Energies, Volume 13, Issue 17 (September-1 2020) – 311 articles

Cement provides zonal isolation and mechanical support, and its integrity is critical to the safety and efficiency of the CO₂ injection process for geologic carbon storage. This work focuses on interfacial debonding at wellbore interfaces and radial cracking in cement during CO₂ injection. It adopts the definition of the energy release rate (ERR) to characterize the propagation of cracks. Based on the finite element method, the proposed model estimates the ERRs of both types of cracks with practical wellbore configurations and injection parameters. Further parametric studies reveal the effects of cement’s mechanical and thermal properties and the crack geometry on crack propagation. Simulation results show that the ERRs of interfacial and radial cracks would surpass 100 J/m² with typical cement properties. The cement’s thermal expansion coefficient is the most influential factor on the ERR, followed by its Young’s modulus, Poisson’s ratio, and thermal conductivity. The initial sizes and positions of the cracks are also important parameters for controlling crack propagation. Moreover, non-uniform in situ stresses would accelerate crack propagation at the interfaces. These findings are valuable and could help to optimize cement sheath design in order to ensure the long-term integrity of wells for geological carbon storage. Full article

The doubly salient permanent magnet (DSPM) machines are very attractive for low-speed power generation. In this work, we propose a design technique to improve the output power of the DSPM generator by an adjustment of pole configuration. The number of stator and rotor poles, split ratio, as well as the stator pole arc of the generator, were proposedly adjusted and optimized. The output characteristics of the generator including the magnetic flux linkage, electromotive force, harmonic, cogging torque, electromagnetic torque, output voltage and output power were analyzed through finite element analysis. The symmetrical magnetic field distribution of all generators was firstly verified. Then, the results indicated that this particular generator was optimized at 18 stator poles and 12 rotor poles, while the split ratio and the stator pole arc should be set as 0.78 and 6.15 degrees, respectively. The proposed optimal generator could provide a significant improvement in the output voltage and the output power compared to the conventional structure. The output power of 1.28 kW can be reached by the optimal structure, which was two times higher than that of the conventional structure. The physical explanation regarding to the structural modification was also given. The proposed design technique can be applied for improving the output power of the DSPM machines. Full article

A simple yet effective optimization technique is developed to solve nonlinear conjugate heat transfer. The proposed Nonlinear Optimization with Replacement Strategy (NORS) is a mutation of several existing optimization processes. With the improvements of 3D metal printing of turbine components, it is feasible to have film holes with unconventional diameters, as these holes are created while printing the component. This paper seeks to optimize each film hole diameter at the leading edge of a turbine vane to satisfy several optimum thermal design objectives with given design constraints. The design technique developed uses linear regression-based machine learning model and further optimizes with strategic improvement of the training dataset. Optimization needs cost and benefit criteria are used to base its decision of success, and cost is minimized with maximum benefit within given constraints. This study minimizes the coolant flow (cost) while satisfying the constraints on average metal temperature and metal temperature variations (benefits) that limit the useful life of turbine components. The proposed NORS methodology provides a scientific basis for selecting design parameters in a nonlinear design space. This model is also a potential academic tool to be used in thesis works without demanding extensive computing resources. Full article

Nowadays, due to the decreasing use of traditional generators in favor of renewable energy sources, power grids are facing a reduction of system inertia and primary frequency regulation capability. Such an issue is exacerbated by the continuously increasing number of electric vehicles (EVs), which results in enforcing novel approaches in the grid operations management. However, from being an issue, the increase of EVs may turn to be a solution to several power system challenges. In this context, a crucial role is played by the so-called vehicle-to-grid (V2G) mode of operation, which has the potential to provide ancillary services to the power grid, such as peak clipping, load shifting, and frequency regulation. More in detail, EVs have recently started to be effectively used for one of the most traditional frequency regulation approaches: the so-called frequency droop control (FDC). This is a primary frequency regulation, currently obtained by adjusting the active power of generators in the main grid. Because to the decommissioning of traditional power plants, EVs are thus recognized as particularly valuable solutions since they can respond to frequency deviation signals by charging or discharging their batteries. Against this background, we address frequency regulation of a power grid model including loads, traditional generators, and several EVs. The latter independently participate in the grid optimization process providing the grid with ancillary services, namely the FDC. We propose two novel control strategies for the optimal control of the batteries of EVs during the frequency regulation service. On the one hand, the control strategies ensure re-balancing the power and stabilizing the frequency of the main grid. On the other hand, the approaches are able to satisfy different types of needs of EVs during the charging process. Differently from the related literature, where the EVs perspective is generally oriented to achieve the optimal charge level, the proposed approaches aim at minimizing the degradation of battery devices. Finally, the proposed strategies are compared with other state-of-the-art V2G control approaches. The results of numerical experiments using a realistic power grid model show the effectiveness of the proposed strategies under the actual operating conditions. Full article

