Average Absolute Deviation Percent Research Articles

Theoretical prediction of molar entropy of modified shifted Morse potential for gaseous molecules

The modified shifted Morse potential is a molecular potential that describes the molecular vibration and atomic interaction. This potential has not received much report as one of the useful potential function. In this study, the molar entropy of a modified shifted Morse is obtained via the vibrational partition function, rotational partition function and translational partition function. Numerical results are obtained for iodine molecule (I2), silicon carbide (SiC), carbon phosphide (CP) and fluorine molecule (F2) for temperatures of 200 K to 6000 K. The predicted results are compared with the experimental results obtained from the National Institute of Standards and Technology (NIST) data base. The predicted values aligned with the experimental results for the four molecules. The average absolute percentage deviation obtained for each molecule is less than one, which shows a good representation of the modified shifted Morse potential for thermodynamic functions.

Density and viscosity measurement of R513A and a modified residual entropy scaling model for predicting the viscosity of HFC/HFO refrigerants

Liquid density and viscosity of alternative refrigerants R513A were measured with a vibrating-wire viscosimeter in the temperature of 253 K to 363 K when pressure changes from 1 MPa to 12 MPa. The combined expended uncertainties with a confidence level of 0.95 (k = 2) of density and viscosity are 0.2 % and 2 %, respectively. In addition, a viscosity model of HFCs (fluorocarbons)/, HFOs (hydro-fluoroolefins) and their mixtures was provided by coupling the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state and a modified residual entropy scaling (RES) method. Obvious quasi-linear relation between the proposed entropy variable and the modified residual viscosity has been confirmed. The overall average absolute percentage deviations between the calculated results with the proposed RES model and the literature experimental data of pure and mixture refrigerants are 2.63 % and 2.05, respectively.

Generalized Analytical Well-Test Solutions for Vertically Fractured Wells in Commingled Reservoirs

Abstract Layered zones with vertical fracture are often found in analyzing well-test data of deep/ultra-deep reservoirs and tight reservoirs. Analyzing and modeling the well-test data in a computation-accurate and easy-program manner have been a challenge for these problems due to the lack of suitable solutions. This work thus presents the generalized analytical well-test solutions for vertically fractured wells in infinite and bounded commingled reservoirs with computation accuracy and functional simplicity. These solutions are derived based on the early-time approximate solution of the infinite/finite-conductivity fracture model, Laplace and Fourier cosine transformation, pressure superposition principle, and Duhamel principle. Subsequently, model validation is carried out by comparing the pressure and derivative results with those of commercial saphir software. The results show that the average absolute percent deviation between the presented analytical solutions and saphir for three kinds of outer boundaries is ∼2% for pressure results and ∼4% for pressure derivative results. Finally, a field case in Xinjiang oilfield is interpreted, indicating that the proposed analytical well-test solutions are feasible to interpret the parameters of commingled reservoirs.

Repeatability and reproducibility of quantitative OCT angiography measurements from table-top and portable Flex Spectralis devices.

The recent development of a portable investigational handheld OCT-angiography (OCTA) device has allowed for expansion of imaging into the operating room (OR) in addition to standard in-clinic imaging. The aim of this study was to assess intravisit repeatability and intervisit reproducibility of retinal microvasculature measures and central retinal thickness for in-clinic table-top and portable OR compatible OCTA devices. Repeated 10 × 10 OCTA images were acquired in 20 healthy adult participants on two separate visit days using Spectralis spectral-domain OCTA table-top and investigational armature suspended Flex systems. Intravisit and intervisit intraclass correlation coefficients and average absolute percent difference were calculated for quantitative microvasculature measures and CRT. 120 OCTA images were acquired from 20 subjects (n = 20, mean age 26.7 ± 1.61years, range 24-30years) with both devices across two separate imaging days. FAZ and CRT measurements had near complete intravisit and intervisit agreement with ICCs between .97 and 1 for both table-top (FAZ ICC .97, .97; CRT ICC .98-1, .98-.99) and Flex (FAZ ICC .97, .95; CRT ICC .99-1, .98-.99) devices. Vessel density measures demonstrated greater variance with only fair to strong agreement (ICC .32-.75) and average absolute percent differences ranging from 2.96 to 6.63%. FAZ and CRT measures for both devices demonstrated high repeatability and reproducibility; retinal vessel density measures demonstrated less. Differences of less than 7% for retinal microvasculature measurements across time and devices are most likely attributable to expectable variance between repeat scans.

