Semi-empirical Prediction Model Research Articles

Experiment and prediction model study on pool boiling heat transfer of water in the electric field with periodically changing direction

The pool boiling heat transfer performance of water could be greatly improved by the constant electric field with the bar electrode. While, the heat transfer of water enhanced by electric field with periodically changing direction is rarely involved. Therefore, the heat transfer performance of water, mechanism analysis and prediction model for heat transfer characteristic affected by the electric field with periodically changing direction were studied. Individually, the heat transfer coefficient (HTC) and critical heat flux (CHF) were compared. Meanwhile, the difference of droplet shape, size of contact angle, bubbles growth and detachment on heat transfer surface were analyzed. Besides, semi-empirical prediction models for HTC and CHF were proposed. Results show that the HTC and CHF are significantly enhanced with decrease of switch period. While, this enhancement is diminished with increase of heat flux. Furthermore, when compared to the constant direction electric field, mechanism analysis indicates that the electric field with periodically changing direction not only enhances the rate of boiling fluid wetting the boiling heat transfer surface, but also accelerates the growth and detachment of bubbles. Moreover, predicted curves of model agree well with the experimental data, and variation of surface tension is considered as the main factor affecting heat transfer.

Strength gain monitoring and construction quality evaluation on non-dispersible underwater concrete using PZT sensors

Owing to the undrained construction, not only the admixtures but also the hardening process is believed to have a significant effect on the final strength of non-dispersible underwater concrete (UWC). To ensure the stability and workability of UWC, this paper defined the hydration stages of UWC by conducting an early-age hydration monitoring experiment based on PZT (Piezoceramic Lead-Zirconate-Titanate) sensors and an isothermal calorimetry experiment. Additionally, a 90-day strength gain monitoring experiment and a compressive strength experiment were also carried out to establish a semi-empirical strength prediction model, which can be utilized to assess the real strength of UWC by substituting the monitoring data from practical structures. With practical application in a corroded bridge pier strengthened by prefabricated Basalt FRP (BFRP) shell and UWC, the strength prediction model presents a high accuracy on the prediction of hardening strength of UWC, and all the indices of the practical bridge pier strengthened by BFRP and UWC meet with the design codes. This indicates that the strength monitoring approach and construction quality evaluation method can apply to practical underwater structures.

Comparison of fatigue life and flexibility between aluminum-composite and aluminum–aluminum bolted joints

Bolted joints are potential fatigue critical points in lightweight aircraft structure. This paper presents a comparative study of fatigue behavior and flexibility of double-shear, aluminum–aluminum and aluminum-composite joint specimens in variable-amplitude loading. Similar failure mode and fatigue life of both specimen types were obtained by experiments and no indication of pretension loss was observed. A semi-empirical fatigue life prediction model was presented in correlation to the comparisons. Extensometer measurements showed decreasing flexibility, which was explained by increasing load transfer by friction and substantiated by comparative finite element analyses. The flexibility was significantly lower in aluminum–aluminum specimens.

Development of a Semi-Empirical Model for Droplet Size Determination of a Three-Channel Spray Nozzle for Pellet Coating Based on the Optical Method Concept

The purpose of this study was to investigate the droplet size obtained with a three-channel spray nozzle typically used in fluid bed devices and to construct a semi-empirical model for prediction of droplet size. With the aid of a custom-made optical method concept, the impact of the type of polymer and solvents used through dispersion properties (viscosity, density, and surface tension), dispersion flow rate, atomization pressure, and microclimate pressure on droplet size was investigated. A semi-empirical model with adequate predictability for calculating the average droplet size (R2 = 0.90, Q2 = 0.73) and its distribution (R2 = 0.84, Q2 = 0.61) was constructed by employing dimensional analysis and design of experiments. Newtonian and non-Newtonian dispersion and process parameters on laboratory and on production scale were included, thereby enabling constant droplet size irrespective of the scale. Based on the model results, it would be possible to scale-up the atomization process (e.g., coating process) from laboratory to production scale in a systematic fashion, regardless of the type of solvent or polymer used. For the system investigated, this can be performed by understanding the dispersion properties, such as viscosity, density, and surface tension, as well as the following process parameters: dispersion flow rate, atomization, and microclimate pressure.

