Maximum Percentage Error Research Articles

Enhanced PINNs with augmented Lagrangian method and transfer learning for hydrodynamic lubrication analysis

Purpose By seamlessly integrating physical laws, physics-informed neural networks (PINNs) have flexibly solved a wide variety of partial differential equations (PDEs). However, encoding PDEs and constraints as soft penalties in the loss function can cause gradient imbalances, leading to training and accuracy issues. This study aims to introduce the augmented Lagrangian method (ALM) and transfer learning to address these challenges and enhance the effectiveness of PINNs for hydrodynamic lubrication analysis. Design/methodology/approach The loss function was reformatted by ALM, adaptively adjusting the loss weights during training. Transfer learning was used to accelerate the convergence of PINNs under similar conditions. Additionally, the iterative process for load balancing was reframed as an inverse problem by extending film thickness as a trainable variable. Findings ALM-PINNs significantly reduced the maximum absolute boundary error by almost 80%. Transfer learning accelerated PINNs for solving the Reynolds equation, reducing training epochs by an order of magnitude. The iterative process for load balancing was effectively eliminated by extending the thickness as a trainable parameter, achieving a maximum percentage error of 2.31%. These outcomes demonstrated strong agreement with FDM results, analytical solutions and experimental data. Originality/value This study proposes a PINN-based approach for hydrodynamic lubrication analysis that significantly improves boundary accuracy and the training process. Additionally, it effectively replaces the load balancing procedure. This methodology demonstrates considerable potential for broader applications across various boundary value problems and iterative processes. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0277/

Performance optimization for solar photovoltaic thermal system with spiral rectangular absorber using Taguchi method

Solar collector systems efficiently transform sunlight into energy that may be used to meet various needs. This research aimed to use the Taguchi method to determine the ideal operating parameters for a solar thermal collector with a rectangular spiral absorber. Controllable parameters including mass flow rate, solar radiation, and absorber design were manipulated during the energy recovery process, and features like PV temperature and outlet water temperature were used to assess the system’s effectiveness. The findings indicate that certain criteria significantly affect response indicators. The observed percentage contribution of absorber design, solar radiation, and the mass flow rate was 69.19%, 27.99%, and 2.83% in PV surface temperature. In comparison, the individual percentage contributions were 73.63%, 13.51%, and 10.57% for absorber design, solar radiation and mass flow rate for water output temperatures. The present model’s R2 values for PV and outlet water temperatures are 97.24% and 99.67%, respectively. The Predictive regression model was found in fine harmony and the maximum percentage error is limited to 0.68%. The maximum analytical electrical efficiency was observed with a spiral rectangular absorber of 14.57% at the lowest mass flow rate of 0.04 kg/s at the lowest radiation level of 600 W/m2. In comparison, maximum analytical thermal efficiency was observed with a spiral rectangular thermal absorber of 63.56% at the highest flow rate of 0.06 kg/s and the highest solar radiation level of 1000 W/m2. The analytical and experiment findings were in better agreement in this study, with the highest relative error of 7.52%. According to the study’s findings, the rectangular absorber-based PVT system is at its best at a higher mass flow rate to lower PV temperature and boost thermal energy recovery via water. The present research work can be extended for exergy, environmental, and economic feasibility analysis.

Combining Multi-Indirect Features Extraction and Optimized GPR Algorithm for Online State of Health Estimation of Lithium-ion Batteries

Abstract With the wide application of lithium batteries (LIBs) in electrified transportation and smart grids, especially in the pure electric vehicle industry, the accurate health maintenance monitor of LIBs has emerged as critical for safe battery operation. Although many data-driven methods with state of health (SOH) estimation for LIBs have been proposed, the problems of industrial generation and computational cost still need to be improved further. In contrast, this paper carried out a low-complexity SOH estimation method for LIBs. Specifically, the seven health indicators are extracted firstly to characterize battery health status from voltage, current, temperature and other data that can be obtained online. Then, the optimized gaussian process regression (GPR) algorithm is proposed with proper computational cost. Ultimately, combining a multi-indirect features extraction and optimized GPR algorithm, the online SOH estimation for LIBs was established and verified with NASA experiment data. The experimental results show that the maximum Mean Absolute Percentage Error (MAPE) of SOH estimation from the proposed method is 1.4496 and the minimum MAPE only reaches 0.5635. More importantly, the optimized GPR for SOH estimation can achieve a maximum 65.37% improvement under multiple evaluation criteria compared to traditional GPR. The method proposed in this paper is helpful for realizing on-line SOH estimation in battery management systems.