Widespread dissemination of electric mobility is highly dependent on the power converters, storage systems and renewable energy sources. The efficiency and reliability, combined with the emerging and innovative technologies, are crucial when speaking of power converters. In this paper the interleaved dc–dc topology has been considered for EV charging, due to its improved reliability. The efficiency comparison of the SiC-MOSFET and Si-IGBT-based converters has been done on wide range of switching frequency and output inductances. The interleaved converters were considered with the optimal switching parameters resulting from the analysis done on a detailed parasitic circuit model, ensuring minimum losses and maintaining the safe operating area. The analysis included the comparison of different inductors, and for the selected ones the complete system efficiency and cost were conducted. The results indicate the benefits when SiC-MOSFETs are applied to the interleaved dc–dc topology for wide ranges of output inductances and switching frequencies, and most importantly, they offer lower total volume but also total cost. The realistic and dynamic models of power devices obtained from the manufacturer’s experimental tests have been considered in both LTspice and PLECS simulation tools. Full article

This paper presents an on-line diagnosis method for large photovoltaic (PV) power plants by using a machine learning algorithm. Most renewable energy output power is decreased due to the lack of management tools and the skills of maintenance engineers. Additionally, many photovoltaic power plants have a long down-time due to the absence of a monitoring system and their distance from the city. The IEC 61724-1 standard is a Performance Ratio (PR) index that evaluates the PV power plant performance and reliability. However, the PR index has a low recognition rate of the fault state in conditions of low irradiation and bad weather. This paper presents a weather-corrected index, linear regression method, temperature correction equation, estimation error matrix, clearness index and proposed variable index, as well as a one-class Support Vector Machine (SVM) method and a kernel technique to classify the fault state and anomaly output power of PV plants. Full article

Durability of concrete designed for radiation shielding structures is an important issue in nuclear power plant safety. An investigation of the permeability of concrete containing heavyweight aggregates and water-bearing aggregates was performed with respect to gaseous and liquid media. Mix design was developed using Portland and slag cement, crushed magnetite and serpentine aggregate. The use of slag cement in concrete containing magnetite and serpentine aggregates resulted in the substantial improvement of the compressive strength in comparison with Portland cement concrete. The application of slag cement was found to reduce the chloride ingress, regardless of the special aggregate use. The coefficient of chloride migration was within the range 5 ÷ 8 × 10⁻¹² m²/s and 17 ÷ 25 × 10⁻¹² m²/s for slag cement concrete and Portland cement concrete, respectively. At the same time, the carbonation depth was increased twice for slag cement concrete in comparison to Portland cement concrete. However, the maximum carbonation depth after one year of exposure to 1% CO₂ was only 14 mm for slag cement concrete, and 7 mm for reference concrete. The total pore volume evaluated using mercury intrusion porosimetry was influenced by the type of special aggregate used. It was shown that concrete with various contents of magnetite aggregate and slag cement achieved the smallest total pore volume. While serpentine coarse aggregate caused an increase in total pore volume in comparison to concrete with magnetite aggregate. Full article

