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This research continues the previous work “Robotic-Arm-Based Force Control in Neurosurgical Practice”. In that study, authors acquired an optimal control arm speed shape for neurological surgery which minimized a cost function that uses an adaptive scheme to determine the brain tissue force. At the end, the authors proposed the use of reinforcement learning, more specifically Deep Deterministic Policy Gradient (DDPG), to create an agent that could obtain the optimal solution through self-training. In this article, that proposal is carried out by creating an environment, agent (actor and critic), and reward function, that obtain a solution for our problem. However, we have drawn conclusions for potential future enhancements. Additionally, we analyzed the results and identified mistakes that can be improved upon in the future, such as exploring the use of varying desired distances of retraction to enhance training.

The nonstationary separated incompressible flows around Darrieus and Savonius rotors of vertical-axis wind turbines were investigated through computational simulation using the Reynolds averaged Navier–Stokes equations and Spalart–Allmaras turbulence model. The implicit finite-volume algorithm, the basis of which was artificial compressibility method, was chosen to obtain the numerical solution. The series of computational and physical experiments for Darrieus rotors with varied numbers and shapes of blades were performed. The detailed visualization of the flow was presented. The turbulent flows surrounding the Darrieus and Savonius rotors were studied, and as a part of these investigations, the major phases of vortex progress were identified. For this purpose, three series of computer tests on the aerodynamic and power properties of Savonius rotors with two and three buckets were performed, and their results are also presented. The influence of tip-speed ratio, solidity, and Reynolds numbers on the power coefficients of the Darrieus and Savonius rotors was investigated. It has been demonstrated that increasing Reynolds number from 104 to 106 causes a rise in Darrieus rotors power coefficient from 0.15 up to 0.5. The maximum values of power coefficient are moved away from higher values of tip-speed ratio from 2 to 5 as a result of a decrease in Darrieus rotor solidity from 1.0 to 0.33. The greatest power coefficient for a Savonius rotor with two blades is 0.23 and for a Savonius rotor with three blades is 0.19.

The main objective of this work was to determine the worth of installing an electrical battery in order to reduce peak power consumption. The importance of this question resides in the expensive terms of energy bills when using the maximum power level. If maximum power consumption decreases, it affects not only the revenues of maximum power level bills, but also results in important reductions at the source of the power. This way, the power of the transformer decreases, and other electrical elements can be removed from electrical installations. The authors studied the Spanish electrical system, and a particle swarm optimization (PSO) algorithm was used to model battery sizing in peak power smoothing applications for an electrical consumption point. This study proves that, despite not being entirely profitable at present due to current kWh prices, implanting a battery will definitely be an option to consider in the future when these prices come down.

Wind energy has become an important source of electricity generation, with the aim of achieving a cleaner and more sustainable energy model. However, wind turbine performance improvement is required to compete with conventional energy resources. To achieve this improvement, flow control devices are implemented on airfoils. Computational fluid dynamics (CFD) simulations are the most popular method for analyzing this kind of devices, but in recent years, with the growth of Artificial Intelligence, predicting flow characteristics using neural networks is becoming increasingly popular. In this work, 158 different CFD simulations of a DU91W(2)250 airfoil are conducted, with two different flow control devices, rotating microtabs and Gurney flaps, added on its Trailing Edge (TE). These flow control devices are implemented by using the cell-set meshing technique. These simulations are used to train and test a Convolutional Neural Network (CNN) for velocity and pressure field prediction and another CNN for aerodynamic coefficient prediction. The results show that the proposed CNN for field prediction is able to accurately predict the main characteristics of the flow around the flow control device, showing very slight errors. Regarding the aerodynamic coefficients, the proposed CNN is also capable to predict them reliably, being able to properly predict both the trend and the values. In comparison with CFD simulations, the use of the CNNs reduces the computational time in four orders of magnitude.

This article, investigates the behaviour of an ionized nanoliquid motion regarding heat transmission between two parallel discs. In the proposed model, the squeezing flow of Cu-water nanofluid with electrical potential force is analysed for studying the flow properties and an uniform magnetic field is applied to that fluid, by taking the surface of the bottom disc porous. We have also studied the effects of different nanomaterials on the transmission of heat through nanofluids. Furthermore, the influence of various physical parameters in the proposed model of nanofluids flow like volume fraction of nanomaterials, squeezing number, Hartmann number, Eckert number, and Prandtl number are analysed and discussed quantitatively through various tables and graphs. The system of nonlinear partial differential equations (PDE’s) has been used to formulate the proposed flow model and later converted to a set of nonlinear ODE’s by mean similarity transformation. Further, the reduced form of ODEs has been solved by Parametric Continuation Method (PCM), which is a stable numerical scheme. The outcomes obtained from the proposed model could also be used to analyse nanofluid flow in several fields, such as polymer processing, power transfer and hydraulic lifts.

In this paper, the 3-D squeezing flow of viscous incompressible fluid between two parallel plates rotating at the same rate is investigated. The flow is observed under the influence of the varying magnetic field. The flow phenomena are modeled by utilizing the basic governing equations, i.e., equation of continuity, coupled Navier Stokes, and Magnetic Field equations. Using appropriate similarity transformations, the resultant partial differential equations are then transformed into a system of ordinary differential equations. The computational technique is developed via the Homotopy Analysis Method (HAM) to obtain the solution of transformed systems of ordinary differential equations. The influence of several engineering fluid parameters, such as squeeze Reynolds number, magnetic field strength parameter, and magnetic Reynolds number, on velocity and magnetic field components, are observed from different graphs. It has been investigated that by increasing the squeeze Reynolds number, fluid velocity in the y and z directions will be increased as well. On the magnetic field component along the y-axis, an increasing influence of squeezing Reynolds number is also noticed. Similarly, raising the magnetic Reynolds number increases the velocity along the y-axis, whereas the inverse relationship is found for magnetic field components. Furthermore, for each flow phenomenon, an error analysis is also presented.