Optimizing High-Performance Predictive Modeling of the Medium-Speed WEDM Processing of Inconel 718

The purpose of this research was to create a predictive model for a medium-speed wire electrical discharge machine (WEDM) utilizing an artificial neural network (ANN). Medium-speed WEDM experiments were developed based on the I-optimal mixture design for machining, the Inconel 718 superalloy. During the experiment, the input parameters were the spark ontime, spark offtime, wire feed, and current, with the material removal rate (MRR) and surface roughness (Ra) selected as performance indicators. The ANN model was trained on experimental data and built using a feed-forward backpropagation neural network with a (4-8-2) structure and the Bayesian regularization (BR) learning approach. The model correctly predicted the relationship between the medium-speed WEDM’s primary process parameters and machining performance. An integrated ANN model and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) were used to determine the ideal parameters for the MRR and Ra, resulting in a set of Pareto-optimal solutions. The confirmation experiment revealed that the mean prediction error between the experimental and ideal solutions had a maximum error percentage of 1% for the MRR and 2% for the Ra, which are within acceptable ranges. This showed that the best process–parameter combinations were better for the MRR and Ra.

Inverse design of cellular structures with the geometry of triply periodic minimal surfaces using generative artificial intelligence algorithms

Triply periodic minimal surfaces (TPMS) exhibit excellent mechanical and energy absorption properties due to their structural advantages. However, existing porous TPMS structural design methods are constrained to a forward process from structural parameters to mechanical properties. This study proposed an inverse design method that combines bidirectional generative adversarial networks (BiGAN) and mechanical performance targets, resulting in a combined TPMS structure of Primitive and IWP types with superior buffering and energy absorption capabilities. The results show that under a single load value target condition of the designed structure, the minimum deviation index (R2) between the load value corresponding to the displacement point and the target load value is only 0.987, and the maximum mean absolute percentage error (MAPE) is only 5.92 %. When considering the elastic modulus target, the approach successfully conducts two sets of combined structural designs meeting the requirements of both high and low elastic moduli. When targeting the specified load-displacement curve conditions, specifically when combining high elastic modulus with ascending plasticity, the designed structures exhibit an error of only 2.2 % compared to the target property. Moreover, the quasi-static uniaxial compression experiments conducted on additively manufactured designed structures confirm that the experimental curves match the target curves in terms of deformation trends and load value ranges. The success of this inverse design approach for cellular TPMS structures has the potential to expedite new structural material development processes.

Regression prediction of critical exhaust volumetric flow rate in tunnel fire with two-point extraction ventilation based on neural network method

In this study, a Genetic Algorithm-Backpropagation Neural Network (GA-BPNN) model was developed to predict critical exhaust volumetric flow rate in tunnel fires with two-point extraction ventilation system. Seven influencing factors served as inputs for the model, while the critical exhaust volumetric flow rate was the output. Experimental data from two reduced-scale tunnels were utilized to both train and validate the GA-BPNN model. The results demonstrate a satisfactory prediction performance, with key metrics such as Mean Absolute Percentage Error (MAPE), Relation Coefficient (R2), and Root Mean Square Error (RMSE) achieving values of 0.0384, 0.9989, and 3.5258, respectively. Importantly, the model maintains a maximum Absolute Percentage Error (APE) below 10 %. In contrast, the traditional BPNN model exhibited an APE of approximately 18.9 %. Moreover, the predictive results of the GA-BPNN model were compared with those of a previously semi-empirical formula. It was observed that the semi-empirical predictions exhibited APE exceeding 20 % for certain data points. This further underscores the superiority of the GA-BPNN model in predicting critical exhaust volumetric flow rates in tunnel fires with two-point ventilation system. These findings affirm the feasibility and effectiveness of GA-BPNN regression model as a reliable method for predicting critical exhaust volumetric flow rates under multiple factors.