The work presented herein is devoted to a unique set of forward and reverse combustion tube (CT) experiments to access the suitability and potential of the in situ combustion (ISC) method for the light oil carbonate reservoir. One forward and one reverse combustion tube tests were carried out using the high-pressure combustion tube (HPCT) experimental setup. However, during reverse combustion, the front moved in the opposite direction to the airflow. The results obtained from experiments such as fuel/air requirements, H/C ratio, and recovery efficiency are crucial for further validation of the numerical model. A quantitative assessment of the potential for the combustion was carried out. The oil recovery of forward combustion was as high as 91.4% of the initial oil in place, while that for the reverse combustion test demonstrated a 43% recovery. In the given conditions, forward combustion demonstrated significantly higher efficiency. However, the stabilized combustion front propagation and produced gases of reverse combustion prove its possible applicability. Currently, there is a limited amount of available studies on reverse combustion and a lack of publications within the last decades despite advances in technologies. However, reverse combustion might have advantages over forward combustion for heavy oil reservoirs with lower permeability or might serve as a reservoir preheating technique. These experiments give the opportunity to build and validate the numerical models of forward and reverse combustion conducted at reservoir conditions and test their field application using different scenarios. Full article

Sulfolane in contact with water and oxygen forms acidic (by-) products that are major factors in accelerating the corrosion of carbon/stainless steel. In consequence, water-induced corrosion damage can be a serious problem in industrial systems. Hence, the determination of the corrosion resistance of AISI 1010 steel immersed in sulfolane containing 0 to 6 vol.% water was the principal objective of the study. Evaluation of the corrosion resistance of steel electrodes was performed using a potentiodynamic technique and scanning Kelvin probe microscopy. It was observed that the corrosion products layer that formed on the surface of AISI 1010 steel partially protects it against corrosion in sulfolane with a water concentration in the range from 1 vol.% to 4 vol.%. Interestingly, amounts of water above 4 vol.% cause a break-down of the corrosion products layer and deteriorate the corrosion resistance of AISI 1010 steel as well. Moreover, the relationship between the fractal dimension, corrosion degree of the steel surface and water concentration in sulfolane was investigated. The fractal dimension was determined using 2D grayscale images of AISI 1010 steel registered through a scanning electron microscope. It was noticed that both the fractal dimension and the corrosion degree rose with the increased water concentration in sulfolane. Full article

Fuel production from hydrogen and carbon dioxide is considered an attractive solution as long-term storage of electric energy and as temporary storage of carbon dioxide. A large variety of CO₂ sources are suitable for Carbon Capture Utilization (CCU), and the process energy intensity depends on the separation technology and, ultimately, on the CO₂ concentration in the flue gas. Since the carbon capture process emits more CO₂ than the expected demand for CO₂ utilization, the most sustainable CO₂ sources must be selected. This work aimed at modeling a Power-to-Gas (PtG) plant and assessing the most suitable carbon sources from a Life Cycle Assessment (LCA) perspective. The PtG plant was supplied by electricity from a 2030 scenario for Italian electricity generation. The plant impacts were assessed using data from the ecoinvent database version 3.5, for different CO₂ sources (e.g., air, cement, iron, and steel plants). A detailed discussion on how to handle multi-functionality was also carried out. The results showed that capturing CO₂ from hydrogen production plants and integrated pulp and paper mills led to the lowest impacts concerning all investigated indicators. The choice of how to handle multi-functional activities had a crucial impact on the assessment. Full article

This study considered the impacts of diesel–soybean biodiesel blends mixed with 3% cerium coated zinc oxide (Ce-ZnO) nanoparticles on the performance, emission, and combustion characteristics of a single cylinder diesel engine. The fuel blends were prepared using 25% soybean biodiesel in diesel (SBME25). Ce-ZnO nanoparticle additives were blended with SBME25 at 25, 50, and 75 ppm using the ultrasonication process with a surfactant (Span 80) at 2 vol.% to enhance the stability of the blend. A variable compression ratio engine operated at a 19.5:1 compression ratio (CR) using these blends resulted in an improvement in overall engine characteristics. With 50 ppm Ce-ZnO nanoparticle additive in SBME25 (SBME25Ce-ZnO50), the brake thermal efficiency (BTE) and heat release rate (HRR) increased by 20.66% and 18.1%, respectively; brake specific fuel consumption (BSFC) by 21.81%; and the CO, smoke, and hydrocarbon (HC) decreased by 30%, 18.7%, and 21.5%, respectively, compared to SBME25 fuel operation. However, the oxides of nitrogen slightly rose for all the nanoparticle added blends. As such, 50 ppm of Ce-ZnO nanoparticle in the blend is a potent choice for the enhancement of engine performance, combustion, and emission characteristics. Full article

