The article presents the results of a simulation of changes in gallium phosphide (GaP) resistivity under the influence of lighting. The adopted model of the defect structure is presented along with the defect parameters. Initial conditions created on the basis of a tested material sample, labeled GaP-1, made of monocrystals of semi-insulating gallium phosphide (SI GaP), are presented. The simulation methodology and the created model of the kinetics equations are described. As a result of the simulation, the values of the photocurrent and the electron-hole pair generation coefficient G were assigned to data obtained experimentally depending on the carrier lifetime coefficient τ. Changes in resistivity and concentration of electrons and holes in the bands for gallium phosphide with a structure consisting of five defects are presented. The proposed simulation method can be used to calculate switch-on and -off times and photocurrent values for the semiconductor materials used to construct... [more]

The development of efficient and sustainable marine energy converter systems is a great challenge, especially in remote areas such as oceanic islands. This work proposes a numerical modeling methodology to assess the mechanical behavior of a wave energy converter (WEC) to be applied outside Fernando de Noronha Island (Pernambuco, Brazil). First, oceanographic data collected in situ were analyzed to determine different sea state scenarios in the region. The Airy theory and second-order Stokes’ theory were used to obtain the velocity profiles for the maximum and operational swells. These profiles were then implemented in a flow model developed in COMSOL Multiphysics software (Burlington, MA, USA) to calculate the wave distributions of pressure on the WEC structure. Finally, wave pressure distributions obtained from simulations were implemented in a static analysis of the system by the finite element method using SolidWorks (France). The results highlighted the most critical system inclin... [more]

Using a direct numerical simulation (DNS), we investigate the onset of non-equilibrium effects and the subsequent emergence of a self-preserving state as a turbulent boundary layer (TBL) encounters a smooth-to-rough (STR) step change. The rough surface comprises over 2500 staggered cuboid-shaped elements where the first row is placed at 50 θ0 from the inflow. A Reθ=4500 value is attained along with δk≈35 as the TBL develops. While different flow parameters adjust at dissimilar rates that further depend on the vertical distance from the surface and perhaps on δSTR/k, an equilibrium for wall stress, mean velocity, and Reynolds stresses exists across the entire TBL by 35 δSTR after the step change. First-order statistics inside the inner layer adapt much earlier, i.e., at 10−15 δSTR after the step change. Like rough-to-smooth (RTS) scenarios, an equilibrium layer develops from the surface. Unlike RTS transitions, a nascent logarithmic layer is identifiable much earlier, at 4 δSTR after t... [more]

A large research effort is currently ongoing within the framework of the EUROfusion consortium for the study and design of a water-cooled lithium−lead (WCLL) breeding blanket (BB). This concept will be tested in ITER through the installation of a test blanket module (TBM) and it is one of the two candidates adopted as driver BBs in DEMO. In this framework, at the ENEA research centre of Brasimone, the realization of the experimental platform, W-HYDRA, is envisaged. The platform is dedicated to the support of the development of WCLL BB and ITER TBM and the investigation of the DEMO balance of plants. One of the most important experimental infrastructures is the water-loop facility, the aim of which is to provide water at a high pressure and temperature (PWR conditions), with a sufficient mass-flow rate and power for the experimental testing of BB and TBM components. The facility will be equipped with a vacuum chamber and an electron beam gun for the reproduction of high surface heat flu... [more]

Geological CO2 sequestration (GCS) has been proposed as an effective approach to mitigate carbon emissions in the atmosphere. Uncertainty and sensitivity analysis of the fate of CO2 dynamics and storage are essential aspects of large-scale reservoir simulations. This work presents a rigorous machine learning-assisted (ML) workflow for the uncertainty and global sensitivity analysis of CO2 storage prediction in deep saline aquifers. The proposed workflow comprises three main steps: The first step concerns dataset generation, in which we identify the uncertainty parameters impacting CO2 flow and transport and then determine their corresponding ranges and distributions. The training data samples are generated by combining the Latin Hypercube Sampling (LHS) technique with high-resolution simulations. The second step involves ML model development based on a data-driven ML model, which is generated to map the nonlinear relationship between the input parameters and corresponding output intere... [more]