A new Frontier in electric load forecasting: The LSV/MOPA model optimized by modified orca predation algorithm

Electric load forecasting is a vital task for energy management and policy-making. However, it is also a challenging problem due to the complex and dynamic nature of electric load data. In this paper, a novel technique, called LSV/MOPA, has been proposed for electric load forecasting. The technique is a hybrid model that combines the advantages of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), two powerful artificial intelligence algorithms. The hybrid model is further optimized by a newly Modified Orca Predation Algorithm (MOPA), which enhances the forecasting accuracy and efficiency. The LSV/MOPA model has been applied to historical electric load data from South Korea, covering four regions and 20 years. The LSV/MOPA model has been compared with other state-of-the-art forecasting techniques, including SVR/FFA, LSTM/BO, LSTM-SVR, and CNN-LSTM. The results show that the LSV/MOPA model with minimum average mean absolute percentage deviation error, including 365 in northern region, 12.8 in southern region, 8.6 in central region, and 30.8 in eastern region, provides the best fitting and outperforms the other techniques in terms of the Mean Absolute Percentage Deviation (MAPD) index, achieving lower values for all regions and years. The LSV/MOPA model also exhibits faster convergence and better generalization than the other techniques. This study demonstrates the effectiveness and superiority of the LSV/MOPA model for electric load forecasting and suggests its potential applications in other sectors where accurate forecasting is crucial.

Analytical optimization of open hole effects on the tensile properties of SS400 sheet specimens using an integrated FFD-CRITIC-DFA method

Structural components are generally composed of material discontinuities, including open holes, which are considered stress concentrators in engineering components. In view of this, assessing the influence of open holes on the tensile properties is crucial to determine the sensitivity and tensile strength of a particular material. Nevertheless, investigation of the impact of open holes on the tensile properties of SS400 steel sheets is very limited and yet to be explored. Therefore, this study was performed to optimize the effects of open holes on the tensile properties of SS400 sheet specimens based on a Full Factorial Design (FFD) experiment. Four input parameters that represent various hole configurations, which include the hole diameter, location of the hole, number of holes, and hole shape, were considered in this study to develop the experimental-based prediction models to optimize the output performance, namely yield strength, ultimate tensile strength, and ultimate elongation, commonly denoted as YS, UTS, and UE respectively. A total of 10 additional experimental trials were then utilized to verify the constructed models. In addition, the weight fractions for YS, UTS, and UE were identified using the Criteria Importance Through Inter-Criteria Correlation (CRITIC) method. Subsequently, the Desirability Function Analysis (DFA) is utilized to pinpoint the optimal parameter conditions for maximizing the tensile properties. Based on the results, all four parameters showed significant effects on the response variables, except the number of holes for UTS and hole location for UE. The diameter also recorded the highest contribution toward UTS and UE, followed by the hole shape. Regarding YS, hole diameter takes precedence, with the number of holes as the second most influential factor. Furthermore, the average absolute percent deviation for the prediction responses of 10 experimental cases were 1.06 %, 0.90 %, and 0.85 % for YS, UTS, and UE, respectively, confirming the validity of the constructed models. Meanwhile, the CRITIC method estimated the weight fractions for YS, UTS and UE from the experimental data, which were 0.3825, 0.2559, and 0.3616, respectively. The DFA-derived composite desirability, rated at 0.9820, suggests optimal conditions: a 1 mm hole diameter, centered hole location, three holes, and a hexagonal shape. The minimal deviations between predicted and experimental values affirm the robustness of the models. Overall, this investigation yields important insights for optimizing open holes and elevating the tensile performance of SS400 sheet specimens.