INSIDER (Improved Nuclear SIte characterization for waste minimization in D&D operations under constrained EnviRonment) was a European project funded under the H2020-EURATOM programme and launched in June 2017. The project was coordinated by the French Commissariat a l'energie atomique et aux energies alternatives (CEA), it had a total duration of 4 years and covered a budget of 4 Meuro. INSIDER's work was performed by 5 technical working groups (WG) which brought together 18 institutions from 10 countries, leading to a total of 68 participating researchers. The objective of the project was to optimise the radiological characterisation of nuclear installations in constrained environments in order to obtain an accurate estimate of the content of contaminated materials as well as to optimise the quantity of contaminated materials to be treated as waste. The focus of this paper is on the statistical analysis of an interteam comparison of measurement results (dose rate, total gamma measurement, and gamma spectrometry) made in situ at the BR3 reactor, Belgium.

In this paper, the 3-D squeezing flow of viscous incompressible fluid between two parallel plates rotating at the same rate is investigated. The flow is observed under the influence of the varying magnetic field. The flow phenomena are modeled by utilizing the basic governing equations, i.e., equation of continuity, coupled Navier Stokes, and Magnetic Field equations. Using appropriate similarity transformations, the resultant partial differential equations are then transformed into a system of ordinary differential equations. The computational technique is developed via the Homotopy Analysis Method (HAM) to obtain the solution of transformed systems of ordinary differential equations. The influence of several engineering fluid parameters, such as squeeze Reynolds number, magnetic field strength parameter, and magnetic Reynolds number, on velocity and magnetic field components, are observed from different graphs. It has been investigated that by increasing the squeeze Reynolds number, fluid velocity in the y and z directions will be increased as well. On the magnetic field component along the y-axis, an increasing influence of squeezing Reynolds number is also noticed. Similarly, raising the magnetic Reynolds number increases the velocity along the y-axis, whereas the inverse relationship is found for magnetic field components. Furthermore, for each flow phenomenon, an error analysis is also presented.

This article, investigates the behaviour of an ionized nanoliquid motion regarding heat transmission between two parallel discs. In the proposed model, the squeezing flow of Cu-water nanofluid with electrical potential force is analysed for studying the flow properties and an uniform magnetic field is applied to that fluid, by taking the surface of the bottom disc porous. We have also studied the effects of different nanomaterials on the transmission of heat through nanofluids. Furthermore, the influence of various physical parameters in the proposed model of nanofluids flow like volume fraction of nanomaterials, squeezing number, Hartmann number, Eckert number, and Prandtl number are analysed and discussed quantitatively through various tables and graphs. The system of nonlinear partial differential equations (PDE’s) has been used to formulate the proposed flow model and later converted to a set of nonlinear ODE’s by mean similarity transformation. Further, the reduced form of ODEs has been solved by Parametric Continuation Method (PCM), which is a stable numerical scheme. The outcomes obtained from the proposed model could also be used to analyse nanofluid flow in several fields, such as polymer processing, power transfer and hydraulic lifts.

Wind energy has become an important source of electricity generation, with the aim of achieving a cleaner and more sustainable energy model. However, wind turbine performance improvement is required to compete with conventional energy resources. To achieve this improvement, flow control devices are implemented on airfoils. Computational fluid dynamics (CFD) simulations are the most popular method for analyzing this kind of devices, but in recent years, with the growth of Artificial Intelligence, predicting flow characteristics using neural networks is becoming increasingly popular. In this work, 158 different CFD simulations of a DU91W(2)250 airfoil are conducted, with two different flow control devices, rotating microtabs and Gurney flaps, added on its Trailing Edge (TE). These flow control devices are implemented by using the cell-set meshing technique. These simulations are used to train and test a Convolutional Neural Network (CNN) for velocity and pressure field prediction and another CNN for aerodynamic coefficient prediction. The results show that the proposed CNN for field prediction is able to accurately predict the main characteristics of the flow around the flow control device, showing very slight errors. Regarding the aerodynamic coefficients, the proposed CNN is also capable to predict them reliably, being able to properly predict both the trend and the values. In comparison with CFD simulations, the use of the CNNs reduces the computational time in four orders of magnitude.

The main objective of this work was to determine the worth of installing an electrical battery in order to reduce peak power consumption. The importance of this question resides in the expensive terms of energy bills when using the maximum power level. If maximum power consumption decreases, it affects not only the revenues of maximum power level bills, but also results in important reductions at the source of the power. This way, the power of the transformer decreases, and other electrical elements can be removed from electrical installations. The authors studied the Spanish electrical system, and a particle swarm optimization (PSO) algorithm was used to model battery sizing in peak power smoothing applications for an electrical consumption point. This study proves that, despite not being entirely profitable at present due to current kWh prices, implanting a battery will definitely be an option to consider in the future when these prices come down.

This article, investigates the behaviour of an ionized nanoliquid motion regarding heat transmission between two parallel discs. In the proposed model, the squeezing flow of Cu-water nanofluid with electrical potential force is analysed for studying the flow properties and an uniform magnetic field is applied to that fluid, by taking the surface of the bottom disc porous. We have also studied the effects of different nanomaterials on the transmission of heat through nanofluids. Furthermore, the influence of various physical parameters in the proposed model of nanofluids flow like volume fraction of nanomaterials, squeezing number, Hartmann number, Eckert number, and Prandtl number are analysed and discussed quantitatively through various tables and graphs. The system of nonlinear partial differential equations (PDE’s) has been used to formulate the proposed flow model and later converted to a set of nonlinear ODE’s by mean similarity transformation. Further, the reduced form of ODEs has been solved by Parametric Continuation Method (PCM), which is a stable numerical scheme. The outcomes obtained from the proposed model could also be used to analyse nanofluid flow in several fields, such as polymer processing, power transfer and hydraulic lifts.

