Observation of the greenhouse effect prompts the consideration of every possibility of reducing anthropogenic carbon dioxide emissions. One of the key methods that has been the subject of much research is Carbon Dioxide Capture and Storage. The purpose of this study was to investigate the main technologies of CO2 capture, separation, and dehydration as well as methods of its transport and methodology of selecting a suitable geological storage site. An installation of dehydration and compression of carbon dioxide captured after the post-combustion was designed at a temperature of 35 °C, a pressure of 1.51 bar, and a mass flow rate of 2.449 million tons/year, assuming that the geological storage site is located at 30 km from the capture place. For the dehydration process, a multistage compression and cooling system were applied, combined with a triethylene glycol (TEG) dehydration unit. The mass flow rate of TEG was selected as 0.5 kg/s. H2O out of the TEG unit was 26.6 ppm. The amount o... [more]

The double-end partial discharge (PD) measurement method is the most common method for measuring and localizing PD sources in power cables. The sensitivity of the PD sensor, the processing speed of the data acquisition unit, and the method of the PD localization algorithm are the three main keys to ensuring the accuracy of the PD source localization on power cables. A new multi-end PD localization algorithm known as segmented correlation trimmed mean (SCTM) has recently demonstrated excellent accuracy in the localization of PD sources on power cables. The algorithm, however, is only applicable to multi-end PD measurement methods. In this paper, the mathematical equation of the SCTM algorithm is customized to match the double-end PD measurement method. A MATLAB simulation was conducted to assess the performance of the SCTM algorithm in the double-end PD measurement method. The maximum peak detection (MPD) algorithm, segmented correlation (SC), and SCTM algorithm were compared as PD loca... [more]

As a result of growing interest in the thermal treatment of sewage sludge with methods such as combustion, gasification or pyrolysis, and also in processes that aim to recover precious components such as phosphorus from this waste, a growing demand has been observed for Computational Fluid Dynamics (CFD) models that provide solutions rapidly and accurately for efficient application in research and development. This study was carried out to develop a computationally inexpensive modelling approach for the combustion of pulverized sewage sludge in entrained flow furnaces. Sewage sludge is a very volatile-rich fuel. Therefore, the Steady Diffusion Flamelet model (SFM), in combination with a validated skeletal reaction mechanism, was applied to consider the pulverized firing of sewage sludge. It was possible to represent the complex composition of volatiles emitted from the sludge particles by releasing surrogate fuels. In addition, the influence of limestone additive (calcination reaction)... [more]

The increasing demand for flexibility in hydropower systems requires pumped storage power plants to change operating modes and compensate reactive power more frequently. In this work, we demonstrate the potential of applying reinforcement learning (RL) to control the blow-out process of a hydraulic machine during pump start-up and when operating in synchronous condenser mode. Even though RL is a promising method that is currently getting much attention, safety concerns are stalling research on RL for the control of energy systems. Therefore, we present a concept that enables process control with RL through the use of a digital twin platform. This enables the safe and effective transfer of the algorithm’s learning strategy from a virtual test environment to the physical asset. The successful implementation of RL in a test environment is presented and an outlook on future research on the transfer to a model test rig is given.

The application of additive manufacturing in the field of aviation hydraulics greatly improves the design freedom of hydraulic valve internal flow channels. Pressure loss in hydraulic valve internal flow channels is a primary factor that designers need to consider, and the rapid prediction of pressure loss is very helpful for flow channel design. At present, most studies only focus on how much the pressure loss in an additive manufacturing (AM) hydraulic channel is reduced compared with an original hydraulic channel, and a mathematical model of pressure loss in an AM curved channel is still lacking. In this paper, the pressure loss in a curved flow channel was firstly studied, and the main parameters affecting the pressure loss were determined using the dimensionless analysis method. Using computational fluid dynamics simulation, the relationships between the flow channel diameter, the flow channel length, the flow channel curvature radius, the fluid velocity and pressure loss were stu... [more]

Wind energy is one of the main sources of renewable energy that does not contaminate and contributes significantly to the reduction of burning fossil fuels that originate global warming by creating greenhouse gasses; therefore, a significant part the electric energy produced presently is of wind origin, and this share is expected to become more important in the next years [...]

