Prediction Of Performance Parameters Research Articles

Modeling of Heat Transfer and Airflow Inside Evacuated Tube Collector With Heat Storage Media: Experimental Validation Powered by Artificial Neural Network

ABSTRACTSolar air heater (SAH) is a widely employed technology for harnessing solar thermal energy in numerous applications. Contemporary research optimizes designs within identical spatial constraints to maximize energy output. However, there is a recognized need to conduct analytical validation to stimulate the experimental setup and formulate an artificial neural network (ANN) model to govern and predict the operation system. This investigation involved developing and assessing an evacuated tube solar air heater (ETSAH) integrated with annulus‐filled heat storage media. Furthermore, this study introduced an ANN model and analytical solution to predict performance parameters, representing a noteworthy contribution. The proposed ANN model achieved its optimal validation performance with a mean square error of 5.4018 × 10−6 after 11 epochs within 31 of training. Also, correlation findings show that the optimal architecture of a feed‐forward backpropagation is achieved when the 5‐40‐40‐40‐2 model architecture is used. In this case, there are five neural nodes in the input layer that represent timing, temperature, radiation, and airflow rate. The power output of the ETSAH device shows a strong correlation with the flow rate, reaching its peak at 0.05 kg/s with a value of 2261 W and dropping to 368 W at 0.006 kg/s. Correspondingly, the greatest energy efficiency was measured at airflow rates of 0.05, 0.01, and 0.006 kg/s, accounting for 48.38%, 27.32%, and 19.65%, respectively.

Aerodynamic optimization of double S-duct caret intake by self-adapting non-dominated sorting genetic algorithm

The caret intake could not only provide compressed airflow to the aircraft engine stably but also should have applicable radar stealth performance. Radar stealth means that the echo intensity of the intake should be low when detected by radar, which could be measured by radar cross section (RCS). The airflow distribution of the double S-duct caret intake was numerically optimized to improve aerodynamic performance, with radar stealth performance as constraints. Self-adaptive genetic algorithm and Kriging model were used to improve the optimization efficiency. There could be multiple boundary-layer separations in the S-duct caret intake, resulting in obvious total pressure distortion at the outlet and secondary flow. To suppress these adverse airflow phenomena, the optimization of the double S-duct intake was defined as a problem with two targets and multiple constraints. By defining the probabilities of selection and variation as functions of the iteration number and the fitness, the self-adapting non-dominated sorting genetic algorithm can be applicable for such a problem. Using the Kriging model, which was proven to have satisfactory accuracy in predicting performance parameters, an optimized intake with both excellent aerodynamic and stealth performance was obtained, with a 30% reduction in DC60 value and a 47% reduction in SC60 value compared to the intake before optimization.

Modeling the Performance Parameters of Pollen Grains of Male Date Palms Using an Artificial Neural Network Based on the Mineral Composition and Morphological Properties of Their Leaves

One of the key factors for sustainability in agricultural systems, particularly, for cultivation of date palms, is the identification of the performance parameters of the pollen grains of male date palms (Phoenix dactylifera L.). This study was carried out to predict the performance parameters of pollen grains using an artificial neural network (ANN) model. The morphological features of spathe length, spathe weight, number of pinnae per leaf, leaf length, leaf width, length of the pinna part, pinna length, pinna width, length of the spathe stem, and spathe width, as well as the concentrations of minerals such as Mg, N, K, P, and Ca in their leaves were used as inputs to the ANN model. For this purpose, we collected the required data from nine male date palms grown in Saudi Arabia. The ANN model utilized in this work included an input layer with 15 parameters, a hidden layer of 30 neurons, and an output layer with 8 neurons. The ANN model was trained with 27 patterns. Seven patterns were utilized for testing purposes. The coefficient of determination (R2) obtained between the observed and predicted performance parameters’ values using the testing dataset was 0.902 for the number of strands per spathe, 0.967 for strand length, 0.963 for the number of flowers per strand, 0.941 for the number of flowers per spathe, 0.985 for the weight of pollen grains per spathe, 0.810 for the pollen grains’ viability, 0.936 for the pollen grains’ length, and 0.992 for the pollen grains’ width. The length of the spathe stem had the most critical effect on how the ANN model predicted the values of the dependent variables, i.e., the number of strands per spathe, with a percentage of contribution of 17.66%; the weight of pollen grains per spathe, with 17.85%; the pollen grains’ length, with 19.78%, and the pollen grains’ width, with a percentage of contribution of 30.59%. Spathe weight had the most critical influence on strand length and pollen grains’ viability, with percentages of 26.29% and 14.92%, respectively. Leaf width had the most critical effect on the number of flowers per spathe, with a percentage of 12.55%. The elemental concentration of K in the male date palm leaves had the most critical effect on the number of flowers per strand, with a percentage of 13.98%. It was therefore concluded that using a modeling process with the ANN technique can help estimate the performance parameters of male date palms’ pollen grains for different purposes, such as providing a starting point for mathematical analyses associated with the physiological mechanisms of male date palm. Moreover, the outcomes of this research work can be supportive as a practical tool in this field of study.