In this paper, the 3-D squeezing flow of viscous incompressible fluid between two parallel plates rotating at the same rate is investigated. The flow is observed under the influence of the varying magnetic field. The flow phenomena are modeled by utilizing the basic governing equations, i.e., equation of continuity, coupled Navier Stokes, and Magnetic Field equations. Using appropriate similarity transformations, the resultant partial differential equations are then transformed into a system of ordinary differential equations. The computational technique is developed via the Homotopy Analysis Method (HAM) to obtain the solution of transformed systems of ordinary differential equations. The influence of several engineering fluid parameters, such as squeeze Reynolds number, magnetic field strength parameter, and magnetic Reynolds number, on velocity and magnetic field components, are observed from different graphs. It has been investigated that by increasing the squeeze Reynolds number, fluid velocity in the y and z directions will be increased as well. On the magnetic field component along the y-axis, an increasing influence of squeezing Reynolds number is also noticed. Similarly, raising the magnetic Reynolds number increases the velocity along the y-axis, whereas the inverse relationship is found for magnetic field components. Furthermore, for each flow phenomenon, an error analysis is also presented.

The main objective of this work was to determine the worth of installing an electrical battery in order to reduce peak power consumption. The importance of this question resides in the expensive terms of energy bills when using the maximum power level. If maximum power consumption decreases, it affects not only the revenues of maximum power level bills, but also results in important reductions at the source of the power. This way, the power of the transformer decreases, and other electrical elements can be removed from electrical installations. The authors studied the Spanish electrical system, and a particle swarm optimization (PSO) algorithm was used to model battery sizing in peak power smoothing applications for an electrical consumption point. This study proves that, despite not being entirely profitable at present due to current kWh prices, implanting a battery will definitely be an option to consider in the future when these prices come down.

Wind energy has become an important source of electricity generation, with the aim of achieving a cleaner and more sustainable energy model. However, wind turbine performance improvement is required to compete with conventional energy resources. To achieve this improvement, flow control devices are implemented on airfoils. Computational fluid dynamics (CFD) simulations are the most popular method for analyzing this kind of devices, but in recent years, with the growth of Artificial Intelligence, predicting flow characteristics using neural networks is becoming increasingly popular. In this work, 158 different CFD simulations of a DU91W(2)250 airfoil are conducted, with two different flow control devices, rotating microtabs and Gurney flaps, added on its Trailing Edge (TE). These flow control devices are implemented by using the cell-set meshing technique. These simulations are used to train and test a Convolutional Neural Network (CNN) for velocity and pressure field prediction and another CNN for aerodynamic coefficient prediction. The results show that the proposed CNN for field prediction is able to accurately predict the main characteristics of the flow around the flow control device, showing very slight errors. Regarding the aerodynamic coefficients, the proposed CNN is also capable to predict them reliably, being able to properly predict both the trend and the values. In comparison with CFD simulations, the use of the CNNs reduces the computational time in four orders of magnitude.

INSIDER (Improved Nuclear SIte characterization for waste minimization in D&D operations under constrained EnviRonment) was a European project funded under the H2020-EURATOM programme and launched in June 2017. The project was coordinated by the French Commissariat a l'energie atomique et aux energies alternatives (CEA), it had a total duration of 4 years and covered a budget of 4 Meuro. INSIDER's work was performed by 5 technical working groups (WG) which brought together 18 institutions from 10 countries, leading to a total of 68 participating researchers. The objective of the project was to optimise the radiological characterisation of nuclear installations in constrained environments in order to obtain an accurate estimate of the content of contaminated materials as well as to optimise the quantity of contaminated materials to be treated as waste. The focus of this paper is on the statistical analysis of an interteam comparison of measurement results (dose rate, total gamma measurement, and gamma spectrometry) made in situ at the BR3 reactor, Belgium.

In this study, a water reattachment length was calculated by adopting two different models. The first was based on Unsteady Reynolds-Averaged Navier–Stokes (URANS) k-omega with Shear Stress Transport (SST); the second was a Large Eddy Simulation (LES) with Wall-Adapting Local Eddy-Viscosity (WALE). Both models used the same mesh and were checked with Taylor length-scale analysis. After the analysis, the mesh had 11,040,000 hexahedral cells. The geometry was a symmetrical expansion–contraction tube with a 4.28 expansion ratio that created mechanical energy losses, which were taken into account. Moreover, the reattachment length was estimated by analyzing the speed values; the change of speed value from negative to positive was used as the criterion to recognize the reattachment point.

The protection provided by wearing masks has been a guideline worldwide to prevent the risk of COVID-19 infection. The current work presents an investigation that analyzes the effectiveness of face shields as personal protective equipment. To that end, a multiphase computational fluid dynamic study based on Eulerian–Lagrangian techniques was defined to simulate the spread of the droplets produced by a sneeze. Different scenarios were evaluated where the relative humidity, ambient temperature, evaporation, mass transfer, break up, and turbulent dispersion were taken into account. The saliva that the human body generates was modeled as a saline solution of 8.8 g per 100 mL. In addition, the influence of the wind speed was studied with a soft breeze of 7 km/h and a moderate wind of 14 km/h. The results indicate that the face shield does not provide accurate protection, because only the person who is sneezed on is protected. Moreover, with a wind of 14 km/h, none of the droplets exhaled into the environment hit the face shield, instead, they were deposited onto the neck and face of the wearer. In the presence of an airflow, the droplets exhaled into the environment exceeded the safe distance marked by the WHO. Relative humidity and ambient temperature play an important role in the lifetime of the droplets.