It is well known that nowadays a significant part of the total energy consumption is related to buildings, so research for improving building energy efficiency is very important. This paper presents our investigations about the dimensioning of horizontal overhangs in order to determine the minimum annual consumption of building primary energy for heating, cooling and lighting. In this investigation, embodied energy for horizontal roof overhangs was taken into account. The annual simulation was carried out for a residential building located in the city of Belgrade (Serbia). Horizontal overhangs (roof and balcony) are positioned to provide shading of all exterior of the building. The building is simulated in the EnergyPlus software environment. The optimization of the overhang size was performed by using the Hooke Jeeves algorithm and plug-in GenOpt program. The objective function minimizes the annual consumption of primary energy for heating, cooling and lighting of the building and energy spent to build overhangs. The simulation results show that the building with optimally sized roof and balcony overhangs consumed 7.12% lessprimary energy for heating, cooling and lighting, compared to the house without overhangs. A 44.15% reduction in cooling energy consumption is also achieved. Full article

Large-scale deployment of both biochemical and thermochemical routes for advanced biofuels production is seen as a key climate change mitigation option. This study addresses techno-economic and environmental aspects of advanced liquid biofuels production alternatives via biomass gasification and Fischer–Tropsch synthesis integrated to a typical sugarcane distillery. The thermochemical route comprises the conversion of the residual lignocellulosic fraction of conventional sugarcane (bagasse and straw), together with eucalyptus and energy-cane as emerging lignocellulosic biomass options. This work promotes an integrated framework to simulate the mass and energy balances of process alternatives and incorporates techno-economic analyses and sustainability assessment methods based on a life-cycle perspective. Results show that integrated biorefineries provide greenhouse gas emission reduction between 85–95% compared to the fossil equivalent, higher than that expected from a typical sugarcane biorefinery. When considering avoided emissions by cultivated area, biorefinery scenarios processing energy-cane are favored, however at lower economic performance. Thermochemical processes may take advantage of the integration with the typical sugarcane mills and novel biofuels policies (e.g., RenovaBio) to mitigate some of the risks linked to the implementation of new biofuel technologies. Full article

One way to perceive the electricity market is as a network of actors connected through transactions and monetary flows. By exploring the monetary flows in the electricity market, one adopts a holistic view which can provide insights on the interactions between different components of the benefits and costs, as well as on the possible conflicts or alliances between the involved actors of the system. The importance of such an analysis becomes even more evident when considering if the system’s state would change due to either the effectuation of a policy measure or a shift in the external drivers of the system. Additionally, by identifying conditions of conflicting interests between the involved actors, one can devise a roadmap of least-resistance for a policy measure to attain its goals. Our work is based on the premise that understanding and quantifying the monetary flows in the electricity market can contribute to the efficiency assessment of policy interventions in the market. We present a structured analytical framework and the results of a quantitative analysis, based on available public domain data, for the identification of the main drivers and interactions that governed the major monetary flows in the Greek wholesale electricity market, from 2009 to 2013 and the ex-post assessment of the market impact of the feed-in-tariffs scheme that was in place during this period. Full article