Accounting for taxi service conditions in estimating route travel time from floating car data using Markov chain model

Recognising the variations in driving behaviour between taxis in the empty and carry conditions is pivotal for enhancing the accuracy of route travel time estimations using floating car data. However, existing methods largely overlook this distinction. In light of this, this study aims to harness these variations for more precise estimations. Utilising taxi data, we segmented the information by service conditions and executed distinct estimations for each segment. The route travel time was deduced through convolutional operation, complemented by a Markov chain model to discern correlations between travel times across various links. Our innovative approach realised a substantial enhancement in accuracy. Notably, when accounting for distinct service conditions, there was a reduction of 51.44% in mean absolute error and a 46.83% decline in maximum percentage error. By providing more accurate and reliable travel time predictions, our methodology enables better-informed traffic management decisions. Accurate travel time estimations are essential for optimising traffic signal timings, planning efficient routing strategies, and managing road network usage. These improvements in traffic management can lead to smoother traffic flow, reduced travel times, and ultimately, diminished congestion in urban areas.

New insight into groundwater 4He ages based on Ne isotopic equilibrium in Jianghan Plain, Central China

The change of hydrostatic pressure caused by the fluctuation of groundwater table in the aquifer will lead to the partial dissolution of excess 4He gas, resulting in the isotope imbalance of 3He/4He-4He. The dissolved Ne in groundwater is mainly derived from the atmosphere, and its isotopic composition can correct the isotopic imbalance of 3He/4He-4He. We collected thirty-eight groundwater samples from the second aquifer of the Jianghan Plain, and the isotopic concentrations and ratios of He and Ne were measured. 21Ne/22Ne-20Ne/22Ne illustration is proposed to estimate the shares of atmospheric and mantle components. The 21Ne content and isotopic ratios of atmospheric and mantle components are used to estimate a calculated Ne content. The difference between the calculated Ne content and the measured Ne content (∆Ne) is used to evaluate the percentage of error estimated“excess air”. The accumulation of crustal 4He is corrected with the measured 4He content and the percentage of error estimated “excess air”. We found the maximum percentage of error estimated “excess air” was 7.57 % occurring in the groundwater samples from the second aquifer of Jianghan Plain, and the disequilibrium of 3He/4He-4He led to overestimation of the share of mantle He. The percentage of mantle He in total dissolved components is reassessed and range from 0.03 % to 0.74 %, indicating the mantle component is minor. The reassessed 4He ages (1.79 ka to 21.90 ka) were uniformly older than those estimated by traditional method which only use the measured 3He/4He ratio to distinguish the crust 4He (1.28 ka to 18.74 ka). 4He age is significantly underestimated up to 47.05 %.

Bayesian Approach to Estimate Proglacial Lake Volume (BE‐GLAV)

AbstractWe present a new model called Bayesian Estimated Glacial Lake Volume (BE‐GLAV) to estimate the volume of proglacial lakes. Presuming the lake cross‐section as trapezoidal, BE‐GLAV uses a Bayesian calibration approach to adjust the cross‐sectional geometry to match modeled and observed lake surface widths. We validated our model using bathymetric measurements from lakes spread across High Mountain Asia (specifically, the Himalaya and Tien‐Shan), with aerial extents ranging from 0.01 to 5.5 km2. The modeled lake volumes agreed with the measured lake volume with a root‐mean‐square absolute uncertainty of ∼14%. With minimum and maximum errors of ∼0.3% and ∼61.2%, BE‐GLAV performed well compared to 10 other models in a model inter‐comparison experiment. Using the measured set of volumes, our model can constrain both the root mean square (RMS) error and the maximum percentage error in modeled lake volume, unlike other models, some of which can compute just the RMS uncertainty.

A fast wide-range air balancing control method based on deep reinforcement learning

Air balancing is a crucial technology for reducing energy consumption in ventilation ducts and improving indoor environmental quality. Existing air balancing methods face challenges due to the complex and diverse internal duct topologies of ventilation duct systems, as well as the strong coupling of airflow between branches, making air balancing tuning difficult. To address the slow convergence speed and difficulty in precise control of air balancing in multi-branch ventilation systems, this paper proposes a fast wide-range air balancing control method based on deep reinforcement learning (DRL-AB). A system airflow control process with Markovian properties is designed, with state, action, and reward functions oriented towards reducing airflow errors. A dynamic single-branch target training mechanism is designed to reduce training costs while improving the agent’s training efficiency and its ability to generalize to different target airflow levels. The proposed method’s performance under various target airflow conditions is verified through experimental platforms. The results indicate that, in all test cases, the maximum percentage error is controlled within 3.33%, ensuring rapid and accurate convergence of a wide range of target airflows. This demonstrates strong universality, prevents waste of airflow energy, and enhances energy utilization efficiency.