Machine learning driven performance enhancement of perovskite solar cells with CNT as both hole transport layer and back contact

We propose a machine learning-based approach to enhance the performance of perovskite solar cells (PSCs) using carbon nanotubes as both hole transport layer (HTL) and back contact. Carbon-based PSCs offer low fabrication costs, long-term mechanical stability, high charge transport, and broad wavelength transparency. Our study estimates and enhances power conversion efficiency (PCE) of carbon-based PSCs through machine learning. PSC with single-walled carbon nanotube (SWCNT)-based HTL and back contact is investigated in simulations using SCAPS-1D and achieved 14.24% PCE, closely aligning with experimental data. Additionally, we explore the impact of various easily tunable fabrication parameters, such as the band gap and electron affinity of SWCNT in HTL, HTL thickness, active layer thickness, electron transport layer (ETL) thickness, HTL dopant concentration, absorber defect density, and ETL dopant concentration, on PCE. We generate a dataset of 20,480 samples to train classical and neural network-based machine learning models to predict performance parameters of PSCs, including short-circuit current density (JSC), open-circuit voltage (VOC), fill factor (FF), and PCE (η). The current density vs. voltage (J-V) characteristics curve is well-fitted by the predicted JSC, VOC, and FF, as evidenced by an average root mean squared error (RMSE) of 0.03 and a goodness of fit (R2) value of 0.99. The fully connected artificial neural network performs best, with RMSE of 0.05% and R2 of 0.99998 for predicting PCE. Genetic algorithm is used to optimize PCE, yielding a maximum PCE of 20.92%. Furthermore, an analysis of feature dimension and dataset size on prediction performance guides future modeling approaches.

Prediction of performance parameters of a hermetic reciprocating compressor under different discharge lift limiter heights by machine learning

Prediction of performance parameters of a hermetic reciprocating compressor under different discharge lift limiter heights by machine learning

Polymer design using machine learning: A quest for high glass transition temperature

This research introduces an advanced data-centric framework tailored for polymer design. Developing a machine learning (ML) model utilizing molecular descriptors, the study aims to forecast the glass transition temperature of polymers. Employing the Retrosynthetically Interesting Chemical Substructures (BRICS) technique, a set of 10,000 novel polymers is synthesized, followed by predicting their glass transition temperature (Tg) values using a pre-existing ML model trained for this purpose. Gradient Boosting and Extra Trees models outperform K Neighbors and Random Forest (KNN) models in predicting performance parameters with R2 scores of 0.99 and 0.99, and lower root mean squared error values of 24 and 32 K, respectively. The selection of polymers is done on the basis of glass transition temperature, those with higher glass transition temperature are selected. We evaluate the ease of synthetic accessibilities of the selected polymers alongwith their structural variations which indicates a diverse range among them. Through the successful identification and enhancement of novel polymers, these innovative methods greatly enhance the possibilities of discovering superior materials tailored for cutting-edge applications.

Performance predication of a solar assisted desiccant air conditioning system using radial basis function neural network: An integrated machine learning approach

