Experimental Output Research Articles

A Comparative and Experimental Study of Parametric Influence on Drilling Process of Hybrid AMMC

Abstract: To aim for the highest quality product quality and a high metal removal rate, metal cutting is crucial. The primary obstacle to attaining optimal quality and high production is the short tool life. The hybrid aluminum metal matrix AA 6082, with reinforcement consisting of titanium dioxide (7.5%) and cenosphere (2.5%), was optimized using the Modified Taguchi optimization method for simultaneous minimization and maximization of surface roughness (Ra), machining time, and material removal rate. The surface finish was affected by different types of geometrical precision values of the CNC drilling process parameters in order to both minimize and maximize the responses. The experiments, including call for the machining of hybrid AMMC and the use of an HSS drill bit, will be performed using a CNC with different process parameters. The L16 orthogonal array will be used for the examinations, and each experiment will be run under a distinct set of circumstances, including feed, speed, and pecking. Using the TAGUCHI design, the optimal geometrical and machining parameters were determined from the experimental output responses, and significant influences were discovered for the corresponding specific output responses.

Behavior of circular concrete-filled double-skin aluminum alloy tubular stub columns: Test, modeling and confinement-based design

Concrete-filled double-skin steel tubular members have been widely adopted in marine structures, thereby being constantly subjected to harsh corrosive environment. This has triggered the emergence of concrete-filled double-skin aluminum alloy tubular (CFDAT) members of late, wherein aluminum alloy tube is taken as an excellent corrosion-resistant material. Nevertheless, the design guidelines of CFDAT columns have not yet been incorporated into existing international standards due to the absence of relevant research, hindering their structural applications. To this end, this study aims to further investigate the behavior of circular CFDAT stub columns under concentric loading and to clarify the confinement mechanism of this member. In total, 12 CFDAT stub columns were axially tested and the corresponding experimental results were discussed. The research findings showed that the ultimate load of CFDAT specimens is significantly affected by the hollow ratio, concrete strength and diameter-to-wall thickness ratio of outer tube. Using the test results, finite element (FE) model was developed and then utilized to ascertain the confinement mechanism of CFDAT columns, followed by a comprehensive parametric study of such members. Subsequently, the feasibility of current design formulas for the design of CFDAT columns was evaluated against the experimental results and outputs of FE analysis, accompanied by an obvious prediction deviation. Accordingly, a new confinement coefficient reflecting the confinement effect of concrete strength in such members was suggested, which can reasonably capture the interaction between of aluminum alloy tubes and sandwiched concrete. Using this coefficient, a unified design model for estimating the ultimate load of CFDAT and CFAT members was suggested and has better predictive ability than existing design codes and empirical formulas.

Identification of the dynamic model of second-life lithium-ion cells through Subspace System Identification and similarity transformation

When lithium-ion batteries (LiBs) reach the end of their first useful life in electric vehicles (EVs), they can still be used in applications with lower power demands, a process known as second-life. However, to ensure that LiBs – or cells – removed from EVs operate safely, efficiently, and reliably in a second application, various tests and procedures must be applied to study their internal conditions. Some information about the conditions of the lithium-ion cells can be obtained from the parameters of its equivalent circuit model(ECM). However, the tests to obtain blackthe ECM parameters usually require taking the LiBs out of service and testing their cells in laboratory benches with special equipment, which is costly and time-consuming. Therefore, this work proposes a methodology to identify the ECM of lithium-ion cells based on Subspace System Identification (SSI) methods. This procedure can be executed online, while the LiBs are being used in the specified application. SSI methods have been widely adopted to identify the dynamic model of lithium-ion cells, but they return models without a physical meaning, called non-parametric models. This type of model cannot be used to address the internal conditions of a lithium cell, because its parameters do not have any physical meaning. To solve this problem, a novel method for estimating the lithium-ion cell physical parameters, in the ECM format, and from its non-parametric model, is proposed in this work. The procedure is based on similarity transformation techniques and special characteristics of the ECM. To validate the proposed method, nine samples of cells from the same manufacturer were studied. These cells had distinct degradation conditions and were removed from heavy-duty EVs at the end of their first life. The results showed that: (a) a good approximation between the identified model voltage output and the actual cells’ experimental voltage output was achieved, with root mean square error values as small as 2.26 mV; (b) it was possible to identify the ECM of the tested cells and their parameters using the proposed similarity transformation; and (c) the identified parameters can be used to detect the internal conditions of the cells.