[EN] In this paper, in-situ radiological characterisation by means of non-destructive techniques is studied and analysed in the context of the different constrained environments (identified as radioactivity, materials, accessibility and other hazards) that may be encountered in the nuclear facilities undergoing decommissioning and dismantling. As a complement to a previous paper (Aspe et al., 2020), the present one gives a global guidance to assist with the decision making process regarding the selection of in-situ measurement techniques that could be applied in such constrained environments. In addition, from the definition of the investigation objectives, and for each one of the most common in-situ measurement techniques, a brief description is given about the impact of the above constraints and how to integrate them onto the system definition, including the experimental design, the mechanical integration and the data management, to properly define the best radiological characterization methodology. Moreover, complementing this general view, all the phases – from initial to final – of a D&D programme were taken into account to provide basic recommendations, together with some particular dispositions, for the appropriate implementation of the chosen instruments. Strengths and weaknesses of the common detectors used for the different in-situ measurement techniques, as well as their recommended applications in nuclear facilities are also outlined.

This work presents a battery management system for lead–acid batteries that integrates a battery-block (12 V) sensor that allows the online monitoring of a cell’s temperature, voltage, and impedance spectra. The monitoring and diagnostic capabilities enable the implementation of improved battery management algorithms in order to increase the life expectancy of lead–acid batteries and report the battery health conditions. The novelty is based on the online monitoring of the evolution of electrochemical impedance spectroscopy (EIS) over a battery’s life as a way to monitor the battery’s performance. Active cell balancing is also proposed as an alternative to traditional charge equalization to minimize excessive electrolyte consumption. A battery-block sensor (VTZ) was validated by using the correlation between experimental data collected from electrochemical impedance spectroscopy lab-testing equipment and sensors that were implemented in a series of 12 V lead–acid battery blocks. The modular design and small size allow easy and direct integration into different commercial cell formats, and the proposed methodology can be used for applications ranging from automotive to stationary energy storage.

Obtaining tools to analyze and predict the performance of batteries is a non-trivial challenge because it involves non-destructive evaluation procedures. At the research level, the development of sensors to allow cell-level monitoring is an innovative path, and electrochemical impedance spectrometry (EIS) has been identified as one of the most promising tools, as is the generation of advanced multivariable models that integrate environmental and internal-battery information. In this article, we describe an algorithm that automatically identifies a battery-equivalent electrochemical model based on electroscopic impedance data. This algorithm allows in operando monitoring of variations in the equivalent circuit parameters that will be used to further estimate variations in the state of health (SoH) and state of charge (SoC) of the battery based on a correlation with experimental aging data corresponding to states of failure or degradation. In the current work, the authors propose a two-step parameter identification algorithm. The first consists of a rough differential evolution algorithm-based identification. The second is based on the Nelder–Mead Simplex search method, which gives a fine parameter estimation. These algorithm results were compared with those of the commercially available Z-view, an equivalent circuit tool estimation that requires expert human input.

[EN] Nearly all decommissioning and dismantling (D&D) projects are steered by the characterisation of the plant being dismantled. This radiological characterisation is a complex process that is updated and modified during the course of the D&D. One of the tools for carrying out this characterisation is the performance of in-situ measurements. There is a wide variety of equipment and methodologies used to carry out on-site measurements, depending on the environment in which they are to be carried out and also on the specific objectives of the measurements and the financial and personnel resources available. The extent to which measurements carried out with different types of equipment or methodologies providing comparable results can be crucial in view of the D&D strategy development and the decision-making process. This paper concerns an on-site benchmarking exercise carried out at the activated biological shield of Belgian Reactor 3 (BR3). This activity allows comparison and validation of characterisation methodologies and different equipment used as well as future interpretation of final results in terms of uncertainties and sensitivities. This paper describes the measurements and results from the analysis of this exercise. Other aspects of this exercise will be reported in separate papers. This paper provides an overview of the on-site benchmarking exercise, outlines the participating organisations and the measurement equipment used for total gamma, dose rate and gamma spectrometry measurements and finally, results obtained and their interpretations are discussed for each type of measurement as a function of detector type. Regarding the dose measurements, results obtained by using a large variety of equipment are very consistent. In view of mapping the inner surface of the biological shield the most appropriate equipment tested might be the organic scintillator, the BGO or even the ionisation chamber. In addition, for mapping this surface, the most appropriate to

The COVID-19 pandemic has pointed to the need to increase our knowledge in fields related to human breathing. In the present study, temperature, relative humidity, carbon dioxide (CO2) concentration, and median particle size diameter measurements were taken into account. These parameters were analyzed in a computer classroom with 15 subjects during a normal 90-minute class; all the subjects wore surgical masks. For measurements, Arduino YUN, Arduino UNO, and APS-3321 devices were used. Natural ventilation efficiency was checked in two different ventilation scenarios: only windows open and windows and doors open. The results show how ventilation affects the temperature, CO2 concentration, and median particle diameter size parameters. By contrast, the relative humidity depends more on the outdoor meteorological conditions. Both ventilation scenarios tend to create the same room conditions in terms of temperature, humidity, CO2 concentration, and particle size. Additionally, the evolution of CO2 concentration as well as the particle size distribution along the time was studied. Finally, the particulate matter (PM2.5) was investigated together with particle concentration. Both parameters showed a similar trend during the time of the experiments.