Synergistic effect of cellulase and hemicellulase (xylanase) was evaluated because lignocellulosic material is a heterogeneous complex of cellulose and hemicellulose. Various effects of HTec2 addition on enzymatic saccharification and fermentation were evaluated using two different substrates such as corn stover and rice straw. Corn stover and rice straw were pretreated by the LMAA (low-moisture anhydrous ammonia) method at the preselected same conditions (90 °C, 120 h, moisture content = 50%, NH₃ loading = 0.1 g NH₃/g). It was observed that the enzymatic saccharification yield of pretreated corn stover (76.4% for glucan digestibility) was higher than that of pretreated rice straw (70.9% for glucan) using CTec2 cellulase without HTec2 addition. Glucan digestibility of pretreated corn stover was significantly increased from 76.4% to 91.1% when the HTec2/CTec2 (v/v) increased from 0 to 10. However, it was interesting that the ethanol production was decreased from 89.9% to 76.3% for SSF and 118.0% to 87.9% for SSCF at higher HTec2/CTec2. As the glucan loading increased from 2.0% to 7.0%, the ethanol yields of both SSF and SSCF were decreased from 96.3% to 88.9% and from 116.6% to 92.4%, respectively. In addition, the smallest inoculum size (optical density of 0.25) resulted in the highest ethanol production (20.5 g/L). Full article

This paper aims to provide a quantitative understanding of the performance of numerical modeling of a wave field equation using general-purpose processors. In particular, this article presents the most important aspects related to the memory workloads and execution time of the numerical modeling of both acoustic and fully elastic waves in isotropic and anisotropic mediums. The results presented in this article were calculated for the staggered grid finite difference method. Our results show that the more realistic the seismic wave simulations that are performed, the more the demand for memory and the computational capacity of the computing environment increases. The results presented in this article allow the estimation of the memory requirements and computational time of wavefield modeling for the considered model (acoustic, elastic or anisotropic) so that their feasibility can be assessed in a given computing environment and within an acceptable time. Understanding the numerical modeling performance is especially important when graphical processing units (GPU) are utilized to satisfy the intensive calculations of three-dimensional seismic forward modeling. Full article

In this study, the growing scientific field of alternative biofuels was examined, with respect to hydrochars produced from renewable biomasses. Hydrochars are the solid products of hydrothermal carbonization (HTC) and their properties depend on the initial biomass and the temperature and duration of treatment. The basic (Scopus) and advanced (Citespace) analysis of literature showed that this is a dynamic research area, with several sub-fields of intense activity. The focus of researchers on sewage sludge and food waste as hydrochar precursors was highlighted and reviewed. It was established that hydrochars have improved behavior as fuels compared to these feedstocks. Food waste can be particularly useful in co-hydrothermal carbonization with ash-rich materials. In the case of sewage sludge, simultaneous P recovery from the HTC wastewater may add more value to the process. For both feedstocks, results from large-scale HTC are practically non-existent. Following the review, related data from the years 2014–2020 were retrieved and fitted into four different artificial neural networks (ANNs). Based on the elemental content, HTC temperature and time (as inputs), the higher heating values (HHVs) and yields (as outputs) could be successfully predicted, regardless of original biomass used for hydrochar production. ANN₃ (based on C, O, H content, and HTC temperature) showed the optimum HHV predicting performance (R² 0.917, root mean square error 1.124), however, hydrochars’ HHVs could also be satisfactorily predicted by the C content alone (ANN₁, R² 0.897, root mean square error 1.289). Full article

Cheap and clean energy demand is continuously increasing due to economic growth and industrialization. The energy sectors of several countries still employ fossil fuels for power production and there is a concern of associated emissions of greenhouse gases (GHG). On the other hand, environmental regulations are becoming more stringent, and resultant emissions need to be mitigated. Therefore, optimal energy policies considering economic resources and environmentally friendly pathways for electricity generation are essential. The objective of this paper is to develop a comprehensive model to optimize the power sector. For this purpose, a multi-period mixed integer programming (MPMIP) model was developed in a General Algebraic Modeling System (GAMS) to minimize the cost of electricity and reduce carbon dioxide (CO₂) emissions. Various CO₂ mitigation strategies such as fuel balancing and carbon capture and sequestration (CCS) were employed. The model was tested on a case study from Pakistan for a period of 13 years from 2018 to 2030. All types of power plants were considered that are available and to be installed from 2018 to 2030. Moreover, capacity expansion was also considered where needed. Fuel balancing was found to be the most suitable and promising option for CO₂ mitigation as up to 40% CO₂ mitigation can be achieved by the year 2030 starting from 4% in 2018 for all scenarios without increase in the cost of electricity (COE). CO₂ mitigation higher than 40% by the year 2030 can also be realized but the number of new proposed power plants was much higher beyond this target, which resulted in increased COE. Implementation of carbon capture and sequestration (CCS) on new power plants also reduced the CO₂ emissions considerably with an increase in COE of up to 15%. Full article