A semi-empirical approach to evaluate the effect of constituent materials on mechanical strengths of GFRP mortar pipes

Major constituents of glass fibre-reinforced polymer (GFRP) mortar pipes are high strength continuous and chopped fibres made up of E-CR (Electrical/chemical resistance) glass and particulate fillers in the form of silica sand, these are embedded in the thermoset matrix (vinyl ester resin). The main objective of current research is to better understand the individual and consolidated effect of constituent materials on mechanical strengths: axial and hoop tensile strengths of glass fibre reinforced polymer mortar pipes. A comprehensive experimental program (685 tests across 48 pipe categories) is completed towards evaluation of the axial and hoop tensile strengths, and composition of constituent materials using loss on ignition tests. The consolidated effects of constituent materials on mechanical strengths of GFRP mortar pipes are studied using a semi-empirical approach. This approach involved aligning the axial tensile (σat) and hoop tensile (σht) strength as a function of the composition of constituent materials: chopped glass (%), hoop glass (%), resin (%), and silica sand (%), and further applying this understanding to the formulation of semi-empirical prediction models to enable future GFRP composite designs to have a reduced design factor of uncertainty. The validation tests were carried out to check the predictive capability of the formulated semi-empirical prediction models. The magnitude of error percentage for predicted axial tensile strength is found to be less than 2.27%, while for hoop strength they are less than 1.13%. To better understand the dominant mode of failure that prevails under applied loading conditions, and to gain insight into the failure mechanisms, microscopic examination of failed surfaces was also performed using the scanning electron microscope (SEM). The semi-empirical prediction models presented in current research allows better understanding of the individual and consolidated effect of constituent materials upon the axial and hoop tensile strengths. This improved understanding provides guidance for practicing engineers, who depending upon the design requirements can use semi-empirical prediction models to selectively reinforce GFRP mortar pipes to have a custom-designed product.

Surface Roughness Analysis of H13 Steel during Electrical Discharge Machining Process Using Cu-TiC Sintered Electrode.

In electrical discharge machining (EDM), the machined surface quality can be affected by the excessive temperature generation during the machining process. To achieve a longer life of the finished part, the machined surface quality plays a key role in maintaining its overall integrity. Surface roughness is an important quality evaluation of a material’s surface that has considerable influence on mechanical performance of the material. Herein, a sintered cermet tooltip with 75% copper and 25% titanium carbide was used as tool electrode for processing H13 steel. The experiments have been performed to investigate the effects of EDM parameters on the machined surface roughness. The findings show that, as the pulse current, pulse length, and pulse interval are increased, the surface roughness tends to rise. The most significant determinant for surface roughness was found to be pulse current. A semi-empirical surface roughness model was created using the characteristics of the EDM technique. Buckingham’s theorem was used to develop a semi-empirical surface roughness prediction model. The semi-empirical model’s predictions were in good agreement with the experimental studies, and the built empirical model based on physical features of the cermet tooltip was tested using dimensional analysis.

A semi-empirical model for thermal resistance and dryout during boiling in thin porous evaporators fed by capillary action

Two-phase passive heat transport devices such as vapor chambers, loop heat pipes, and capillary pumped loops utilize porous evaporators for phase change and to drive fluid transport. Nucleate boiling can occur within such capillary-fed porous evaporators, especially under high-heat-flux operation, as has been visually observed in various experimental studies in the literature. However, prior modeling efforts have typically only considered single-phase flow of liquid through a completely saturated porous medium for characterizing the dryout limit and thermal performance. The present work offers a new semi-empirical model for prediction of thermal resistance and dryout during boiling in capillary-fed evaporators. Thermal conduction across the solid and volumetric evaporation within the pores are solved to obtain the temperature distribution in the porous structure. Capillary-driven lateral liquid flow from the outer periphery of the evaporator to its center, with vapor flow across the thickness, is considered to obtain the local liquid and vapor pressures. The capillary pressure and the relative permeabilities (fraction of single-phase permeabilities) for two-phase flow in the porous medium are modeled as a function of the local liquid saturation. The heat flux at which the liquid saturation at the center of the evaporator becomes zero is defined as the dryout limit of the evaporator. Experiments are conducted on sintered copper particle evaporators of different particle sizes and heater areas to collect data for model calibration. To demonstrate the wider applicability of the model for other types of porous evaporators, the model is further calibrated against a variety of dryout limit and thermal resistance data collected from the literature. The model is shown to predict the experimentally observed trends in the dryout limit with mean particle/pore size, heater size, and evaporator thicknesses.