Implementation of a motion planning technique for a low-frequency piezo-actuated inchworm drive

A novel piezoelectric inchworm drive capable of long-range motion has been designed, fabricated, and tested in this research work. To control the motion of the inchworm drive, trajectory planning has been proposed. The trajectory planning ensures that the inchworm drive achieves smooth and continuous motion with high accuracy. Two trajectory planning methods were incorporated for the developed inchworm drive: a linear function with a parabolic blend trajectory and a cubic polynomial trajectory. Simulations for both the cubic polynomial and trapezoidal trajectories were conducted, with the estimated displacement results closely verified through experimental validation. The fabricated inchworm drive is tested for varying input voltages and frequencies. The experimental findings demonstrate that the proposed piezoactuated Inchworm drive can achieve substantial displacement, constrained only by the linear slide’s length. When an input signal of 150 V peak to peak and frequency of 10 Hz is applied to the inchworm drive, it was capable of moving at a speed of 1425 μm/s. When incorporating trajectory planning for the Inchworm drive the experimental results show that the maximum percentage error for the trapezoidal motion profile is 1.56% and the cubic polynomial profile trajectory is within 1% for the corresponding target position for a travel range of 25 mm of the inchworm drive.

Rancang Bangun Sistem Kontrol Dan Monitoring Beban 1 dan 3 Phasa Pada Panel Distribusi Berbasis Internet of Things

The distribution panel is a place to distribute and distribute electrical energy from the power panel to the load panel (consumer) for both power installations and lighting installations. The main problem with every disturbance or improvement in electricity usage is that people need to come to the area to control and monitoring the panel manually. This research focuses on the design and manufacture of control and monitoring devices at 3 and 1 phase loads, with the method using NodeMCU esp 32/8266 with pzem004t sensor as control and reading of voltage, current and frequency on the panel, which can be viewed and controlled offline at panel or online using the blynk application. The testing of the tool is carried out using a 3-phase motor, PC monitor, fan, laptop charger, and chicken lamp. By turning on the load one by one or simultaneously. From the test results, the tool can turn on and off online through the blynk application and manually using a push button. the results obtained are quite accurate with a maximum error percentage of 8.70%. However, the microcontroller between control and monitoring is used separately.

Short-term wind speed prediction of wind farm based on TSO-VMD-BiLSTM

Aiming at the random and intermittent characteristics of wind speed, a short-term wind speed prediction (SWSP) method based on TSO-VMD-BiLSTM is proposed in this article. Firstly, open-source historical data from a certain region in 2022, including wind speed, direction, pressure, and temperature is analyzed. The data is processed through variational mode decomposition (VMD) to fully extract feature data from historical wind speed records. Secondly, taking historical wind speed, direction, pressure, and temperature as inputs and wind speed as output, a SWSP model based on long short-term memory (LSTM) network is constructed. Thirdly, the tuna swarm optimization (TSO) algorithm is utilized for parameters optimization, and a bi-directional long short-term memory (BiLSTM) network is incorporated to enhance prediction accuracy for micrometeorological parameters. The proposed TSO-VMD-BiLSTM model is validated through comparison with other models, demonstrating its higher accuracy with the maximum absolute error of only 2.52 m/s, the maximum root mean square error of 0.81, the maximum mean absolute error of only 0.54, and the maximum mean absolute percentage error of 6.89%.

Modeling, simulation and efficiency assessment of a direct coupled water pumping PV system in semi-arid coastal areas

Photovoltaics (PV) energy is a solution for the electrification of developing countries, especially for remote rural zones. The use of solar energy in water pumping is the most adopted solution for rural and desert regions. The modeling, simulation and analysis of the PV system of water pumping is a vital step before assembling this system on any place, which allows a better understanding of its behavior in real weather conditions on that place. In this paper, we investigate the performance of a water pumping photovoltaic system consisting of a PV generator coupled with a DC-motor which drives a centrifugal pump that draws water from a well and delivers it to a reservoir. The characteristics of the components of this system allow researchers, manufacturers, and social communities to understand better the functioning of these components. Our simulation results were meticulously cross-verified against the module manufacturer’s data sheet, revealing a maximum relative percentage error of 1.12 %. This attests to the coherence between the manufacturer’s stipulations and our simulated values. Furthermore, we ascertain commendable efficiency in the motor-pump system, particularly noteworthy for elevated irradiation levels, while maintaining acceptable performance even under minimal irradiation conditions.