Generalized Analytical Solutions of Vertically Fractured Wells in Commingled Reservoirs: Field Case Study

Summary Accurate identification of the individual-layer parameters for vertically fractured wells in commingled reservoirs is essential for development plan design, reservoir numerical simulation, and stimulation measure selection. Different semi-analytical and numerical models are generally applied in multilayer transient testing (MLT) analysis to determine the properties of individual layer. However, these approaches require numerous computations and are complicated to program due to the fracture and reservoir discretization. This work thus presents the generalized analytical solutions of vertically fractured wells in infinite, closed, or constant-pressure commingled reservoirs with both computational and functional simplicity. The fully analytical solutions are derived based on the early-time approximate solutions of infinite-conductivity fracture and trilinear flow models, infinite-conductivity fracture solutions, pressure superposition principle, and Duhamel principle. A systematic verification by employing a standardized well testing software and trilinear flow model is conducted to ensure the general application accuracy of the presented solutions. The results show that the developed analytical solutions are valid when the dimensionless fracture conductivity is more than 2 (FcD > 2) with an average absolute percent deviation (AAD) of ~2% for pressure and that is ~4% for pressure derivative. The developed analytical solutions also exhibit improvements in early-time pressure and derivative calculation. Finally, a field case of a four-layer fractured well is interpreted by the developed solutions and well testing software to illustrate the feasibility. The interpretation results of two methods are nearly identical, with only a minor difference. The developed analytical solutions are computationally accurate while maintaining functional simplicity and can be considered as an alternative to the current semi-analytical and numerical approaches in MLT analysis.

Bound-state energy spectrum and thermochemical functions of the deformed Schiöberg oscillator

In this study, a diatomic molecule interacting potential such as the deformed Schiöberg oscillator (DSO) have been applied to diatomic systems. By solving the Schrödinger equation with the DSO, analytical equations for energy eigenvalues, molar entropy, molar enthalpy, molar Gibbs free energy and constant pressure molar heat capacity are obtained. The obtained equations were used to analyze the physical properties of diatomic molecules. With the aid of the DSO, the percentage average absolute deviation (PAAD) of computed data from the experimental data of the 7Li2 (2 3Πg), NaBr (X 1Σ+), KBr (X 1Σ+) and KRb (B 1Π) molecules are 1.3319%, 0.2108%, 0.2359% and 0.8841%, respectively. The PAAD values obtained by employing the equations of molar entropy, scaled molar enthalpy, scaled molar Gibbs free energy and isobaric molar heat capacity are 1.2919%, 1.5639%, 1.5957% and 2.4041%, respectively, from the experimental data of the KBr (X 1Σ+) molecule. The results for the potential energies, bound-state energy spectra, and thermodynamic functions are in good agreement with the literature on diatomic molecules.

Modeling of multiphase heat and mass transport and drying behavior of potash particles in a packed bed: Experiments and simulation

The drying behavior of potash particles was experimentally investigated in a convective packed bed dryer under different operating conditions, including inlet gas temperature of 40 °C, 50 °C, and 60 °C, moisture content of 2.8 wt%, and superficial gas velocity of 0.3 m/s. When the temperature increased from 40 °C to 50 °C and then to 60 °C, the drying time decreased from 930 s to 870 s and then to 720 s, respectively. The thermal conductivity and specific heat capacity of potash particles as a function of moisture content and porosity of the packed bed was determined at a 95 % confidence interval to have R2 values of 0.94 and 0.91, respectively. During drying of potash particles, solid bridges form, and the whole bed behaves like a porous medium with void spaces that expand. In this work, a conjugate heat and mass transfer model was developed for batch packed bed drying of wet potash particles. Computational fluid dynamics was used to simulate the momentum, mass, and energy transport in a porous medium based on the physical properties of potash particles. The model successfully correlated the experimental particle moisture content versus time with average absolute deviation percentage values of 14.8, 3.2, and 14.7 for the inlet gas temperatures of 40 °C, 50 °C, and 60 °C, respectively.