In this Paper solar desiccant air conditioning system integrated with cross flow Maisotsenko cycle (M-cycle) indirect evaporative cooler is used to investigate the performance of whole system in different range of parameters. Solar evacuated tube electric heater is used to supply the regeneration temperature to the desiccant wheel, whereas, Desiccant Wheel (DW) and M-cycle is used to handle latent load and sensible load separately. Major contribution of this research is to predict system level performance parameters of a Solar Assisted Desiccant Air Conditioning (Sol-DAC) system using Radial Basis Function Neural Network (RBF-NN) under real transient experimental inlet conditions. Nine parameters are mainly considered as input parameters to train the RBF-NN model, which are, supply Air temperature at the process side of desiccant wheel, supply air humidity ratio at process side of the desiccant wheel, outlet temperature from the desiccant wheel at process side, outlet humidity ratio from the desiccant wheel at process side, regeneration temperature at regeneration side of the DW, outlet temperature from the heat recovery wheel at process side, outlet humidity ratio out from the Heat Recovery Wheel (HRW) at process side, temperature before heat recovery wheel regeneration side of the system, humidity ratio before heat recovery wheel regeneration side of the system. Four parameters are considered as the output of the RBF-NN model, namely: output temperature, output humidity, Cooling Capacity (CC), and Coefficient of Performance (COP). The results of the RBF-NN model shows that the best Mean Squared Error (MSE) and Regression coefficient (R) for outlet temperature prediction are 0.00998279 and 0.99832 when regeneration temperature is 70 °C and inlet humidity at 18 g/kg. Best MSE and R for predication of outlet humidity are 0.0102932 and 0.99485 when the regeneration temperature is 70 °C and inlet humidity at 16 g/kg. Best MSE and R for predication of COP are 0.0106691 and 0.9981 when the regeneration temperature is 70 °C and inlet humidity 12 g/kg. Best MSE and R for predication of CC are 0.0144943 and 0.99711 when the regeneration temperature is 70 °C and inlet humidity 14 g/kg. Experimental and predicted performance parameters were in close agreement and showed minimal deviation. Investigations of predicted results revealed that trained RBF-NN model was capable of predicting the trend of output result under the varying input condition.

Design of Modified Polymer Membranes Using Machine Learning.

Surface modification is an attractive strategy to adjust the properties of polymer membranes. Unfortunately, predictive structure-processing-property relationships between the modification strategies and membrane performance are often unknown. One possibility to tackle this challenge is the application of data-driven methods such as machine learning. In this study, we applied machine learning methods to data sets containing the performance parameters of modified membranes. The resulting machine learning models were used to predict performance parameters, such as the pure water permeability and the zeta potential of membranes modified with new substances. The predictions had low prediction errors, which allowed us to generalize them to similar membrane modifications and processing conditions. Additionally, machine learning methods were able to identify the impact of substance properties and process parameters on the resulting membrane properties. Our results demonstrate that small data sets, as they are common in materials science, can be used as training data for predictive machine learning models. Therefore, machine learning shows great potential as a tool to expedite the development of high-performance membranes while reducing the time and costs associated with the development process at the same time.

A machine learning approach for optimizing and accurate prediction of performance parameters for stacked nanosheet transistor

In this article, the possibilities of accurate prediction of wide range of parameters and optimizing the same through machine learning (ML) approach have been demonstrated for the multi stacked nanosheet transistor (NSFET). The machine is trained by the generated data of the tedious calibrated technology computer aided (TCAD) simulations. An innovative strategy is employed that combines ML with device simulations. Numerous devices are simulated with different geometric parameters like height, width, length and equivalent oxide thickness of the channel. The input, output, and CV characteristics are extrapolated from the simulation which is predicted by ML models. The DC, Analog and RF parameters are derived with a domain expertise approach. The device parameters are anticipated with actual values by the ML approach. Random forest regression, linear regression, polynomial regression and decision tree regression are employed for the prediction of the performance parameters. Random forest regression models provide significant R2 score with minimal error percentage. It indicates that TCAD-augmented ML can be considered an alternative to device simulation due to its reduction in computational cost. This work can also be treated as a benchmark for accurate prediction of the NSFET.

Prediction of performance parameters of a hermetic reciprocating compressor applying an artificial neural network

The efficiency of the compressor's thermodynamic, mechanical, and electric motors can be increased by minimizing their losses. Based on the design characteristics of the compressor, the current study proposes an artificial neural network (ANN) model to forecast the mass flow rate, cooling capacity, compression power, and discharge line loss of the hermetic reciprocating compressor as outputs. This study uses experimental and numerical results obtained by the fluid–structure interaction (FSI) method, and the whole findings are used as inputs for the machine learning method. Using 36 numerical and 3 experimental data sets, a multilayer network is created with input parameters defining mass flow rate, cooling capacity, compression power, and discharge line energy loss. The input parameters for the network model are the compressor's design parameters, which include the compressor speed, discharge valve thickness, and discharge valve length. When comparing the experimental and FSI methods for compressor speeds of 1300, 2100, and 3000 rpm, the FSI study's mass flow rate and cooling capacity parameters converged at 11.9%, 9.1%, and 9.3%, respectively, to their actual values. The convergence rate to experimental compression power data was −0.3%, 2.6%, and 11%. The numerical-experimental deviation for discharge line energy losses is 14.9%, −3.6%, and 7.8%, respectively, for 1300, 2100, and 3000 rpm. With the Levenberg–Marquardt (LM) ANN model, which is used in this study, the average squared error value is calculated as 1.75E-02 and the R2 value is calculated as 0.99976. ANN model can predict outputs with average deviation rates of less than 0.88%. It is seen that results obtained with the ANN method provide high convergence on the experimental and FSI results, while the results obtained with the regression model deviate from the compressor exhaust line results by more than ±20%, and the ANN method is more successful than regression.