The solar trough concentrator is used to increase the solar radiation intensity on absorbers for water heating, desalination, or power generation purposes. In this study, optical performances of four solar trough concentrators, viz. the parabolic trough concentrator (PTC), the compound parabolic concentrator (CPC), the surface uniform concentrator (SUC), and the trapezoid trough concentrator (TTC), are simulated using the Monte Carlo Ray Tracing method. Mathematical models for the solar trough concentrators are first established. The solar radiation distributions on their receivers are then simulated. The solar water heating performances using the solar trough concentrators are finally compared. The results show that, as a high-concentration ratio concentrator, the PTC can achieve the highest heat flux, but suffers from the worst uniformity on the absorber, which is only 0.32%. The CPC can generate the highest heat flux among the rest three low-concentration ratio solar trough concentr... [more]

The accurate prediction of heat fluxes and, thus, metal wall temperatures of gas turbine (GT) combustor liners is a complicated and numerically expensive task. Computational Fluid Dynamics (CFD) support for the design of cooling systems is essential to ensure safe and proper operation of the entire gas turbine engine. Indeed, it is well known how complicated, and, at the same time, expensive it is to carry out experimental campaigns inside combustors operating under working conditions, and, therefore, pressurized and having high temperatures. The correct prediction of thermal fluxes in a CFD simulation depends on the proper modeling of all the involved phenomena and their interactions with each other. For this reason, Conjugate Heat Transfer (CHT) simulations are mandatory in gas turbine cooling system applications. Multiphysics and multiscale simulations, based on loosely-coupled approaches, have emerged as extremely effective numerical tools, providing enormous computational time sav... [more]

Coughing and sneezing are the main ways of spreading coronavirus-2019 (SARS-CoV-2). People sometimes need to work together at close distances. This study presents the results of the computational fluid dynamics (CFD) simulation of the dispersion and transport of respiratory droplets emitted by an infected person who coughs in an indoor space with an air ventilation system. The resulting information is expected to help in risk assessment and development of mitigation measures to prevent the infection spread. The turbulent flow of air in the indoor space is simulated using the k-ε model. The particle equation of motion included the drag, the Saffman lift, the Brownian force and gravity/buoyancy forces. The innovation of this study includes A: Using the Eulerian−Lagrangian CFD model for the simulation of the cough droplet dispersion. B: Assessing the infection risk by the Wells−Riley equation. C: A safer design for the ventilation system (changing the ventilation supplies and exhausts in... [more]

In a physical microgrid system, equipment failures, manual misbehavior of equipment, and power quality can be affected by intentional cyberattacks, made more dangerous by the widespread use of established communication networks via sensors. This paper comprehensively reviews smart grid challenges on cyber-physical and cyber security systems, standard protocols, communication, and sensor technology. Existing supervised learning-based Machine Learning (ML) methods for identifying cyberattacks in smart grids mostly rely on instances of both normal and attack events for training. Additionally, for supervised learning to be effective, the training dataset must contain representative examples of various attack situations having different patterns, which is challenging. Therefore, we reviewed a novel Data Mining (DM) approach based on unsupervised rules for identifying False Data Injection Cyber Attacks (FDIA) in smart grids using Phasor Measurement Unit (PMU) data. The unsupervised algorithm... [more]

This paper describes testing of the INSIM-FT proxy simulation method (interwell-numerical-simulation model improved with front-tracking method) to assess the dependencies between production and injection wells, as well as to assess the forecast of oil/liquid production by wells depending on their operation parameters. The paper proposes the approach of taking into account the influence of various production enhancement operations. The method was tested on a synthetic hydrodynamic model and on a sector of a real field. The results show a good match between historical data and simulation results and indicate significant computational efficiency compared to classical reservoir simulators.