A new electrically driven gas booster is described as an alternative to the classical air-driven gas boosters known for their poor energetic efficiency. These boosters are used in small scale Hydrogen storage facilities and in refueling stations for Hydrogen vehicles. In such applications the overall energy count is of significance and must include the efficiency of the compression stage. The proposed system uses an electric motor instead of the pneumatic actuator and increases the total efficiency of the compression process. Two mechanical principles are studied for the transformation of the rotational motion of the motor to the linear displacement of the compressor pistons. The strongly fluctuating power of the compressor is smoothed by an active capacitive auxiliary storage device connected to the DC circuit of the power converter. The proposed system has been verified by numeric simulation, including the thermodynamic phenomena, the kinetics of the new compressor drive and the the... [more]

Convective heat transfer as an energy transport phenomenon occurs in various engineering and natural systems such as heat exchangers, chemical reactors and solar collectors, cooling systems, transportation of contaminant in urban landscape and air pollution, motion of sea or ocean waves and others [...]

The concept of local energy communities is receiving increasing attention. However, the question of how to distribute the benefit of a community among its members is still open. It is commonly desired that the benefit distribution is fair and stable. While benefit distribution schemes such as the nucleolus, Shapley value and Shapley-core are known to perform well in terms of fairness and stability, studies have shown that none of them can guarantee full fairness and stability at the same time. However, the existing studies neglect the temporal component. Hence, in order to gain more insights into the stability and fairness of the three aforementioned distributions in practice, we investigate their performance over time in simulation experiments on real-world data from Australian households. In about 90% of the cases, the Shapley value yielded a reasonably stable distribution, while the nucleolus yielded a reasonably fair distribution in about 75% of the cases. Furthermore, the experime... [more]

As early as at the end of the 19th century, shortly after mathematical rules describing fluid flow—such as the Navier−Stokes equations—were developed, the idea of using them for flow simulations emerged. However, it was soon discovered that the computational requirements of problems such as atmospheric phenomena and engineering calculations made hand computation impractical. The dawn of the computer age also marked the beginning of computational fluid mechanics and their subsequent popularization made computational fluid dynamics one of the common tools used in science and engineering. From the beginning, however, the method has faced a trade-off between accuracy and computational requirements. The purpose of this work is to examine how the results of recent advances in machine learning can be applied to further develop the seemingly plateaued method. Examples of applying this method to improve various types of computational flow simulations, both by increasing the accuracy of the resu... [more]

The ammonium nitrate (AN) and fuel oil (FO) mixture known as ANFO is a typical representative of non-ideal explosives. In contrast to ideal explosives, the detonation behavior of ANFO exhibits a strong dependence on charge diameter, existence, and properties of confinement, with a large failure diameter and long distance required to establish steady-state detonation. In this study shock initiation and propagation of detonation in ANFO were studied experimentally by determining the detonation velocity at different distances from the initiation point, as well as by numerical modeling using AUTODYN hydrodynamics code and a Wood−Kirkwood detonation model incorporated into EXPLO5 thermochemical code. The run-to-steady-state detonation velocity distance was determined as a function of charge diameter, booster charge mass, and confinement. It was demonstrated that a Lee−Tarver ignition and growth reactive flow model with properly calibrated rate constants was capable of correctly ascertaining... [more]

The paper deals with the self-ignition and combustion of hydrogen jets in a high-speed transverse flow of hot vitiated air in a duct. The Improved Delayed Detached Eddy Simulation (IDDES) approach based on the Shear Stress Transport (SST) model is used, which in this paper is applied to a turbulent reacting flow with finite rate chemical reactions. An original Adaptive Implicit Scheme for unsteady simulations of turbulent flows with combustion, which was successfully used in IDDES simulation, is described. The simulation results are compared with the experimental database obtained at the LAERTE experimental workbench of the ONERA—The French Aerospace Laboratory. Comparison of IDDES with experimental results shows a strong sensitivity of the simulation results to the surface roughness and temperature of the duct walls. The results of IDDES modeling are in good agreement with experimental pressure distributions along the wall and with the results of videoregistration of the excited radic... [more]

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.