Strength matching factor of pipeline girth weld designed by reliability method

Crack-like defects frequently form in pipes exposed to aggressive environments or during welding fabrication. Appropriate strength matching factors should be determined for welding process evaluation based on the reliability aspect. In this paper, the current research status of codes and standards related to the strength requirement of weld metals in the pipeline industry were reviewed. The results showed that almost all standards considered the lower limit of pipe tensile strength as a requirement for weld strength evaluation for specimens of cross-weld test breaking at the weld position. This was contradictory to the original intention of even-match or over-match of pipeline girth weld. The Monte Carlo Method was then employed to investigate the evolution of girth weld strength matching factor and pipeline failure probability under different high-low misalignments, fracture toughnesses, and standard deviations of pipe strength distribution. The data revealed significantly reduced probability of pipe failure as the weld metal strength matching factor increased. At the same strength matching factor, the failure probability of pipe decreased as fracture toughness of girth weld. The failure probability of pipe also declined as the misalignment and standard deviation of pipe strength distribution diminished. Based on the requirement of certain target reliability and strain demand, a semi-empirical prediction model of critical strength matching factor was proposed for engineering applications. The analysis of parameter sensitivity showed the relative importance of several parameters on the critical strength matching factor of girth weld, including high-low misalignment, standard deviation of pipe material strength distribution, and fracture toughness.

A novel approach based on semi-empirical model for degradation prediction of fuel cells

This work proposes a semi-empirical model-based prognostics method which can achieve degradation prediction and estimate the remaining useful life of proton exchange membrane fuel cells (PEMFCs) under automotive profiles. Based on the polarization behavior of PEMFCs, a voltage prediction model is proposed to predict the degradation trend of PEMFCs by introducing degradation models of electrochemical surface area and equivalent resistance, respectively. In addition, a recovery factor is introduced to characterize the performance recovery of PEMFCs after shutdown and restart. When analyzing the aging parameters that affect the performance of PEMFCs, system identification is introduced to achieve the one-to-one correspondence between the aging parameters and the experimental conditions. Two experimental cases with different PEMFCs, which are loaded with different profiles, have been performed to validate the proposed voltage prediction model's accuracy. The results show that the proposed voltage prediction model can achieve accurate degradation trend prediction of PEMFCs, whether predicting the complete profile or the degradation corresponding to the maximum current.

Predictive Modelling for Concrete Failure at Anchorages Using Machine Learning Techniques.

Anchorage to concrete plays a significant role in various aspects of modern construction. The structural performance of anchors under direct tensile load can lead to failure by concrete cone breakout. Concrete related failure modes are quasi-brittle, and as such, they may develop without prior warning indications of damage, while it also exposes the bearing component to damage propagation. As such, an adequate reliability assessment of anchors against concrete cone failure is of high importance, and improved precision and minimisation of uncertainty in the predictive model are critical. This contribution develops predictive models for the tensile breakout capacity of fastening systems in concrete using the Gaussian Process Regression (GPR) and the Support Vector Regression (SVR) machine learning (ML) algorithms. The models were developed utilising a set of 864 experimental anchor tests. The efficiency of the developed models is assessed by statistical comparison to the state-of-practice semi-empirical predictive model, which is embedded in international design standards. Furthermore, the algorithms were evaluated based on a newly introduced Model Explainability concept based on Analogous Rational and Mechanical phenomena (MEARM). Finally, a discussion is provided regarding the developed ML models’ suitability for use as General Probabilistic Models in a reliability framework.

