nearly identical numerical methods and the same initial and boundary conditions. A detailed discussion of the advantages and limitations of both approaches is provided, including the difference in carbon footprint for establishing the database. It is shown that displacement speed statistics and their interrelation with curvature and tangential strain rate are in very good qualitative and reasonably good quantitative agreement between simple and detailed chemistry Direct Numerical Simulations. Hence, it is concluded that simple chemistry simulations should retain their importance for future combustion research, a... [more]

Compared with the marine shale from North America, Chinese lacustrine basins have more complex geological and reservoir conditions, which makes the selection of sweet spot intervals in the shale oil reservoir particularly difficult. It is thus crucial to accurately predict the shale oil sweet spots for effective exploration and development of shale oil in a lacustrine basin. In this paper, we propose an innovative evaluation method of shale oil sweet spots, which considers five primary parameters (i.e., total oil content, movable oil ratio, reservoir pressure coefficient, permeability, and rock brittleness index) to construct a comprehensive weighting factor, which is used to quantitatively identify a favorable shale oil reservoir. This method firstly constructs an evaluation function for each of the parameters, and then calculates a comprehensive weighting factor to determine the shale oil sweet spot. Statistical results show that the oil production of formation testing intervals have... [more]

Unmanned Ground Vehicles (UGV) are devices capable of performing basic working movements without the operator being in their immediate working environment. Their capabilities include but are not limited to the perception of the environment with the use of sensors, determining the platform’s position, and planning and executing its movement. Ultra Wideband (UWB) is one of the wireless communication technologies which is increasingly used in location systems. This article presents the use of UWB technology in developing a guide localization system for a UGV (one of the stages of implementing a follow-me system). The article describes tests carried out on the developed testbed. Their aim was to determine the hardware configuration of the anchor arrangement characterized by the minimum number of lost data packets during operation. In order to determine the influence of the analysed variables on the output values, the method of global sensitivity analysis for neural networks was used.

Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error... [more]

In this paper, a heavy-duty loader operated in an underground mine is discussed. Due to extremely harsh operational conditions, an important maintenance problem is related to engine oil pressure. We have found that when the degradation process appears, the nature of variation of pressure engine oil changes. Following this observation, we have proposed a data analysis procedure for the structure break point detection. It is based on specific data pre-processing and further statistical analysis. The idea of the paper is to transform the data into a nearly monotonic function that describes the variation of machine condition or in the statistical language—change of the regime inside the process. To achieve that goal we proposed an original data processing procedure. The dataset analyzed in the paper covers one month of observation. We have received confirmation that during that period, maintenance service has been done. The purpose of our research was to remove ambiguity related to direct... [more]

We present the current status of time-lapse seismic integration at the Farnsworth (FWU) CO2 WAG (water-alternating-gas) EOR (Enhanced Oil Recovery) project at Ochiltree County, northwest Texas. As a potential carbon sequestration mechanism, CO2 WAG projects will be subject to some degree of monitoring and verification, either as a regulatory requirement or to qualify for economic incentives. In order to evaluate the viability of time-lapse seismic as a monitoring method the Southwest Partnership (SWP) has conducted time-lapse seismic monitoring at FWU using the 3D Vertical Seismic Profiling (VSP) method. The efficacy of seismic time-lapse depends on a number of key factors, which vary widely from one application to another. Most important among these are the thermophysical properties of the original fluid in place and the displacing fluid, followed by the petrophysical properties of the rock matrix, which together determine the effective elastic properties of the rock fluid system. We... [more]

An alternative multirod solar laser end-side-pumping concept, based on the megawatt solar furnace in France, is proposed to significantly improve the TEM00-mode solar laser output power level and its beam brightness through a novel zigzag beam merging technique. A solar flux homogenizer was used to deliver nearly the same pump power to multiple core-doped Nd:YAG laser rods within a water-cooled pump cavity through a fused silica window. Compared to the previous multibeam solar laser station concepts for the same solar furnace, the present approach can allow the production of high-power TEM00-mode solar laser beams with high beam brightness. An average of 1.06 W TEM00-mode laser power was numerically extracted from each of 1657 rods, resulting in a total of 1.8 kW. More importantly, by mounting 399 rods at a 30° angle of inclination and employing the beam merging technique, a maximum of 5.2 kW total TEM00-mode laser power was numerically extracted from 37 laser beams, averaging 141 W fr... [more]

Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simplified modeling techniques demanding a small number of inputs and low computational resources. Data-driven techniques are envisaged as one of the best options to meet these constraints. This paper presents a novel methodology consisting of the combination of an optimization algorithm, two auto-regressive models and a control loop algorithm able to virtually replicate the control of thermostatically driven systems. This combined strategy includes all the thermostatically controlled modes governed by the set point temperature and enables automatic assessment of the energy consumption impact of multiple scenarios. The required inputs are limited to available historical readings from smart thermostats and externa... [more]

Corrosion in the marine environment is a complex mechanism. One of the most damaging forms of corrosion is pitting corrosion, which is difficult to design and inspect against. In the North Sea, multiple offshore wind structures have been deployed that are corroding from the inside out. One of the most notable corrosion mechanisms observed is pitting corrosion. This study addresses the lack of information both in the literature and the industry standards on the pitting corrosion profile for water depth from coupons deployed in the North Sea. Image processing was therefore conducted to extract the characteristics of the pit, which were defined as pit major length, minor length, area, aspect ratio, and count. The pit depth was measured using a pit gauge and the maximum pit depth was found to be 1.05 mm over 111 days of exposure. The goal of this paper is to provide both deterministic models and a statistical model of pit characteristics for water depth that can be used by wind farm operat... [more]

Road transport is one of the key elements of economic development that helps build social and territorial cohesion. The economic development that has taken place in Poland over the last three decades has led to an improvement in road infrastructure throughout the country. Construction of new roads and improvement of existing ones promotes economic development. However, as the number of cars increases, so does the level of air emissions. In reducing pollutant emissions, the analysis of the technological possibilities used and the improvement of their efficiency with the simultaneous minimization of generated pollution is also of particular importance. The purpose of the publication is to present development trends in road transport in Poland and the possibility of reducing emissions in this respect. The method of analysing strategic documents and statistical data was used to achieve this goal. Moreover, the article shows perspectives for reducing the level of emissions from road transpo... [more]

Fatigue affects multiple aspects of cognitive performance among drivers. However, even after fatigue builds up, some are still able to maintain the level of behavioral performance. To evaluate these adaptations on the neural network level, we utilized functional magnetic resonance imaging (fMRI). Seventeen male professional drivers underwent two fMRI sessions, once while rested and once in a fatigued condition after 10-h of overnight driving. The cognitive task used in the study involved the detection of visual feature conjunctions, namely the shape and the color. There were no significant differences in the task performance between the conditions except for longer reaction times in the fatigued condition. However, we observed substantial differences in the activation patterns during the cognitive task involving selective attention between the conditions. On the global level, we observed a general decrease in activation strength in the fatigued condition, which appeared to be more pron... [more]

The heating, ventilation, and air conditioning (HVAC) system serving the test room of the SENS i-Lab of the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Aversa, south of Italy) has been experimentally investigated through a series of tests performed during both summer and winter under both normal and faulty scenarios. In particular, five distinct typical faults have been artificially implemented in the HVAC system and analyzed during transient and steady-state operation. An optimal artificial neural network-based system model has been created in the MATLAB platform and verified by contrasting the experimental data with the predictions of twenty-two different neural network architectures. The selected artificial neural network architecture has been coupled with a dynamic simulation model developed by using the TRaNsient SYStems (TRNSYS) software platform with the main aims of (i) making available an experimental dataset characterized b... [more]