Influence of electrochemical machining on shape memory and surface characteristics of nickel-titanium-cobalt shape memory alloy: An experimental study

ABSTRACT Electro-chemical machining (ECM) can safely be categorized as being a unique unconventional machining technique, in which the role of thermal defects are not involved. The NiTiCo shape memory alloy has high yield strength coupled with good resistance to aggressive aqueous environments, which makes it a potential candidate for use in several bio-medical applications. This research concentrates on electro-chemical machining of a NiTiCo alloy. Taguchi’s Design of Experiment (DOE) containing three-parameters and three-factors was used to conduct the experimental study. The influence of concentration of the sodium chloride electrolyte, voltage, and frequency on output characteristics, such as (i) material removal rate (MRR), (ii) surface roughness (SR), and (iii) over-cut (OC), was analyzed. Experimental outputs revealed the voltage (45.29%) to be the most influential among the three controlling parameter. Grey relational analysis (GRA) was utilized to discover the ideal parametric relation and optimize machining characteristics.

Combining simulation and experimental data via surrogate modelling of continuum dislocation dynamics simulations

Several computational models have been introduced in recent years to yield comprehensive insights into microstructural evolution analyses. However, the identification of the correct input parameters to a simulation that corresponds to a certain experimental result is a major challenge on this length scale. To complement simulation results with experimental data (and vice versa) is not trivial since, e.g. simulation model parameters might lack a physical understanding or uncertainties in the experimental data are neglected. Computational costs are another challenge mesoscale models always have to face, so comprehensive parameter studies can be costly. In this paper, we introduce a surrogate model to circumvent continuum dislocation dynamics simulation by a data-driven linkage between well-defined input parameters and output data and vice versa. We present meaningful results for a forward surrogate formulation that predicts simulation output based on the input parameter space, as well as for the inverse approach that derives the input parameter space based on simulation as well as experimental output quantities. This enables, e.g. a direct derivation of the input parameter space of a continuum dislocation dynamics simulation based on experimentally provided stress–strain data.

Experimental investigation of bidirectional series resonant DC-DC converter in different operating modes

This paper presents a study of bidirectional series resonant DC/DC converter. The main operating modes of the converter with phase-shift control technique are observed. An experimental survey with a laboratory model of such type of converter working over the resonant frequency is made. The functionality of the studied device operating in a wide area of loading is demonstrated with waveforms of the main parameters. The conducted measurements are used to build the experimental output characteristics of the converter. A similarity of the experimental results with the theoretical are denoted, and the ability to achieve current source characteristics for the whole range of loading is proven. The possibility of the studied converter for operation both in forward and reverse modes of energy flow is proved, which positions the device as an appropriate variant for implementation in the storage system control area.

Resonant Ring with a Gain of 36 for Use with a 1 MW 110 GHz Gyrotron

A 110 GHz quasi-optical ring resonator, designed for use with a 1 MW pulsed gyrotron, has been built and successfully tested using a 100 mW solid-state source. A low reflectance (2.4%) input coupler and a low-loss, four-mirror ring demonstrated a compression ratio, defined as the ratio of output to input power, of 36. The 6 ns output pulses were generated from the 2 m length ring using a silicon laser-driven semiconductor switch (LDSS). The quasi-optical ring resonator was designed with large waist sizes so that input pulses of up to 1 MW will stay under the 35 kV/cm electric field limit for ionization in ambient air. Maximum compression gain was achieved by matching the input coupling fraction to the round trip loss in the ring, achieving close to critical coupling. The experimental output pulse shape obtained after firing the LDSS was modeled using the reflectance, transmittance, and absorptance of the switch vs. time and vs. laser pulse fluence, with good agreement found with theory. The timing for the peak energy efficiency of 32% was found and the main loss mechanism limiting that efficiency was found to be the absorptance in the silicon wafer.