Vortex Generators (VGs) are applied before the expected region of separation of the boundary layer in order to delay or remove the flow separation. Although their height is usually similar to that of the boundary layer, in some applications, lower VGs are used, Sub-Boundary Layer Vortex Generators (SBVGs), since this reduces the drag coefficient. Numerical simulations of sub-boundary layer vane-type vortex generators on a flat plate in a negligible pressure gradient flow were conducted using the fully resolved mesh model and the cell-set model, with the aim on assessing the accuracy of the cell-set model with Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) turbulence modelling techniques. The implementation of the cell-set model has supposed savings of the 40% in terms of computational time. The vortexes generated on the wake behind the VG; vortical structure of the primary vortex; and its path, size, strength, and produced wall shear stress have been studied. The results show good agreements between meshing models in the higher VGs, but slight discrepancies on the lower ones. These disparities are more pronounced with LES. Further study of the cell-set model is proposed, since its implementation entails great computational time and resources savings.

Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.

One of the materials that is used widely for wind turbine blade manufacturing are fiber-reinforced composites. Although glass fiber reinforcement is the most used in wind turbine blades, the use of carbon fiber allows larger blades to be manufactured due to their better mechanical characteristics. Some turbine manufacturers are using carbon fiber in the most critical parts of the blade design. The larger rotors are exposed to complex loading conditions in service. One of the most relevant structures on a wind turbine blade is the spar cap. It is usually manufactured by means of unidirectional laminates, and one of its major failures is the delamination. The determination of material features that influence delamination initiation and advance by appropriate testing is a fundamental topic for the study of composite delamination. The fracture behavior is studied across coupons of carbon fiber reinforcement epoxy laminates. Fifteen different test conditions have been analyzed. Fracture surfaces for different mode ratios have been explored using optical microscope and scanning electron microscope. Experimental results shown in the paper for critical fracture parameters agree with the theoretically expected values. Therefore, this experimental procedure is suitable for wind turbine blade material characterizing at the initial coupon-scale research level.

The thermal behavior of three different walls, made with and without by-products, is assessed by means of the Finite Element Method, aiming to evaluate its performance in terms of the sustainable construction of the blocks. Results were compared to those obtained from an experimental campaign, aiming at validation of the model. The by-products used for the blocks were “lime sludge” and “sawdust”, whose performance was compared against the traditional blocks made of concrete as a reference, aiming to demonstrate its sustainability, showing decreases of the thermal transmittance up to 10.5%. Additionally, following the same methodology, the thermal behavior of these above-mentioned blocks but now with added internal insulation made of “recycled cellulose” was assessed, showing higher decreases up to 25.5%, increasing sustainability by addressing an additional reduction in waste, so the right combination of using by-products and the insulating filler in their cavities has been revealed as a promising way of optimizing the walls, offering a relevant improvement in energy savings. Finally, when comparing the U-values of the blocks made of concrete without insulation versus those made of by-products, with insulation, improvements up to 33.3% were reached. The adaptation of the procedure through a moisture correction factor was also incorporated.

presented at the 6th IAHR Europe Congress, February 15th to February 18th, 2021, Warsaw, Poland, During the last few decades, resolution for hydrologic models has increased considerably. In addition, the growing development of Sustainable Urban Drainage Systems (SUDS) has led to the implementation of new parameters and modules in those models. For example, Storm Water Management Model (SWMM) implemented the Low Impact Development (LID) module, which is designed to model SUDS techniques. However, hydrological modelling with the LID module presents relevant differences from the general SWMM model procedure. This research proposes a new approach for modelling the hydraulic response of impervious pavements in urban areas, based on the model provided by the SWMM LID module, instead of the general model. Results show that, when modelling with the SWMM LID module, it is possible to obtain runoff hydrographs that match those obtained by the general SWMM model, modifying just one parameter: the pavement permeability. This approach would be useful to compare alternatives that can be implemented in a given area by modifying only one model parameter. It would be possible to switch from pervious to impervious surfaces without changing the model itself.

The introduction of hybrid nanofluids is an important concept in various engineering and industrial applications. It is used prominently in various engineering applications, such as wider absorption range, low-pressure drop, generator cooling, nuclear system cooling, good thermal conductivity, heat exchangers, etc. In this article, the impact of variable magnetic field on the flow field of hybrid nano-fluid for the improvement of heat and mass transmission is investigated. The main objective of this study is to see the impact of hybrid nano-fluid (ferrous oxide water and carbon nanotubes) CNTs-Fe3O4, H2O between two parallel plates with variable magnetic field. The governing momentum equation, energy equation, and the magnetic field equation have been reduced into a system of highly nonlinear ODEs by using similarity transformations. The parametric continuation method (PCM) has been utilized for the solution of the derived system of equations. For the validity of the model by PCM, the proposed model has also been solved via the shooting method. The numerical outcomes of the important flow properties such as velocity profile, temperature profile and variable magnetic field for the hybrid nanofluid are displayed quantitatively through various graphs and tables. It has been noticed that the increase in the volume friction of the nano-material significantly fluctuates the velocity profile near the channel wall due to an increase in the fluid density. In addition, single-wall nanotubes have a greater effect on temperature than multi-wall carbon nanotubes. Statistical analysis shows that the thermal flow rate of (Fe3O4-SWCNTs-water) and (Fe3O4 -MWCNTs-water) rises from 1.6336 percent to 6.9519 percent, and 1.7614 percent to 7.4413 percent, respectively when the volume fraction of nanomaterial increases from 0.01 to 0.04. Furthermore, the body force accelerates near the wall of boundary layer because Lorentz force is small near the squeezing plate, as the current being alm

There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path.