Solubility of Sulfanilamide and Sulfacetamide in neat solvents: Measurements and interpretation using theoretical predictive models, first principle approach and artificial neural networks

Solubilities of Sulfanilamide and Sulfacetamide in neat solvents were both measured and collected from the literature. The set comprising 35 systems was interpreted in terms of the empirical and semi-empirical predictive models including Buchowski-Ksiazczak model (λh-equation), extended Buchowski's model (λβ-equation), modified Apelblat equation, van't Hoff-Yaws model, Non-Random Two Liquid (NRTL) model, Wilson model and the Weibull two-parameter extrapolation model. The advantage of corrected Akaike information criterion was used for precise quantification of the quality of each model. It turned out that the lowest values of this measure were found for the Buchowski-Ksiazczak approach indicating the best correlation with experimental solubility data. Interestingly, λβ-equation and van't Hoff–Yaws model give a fitting of almost the same quality but were slightly less accurate compared to λh-equation. The results of applied COSMO-RS computations suffered seriously from inaccuracies providing only a qualitative guess of solubility. Hence, machine learning procedure was applied for building a non-linear fully predictive model. The set of used molecular descriptors comes from COSMO-RS computations and characterizes intermolecular interactions in pure solvents. Based on the quality of obtained machine learning model it is possible to confirm that the selected set of descriptors can be used for solubility modeling and carries the most important information necessary for quantifying of saturated solutions.

Assessing Climate Change Impact on Soil Salinity Dynamics between 1987–2017 in Arid Landscape Using Landsat TM, ETM+ and OLI Data

This paper examines the climate change impact on the spatiotemporal soil salinity dynamics during the last 30 years (1987–2017) in the arid landscape. The state of Kuwait, located at the northwest Arabian Peninsula, was selected as a pilot study area. To achieve this, a Landsat- Operational Land Imager (OLI) image acquired thereabouts simultaneously to a field survey was preprocessed and processed to derive a soil salinity map using a previously developed semi-empirical predictive model (SEPM). During the field survey, 100 geo-referenced soil samples were collected representing different soil salinity classes (non-saline, low, moderate, high, very high and extreme salinity). The laboratory analysis of soil samples was accomplished to measure the electrical conductivity (EC-Lab) to validate the selected and used SEPM. The results are statistically analyzed (p ˂ 0.05) to determine whether the differences are significant between the predicted salinity (EC-Predicted) and the measured ground truth (EC-Lab). Subsequently, the Landsat serial time’s datasets acquired over the study area with the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and OLI sensors during the last three decades over the intervals (1987, 1992, 1998, 2000, 2002, 2006, 2009, 2013, 2016 and 2017) were radiometrically calibrated. Likewise, the datasets were atmospherically and spectrally normalized by applying a semi-empirical line approach (SELA) based on the pseudo-invariant targets. Afterwards, a series of soil salinity maps were derived through the application of the SEPM on the images sequence. The trend of salinity changes was statistically tested according to climatic variables (temperatures and precipitations). The results revealed that the EC-Predicted validation display a best fits in comparison to the EC-Lab by indicating a good index of agreement (D = 0.84), an excellent correlation coefficient (R2 = 0.97) and low overall root mean square error (RMSE) (13%). This also demonstrates the validity of SEPM to be applicable to the other images acquired multi-temporally. For cross-calibration among the Landsat serial time’s datasets, the SELA performed significantly with an RMSE ≤ ± 5% between all homologous spectral reflectances bands of the considered sensors. This accuracy is considered suitable and fits well the calibration standards of TM, ETM+ and OLI sensors for multi-temporal studies. Moreover, remarkable changes of soil salinity were observed in response to changes in climate that have warmed by more than 1.1 °C with a drastic decrease in precipitations during the last 30 years over the study area. Thus, salinized soils have expanded continuously in space and time and significantly correlated to precipitation rates (R2 = 0.73 and D = 0.85).