Experimental Validation of the Capture Chamber Model in Mutriku MOWC Wave Power Plant

Wave energy holds the potential to fulfill 15% of the EU's energy demand by 2050, thereby reducing CO2 emissions by 136 million metric tons per megawatt-hour, as outlined in the EU Energy Road Map. Similarly, the Spanish Renewable Energies Plan underscores the significant marine energy potential in Spain, particularly emphasizing wave energy. Within this framework, Oscillating Water Column (OWC) converters currently stand as among the most promising wave energy conversion technologies, offering the capability to harness ocean energy from various on-shore and floating structures. This paper introduces an analytical model of the wave capture chamber parameterized for a specific on-shore OWC wave power plant. The model is specifically adapted and parameterized for the Mutriku Marine Offshore Wave Power Plant located on the coast of the Spanish Basque Country. Subsequently, validation is conducted using both real wave entry data measured on-site and experimental output power data generated in the plant.

Performance prediction model for desalination plants using modified grey wolf optimizer based artificial neural network approach

Desalination represents an effective method for alleviating water scarcity, applying algorithmic techniques to predict the performance of reverse osmosis (RO) desalination plants, Modified Grey Wolf Optimizer (MGWO) based Artificial Neural Networks (ANN) can predict the performance of membrane distillation (MD) equipment. Four experimental inputs are selected: feed salt concentration(35–140 g/h), feed flow rate(400–600 L/h), evaporator inlet temperature (60–80 ℃), and condenser inlet temperature (20–30 ℃). The permeate flux (L/h m2) is selected as the experimental output. Ten prediction models were proposed and compared with the existing models (ANN, WOA-ANN, and GWO-ANN). The results showed that the MGWO-ANN model-5 showed the best regression results: R2 = 99.3 %, mean square error (MSE)= 0.004. This model outperformed the existing ANN model (R2 =98.8 %, MSE=0.060), WOA-ANN model (R2 =99.1 %, MSE=0.005) and GWO-ANN model (R2 =98.9 %, MSE=0.007). Model-5 has a single hidden layer (H=1), 13 hidden nodes (n = 13), 10 search agents (SA=10), and 75 %− 20 %− 05 % dataset division. Its residual error is within acceptable limits (spanning −0.1 to 0.2). Optimizing the number of hidden layer nodes (n) and the number of search agents (SA) can improve the training efficiency and prediction accuracy of the model, and the MGWO-ANN model is capable of more accurately predicting the performance of desalination plants.

A decision fusion-based method for global sensitivity analysis of complicated experiments

In real world, the relationship between experimental input and output has become more and more complex, which brings great challenges to the sensitivity analysis. This paper proposes a decision fusion based global sensitivity analysis method for complicated experiments, which not only provides quantitative evluation of the input factor influence on experimental results, but also mines the correlation and form the explicit criteria in IF-THEN fomation for further guidance. The theory of decision information system and continuous attribute discretization is presented first for transforming the experimental input and output into a decision table. In order to calculate the sensitivity of the factors and extract valid correlation criterions between conditional attributes and decision attributes simultaneously, the discrimination matrx is utilized for attribute reduction. Then a sensitivity analysis method based on decision fusion is proposed by organically assembling experiment design, attribute discretization, the discrimization matrix, and attribute reduction. Finally, the effectiveness and practicality of the proposed method were verified by the application of sensitivity analysis in hypersonic vehicle re-entry trajectory experiment.

Comparative analysis of anti-corrosive performance: Azure C dye vs. alternative Azure dyes on mild steel in 1 N HCl at elevated temperatures

Organic derivatives and dyes represent a promising and efficient class of corrosion inhibitors, demonstrating considerable potential in fulfilling the criteria for utilization as corrosion inhibitors. In the present study, the anti-corrosive impact of some Thiazines dyes: Azure A (D-1), Azure B (D-2) and Azure C (D-3), has shown a decelerative effect on mild steel in 1 N HCl environment and was investigated using experimental {Weight loss (WL), Electrochemical method and Atomic Force Microscope (AFM)} and theoretical {Density Functional Theory (DFT)} studies. It was found to have an efficiency in the order of D-3 (98.09 %) > D-1 (89.94 %) > D-2 (76.44 %) at elevated temperatures (338 K for 100 ppm concentration) deliberated through WL method. D-3 had greater stability than D-1 and D-2 at elevated temperatures which is indicative in the obtained experimental data. As examined via electrochemical and WL analysis, the cathodic type of inhibition mechanism and adsorption isotherm viz. Langmuir, Temkin and El-Awady were followed by all three dyes. Results of surface analysis and thickness of deposited layer by AFM agreed with other experimental outputs. A theoretical study for these thiazine dyes was probed employing DFT that illustrated the formation of a complex among the dyes and mild steel. The corrosion inhibition efficacy of dyes, as deduced from both experimental and theoretical data, aligns cohesively, thereby establishing a comparatively durable monolayer at the mild steel/medium interface, preventing steel deterioration in a specified range of temperatures.