NUMERICAL ANALYSIS AND DIFFUSER VANE SHAPE OPTIMIZATION OF A RADIAL COMPRESSOR WITH THE OPEN-SOURCE SOFTWARE SU2

In recent years, the usage of open-source computational fluid dynamics tools is on a rise both in industry and academia. SU2 is one of these open-source tools. Unlike other open-source alternatives, SU2 is equipped with boundary condition types, solvers and methods that are especially developed for the analysis and design of turbomachinery. The aim of this work is to explore and investigate the capabilities of SU2 in the prediction of performance parameters of radial compressors. Two different single stage shrouded compressor geometries, one with a vaneless diffuser and the other with a vaned diffuser have been investigated with steady state CFD. The compressors were designed by MAN Energy Solutions Schweiz AG. Computational results with SU2 showed a satisfactory agreement with both the experimental data and reference CFD solutions obtained with Fidelity Flow, which is formerly known as Numeca Fine TURBO. Only at the relatively higher mass flow rates the difference between references and SU2 were higher compared to other operating points. After performance parameters were successfully calculated with SU2, the optimization tools that come with SU2 were also used. A 2D adjoint optimization study on the vane of the vaned diffuser was carried out. The study was carried out at a single operating point that is close to choke conditions. The loss generated by the large separated flow region at the suction side of the diffuser vane was reduced by 0.55 % in the optimized geometry using minimal modifications on the existing vane geometry to keep the performance of the compressor intact at other operating points. However, the resulting modification increased the total pressure loss by 0.86 % at one of the design operating points. This performance penalty could be due to the discontinuity in the vane geometry generated by the optimizer. Overall, the study shows that SU2 has the basic numerical schemes and models that are required for the analysis of radial turbomachinery flows and geometry optimization.

Aerodynamic characteristics simulation of helicopter rotor in hover using HeliX software

In order to predict hover performance of a new helicopter rotor, the aerodynamic performance and flow field of the rotor in hover is calculated by using HeliX software. The comparison between numerical results and wind tunnel test data of S-76 rotor as a validation case of CFD method demonstrate that HeliX software has the capability to consistently and reliably predict performance parameters for a helicopter rotor. The numerical results of the new rotor in hover suggest that the maximum hover efficiency of the new rotor is about 73% with collective pitch angle of 9.2°, while the shock induced separation and the reattachment phenomenon begin to appear on the upper surface near the blade tip. As the increase of collective pitch angle from 9.2° to 11.2°, hover efficiency decreases significantly due to the serious flow separation near the blade tip. The research in this paper can provide reference and guidance for aerodynamic optimization design of helicopter rotor.

Prediction of centrifugal pump performance parameters based on a multilayer hybrid model

The present paper presents a multilayer hybrid model for sensorless measurement of pump operating status, with the objective of enabling safe and stable operations while reducing energy losses. The model takes easily measurable variables such as rotational frequency and valve opening as input features to predict the performance parameters of the centrifugal pump. By integrating just-in-time learning (JITL) with Gaussian process regression (GPR) and leveraging the unique probability features of GPR, a just-in-time-learning GPR (JGPR) is developed to extract valuable feature information. The JGPR sequentially predicts the flow rate, coefficient for dynamic head, and shaft power. The predicted values are extended to other input features, which can accurately capture the characteristics of the centrifugal pump and effectively replace the process of acquiring process parameters through sensors. Finally, the mechanism model is integrated into the multilayer JGPR model to calculate the performance parameters of centrifugal pump. The validation results indicate a strong agreement between predicted and experimental results, with predicted meeting performance parameters all engineering requirements. Compared to a single model, the multilayer hybrid model significantly improves the reliability of predictions, demonstrating the feasibility of using this approach to predict performance parameters. This research provides valuable insights into the measurement of sensorless pump operating states, enabling safe and efficient operation in complex conditions.