This paper presents a gain scheduling output feedback control method to reduce driver workload and improve driving performance by considering input saturation. The driver–vehicle system model is developed by considering tire cornering stiffness uncertainties and different driver parameter uncertainties. Meanwhile, the input saturation is also considered in the driver-vehicle system. A quadratic Lyapunov function is designed to solve the optimization problem with uncertainties and input saturation. The results, which are based on the MATLAB-CarSim co-simulation platform, indicate that the robust controller not only improves the convergence rate of the state but also reduces the steering workload of the driver. Full article

The paper is focused on galvanically isolated series resonant DC–DC converters (SRCs) with a low quality factor of the resonant tank. These converters provide input voltage regulation at fixed switching frequency and good power density. Different modulation methods at the fixed switching frequency enable the implementation of the voltage buck functionality in these converters. The SRC under study is considered as a step-up front-end DC–DC converter for the integration of renewable energy sources in DC microgrids. The paper evaluates the voltage buck performance of the SRC achieved by using different pulse-width modulation (PWM) methods including conventional PWM and shifted PWM. Moreover, the new PWM methods, i.e., the hybrid shifted PWM (HSPWM), improved shifted PWM (ISPWM), and hybrid PWM (HPWM), are proposed to overcome the disadvantages of the existing methods. They improve the power conversion efficiency in the buck mode by reducing the power losses in the semiconductor switches and the isolating transformer of the SRC. The proposed and the existing methods are benchmarked in terms of the components stresses and power conversion efficiency. The presented findings have been experimentally validated by the help of a 200 W prototype, which demonstrated the lowest power loss in the case of the HPWM. Full article

The paper presents a general approach to the steady-state efficiency analysis of one degree of freedom (1-DOF) speed increasers with one or two inputs, and one or two outputs, applicable to wind, hydro and marine-current power generating systems. The mechanical power flow, and the efficiency of this type of complex speed increasers, are important issues in the design and development of new power-generating systems. It is revealed that speed increases, with in-parallel transmission of the mechanical power from the wind or water rotors to the electric generator, have better efficiency than serial transmissions, but their efficiency calculus is still a challenging problem, solved in the paper by applying the decomposition method of complex speed increasers into simpler component planetary gear sets. Therefore, kinematic, steady-state torque and efficiency equations are derived for a generic 1-DOF speed increasers with two inputs and two outputs, obtained by connecting in parallel two gear mechanisms. These equations allow any speed increaser to be analysed with two inputs and one output, with one input and two outputs, and with one input and one output. We discuss a novel design of a patent-pending planetary-gear speed increaser, equipped with a two-way clutch, which can operate (in combination with the pitch adjustment of the rotors blades) in four distinct configurations. It was found that the mechanical efficiency of this speed increaser in the steady-state regime is influenced by the interior kinematic ratios, the input-torque ratio and by the meshing efficiency of its individual gear pairs. The efficiency of counter-rotating dual-rotor systems was found to be the highest, followed by systems with counter-rotating electric generator, and both have higher efficiency than conventional systems with one rotor and one electric generator with fixed-stator. Full article

Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. Both functions turn out to be very similar according to the performance of these indicators, unlike the ranking index, in which the DRF has better performance. The DRF reduces to 25% the minimum demand to avoid non-optimal situations, such as non-supplying the demand and has potential benefits, such as the elimination of finite combinations and easy implementation. Full article

Pattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) supervised fine-tuned by the adaptive moment estimation (ADAM) algorithm is proposed to recognize the four typical insulation defects of DC cable according to the PD pulse waveforms. Moreover, the effect of the training sample set size on recognition accuracy is analyzed. Compared with naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural networks (BPNN), the ADAM-DBN method has higher accuracy on four different defect types due to the excellent ability in terms of the feature extraction of PD pulse waveforms. Moreover, the increase of training sample set size would lead to the increase of recognition accuracy within a certain range. Full article