Numerical modelling and experimental validation on the discharging performance of paraffin/graphite matrix composite with various configurations of bulk density and wall temperature

Graphite material in the form of expanded has proven to be an effective material to remedy the low thermal conductivity of paraffin storage medium. The present work focuses on the solidification performance of carbon-based approach via graphite matrix composite with phase change to eliminate the bottleneck of low thermal conductivity issue of phase change materials for thermal energy storage applications beyond the current literature. Paraffin/graphite matrix composites with various bulk density values of 0 kg/m3 (Case 1), 23 kg/m3 (Case 2), 50 kg/m3 (Case 3), 100 kg/m3 (Case 4), and 143 kg/m3 (Case 5) under various operating conditions of wall temperature (Twall: 15 °C, 25 °C, 35 °C, and 45 °C), which mimic real applications' fluid temperature, are performed numerically in detail. The numerical model was validated with experimental outputs performed by the authors. Numerical results show that paraffin/graphite matrix composites have a great potential for the solidification/discharging process to recover the heat stored in the charging/melting period. Uniform and rapid solidification behaviour is achieved with the paraffin/graphite matrix composite in terms of conduction-dominated heat transfer via abundant thermal bridges compared to the pure paraffin case. Higher bulk density values and lower cold surface temperatures present superior performance in solidification rate and thermal energy recovery rate. However, the effect of the bulk density is slowed down with the beyond of certain value of bulk density (100 kg/m3). For 100 kg/m3 (Case 4) at Twall = 15 °C, the heat recovery rate is enhanced by 31.17 times and 3.84 times compared to the 0 kg/m3 and 23 kg/m3. The heat recovery rate of 100 kg/m3 is improved by 350.8 % for Twall = 15 °C in comparison with Twall = 45 °C. The total solidification rate (t = 17.5 min) increased by 90.94 % and 62.94 % for 100 kg/m3 at Twall = 35 °C compared to 0 kg/m3 and 23 kg/m3, respectively.

An electrochemical sensor based on a modified glassy carbon electrode for detection of epinephrine in the presence of theophylline.

Neurotransmitters are chemical messengers that enhance and balance signals between cells and target cells in the body. They are vital to the body's ability to function. Epinephrine is one of the most essential catecholamine neurotransmitters with an important biological and pharmacological role in the mammalian central nervous system. Therefore, it is very important to develop sensitive, simple, and fast methods for the determination of this compound. In the present work, a glassy carbon electrode (GCE) modified with the cerium oxide-zinc oxide (CeO2-ZnO) nanocomposite (CeO2-ZnO/GCE) was developed for the sensitive and quick detection of epinephrine. The CeO2-ZnO nanocomposite was prepared by hydrothermal method. Electrochemical methods such as voltammetry and chronoamperometry techniques were used to investigate the performance of the developed sensor. The resulting CeO2-ZnO/GCE showed a remarkable response towards the determination of epinephrine. The electrochemical sensor demonstrated a wide dynamic linear range from 0.1 to 900.0 μM for analysis of epinephrine. The LOD equalled 0.03 μM for epinephrine. In addition, the electrochemical sensor had good feasibility for concurrent detection of epinephrine and theophylline. Furthermore, experimental outputs indicated that the oxidation peaks of epinephrine and theophylline were separated by a 685 mV difference between the two peaks in PBS at a pH of 7.0. Also, an electrochemical sensor has been employed to analyse epinephrine in real samples (urine and epinephrine Injection). The good and acceptable analytical performance of the developed sensor can provide a promising tool for the analysis of epinephrine in real samples.

Impact of Big Data Analytics on Digital Marketing: Academic Review

Strategic decisions require the use of analytics and big data technologies. Previous study has focused on big data applications, ethics, benefits, drawbacks, and analytical viewpoints, among other things. The goal of this study is to conduct a comprehensive literature assessment in these areas and to fill any research gaps on the impact of big data analytics on digital marketing approaches. We attempted to cover as many as 200 articles, news, publications, and other portals to investigate studies conducted from the past to the present. As a result, this study evaluated the current literature on big data applications and discovered that digital marketing is a vast sector in which big data has a considerable influence on the creation of digital advertising strategies and how advertising is influenced by big data. Using the greatest big data applications, according to past research, can assist selected organizations in overcoming severe limits during one of the world's most catastrophic pandemics. This outcome will benefit academics and industry in two ways: first, the experimental output will navigate the state of mind on the relationship between digital marketing, digital advertising, and big data analytics; second, the data-based result will improve the ability to think more creatively in the future with other industry affiliations.