Prediction of Electron Beam Welding Penetration Depth Using Machine Learning-Enhanced Computational Fluid Dynamics Modelling

The necessity for precise prediction of penetration depth in the context of electron beam welding (EBW) cannot be overstated. Traditional statistical methodologies, including regression analysis and neural networks, often necessitate a considerable investment of both time and financial resources to produce results that meet acceptable standards. To address these challenges, this study introduces a novel approach for predicting EBW penetration depth that synergistically combines computational fluid dynamics (CFD) modelling with artificial neural networks (ANN). The CFD modelling technique was proven to be highly effective, yielding predictions with an average absolute percentage deviation of around 8%. This level of accuracy is consistent across a linear electron beam (EB) power range spanning from 86 J/mm to 324 J/mm. One of the most compelling advantages of this integrated approach is its efficiency. By leveraging the capabilities of CFD and ANN, the need for extensive and costly preliminary testing is effectively eliminated, thereby reducing both the time and financial outlay typically associated with such predictive modelling. Furthermore, the versatility of this approach is demonstrated by its adaptability to other types of EB machines, made possible through the application of the beam characterisation method outlined in the research. With the implementation of the models introduced in this study, practitioners can exert effective control over the quality of EBW welds. This is achieved by fine-tuning key variables, including but not limited to the beam power, beam radius, and the speed of travel during the welding process.

Using the method of self-similar functions for estimating the normal boiling temperatures of homologous series

An equation for estimating the normal boiling points of homologous series with the general formula R1(CH2)nR2 has been developed. The equation is based on the hypothesis of functional self-similarity. The parameters of the equation have been determined using a current experimental database for 10 homologous series. The equation allows predicting the normal boiling temperature of any member of any homologous series of the type R1(CH2)nR2 if the normal boiling temperature is known for one compound which belongs to this series. The proposed method gives an absolute average percentage deviation from the experimental values of the normal boiling point of 0.72 % for all the homologous series studied. A thermodynamically consistent model for estimating the critical temperature, critical pressure, and normal boiling point of n-alkanes has been developed.

3D Brain Tumour Segmentation Using UNet with Quantitative Analysis of the Tumour Features

Brain tumours are abnormal cells that grow within the brain and may cause severe harm to an individual. The diagnosis of tumours often requires a manual review of Magnetic Resonance (MR) images by a skilled radiologist, which is a laborious task. The study aims to use a deep learning-based approach to segment the brain tumour and quantitatively evaluate the performance of the model by comparing the statistical distribution of five textural and four non-textural feature differences between the ground truth and the prediction. Textural features were extracted from a Grey Level Co-occurrence Matrix (GLCM) and averaged over four different directions. A UNet model was constructed with MONAI using BraTS 2020 dataset. Due to hardware constraints, the model was trained over a short epoch range of 100 but managed to achieve a mean Dice similarity coefficient (DSC) of 0.86. The quantitative analysis showed that the ground truth and the prediction had low differences in most features, except for percentage variation in signal intensity, energy, correlation and Hausdorff distance, which could be attributed to false positive voxels that are far from the ground truth. The textural properties of homogeneity, entropy, and correlation had an average absolute percentage difference of less than 5% from the ground truth.

Performance prediction of a supercritical CO2 Brayton cycle integrated with wind farm-based molten salt energy storage: Artificial intelligence (AI) approach

Using energy storage technologies in combination with renewable energy sources can improve power generation efficiency and reliability. Here, we predict the performance of a wind farm-based molten salt energy storage system combined with a supercritical CO2 Brayton cycle (s-CO2BC) with the aid of artificial intelligence (AI). For this purpose, the Grasshopper Optimization Algorithm (GOA) and Particle Swarm Optimization (PSO) are applied to train a Multilayer Perceptron Artificial Neural Network (MLP-ANN). The proposed AI approaches model the complex interactions between the wind farm, the molten salt energy storage, and the s-CO2BC components. Integrated system simulations under various operating conditions are used for training the performance prediction model. Power generation, energy storage, and system efficiency can all be accurately predicted by a trained AI model. In addition to providing valuable insight into the optimal operation and control strategies of the integrated system, the proposed approach enables the efficient utilization of renewable energy resources and energy storage to generate sustainable power. This study contributes to the development of AI-based optimization approaches and predicts the performance of integrated renewable energy systems, resulting in improved grid integration and stability. According to the results, the PSO method yields average absolute percentage deviations (AAPDs) between 0.47% and 0.51% across different parameters, while GOA yields AAPDs between 0.05% and 0.27%.