Analyzing energy consumption is currently of great interest to define efficient energy management strategies. In particular, studying the evolution of the behavior of the consumption pattern can allow energy policies to be defined according to the time of the year. In this sense, this work proposes to study the evolution of energy behavior patterns using online clustering techniques. In particular, the centroids of the groups constructed by the techniques will represent their consumption patterns. Specifically, two unsupervised online machine learning techniques ideal for the stated objective will be analyzed, X-Means and LAMDA, since they are capable of varying and adapting the number of clusters at runtime. These techniques are applied to energy consumption data in commercial buildings, making groupings on previous groups, in our case, monthly and quarterly. We compared their performance by analyzing the evolution of the patterns over time. The results are very promising since the qu... [more]

Reducing CO2 emissions from coal-fired power plants is an urgent global issue. Effective and precise monitoring of CO2 emissions is a prerequisite for optimizing electricity production processes and achieving such reductions. To obtain the high temporal resolution emissions status of power plants, a lot of research has been done. Currently, typical solutions are utilizing Continuous Emission Monitoring System (CEMS) to measure CO2 emissions. However, these methods are too expensive and complicated because they require the installation of a large number of devices and require periodic maintenance to obtain accurate measurements. According to this limitation, this paper attempts to provide a novel data-driven method using net power generation to achieve near-real-time monitoring. First, we study the key elements of CO2 emissions from coal-fired power plants (CFPPs) in depth and design a regression and physical variable model-based emission simulator. We then present Emission Estimation N... [more]

The goal of this work is to give a full review of how machine learning (ML) is used in thermal comfort studies, highlight the most recent techniques and findings, and lay out a plan for future research. Most of the researchers focus on developing models related to thermal comfort prediction. However, only a few works look at the current state of adaptive thermal comfort studies and the ways in which it could save energy. This study showed that using ML control schemas to make buildings more comfortable in terms of temperature could cut energy by more than 27%. Finally, this paper identifies the remaining difficulties in using ML in thermal comfort investigations, including data collection, thermal comfort indices, sample size, feature selection, model selection, and real-world application.

Bladeless fans are more energy efficient, safer due to the hidden blades, easier to clean, and more adjustable than conventional fans. This paper investigates the influence of the airfoil’s outlet slit thickness on the discharge ratio by varying the outlet slit thickness of an Eppler 473 airfoil from 1.2 mm to 2 mm in intervals of 0.2 mm by using a k-omega SST turbulence model with an all y+ wall treatment used to numerically simulate in CFD. The computational results indicated that smaller slits showed higher discharge ratios. The airfoil with a 1.2 mm slit thickness showed a discharge ratio of 18.78, a 24% increase from the discharge ratio of the 2 mm slit. The effect of outlet angle on the pressure drop across the airfoil was also studied. Outlet angles were varied from 16° to 26° by an interval of 2°. The airfoil profile with a 24° outlet angle showed a maximum pressure difference of 965 Pa between the slit and leading edge. In contrast, the 16° outlet angle showed the least pressu... [more]

Efficient simulation of the helical coiled once-through steam generator (H-OTSG) is crucial in the design and safety analysis of the high-temperature gas-cooled reactor (HTGR). The physical property and phase transformation of water in the steam generator brings great challenges during simulation. The water properties calculation routine occupies a large part of the computational time in the steam generator solution process. Thus, a thermohydraulic property library is developed based on the IAPWS-IF97 formulation in this work to reduce the computational cost. Here the formulation adopts the backward equation method to avoid iterations in thermodynamic property calculation. Moreover, two Newton-method-based simultaneous solutions are implemented as implicitly nonlinear solvers, including Jacobian-Free Newton−Krylov (JFNK) and Newton−Krylov (NK) methods, due to its excellent computational performance. These simultaneous solution algorithms are combined with the developed water property l... [more]