Dynamic Bayesian network based prediction of performance parameters in cloud computing

Dynamic Bayesian network based prediction of performance parameters in cloud computing

Dynamic Bayesian network based prediction of performance parameters in cloud computing

Dynamic Bayesian network based prediction of performance parameters in cloud computing

Experimental Analysis of Different Refrigerants’ Thermal Behavior and Predicting Their Performance Parameters

This study experimentally compares thermodynamic performance of R290 and R404A refrigerants in a refrigeration system. In the first part of the paper, energy analysis of the refrigeration system was performed at various evaporator and condenser temperatures. Results revealed that R404A refrigerant caused an 18.6% increase in compressor power consumption. The highest coefficient of performance values in the system for R290 and R404A were 3.99 and 3.21, respectively. The second part of the paper includes artificial intelligence prediction studies. The pace and elastic net regression models were used to predict performance parameters. A single equation that can predict the cooling capacity and compressor power consumption of R290 and R404A simultaneously was derived. For the cooling capacity, pace regression showed mean absolute error of 0.0252 and root-mean-squared error of 0.0334, while elastic net regression indicated mean absolute error of 0.1103 and root-mean-squared error of 0.1262. It was concluded that R290 had better thermodynamic performance than R404A and the equations obtained with artificial intelligence were applicable to predict the experimental findings, regardless of which refrigerant gas was used.

Experimental investigation and theoretical prediction of the multistage reciprocating evaporative cooler

Present work comprises the fabrication of a multistage reciprocating humidifier where four Celdek 7090 packings are arranged at different positions run by a motor connected to a cam follower. Packing dips in the water and comes in contact with air in cross flow direction in a duct. Air velocities and camshaft speeds are varied to evaluate the performance parameters such as outlet dry bulb temperature, outlet specific humidity, evaporation rate, coefficient of performance, cooling effect and humidification efficiency. A theoretical model is constructed to predict the performance of the multistage dynamic humidifier. Stage-wise performance is also determined to analyse the influence of number of stages on the cooler performance. The system gave a maximum evaporation rate, energy conversion factor, humidification efficiency equal to 1.864 g/s, 4.84, and 72.62 %. The predicted performance parameters for various conditions are found to be in agreement with the experimental results. Experimental and empirical results are close to each other with a maximum deviation of 2.1 %, 2.4 %, 8.3 % and 8.5 % for exit air temperature, exit humidity ratio, saturation efficiency and energy conversion factor respectively for various tested air flow rates. Stage wise performance revealed that higher number of stages will drastically deteriorate the performance and simultaneously increase the energy consumption.

Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery

The performance and reliability of turbomachinery directly affect the efficiency and safety of energy conversion systems. A dual graph neural network (DGNN) for turbomachinery flow field reconstruction and performance prediction is proposed, which utilizes flow field data at grid vertices and characterizes the neighborhood relationships of grids through the adjacency matrix. Different from previous work, this work extends deep learning methods to the reconstruction of global turbomachinery fields defined on arbitrary structured/unstructured grids. The flow field reconstruction and performance prediction of low aspect ratio rotors are used to verify the generalizability of DGNN. The proposed method can not only accurately predict performance parameters, but also achieve excellent performance in flow field reconstruction. The superiority of DGNN over the artificial neural network (ANN) in flow field reconstruction is clarified. The optimal GNN operator with the highest accuracy and lowest computation costs is obtained as SAGE. A well-trained DGNN can give the flow field distribution as well as the performance of turbines within 0.05 s. The proposed approach could be a real-time simulation and analysis approach to assist turbomachinery design and optimization. It may provide an efficient solution for establishing the digital twin system of turbomachinery in the future.

A Numerical Model for the Prediction of Performance Parameters and Hydraulic Losses in Centrifugal Pump Systems such as Turbine

The prediction of the energy performance of centrifugal pump systems such as turbines is one of the main needs to evaluate the viability of projects focused on energy recovery in high pressure flows rate. Due to the above, in this investigation, the construction of a numerical model has been carried out for the prediction of the performance parameters in pump systems such as turbines under different operating conditions. The analysis of the performance parameters includes the head, the generated power, and the efficiency. Additionally, the numerical model developed takes into consideration the hydraulic losses present in the impeller, the inlet pipe, and the pump volute. From the results obtained, it has been demonstrated that the proposed numerical model has the ability to predict the performance parameters of the pump as a turbine. It has been possible to obtain an average relative error of 2.82% and 3.52% in the prediction of the head and the efficiency of the PAT system. The analysis of the efficiency of the pump as a turbine has showed that this type of system could reach an efficiency of 84.7%. It has been evidenced that the rotation speed reduction causes a greater restriction in the efficient operating areas of the PAT system. In general, the proposed numerical model is an alternative tool to evaluate the behavior of centrifugal pumps as a turbine under different operating conditions.

Research output software for energetic materials based on observational modelling 2.1 (RoseBoom2.1©)

With the development of the program RoseBoom©, the development of new modern energetic materials will be advanced, since it aims to provide access to quick and easy prediction of performance parameters before they have been synthesized.