A new setup for speed of sound measurements and experimental data for nanofluids Al2O3 or Ag with water or ethylene glycol

The speed of sound of four different nanofluids consisting of Ag or Al2O3 nanoparticles and water or ethylene glycol base fluid is measured in the temperature range (298.15–343.15) K and at atmospheric pressure. One aim of this analysis is to learn whether the change from pure base fluid speed of sound to the nanofluid situation is a smooth or disruptive transition when adding nanoparticles. The measurements were conducted on the newly proposed apparatus consisting mainly of function generator, ultrasonic sensor, switch, voltage source, oscilloscope, ultrasonic container and the necessary sensors and equipment for data acquisition. The values for speed of sound were calculated based on the Time of Flight method. Deionised water and ethanol were used to calibrate the apparatus which is additionally tested with pure base fluids water and ethylene glycol and showed excellent agreement with average absolute percentage deviations between experimental and literature data for speed of sound of 0.12% and 0.36%, respectively,. Beside the temperature influence, the influence of the nanoparticle concentration on the speed of sound of nanofluid were investigated and followed. The analysis of the sound velocity allows insight into the molecular level interactions taking place between the nanoparticles and the base fluid molecules of the nanofluid. The decrease in velocity values with the increase of concentration, noticeable in the tested nanofluids, is a result of decrease in nanoparticle-fluid interaction and dominance of particle–particle interaction.

Wet fluidization characterization of potash particles in a pulsation-assisted fluidized bed

In order to improve fluidization behavior, pulsed airflow was employed to break agglomeration and eliminate channeling in a fluidized bed with wet potash particles. The effects of pulsation frequency, pulsed air to continuous flow ratio (r), and inlet relative humidity of the air on bubbling behavior were investigated via an electrical capacitance tomography system. A frequency of 1.0 Hz and an r of 0.33 with 0.1 s opening time were found to be the optimum condition, leading to the lowest minimum fluidization velocity (umf) and generation of homogeneous bubbles in size and shape. A new theoretical model was developed to predict the umf of wet particles in a fluidized bed by considering both the liquid bridge force and resonant force resulting from the pulsation. Average absolute deviation percentage values between the predicted umf and the experimental data were 13.8 and 20.7 for the dry and wet particles, respectively, indicating good agreement.

Demand Side Control for Energy Saving in Renewable Energy Resources Using Deep Learning Optimization

Convolutional neural networks a type of deep learning technology, are used for forecasting future power usage. The mean absolute error, mean square error, root mean square error, and mean constant percentage error are used to evaluate the performance of the models. These metrics are used to rank the models. Among the models tested, the CNN stack demonstrates the highest precision in estimating energy consumption and solar power output, with a mean absolute error of 0.015% points, a root mean square error of 0.23% points, and an average absolute percentage deviation of 1.71% points. However, for wind turbine (WT) energy generation, the recurrent neural network proves for most accurate model, achieving 0.070 as the median absolute percentage error is 2.65, Both the root of the mean square error and the average absolute error are 0.38. The training and validation data utilized in this study comprise the International Renewable Energy Agency’s data on solar power production, WT power generation, and the “ensue” dataset, which includes hourly power consumption information from the Pennsylvania, New Jersey, and Maryland interconnection.

Equilibrium absorption of CO2 in aqueous solution of N-dimethylamino ethanol and 2-(ethylamino)ethanol, measuring and thermodynamic modeling

The equilibrium carbon dioxide solubility in aqueous solutions of N-dimethylaminoethanol (DMAE) 40 wt% + 2-(ethylamino) ethanol (EAE) 5 wt% and DMAE 30 wt% + EAE 5 wt% was measured at various temperatures (313.15, 328.15, 343.15, 358.15 K) in the range of pressures from 6.5 to 236 kPa. The enthalpy of absorption of carbon dioxide was measured for the mixture of DMAE (40 wt%) + EAE (5 wt%), at 328.15 K. The Deshmukh-Mather model was used to correlate the equilibrium solubility of carbon dioxide data, and the model parameters were optimized. The optimized model could correlate the vapor-liquid equilibrium with the 9.4 average absolute percentage deviation (AAD%), and the obtained AAD% for enthalpy of absorption in aqueous solution of DMAE (40 wt%) + EAE (5 wt%) was 24.0.