MSACN: A Cloud Extraction Method from Satellite Image Using Multiscale Soft Attention Convolutional Neural Network

In satellite remote sensing images, the existence of clouds has an occlusion effect on ground information. Different degrees of clouds make it difficult for existing models to accurately detect clouds in images due to complex scenes. The detection and extraction of clouds is one of the most important problems to be solved in the further analysis and utilization of image information. In this article, we refined a multi-head soft attention convolutional neural network incorporating spatial information modeling (MSACN). During the encoder process, MSACN extracts cloud features through a concurrent dilated residual convolution module. In the part of the decoder, there is an aggregating feature module that uses a soft attention mechanism. It integrates the semantic information with spatial information to obtain the pixel-level semantic segmentation outputs. To assess the applicability of MSACN, we compare our network with Transform-based and other traditional CNN-based methods on the ZY-3 dataset. Experimental outputs including the other two datasets show that MSACN has a better overall performance for cloud extraction tasks, with an overall accuracy of 98.57%, a precision of 97.61%, a recall of 97.37%, and F1-score of 97.48% and an IOU of 95.10%.

A model for tensile strength of cellulose nanocrystals polymer nanocomposites

Cellulose nanocrystals (CNCs) are rod-shaped, highly crystalline, and possess a high aspect ratio, extensive surface area, and exceptional mechanical properties, making them ideal for reinforcing polymer matrices. However, few models exist for predicting the mechanical properties of CNC-based polymer nanocomposites (PNCs). Existing models often overlook the interphase's role in the mechanical performance of CNC-filled samples, leading to unreliable predictions. This study introduces a straightforward model emphasizing the interphase region and CNC properties to determine the strength of CNC-based system. The model is validated by experimental data and parametric analyses. The results indicate that interphase thickness and strength depending on the interfacial bonds between polymer chains and CNCs along with CNC volume fraction directly control the nanocomposite strength, while a larger CNC diameter diminishes it. Optimal strength in PNCs is achieved with thinner CNCs, thicker and stronger interphase, and higher CNC content. In addition, numerous experimental outputs of various samples show good fitting with model’s predictions.

Optimization and PID Control of pH and Temperature in an Electrocoagulation Process

In this work, effects of temperature and pH in batch treatment of pulp and paper mill wastewater using electrocoagulation has been investigated. Conductivity, temperature, and pH are selected as controlled variables; supporting electrolyte, cooling water, acid and base flow rates are selected as manipulated variables, respectively. Real time experimental multi input-multi output (MIMO) control of conductivity, temperature, and pH under constant current conditions are achieved using MIMO Proportional Integral Derivative (PID) control algorithms coded in MATLAB™. A central composite design (CCD) has been applied to the system under controlled conditions and optimum pH and temperature values are obtained using response surface methodology (RSM). Both controlled and uncontrolled experiments are performed using optimum values and results are compared in terms of removal efficiencies of pollutants. Results show that 34.47% chemical oxygen demand (COD), 98.06% total suspended solids (TSS), 99.80% turbidity, 99.93% color, and 13.40% SO42- removal is achieved in 45 minutes of process operation under controlled conditions and COD, TSS, turbidity, color and SO42- removal are increased by 10.92, 2.97, 4.06, 2.89, 3.17 respectively in comparison with uncontrolled operation. The highest removal percentages are obtained under controlled operating conditions as 98.5% and 98.3% for turbidity and color, respectively, for 10 minutes operation. It is concluded that optimum process operating conditions for removal of turbidity and color of pulp and paper mill wastewater is obtained under constant 6.45 pH, 23.24 °C temperature, 1.78 mS/cm conductivity, and power consumption is reduced by 25.3% under controlled conditions.