Haque’s approach with mickens’ iteration method to find a modified analytical solution of nonlinear jerk oscillator containing displacement time velocity and time acceleration

AbstractHaque’s approach with Mickens’ iteration method has been used to obtain the modified analytical solutions of the nonlinear jerk oscillator, including displacement time velocity and acceleration. The jerk oscillator represents the features of chaotic behavior in numerous nonlinear phenomena, cosmological analysis, kinematical physics, pendulum analysis, etc., such as electrical circuits, laser physics, mechanical oscillators, damped harmonic oscillators, and biological systems. In this paper, we have used different harmonic terms for different iterative stages using the truncated Fourier series. A comparison is made between the iteration method, the improved harmonic balance method, and the homotopy perturbation method. After comparison, the suggested approach has been shown to be more precise, efficient, simple, and easy to use. Furthermore, there was remarkable accuracy in the comparison between the numerical results and the generated analytical solutions. For the third approximate period, the maximum percentage error is 0.014.

Comprehensive thermal performance analysis of an air-cooled staggered configured Li-ion battery pack- A numerical and experimental approach

The discharge rate, ambient temperature, and airflow cooling velocity of a Li-ion battery pack BTMS have a direct impact on the battery's temperature, performance, and safety. Monitoring these is essential for avoiding overheating, ensuring optimal performance, and extending the battery's lifespan. This study comprehensively assesses the thermal performance of staggered structured cells of a 24.00V, 10.00Ah Li-ion battery pack cooled by ambient air. Both experimental and numerical methods are employed to achieve this evaluation. The primary objective is to investigate how varying discharge rates (0.50, 1.00, 1.50, 2.00, and 2.50; C), the seasonal temperatures in Prayagraj, India (282.00, 296.00, 305.00, and 315.00; K), and airflow velocities (0.50, 1.00, 1.50, and 2.00; m/s) influence the battery pack's thermal behaviour. A comparative analysis is also conducted between the present study and the author's prior research, focusing on the aligned configuration. The study observed a temperature increase in the airflow direction, from the inlet to the outlet, with the highest temperature recorded in the second-to-last column of the battery pack. Both numerical analysis and experimental testing confirmed that decreasing the airflow velocity from 2.00 m/s to 0.50 m/s reduced thermal performance by enhancing temperature in the airflow path, as well as transverse and circumferential temperature variations. Conversely, as the load on the battery (discharge rate) decreased from 2.50 C to 0.50 C, the thermal performance estimating parameters; Tmax, Tavg., ΔTmax, and σT, exhibited a declined trend. This is due to the greater heat generation at higher discharge rates. Furthermore, it was observed that as the ambient temperature increased from 282.00 K to 315.00 K, the temperature of the battery cells also increased. Nonetheless, the cooling patterns remained similar across these different temperature range conditions. Moreover, experimental results compete with the numerical outcomes with a maximum absolute percentage error of 1.21%. The staggered configuration gives lower and uniform temperature distributions within the battery packs than the aligned arrangements.

Vibration analysis of a sandwich Timoshenko beam reinforced by GOAM/CNT with various boundary conditions using VIM

In the present study, a sandwich beam reinforced by carbon nanotubes (CNTs)/graphene origami auxetic metamaterials (GOAM) and porous cores is studied. Various distributions of CNTs in facesheets and porosity in the core are stated in terms of thickness. The equations of motion for a sandwich beam are derived and solved under various boundary conditions using variational iteration method (VIM), in which, it is a powerful technique with higher convergence with the computational method that gives analytical response to a linear problem. The effect of the types of CNT distributions, porosity coefficient, types of porous cores, porosity parameter, weight fraction and folding degree of GOAM, different geometric dimensions and different B.C.’s on the natural frequency is investigated. The obtained results are compared with the other literature, that there is a good agreement between them. On the other hands, the maximum error percentage of the present work (VIM) with Civalek and Kiracioglu [66] (Discrete singular convolution method) for the first five frequencies is equal to 0.3% according to Table 2, while in Table 3, the maximum error percentage of the present work (VIM) with Yas and Samadi [67] (differential quadrature method) for the first frequency with various boundary conditions and nanocomposite material is equal to 0.22%. In this regard, the present study can be a suitable platform to find the natural frequency and prevent the phenomenon of resonance and increase the life of the structure. Moreover, it is seen that with enhancing of weight fraction of GOAM, frequency increases because increasing the stiffness of structures. The importance of this research is to extract the formulation of sandwich beam structure for GOAM under different boundary conditions and compare the results between them by repeating the changes.