Measurement of gas-liquid flows with the combination of thermal sensors and conductance sensor

Gas-liquid two-phase flow is a typical phenomenon in energy and industrial production. Thus, flow measurement of gas-liquid flow is vital in the exploration and extraction of oil and natural gas. The thermal sensors are based on convection heat transfer between heating elements and fluid. These sensors have been widely used in flow measurement because of their high precision, and low-pressure loss, and it is not affected by fluid conductivity and viscosity. This study utilized the thermal conductivity difference in liquid and gas to design a measurement system that combines thermal sensors and a conductance sensor. Among them, the thermal sensors contain 2 upstream and downstream thermal resistors and a total of 28 thermocouples distributed over 7 sections (4 for each section) to capture axial and sectional thermal information. A rotating electric field conductance sensor (REFCS) serves to accurately determine gas holdups. The model tests show that the separated flow models have better accuracy than the liquid-phase acceleration models and pressure drop models. Inspired by the classical separated flow models, an improved heat transfer model based on flow patterns is developed by adding an additional superficial velocity term. Based on the mechanism that the heat transfer is dominated by the liquid phase, the superficial velocities are measured in combination with the drift-flux model. Overall, the average absolute percentage deviations (AAPD) for both the heat transfer coefficients and superficial velocities are less than 5%, indicating that the improved model has excellent performance and robustness for flow prediction in 20 mm pipes.

A precise estimation for vibrational energies of diatomic molecules using the improved Rosen–Morse potential

In the context of the generalized fractional derivative, novel solutions to the D-dimensional Schrödinger equation are investigated via the improved Rosen-Morse potential (IRMP). By applying the Pekeris-type approximation to the centrifugal term, the generalized fractional Nikiforov-Uvarov method has been used to derive the analytical formulations of the energy eigenvalues and wave functions in terms of the fractional parameters in D-dimensions. The resulting solutions are employed for a variety of diatomic molecules (DMs), which have numerous uses in many fields of physics. With the use of molecular parameters, the IRMP is utilized to reproduce potential energy curves for numerous DMs. The pure vibrational energy spectra for several DMs are determined using both the fractional and the ordinary forms to demonstrate the effectiveness of the method utilized in this work. As compared to earlier investigations, it has been found that our estimated vibrational energies correspond with the observed Rydberg-Klein-Rees (RKR) data much more closely. Moreover, it is observed that the vibrational energy spectra of different DMs computed in the existence of fractional parameters are superior to those computed in the ordinary case for fitting the observed RKR data. Thus, it may be inferred that fractional order significantly affects the vibrational energy levels of DMs. Both the mean absolute percentage deviation (MAPD) and average absolute deviation (AAD) are evaluated as the goodness of fit indicators. According to the estimated AAD and MAPD outcomes, the IRMP is an appropriate model for simulating the RKR data for all of the DMs under investigation.

Liquid densities of n-octane, ethylcyclohexane and their binary mixtures {(x) n-octane) + (1 − x) ethylcyclohexane} at temperatures from (283 to 363) K and pressures up to 60 MPa

In this work, liquid densities of n-octane, ethylcyclohexane and their binary mixtures {(x) n-octane) + (1 − x) ethylcyclohexane} with mole fraction x= (0.200, 0.400, 0.600, 0.800) are reported. The density measurements were performed with a high-pressure vibrating-tube densimeter at temperatures from (283 to 363) K and pressures up to 60 MPa. The combined expanded uncertainties (0.95 level of confidence, k = 2) are estimated to be 16 mK for temperature, 0.062 MPa for pressure, and up to 0.6 kg/m3 for density. The experimental densities were correlated using the modified Tait equation. The average absolute percentage deviations of measurement results from the modified Tait equation correlations of n-octane, ethylcyclohexane and their binary mixtures {(x) n-octane) + (1 − x) ethylcyclohexane} are all lower than 0.1%. The isothermal compressibility and isobaric expansivity have also been derived from the experimental densities.