Geothermal heat flow is an essential parameter for the exploration of geothermal energy. The cost is often prohibitive if dense heat flow measurements are arranged in the study area. Regardless, an increase in the limited and sparse heat flow observation points is needed to study the regional geothermal setting. This research is significant in order to provide a new reliable map of terrestrial heat flow for the subsequent development of geothermal resources. The Gradient Boosted Regression Tree (GBRT) prediction model used in this paper is devoted to solving the problem of an insufficient number of heat flow observations in North China. It considers the geological and geophysical information in the region by training the sample data using 12 kinds of geological and geophysical features. Finally, a robust GBRT prediction model was obtained. The performance of the GBRT method was evaluated by comparing it with the kriging interpolation, the minimum curvature interpolation, and the 3D int... [more]

This paper studies the implementation strategy of an online tracking simulation system for the 660 MW ultra supercritical circulating fluidized bed boiler (USCFB). The establishment of the online system can be divided into offline and online phases. In the offline phase, the high-precision model and initial condition database of the boiler are first established. After the initial conditions are loaded, the initial conditions are adjusted through mass and energy compensation to make the initial state of the model consistent with the current operating state of the unit. In the online phase, this paper analyzes the global sensitivity of the 660 MW USCFB, establishes an online tracking mechanism based on the PI controller, and conducts simulation tests. The experiment shows that the tracking simulation system established in this paper can track the analog signal quickly and accurately.

Hydraulic fracturing is a complex nonlinear hydro-mechanical coupled process. Accurate numerical simulation is of great significance for reducing fracturing costs and improving reservoir development benefits. The aim of this paper is to propose an efficient numerical simulation method for the fracturing-to-production problem under a unified framework that has good convergence and accuracy. A hydro-mechanical coupled fracturing model (HMFM) is established for poroelastic media saturated with a compressible fluid, and the local characteristics of the physical field are fully considered. Each fracture is explicitly characterized using the discrete fracture model (DFM), which can better reflect the physical characteristics near fractures. Based on the extended finite element method (XFEM) and the Newton−Raphson method, a fully coupled approach named Unified Extended Finite Element (UXFEM) is developed, which can solve the nonlinear system of equations that describe the solution under a uni... [more]

The increasing demand for robust and high-performance centrifugal compressor stages has led to the development of several optimization and uncertainty quantification approaches. However, in the industrial scenario, geometric variations of such pre-engineered stages can occur during customer orders or non-conformity evaluations. In this regard, a rapid low-effort quantification of the impact of these changes has become critical for manufacturers. Against this backdrop, the present study provides an approach based on the joint use of computational fluid dynamics (CFDs) and artificial neural networks to instantly assess the impact of geometric variations on the aerodynamic performance and operating range of centrifugal compressor stages. As a theoretical contribution, the research investigates the capacity of a CFD-based surrogate approach for evaluating variations of stage efficiency and work coefficient. On a practical level, a business-friendly tool for stage performance assessment is... [more]

Pre-processing and analysis of sensor data present several challenges due to their increasingly complex structure and lack of consistency. In this paper, we present TSxtend, a software tool that allows non-programmers to transform, clean, and analyze temporal sensor data by defining and executing process workflows in a declarative language. TSxtend integrates several existing techniques for temporal data partitioning, cleaning, and imputation, along with state-of-the-art machine learning algorithms for prediction and tools for experiment definition and tracking. Moreover, the modular architecture of the tool facilitates the incorporation of additional methods. The examples presented in this paper using the ASHRAE Great Energy Predictor dataset show that TSxtend is particularly effective to analyze energy data.