[EN] In this paper, in-situ radiological characterisation by means of non-destructive techniques is studied and analysed in the context of the different constrained environments (identified as radioactivity, materials, accessibility and other hazards) that may be encountered in the nuclear facilities undergoing decommissioning and dismantling. As a complement to a previous paper (Aspe et al., 2020), the present one gives a global guidance to assist with the decision making process regarding the selection of in-situ measurement techniques that could be applied in such constrained environments. In addition, from the definition of the investigation objectives, and for each one of the most common in-situ measurement techniques, a brief description is given about the impact of the above constraints and how to integrate them onto the system definition, including the experimental design, the mechanical integration and the data management, to properly define the best radiological characterization methodology. Moreover, complementing this general view, all the phases – from initial to final – of a D&D programme were taken into account to provide basic recommendations, together with some particular dispositions, for the appropriate implementation of the chosen instruments. Strengths and weaknesses of the common detectors used for the different in-situ measurement techniques, as well as their recommended applications in nuclear facilities are also outlined.

The protection provided by wearing masks has been a guideline worldwide to prevent the risk of COVID-19 infection. The current work presents an investigation that analyzes the effectiveness of face shields as personal protective equipment. To that end, a multiphase computational fluid dynamic study based on Eulerian–Lagrangian techniques was defined to simulate the spread of the droplets produced by a sneeze. Different scenarios were evaluated where the relative humidity, ambient temperature, evaporation, mass transfer, break up, and turbulent dispersion were taken into account. The saliva that the human body generates was modeled as a saline solution of 8.8 g per 100 mL. In addition, the influence of the wind speed was studied with a soft breeze of 7 km/h and a moderate wind of 14 km/h. The results indicate that the face shield does not provide accurate protection, because only the person who is sneezed on is protected. Moreover, with a wind of 14 km/h, none of the droplets exhaled into the environment hit the face shield, instead, they were deposited onto the neck and face of the wearer. In the presence of an airflow, the droplets exhaled into the environment exceeded the safe distance marked by the WHO. Relative humidity and ambient temperature play an important role in the lifetime of the droplets.

There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path.

Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.

In this study, a water reattachment length was calculated by adopting two different models. The first was based on Unsteady Reynolds-Averaged Navier–Stokes (URANS) k-omega with Shear Stress Transport (SST); the second was a Large Eddy Simulation (LES) with Wall-Adapting Local Eddy-Viscosity (WALE). Both models used the same mesh and were checked with Taylor length-scale analysis. After the analysis, the mesh had 11,040,000 hexahedral cells. The geometry was a symmetrical expansion–contraction tube with a 4.28 expansion ratio that created mechanical energy losses, which were taken into account. Moreover, the reattachment length was estimated by analyzing the speed values; the change of speed value from negative to positive was used as the criterion to recognize the reattachment point.

The introduction of hybrid nanofluids is an important concept in various engineering and industrial applications. It is used prominently in various engineering applications, such as wider absorption range, low-pressure drop, generator cooling, nuclear system cooling, good thermal conductivity, heat exchangers, etc. In this article, the impact of variable magnetic field on the flow field of hybrid nano-fluid for the improvement of heat and mass transmission is investigated. The main objective of this study is to see the impact of hybrid nano-fluid (ferrous oxide water and carbon nanotubes) CNTs-Fe3O4, H2O between two parallel plates with variable magnetic field. The governing momentum equation, energy equation, and the magnetic field equation have been reduced into a system of highly nonlinear ODEs by using similarity transformations. The parametric continuation method (PCM) has been utilized for the solution of the derived system of equations. For the validity of the model by PCM, the proposed model has also been solved via the shooting method. The numerical outcomes of the important flow properties such as velocity profile, temperature profile and variable magnetic field for the hybrid nanofluid are displayed quantitatively through various graphs and tables. It has been noticed that the increase in the volume friction of the nano-material significantly fluctuates the velocity profile near the channel wall due to an increase in the fluid density. In addition, single-wall nanotubes have a greater effect on temperature than multi-wall carbon nanotubes. Statistical analysis shows that the thermal flow rate of (Fe3O4-SWCNTs-water) and (Fe3O4 -MWCNTs-water) rises from 1.6336 percent to 6.9519 percent, and 1.7614 percent to 7.4413 percent, respectively when the volume fraction of nanomaterial increases from 0.01 to 0.04. Furthermore, the body force accelerates near the wall of boundary layer because Lorentz force is small near the squeezing plate, as the current being alm

The COVID-19 pandemic has pointed to the need to increase our knowledge in fields related to human breathing. In the present study, temperature, relative humidity, carbon dioxide (CO2) concentration, and median particle size diameter measurements were taken into account. These parameters were analyzed in a computer classroom with 15 subjects during a normal 90-minute class; all the subjects wore surgical masks. For measurements, Arduino YUN, Arduino UNO, and APS-3321 devices were used. Natural ventilation efficiency was checked in two different ventilation scenarios: only windows open and windows and doors open. The results show how ventilation affects the temperature, CO2 concentration, and median particle diameter size parameters. By contrast, the relative humidity depends more on the outdoor meteorological conditions. Both ventilation scenarios tend to create the same room conditions in terms of temperature, humidity, CO2 concentration, and particle size. Additionally, the evolution of CO2 concentration as well as the particle size distribution along the time was studied. Finally, the particulate matter (PM2.5) was investigated together with particle concentration. Both parameters showed a similar trend during the time of the experiments.

presented at the 6th IAHR Europe Congress, February 15th to February 18th, 2021, Warsaw, Poland, During the last few decades, resolution for hydrologic models has increased considerably. In addition, the growing development of Sustainable Urban Drainage Systems (SUDS) has led to the implementation of new parameters and modules in those models. For example, Storm Water Management Model (SWMM) implemented the Low Impact Development (LID) module, which is designed to model SUDS techniques. However, hydrological modelling with the LID module presents relevant differences from the general SWMM model procedure. This research proposes a new approach for modelling the hydraulic response of impervious pavements in urban areas, based on the model provided by the SWMM LID module, instead of the general model. Results show that, when modelling with the SWMM LID module, it is possible to obtain runoff hydrographs that match those obtained by the general SWMM model, modifying just one parameter: the pavement permeability. This approach would be useful to compare alternatives that can be implemented in a given area by modifying only one model parameter. It would be possible to switch from pervious to impervious surfaces without changing the model itself.