This study focuses on establishing a surrogate model based on machine learning techniques to predict the time-averaged spatially distributed behaviors of vaporizing liquid jets in turbulent air crossflow for momentum flux ratios between 5 and 120. This surrogate model extends a previously developed Gaussian-process-based framework applicable to laminar flows to accommodate turbulent flows and demonstrates that in addition to detailed fields of primitive variables, second-order turbulence statistics can also be predicted using machine learning techniques. The framework proceeds in 3 steps—(1) design of experiment studies to identify training points and conducting high-fidelity calculations to build the training dataset; (2) Gaussian process regression (supervised training) for the range of operating conditions under consideration for gaseous and dispersed phase quantities; and (3) error quantification of the surrogate model by comparing the machine learning predictions with the truth model for test conditions (i.e., conditions not used for training). The framework was trained using data generated by high-fidelity large eddy simulation (LES)-based calculations (also referred to as the truth model), which solves the complete set of conservation equations for mass, momentum, energy, and species in an Eulerian reference frame, coupled with a Lagrangian solver that tracks the dispersed phase. Simulations were conducted for the range of momentum flux ratios between 5 and 120 for liquid water injected into crossflowing air at a pressure of 1 atm and temperature of 600 K. Results from the machine-learned surrogate model, also called emulations, were compared with the truth model under testing conditions identified by momentum flux ratios of 7 and 40. L₁ errors for time-averaged field quantities, including velocity magnitudes, pressure, temperature, vapor fraction of the evaporated liquid, and turbulent kinetic energy in the gas phase, and spray penetration and Sauter mean diameters in the dispersed phase are reported. Speedup of 65 was achieved with this emulator when compared against LES simulation of the same test conditions with errors for all quantities below 14%, thus demonstrating the potential benefits of using machine learning techniques for design space exploration of devices that are based on turbulent multiphase fluid flows. This is the first effort of its kind in the literature that demonstrates the application of machine learning techniques on turbulent multiphase flows. Full article

The recuperation of otherwise lost waste heat provides a formidable way to decrease the primary energy consumption of many technical systems. A possible route to achieve that goal is through the use of Stirling engines, which have shown to be reliable and efficient devices. One can increase their performance by optimizing the piston motion. Here, it is investigated to which extent the cycle averaged power output can be increased by using a special class of adjustable sinusoidal motions (the AS class). In particular the influence of the regeneration effectiveness on the piston motion is examined. It turns out that with the optimized piston motion one can achieve performance gains for the power output of up to 50% depending on the loss mechanisms involved. A remarkable result is that the power output does not depend strongly on the limitations of the regenerator, in fact—depending on the loss terms—the influence of the regenerator practically vanishes. Full article

This paper discusses the benefits of an advanced highly-efficient approach to static and dynamic electrothermal simulations of multicellular silicon carbide (SiC) power MOSFETs. The strategy is based on a fully circuital representation of the device, which is discretized into an assigned number of individual cells, high enough to analyze temperature and current nonuniformities over the active area. The cells are described with subcircuits implementing a simple transistor model that accounts for the utmost influence of the traps at the SiC/SiO₂ interface. The power-temperature feedback is emulated with an equivalent network corresponding to a compact thermal model automatically generated by the FANTASTIC tool from an accurate 3D mesh of the component under test. The resulting macrocircuit can be solved by any SPICE-like simulation program with low computational burden and rare occurrence of convergence issues. Full article

For the problem of poor accuracy of the existing multi-rate simulation methods, this paper proposes a multi-rate real-time simulation method based on the Norton equivalent, compared with multi-rate simulation method based on the ideal source equivalent. After the Norton equivalence of the fast subsystem and the slow subsystem are established, they are solved simultaneously at the junction nodes. In order to reduce the amount of the simulation calculation, the Norton equivalent circuit is obtained by incremental calculation. The data interaction between the fast subsystem and the slow subsystem is realized by extrapolation method. For ensuring the real-time performance of the simulation, the method of the slow subsystem calculates ahead of the fast subsystem is given for the slow subsystem with a large amount of calculation. Finally, the AC/DC hybrid power system was simulated on the real-time simulation platform (FPGA-based Real-Time Digital Solver, FRTDS), and the simulation results were compared with the single-rate simulation, which verified the correctness and accuracy of the proposed method. Full article