Multilevel-Modeling Interpretation of Trailing-Edge Noise Models for Wind Turbines with NACA 0012 Airfoil

Recently, good interest in renewable energy has been increased and thereby the need for noise prediction model is required for the design of wind turbines with less noise. In particular, trailing edge noise is a major noise source. A number of results using complex predictive models with machine learning techniques and artificial neural network have been reported, and the results are generally good, depending on the application. However, the rigorous verification of the model itself that produces such predictive results is very insufficient, and there is a lack of understanding of the process in which the results are produced, and an explanation of the various physical phenomena observed in the experiment is not easy. In this study, Brooks, Pope, and Marcolini (BPM) model, the most popular semi-empirical model for airfoil noise prediction, was subjected to an analysis using multilevel modeling. The multilevel model is a statistical model containing both fixed effects and random effects. We were able to reckon the interpretation of two issues that are left unexplained in BPM model and to improve our understanding of the noise phenomenon of wind turbines. With further research, multilevel modeling is expected to be an effective tool for better design and analysis of complex predictive models for wind turbines noise.

Assessment of biomechanical stability and formulation of a statistical model on magnesium based composite in two different milieus

Magnesium (Mg) based temporary implants are an appealing new solution to counter the problems associated with the currently available temporary orthopaedic implants, used in fracture fixing. To make the extensive use of Mg-based implants in-vivo, mechanical integrity in the physiological environment is a prerequisite. This study presents an insight into the biomechanical stability of Mg-3Zn/HA (0, 5, and 15 wt % of HA) composites in two different milieus (simulated body fluid (SBF) and serum contained SBF (m-SBF)). After 14 days of static immersion in SBF, ~65% mechanical strength was compromised in the case of 15 wt % HA reinforcement. However, the degradation rate was slowed down by ~35% with the addition of 15 wt % HA in Mg-3Zn. Mg-3Zn/HA composite, when soaked in both fluids, was found to induce apatite layer formation on the surfaces for several days. However, in the case of m-SBF immersion, 15 wt % HA facilitated less precipitation of apatite growth when compared to SBF immersion. Nevertheless, m-SBF immersed 15 wt % HA composite facilitated better corrosion resistance and excellent mechanical stability after 14 days of immersion. The approach thereby assists in establishing an effective mechanism between the degradation and mechanical stability in in-vitro immersion. In addition, this study has also developed a semi-empirical model for prediction of the compressive strength of these composites as a function of the number of days of immersion and the content of hydroxyapatite (HA). This semi-empirical model will help in predicting the biomechanical stability for long-term in-vitro exposures, which might be of use in evaluating the effect of the in-vivo environment.

On the effect of free-stream turbulence on boundary-layer transition

Abstract

Efficacy of semi-empirical models for prediction of forming limit curve of IN718 alloy at elevated temperatures

ABSTRACT Inconel 718 alloy (IN718) is a high-strength material and is widely used in aerospace and nuclear applications for extreme working conditions. IN718 alloy is difficult to form at room temperature (RT) conditions. Warm/hot forming is used as an alternative approach for forming of complex shape components for IN718 alloy. The present study is focused on the influence of processing temperatures (RT, 400°C and 700°C) on the formability of IN718 alloy. Experimental forming limit curves (FLCs) have been plotted at different temperatures using standard Nakazima test. Improvement in maximum major safe strain values for T-C (tension–compression) and T-T (equi-biaxial tension) region is found to be 54.35% and 66.16%, respectively, at 700ºC. Further, the effect of processing temperatures on surface strain distribution, thickness distribution and limiting dome height (LDH) is also analysed. Surface strain distribution at test temperature mainly depends on strain path throughout stretching. LDH is increased by approximately 33–44% at 700ºC. FLCs are predicted based on Paul and Chow semi-empirical models based on uniaxial anisotropic properties and Hosford’s constants. The predicted FLCs are validated with experimental FLCs based on average absolute error (AAE). Predicted FLCs by Paul method show good agreement with experimental limiting strains.