Elucidation of Mass Transport Phenomena in Highly Concentrated Electrolytes during Current Cycling Using In-Situ Interferometry and Finite Difference Method

Understanding electrolyte mass transfer during charge–discharge reactions is essential for developing next-generation storage batteries with high energy densities. In this study, we investigated Li+ transport in a highly concentrated electrolyte (HCE) consisting of an equimolar mixture of lithium bis(fluorosulfonyl)amide (LiFSA) and tetraglyme (G4) under current reversal and re-reversal. Concentration profiles of the electrolyte at a distance of 0–600 μm from the Li electrodes were obtained using in situ laser interferometry. The Li+ transference numbers and LiFSA diffusion coefficients were calculated from these profiles. Raman spectroscopy suggested that the coordination structure surrounding Li+ ions in the electrolytes mainly contributed to the transference number. A one-dimensional unsteady diffusion equation and the finite difference method were employed to simulate the concentration profiles. The maximum error percentage between the measured and simulated values was only 3%, confirming the accuracy and validity of the interferometric measurements. Our findings on Li-ion transfer in HCEs could promote the rational design of high-energy-density Li-ion batteries with higher cation transference numbers of electrolytes and charge–discharge rates.

Design and Implementation of an IoT Based Patient's Health Monitoring System

This paper presents an Internet of Things (IoT) based patient's health monitoring system that can be given to patients, especially in remote areas from the hospitals. This system is built around Node MCU (ESP8266) which is used to control the activities of the system. The microcontroller is interfaced to MLX90614 which is the contactless infrared (IR) temperature sensor and MAX30101 which is the High-Sensitivity Pulse Oximeter and Heart Rate Sensor. The software implementation involves the programming of the microcontroller in Arduino IDE environment and the setting up of the Blynk cloud platform for receiving data from the system. This data can be accessed remotely in the Blynk platform through internet connection. The results obtained show that the system performed optimally as the percentage error margin in readings obtained from the system for body temperature, heart rate (pulse rate), and blood Oxygen level when compared with commercially available health measuring devices show a maximum percentage error of 3.51 in heart rate and a minimum percentage error of - 3.42 in body temperature. Again, the Root Mean Square Error (RMSE) for body temperature, heart rate (pulse rate), and blood Oxygen level when compared with commercially available health measuring devices are 0.351, 0.935, and 2.503 respectively. These values are less than the 2% stipulated by the US Food and Drug Administration (FDA) requirement of root mean square accuracy for pulse oximeters, and the percentage error values in this work are less than ±5% for oximeter. This means that the system is reliable.

Оптимизация параметров импульсной лазерной наплавки стали 30ХГСН2А с использованием генетического алгоритма

The paperpresents the optimization of pulsed laser cladding of structural steel using a genetic algo-rithm. Using the ANSYS Workbench software, finite element modelling of laser cladding on a 30ХГСН2А steel substrate with an additive in the form of wire was conducted, considering the temperature dependence of the material's thermophysical properties. A surrogate model for pulsed laser cladding of 30ХГСН2Аsteel was developed employing a face-centered version of the central composite design experiment. The time inter-vals corresponding to the end time of the three fronts of the laser pulse and the diameter of the filler wire were considered as variable factors. The maximum temperatures in the treatment zone were used as re-sponses. In order to optimize pulsed laser cladding of 30ХГСН2А steel, the maximum temperature limit val-ues in the treatment zone were established for three moments of time that corresponded to the laser pulse fronts at the three points in the finite element model. A comparison was made between the parameters ob-tained from optimization and those derived from finite element modelling. When determining temperatures, the maximum percentage error of the results obtained via the genetic algorithm did not exceed 3.5 %