This work presents a battery management system for lead–acid batteries that integrates a battery-block (12 V) sensor that allows the online monitoring of a cell’s temperature, voltage, and impedance spectra. The monitoring and diagnostic capabilities enable the implementation of improved battery management algorithms in order to increase the life expectancy of lead–acid batteries and report the battery health conditions. The novelty is based on the online monitoring of the evolution of electrochemical impedance spectroscopy (EIS) over a battery’s life as a way to monitor the battery’s performance. Active cell balancing is also proposed as an alternative to traditional charge equalization to minimize excessive electrolyte consumption. A battery-block sensor (VTZ) was validated by using the correlation between experimental data collected from electrochemical impedance spectroscopy lab-testing equipment and sensors that were implemented in a series of 12 V lead–acid battery blocks. The modular design and small size allow easy and direct integration into different commercial cell formats, and the proposed methodology can be used for applications ranging from automotive to stationary energy storage.

Obtaining tools to analyze and predict the performance of batteries is a non-trivial challenge because it involves non-destructive evaluation procedures. At the research level, the development of sensors to allow cell-level monitoring is an innovative path, and electrochemical impedance spectrometry (EIS) has been identified as one of the most promising tools, as is the generation of advanced multivariable models that integrate environmental and internal-battery information. In this article, we describe an algorithm that automatically identifies a battery-equivalent electrochemical model based on electroscopic impedance data. This algorithm allows in operando monitoring of variations in the equivalent circuit parameters that will be used to further estimate variations in the state of health (SoH) and state of charge (SoC) of the battery based on a correlation with experimental aging data corresponding to states of failure or degradation. In the current work, the authors propose a two-step parameter identification algorithm. The first consists of a rough differential evolution algorithm-based identification. The second is based on the Nelder–Mead Simplex search method, which gives a fine parameter estimation. These algorithm results were compared with those of the commercially available Z-view, an equivalent circuit tool estimation that requires expert human input.

One of the materials that is used widely for wind turbine blade manufacturing are fiber-reinforced composites. Although glass fiber reinforcement is the most used in wind turbine blades, the use of carbon fiber allows larger blades to be manufactured due to their better mechanical characteristics. Some turbine manufacturers are using carbon fiber in the most critical parts of the blade design. The larger rotors are exposed to complex loading conditions in service. One of the most relevant structures on a wind turbine blade is the spar cap. It is usually manufactured by means of unidirectional laminates, and one of its major failures is the delamination. The determination of material features that influence delamination initiation and advance by appropriate testing is a fundamental topic for the study of composite delamination. The fracture behavior is studied across coupons of carbon fiber reinforcement epoxy laminates. Fifteen different test conditions have been analyzed. Fracture surfaces for different mode ratios have been explored using optical microscope and scanning electron microscope. Experimental results shown in the paper for critical fracture parameters agree with the theoretically expected values. Therefore, this experimental procedure is suitable for wind turbine blade material characterizing at the initial coupon-scale research level.

The thermal behavior of three different walls, made with and without by-products, is assessed by means of the Finite Element Method, aiming to evaluate its performance in terms of the sustainable construction of the blocks. Results were compared to those obtained from an experimental campaign, aiming at validation of the model. The by-products used for the blocks were “lime sludge” and “sawdust”, whose performance was compared against the traditional blocks made of concrete as a reference, aiming to demonstrate its sustainability, showing decreases of the thermal transmittance up to 10.5%. Additionally, following the same methodology, the thermal behavior of these above-mentioned blocks but now with added internal insulation made of “recycled cellulose” was assessed, showing higher decreases up to 25.5%, increasing sustainability by addressing an additional reduction in waste, so the right combination of using by-products and the insulating filler in their cavities has been revealed as a promising way of optimizing the walls, offering a relevant improvement in energy savings. Finally, when comparing the U-values of the blocks made of concrete without insulation versus those made of by-products, with insulation, improvements up to 33.3% were reached. The adaptation of the procedure through a moisture correction factor was also incorporated.

Vortex Generators (VGs) are applied before the expected region of separation of the boundary layer in order to delay or remove the flow separation. Although their height is usually similar to that of the boundary layer, in some applications, lower VGs are used, Sub-Boundary Layer Vortex Generators (SBVGs), since this reduces the drag coefficient. Numerical simulations of sub-boundary layer vane-type vortex generators on a flat plate in a negligible pressure gradient flow were conducted using the fully resolved mesh model and the cell-set model, with the aim on assessing the accuracy of the cell-set model with Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) turbulence modelling techniques. The implementation of the cell-set model has supposed savings of the 40% in terms of computational time. The vortexes generated on the wake behind the VG; vortical structure of the primary vortex; and its path, size, strength, and produced wall shear stress have been studied. The results show good agreements between meshing models in the higher VGs, but slight discrepancies on the lower ones. These disparities are more pronounced with LES. Further study of the cell-set model is proposed, since its implementation entails great computational time and resources savings.