Placement of fins in enclosures has promising utilization in advanced technological processes due to their role as heat reducing/generating elements such as in conventional furnaces, economizers, gas turbines, heat exchangers, superconductive heaters and so forth. The advancement in technologies in power engineering and microelectronics requires the development of effective cooling systems. This evolution involves the utilization of fins of significantly variable geometries enclosed in cavities to increase the heat elimination from heat-generating mechanisms. Since fins are considered to play an effective role in the escalation of heat transmission, the current study is conducted to examine the transfer of heat in cavities embedding fins, as well as the effect of a range of several parameters upon the transmission of energy. The following research is supplemented with the interpretation of the thermo-physical aspects of a power-law liquid enclosed in a trapezoidal cavity embedding a U-... [more]

The aim of the study was to determine the association between parental attitudes and saving energy in the perception of young people with autism spectrum (13−17 years old). The character of studies was pilot. The diagnostic survey method was used, along with The Scale of Parental Attitudes by M. Plopa and an authored survey questionnaire. Deliberate random sampling was applied. Statistical analysis showed that an attitude towards autonomy manifested by mothers, as well as an attitude of acceptance in mothers and fathers, turned out to be significant in adolescents with ASD showing a positive attitude towards energy saving, and these correlations were positive, strong and statistically significant. An overly protective attitude of mothers turned out to be significant in shaping a negative attitude towards energy saving among adolescents with ASD with a significant, positive moderate correlation. Regression analysis showed that the perception of the father’s parental attitude as acceptin... [more]

This article presents the results of the optimization of steam generator control systems powered by mixtures of liquid fuels containing biofuels. The numerical model was based on the results of experimental research of steam generator operation in an open system. The numerical model is used to build control algorithms that improve performance, increase efficiency, reduce fuel consumption and increase safety in the full range of operation of the steam generator and the cogeneration system of which it is a component. In this research, the following parameters were monitored: temperature and pressure of the circulating medium, exhaust gas temperature, oxygen content in exhaust gas, percentage control of oil burner power. Two methods of controlling the steam generator were proposed: the classic one, using the PID regulator, and the advanced one, using artificial neural networks. The work shows how the model is adapted to the real system and the impact of the control algorithms on the effic... [more]

This paper develops a novel hybrid feature learner and classifier for vibration-based fault detection and isolation (FDI) of industrial apartments. The trained model extracts high-level discriminative features from vibration signals and predicts equipment state. Against the limitations of traditional machine learning (ML)-based classifiers, the convolutional neural network (CNN) and deep neural network (DNN) are not only superior for real-time applications, but they also come with other benefits including ease-of-use, automated feature learning, and higher predictive accuracies. This study proposes a hybrid DNN and one-dimensional CNN diagnostics model (D-dCNN) which automatically extracts high-level discriminative features from vibration signals for FDI. Via Softmax averaging at the output layer, the model mitigates the limitations of the standalone classifiers. A diagnostic case study demonstrates the efficiency of the model with a significant accuracy of 92% (F1 score) and extensive... [more]

This paper is the first country-specific analysis of the market-driven Smart Metering innovation system, covering technologies, actors, and policies. It provides new insights on the key enablers and barriers in the rollout of electricity Smart Meters (SMs) without binding regulatory mandate. The presented research is based on the Technology Innovation System (TIS) analysis for Poland, where an obligation scheme for the rolling out of SMs has been introduced very recently. Still, the number of SMs installed places the country in the top 10 Member States of the European Union. The implementation of SMs is progressing in a complex, multi-actor innovation system, shaped by the leading role of Distribution System Operators (DSOs). The article analyses the key elements of the SM innovation system (technologies and infrastructures, actors and networks, institutions and policies) and characterizes their interaction based on desk research and a critical assessment of regulations, statistics, an... [more]

In this study, we propose a novel workflow to predict the production of existing and new multi-wells. To perform reliable production forecasting on heterogeneous shale formations, the features of these formations must be analyzed by classifying the formations into various groups; the groups have different production characteristics depending on the key factors that affect the shale formation. In addition, the limited data obtained from nearby existing multi-wells should be used to estimate the production of new wells. The key factors that affect shale formation were derived from the correlation and principal component analysis of available production-related attributes. The production of existing wells was estimated by classifying them into groups based on their production characteristics. These classified groups also identified the relationship between hydraulic fracturing design factors and productivity. To estimate the production of new wells (blind wells), we generated groups with... [more]