Building information modelling (BIM) is the first step towards implementing Building 4.0, where virtual reality and digital twins are key elements. The use of unmanned aircraft systems (UAS/drones) to capture data from buildings is nowadays a very popular method, so a methodology was developed to digitally integrate the photogrammetric surveys of a building into BIM, exclusively with the use of drones. Currently, buildings are responsible for 40% of energy consumption in Europe; therefore, the interconnection between BIM and building energy modelling (BEM) is essential to digitalize the construction sector, increasing competitiveness through cost reduction. In this context, the BlueWoodenHouse Project aims, among other activities, to characterize the solutions/systems of building materials and monitor the temperature, relative humidity and CO2, as well as energy consumption, of a single-family modular wooden house located in the north of Portugal, with 190 m2 and three users. Thus, the... [more]

The Japan Electric Power Exchange (JEPX) provides a platform for the trading of electric energy in a manner similar to more traditional financial markets. As the number of market agents increase, there is an increasing need for effective price-forecasting models. Electricity price data are observed to exhibit periods of relatively stable, i.e., low-magnitude, low-variance prices interspersed with periods of higher prices accompanied by larger uncertainty. The price data time series therefore exhibits a temporal non-stationarity characteristic that is difficult to capture with typical time series modeling frameworks. In this paper, we implement models for the occurrence of price spike events where spikes are defined as observing prices above a predefined threshold set here at 25 JPY/kWh. This value corresponds to about the 90th percentile of observed prices during peak trading periods. The price spikes time series is observed to be rare events that occur in clusters. We therefore propos... [more]

In this study, drying characteristics, kinetic modelling, energy and exergy analyses of a convective hot air dryer are presented for water yam. The drying experiments were carried out at temperature levels of 50, 60, and 70°C and slice thicknesses of 3, 6, and 9 mm. The effects of drying variables on the drying rate (DR), moisture diffusivity (Deff), activation energy (Ea), energy utilization (EU), energy utilization ratio (EUR), exergy loss (EXL), exergy efficiency (EXeff), improvement potential (IP), and exergetic sustainability index (ESI) were investigated. The results showed that increasing air temperature increased the DR, Deff, EU, EUR, EXL, EXeff, IP, and ESI, while increasing the slice thickness increased Deff and Ea, but decreased the DR. The highest Deff and Ea values were 4.2 × 10−8 m2/s, and 53 KJ/mol, respectively. EU and EUR varied from 10 to 150 J/s and 0.39 to 0.79, respectively. EXL and EXeff varied between 2 and 12.5 J/s and 58 to 75 %, respectively. Midilli’s model... [more]

Wind-driven turbines utilizing the doubly-fed induction generators aligned with the progressed IEC 61400 series standards have engrossed specific consideration as of their benefits, such as adjustable speed, consistent frequency mode of operation, self-governing competencies for voltage and frequency control, active and reactive power controls, and maximum power point tracking approach at the place of shared connection. Such resource combinations into the existing smart grid system cause open-ended problems regarding the security and reliability of power system dynamics, which needs attention. There is a prospect of advancing the art of wind turbine-operated doubly-fed induction generator control systems. This section assesses the smart grid-integrated power system dynamics, characteristics, and causes of instabilities. These instabilities are unclear in the wind and nonlinear load predictions, leading to a provisional load-rejection response. Here, machine learning computations and tr... [more]

A novel roof solar chimney with wind-induced channel was designed herein to augment indoor natural ventilation under combined action of wind and solar energy. Compared with the traditional solar chimney, the new rooftop solar chimney improves the air flow due to the wind-induced channel. The effects of channel width ratio, chimney inclination at different outdoor wind speeds on the natural ventilation performance of the roof solar chimney were studied by numerical simulation. Finite-volume method was used for the numerical calculation. It was found that the trends of ventilation rate are different when the channel widths increase. The mass flow rate of the new structure increases with the increase in the inclination angle. When the inclination angles increase from 30° to 90°, the ventilation rate increases by 212% for U = 1.0 m·s−1, 166% for U = 2.0 m·s−1, and 127% for U = 3.0 m·s−1 under the condition of and the solar radiation I = 600 W·m−2.