A Computational Platform for Automatic Signal Processing for Bender Element Sensors

The small strain shear modulus is an important characteristic of geomaterials that can be measured experimentally using piezoelectric sensors (bender elements). However, most conventional signal interpretation techniques are based on the visual observation of the output signal and therefore inherently subjective. Objective techniques also exist, like the cross-correlation of the input and output signals, but they lack physical insight, as they rely on the (incorrect) assumption that input and output signals are similar. This paper presents GeoHyTE, the first objective and physically consistent toolbox for the automatic processing of the output signal of bender element sensors. GeoHyTE updates a finite element model of the experiment, iteratively searching for the small strain shear modulus that maximises the correlation between the experimental and numerical output signals. The method is objective, as the results do not depend on the experience of the user, and physically consistent, as the wave propagation process is modelled in full and signals of the same nature (output) are correlated. Moreover, GeoHyTE is nearly insensitive to grossly erroneous input by the user, both in terms of the starting point of the iterative maximisation process and refinement of the finite element model. The results obtained with GeoHyTE are validated against benchmark measurements reported in the literature and experimental data obtained by the authors. A detailed statistical analysis of the results obtained with GeoHyTE and conventional interpretation techniques is also presented.

Process Parameter Optimization via RSM of a PEM based Water Electrolysis Cell for the Production of Green Hydrogen

In the present work, the operating parameters were optimized using Box Behnken Design (BBD) in response surface methodology (RSM) to maximize the hydrogen production rate (R1) and hydrogen production rate per unit watt consumed (R2) of a proton exchange membrane electrolysis cell (PEMEC), a third response (R3) which was the sum of the scaled values of R1 and R2 were selected to be maximized so that both hydrogen production rate and hydrogen production rate per unit watt consumed could be maximized. The major parameters which were influencing the experiment for enhancing the output responses were oxygen electrode/anode electrocatalyst loading (A), current supplied (B) and water inlet temperature (C). The commercial proton exchange membrane Nafion® was used as the electrolyte. The acetylene black carbon (CAB) supported IrO2 was used as the electrocatalyst for preparing oxygen electrode/anode whereas commercial Pt (40 wt%)/CHSA was used as the H2 electrode/cathode electrocatalyst. The quadratic model was developed to predict the output/responses and their proximity to the experimental output values. The developed model was found to be significant as the P values for both the responses were < 0.0001 and F values were greater than 1. The optimum condition for both the responses were O2 electrode/anode electrocatalyst loading of 1.78 mg/cm2, supplied current of 0.33 A and water inlet temperature of 54°C. The predicted values for hydrogen production rate (R1) and hydrogen production rate per unit watt consumed (R2) were 2.921 mL/min and 2.562 mL/(min·W), respectively obtained from the quadratic model. The error % between the predicted response values and experimental values were 1.47% and 3.08% for R1 and R2, respectively. This model predicted the optimum conditions reasonably in good agreement with the experimental conditions for the enhancement of the output responses of the developed PEM based electrolyser.

CPV System Optical Performance Evaluation by Means of Direct Experimental Measurement Procedure

The optics is the component that most affects the concentrating photovoltaic (CPV) system performance, depending above all on the concentration factor and optical efficiency. Hence, a basic aspect is the concentrated solar flux measure on the receiving area, the evaluation of which is principally realized by indirect measurement methods. First, a literature review on indirect and direct methods used for the evaluation of concentrated solar flux and optical parameters is presented in this paper. The experimental measurement procedure, which is able to evaluate the optical parameters and concentrated solar flux in CPV systems, is also presented. The main steps of this procedure are represented by experimental system setup, sensor selection for concentrated solar flux estimation, identification of all the factors affecting optical performances, and development of an experimental campaign and output analysis. In particular, the optical characterization results of a CPV system are obtained by means of in-depth experimental analysis using Triple-Junction (TJ) solar cells with areas of 5.5 × 5.5 mm2 and 10 × 10 mm2. Three different setups have been analyzed related to primary and secondary optics composition. The main aim of this paper is the determination of a direct measuring technique, rarely adopted in literature in comparison to the established techniques, that is able to evaluate experimentally the optical parameter values and that can be standardized for other CPV systems. In particular, equations that link the optical concentration factor (C) and efficiency (ηopt) with focal distance (h) represent the fundamental results. They can be used for similar point-focus configurations presenting the same TJ cell size and ranges of C, ηopt and h. Finally, the experimental results of the direct method are compared with those of an indirect method adopting the same CPV system and operational conditions.