[EN] Nearly all decommissioning and dismantling (D&D) projects are steered by the characterisation of the plant being dismantled. This radiological characterisation is a complex process that is updated and modified during the course of the D&D. One of the tools for carrying out this characterisation is the performance of in-situ measurements. There is a wide variety of equipment and methodologies used to carry out on-site measurements, depending on the environment in which they are to be carried out and also on the specific objectives of the measurements and the financial and personnel resources available. The extent to which measurements carried out with different types of equipment or methodologies providing comparable results can be crucial in view of the D&D strategy development and the decision-making process. This paper concerns an on-site benchmarking exercise carried out at the activated biological shield of Belgian Reactor 3 (BR3). This activity allows comparison and validation of characterisation methodologies and different equipment used as well as future interpretation of final results in terms of uncertainties and sensitivities. This paper describes the measurements and results from the analysis of this exercise. Other aspects of this exercise will be reported in separate papers. This paper provides an overview of the on-site benchmarking exercise, outlines the participating organisations and the measurement equipment used for total gamma, dose rate and gamma spectrometry measurements and finally, results obtained and their interpretations are discussed for each type of measurement as a function of detector type. Regarding the dose measurements, results obtained by using a large variety of equipment are very consistent. In view of mapping the inner surface of the biological shield the most appropriate equipment tested might be the organic scintillator, the BGO or even the ionisation chamber. In addition, for mapping this surface, the most appropriate to

Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.

presented at the 6th IAHR Europe Congress, February 15th to February 18th, 2021, Warsaw, Poland, During the last few decades, resolution for hydrologic models has increased considerably. In addition, the growing development of Sustainable Urban Drainage Systems (SUDS) has led to the implementation of new parameters and modules in those models. For example, Storm Water Management Model (SWMM) implemented the Low Impact Development (LID) module, which is designed to model SUDS techniques. However, hydrological modelling with the LID module presents relevant differences from the general SWMM model procedure. This research proposes a new approach for modelling the hydraulic response of impervious pavements in urban areas, based on the model provided by the SWMM LID module, instead of the general model. Results show that, when modelling with the SWMM LID module, it is possible to obtain runoff hydrographs that match those obtained by the general SWMM model, modifying just one parameter: the pavement permeability. This approach would be useful to compare alternatives that can be implemented in a given area by modifying only one model parameter. It would be possible to switch from pervious to impervious surfaces without changing the model itself.

One of the materials that is used widely for wind turbine blade manufacturing are fiber-reinforced composites. Although glass fiber reinforcement is the most used in wind turbine blades, the use of carbon fiber allows larger blades to be manufactured due to their better mechanical characteristics. Some turbine manufacturers are using carbon fiber in the most critical parts of the blade design. The larger rotors are exposed to complex loading conditions in service. One of the most relevant structures on a wind turbine blade is the spar cap. It is usually manufactured by means of unidirectional laminates, and one of its major failures is the delamination. The determination of material features that influence delamination initiation and advance by appropriate testing is a fundamental topic for the study of composite delamination. The fracture behavior is studied across coupons of carbon fiber reinforcement epoxy laminates. Fifteen different test conditions have been analyzed. Fracture surfaces for different mode ratios have been explored using optical microscope and scanning electron microscope. Experimental results shown in the paper for critical fracture parameters agree with the theoretically expected values. Therefore, this experimental procedure is suitable for wind turbine blade material characterizing at the initial coupon-scale research level.

This study analysed temporal and spatial changes in offshore wind power density (WPD) and capacity factor (CF) around the Iberian Peninsula during the 20th century by analysing data from ERA20 and ERA5. Both WPD and CF were calculated using reanalysis data considering a wind turbine with a hub height of 90 m and incorporating the effect of air density changes. Since ERA5 assimilates more observations, the data from ERA20 was bias-corrected using quantile matching, with ERA5 reanalysis data as the reference. As both variables are based on wind speed (WS), this variable was also corrected and analysed. The results show that the mean values for WPD, CF, and WS during the 20th century were highest in the Atlantic zone and the Gulf of Lyon and lowest around the Balearic Islands. The results of the assessment of decadal trends using the Theil–Sen estimator show that all indicators increased significantly in the waters of the Iberian Peninsula during the study period (1900–2010). Considering the mean slope over this period, the change over the entire period could amount to 174 Wm-2 for WPD, 8.8% for CF, and 1.1 ms−1 for WS. Based on these changes, offshore wind turbines would have increased their returns by approximately 20% over the 11 decades.

Mutriku is a wave farm on the Spanish coast of the Bay of Biscay that has now been continuously supplying electricity for more than nine years. Since 1979, there has been a growing trend in wave energy flux for the whole Bay of Biscay. ERA5 data at the grid point nearest to Mutriku indicate an increase of 0.146 kW/m per decade for the 1979–2019 period. In this paper, a Self-Organizing Map (SOM) with a 2X5 architecture has been fitted to identify ten major sea-state types, each with a distinctive electricity generation pattern at a daily scale. This rendered it possible to reconstruct the daily electric power that would have been generated if the Mutriku wave farm had been operational over the entire 1979–2019 period and, accordingly, evaluate the impact that the observed changes in the wave climate and associated wave energy flux would have had on the electricity production. The results indicate that the electricity production or power flow would have remained constant during that period despite the increasing trend in wave energy flux. This is due to the regulation procedures and mechanisms used in the operation of Mutriku's Oscillating Water Column (OWC) wave energy converters, which dampen the effect of the increasing trend observed. The main conclusion is that the power flow levels off above a given threshold, making it more stable than the wave energy flux.

Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.