Growing interest has arisen to adopt Variable Valve Timing (VVT) technology for automotive engines due to the need to fulfill the pollutant emission regulations. Several VVT strategies, such as the exhaust re-opening and the late exhaust closing, can be used to achieve an increment in the after-treatment upstream temperature by increasing the residual gas amount. In this study, a one-dimensional gas dynamics engine model has been used to simulate several VVT strategies and develop a control system to actuate over the valves timing in order to increase diesel oxidation catalyst efficiency and reduce the exhaust pollutant emissions. A transient operating conditions comparison, taking the Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC) as a reference, has been done by analyzing fuel economy, HC and CO pollutant emissions levels. The results conclude that the combination of an early exhaust and a late intake valve events leads to a 20% reduction in CO emissions with a fuel penalty of 6% over the low speed stage of the WLTC, during the warm-up of the oxidation catalyst. The same set-up is able to reduce HC emissions down to 16% and NO_x emission by 13%. Full article

Horizontal axis turbines are commonly used for harnessing renewable hydrokinetic energy, contained in marine and river currents. In order to encourage the expansion of electricity generation using micro-hydrokinetic turbines, several design improvements are investigated. Firstly, optimization-based design of rotor blade is used to get as close as possible to the efficiency limit of 59.3% (known as Betz limit), that counts for bare turbine rotors, placed in the free flow. Additional diffuser elements are further added to examine the potential to overcome the theoretical efficiency limit by accelerating water at the axial direction. Various diffuser geometrical configurations are investigated using the computational fluid dynamics (CFD) to obtain insight into hydrodynamics of augmented micro-hydrokinetic turbines. Moreover, the turbines are compared from the energy conversion efficiency point of view. The highest maximum power coefficient increase of 81% is obtained with brimmed (flanged) diffuser. Diffusers with foil-shaped cross-sections have also been analyzed but power augmentation is not significantly greater than in the case of simple cross-section designs of the same dimensions. The power coefficients’ comparison indicate that considerable power augmentation is achievable using brimmed diffuser with higher value of length-to-diameter ratio. However, the impact of diffuser length increase on the power coefficient enhancement becomes weaker as the length-to-diameter ratio reaches a value of 1. Full article

Coal structure directly correlates to permeability and hydraulic fracturing effects. Underground coal mining indicates that a single coal section generally contains multiple coal structures in superposition, making how to recognise the coal structure combination and predict its influence on coal permeability a challenging problem. Based on well-drilling sampled cores, the geological strength index (GSI), and well-logging data, the DEN, GR, CALX, and CALY were selected to establish a model to predict GSI by multiple regression to identify coal structure from 100 coalbed methane wells. Based on fitting GSI and corresponding permeability test values, injection fall-off (IFO) testing, and hydraulic fracturing results, permeability prediction models for pre- and post-fracturing behaviour were established, respectively. The fracturing effect was evaluated by the difference in permeability. The results show that a reservoir can be classified into one of nine types by different coal structure thickness proportion (and combinations thereof) and the fracturing curves can be classified into four categories (and eight sub-categories) by the pressure curve. Up-down type I and type II reservoirs (proportion of hard coal >60%) and intervening interval type I reservoir (proportion of hard coal >70%) are prone to form stable and descending fracturing curves and the fracturing effects are optimal. Intervening interval type II (hard coal:soft coal:hard coal or soft coal:hard coal:soft coal ≈1:1:1) and up-down type III (hard coal:soft coal =1:1) form descending type II, rising type I and fluctuating type I fracturing curves and fracturing effect ranks second; up-down type IV and V (proportion of hard coal <40%), interval type III (proportion of hard coal <30%), and multi-layer superposition-type reservoirs readily form fluctuating and rising fracturing curves and fracturing effects therein are poor. The research results provide guidance for the targeted stimulation measured under different coal structure combinations. Full article