Semi-empirical prediction method for monthly precipitation prediction based on environmental factors and comparison with stochastic and machine learning models

ABSTRACT Precipitation prediction is central in hydrology and water resources planning and management. This paper introduces a semi-empirical predictive model to predict monthly precipitation and compares its predictive skill with those of machine learning (ML) methods. The stochastic method presented herein estimates monthly precipitation with one-step-ahead prediction properties. The ML predictive skill of the algorithms is evaluated by predicting monthly precipitation relying on the statistical association between precipitation and environmental and topographic factors. The semi-empirical predictive model features non-negative matrix factorization (NMF) for investigating the influence of multiple predictor variables on precipitation. The semi-empirical predictive model’s parameters are optimized with the hybrid genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM), or GALMA, yielding a validated model with high predictive skill. The methodologies are illustrated with data from Hubei Province, China, which comprise 27 meteorological station datasets from 1988–2017. The empirical results provide valuable insights for developing semi-empirical rainfall prediction models.

Temporal variation of power production via reverse electrodialysis using coastal North Carolina waters and its correlation to temperature and conductivity

Global estimates of electricity generation from coastal salinity gradient energy resources rely on the underlying assumption that these gradients are spatially and temporally stable. Refining these estimates requires a better understanding of coastal variations in water properties and their impact on power production. This study investigated power output in reverse electrodialysis (RED) cells by coupling seawater samples collected from three different sites along coastal North Carolina at five different sampling dates between 2016 and 2017 with wastewater effluent from a wastewater treatment facility as the dilute solution. We found that power density did not vary substantially across the sampling dates except for one notable drop in power for a sample collected during an approaching hurricane. For all sites, power output peaked during the summer season. Using our experimental results, we developed a semi-empirical predictive model of RED power output as a function of temperature and conductivity. The model was able to predict power density within approximately 20% of the experimental power densities for the seawater samples used in this study and others in the literature. Combining our modeling approach with temporal conductivity and temperature data may help identify promising sites for coastal salinity gradient energy installations.

A semi-empirical model for ship speed loss prediction at head sea and its validation by full-scale measurements

This paper proposes a semi-empirical model to estimate a ship’s speed loss at head sea. In the model, the formulas to estimate a ship’s added resistance due to waves have been further developed to better consider the ship hull forms, in addition to other main particulars. Based on the model experimental tests of 11 ships in regular head waves, the new formulas have more flexible forms and can better fit the test results than other similar models. In addition, this model proposes a significant wave height based correction factor multiplied to the conventional integration to compute wave resistance in irregular waves. This factor is supposed to consider the impact of coupled ship motions in high waves on a ship’s added resistance due to waves. The model is validated by the full-scale measurement from two vessels, a PCTC and a chemical tanker. The encountered weather conditions along the sailing routes are extracted from the reanalysis metocean data. The results indicate that the proposed model can provide quite accurate predictions of ship speed loss in head sea operations.

Life prediction for rate-dependent low-cycle fatigue of PA6 polymer considering ratchetting: Semi-empirical model and neural network based approach

Based on the experimental results in [1,2], a semi-empirical low-cycle fatigue life prediction model is constructed for a polyamide-6 (PA6) polymer, in which the rate dependence of fatigue life and the effect of ratchetting strain on the life are considered. Since the fatigue damage in PA6 is not only loading-cycle-dependent but also time-dependent, a function to describe the complicate effect of stress rate on the fatigue life is obtained. In the proposed model, the detrimental effect of ratchetting strain on the fatigue life is characterized by introducing a function of mean stress. Comparison of the predicted and experimental results shows that the proposed model presents a good prediction. Furthermore, the neural network based method is also used to correlate the low-cycle fatigue data of PA6. The results show that the established neural network based approach achieves a better prediction to the fatigue life of PA6 with the occurrence of ratchetting than that by the proposed semi-empirical model, which demonstrates a possibility to apply the neural network based machine learning method to deal with the fatigue of polymer.