Multiple Nonlinear Regression Analysis Research Articles

Experimental study on the effect of simulated grass and stem coverage on resistance coefficient of overland flow

AbstractVegetation plays a significant role in preventing desertification, conservation of soil and water. However, nowadays, there is still uncertainty regarding the mechanisms of flow resistance caused by dissimilar vegetation covers. To address this gap, a series of these was conducted in this study to investigate the influence of synthetic grass and stems on the variation of resistance on overland flows. Thirty vegetation configurations were selected (combining five synthetic grass coverage options and six synthetic stem coverage ones), and tested against five unit discharges (flow range = 0.28–2.22 L m−1 s−1) and four slope gradients (3.49%–20.78%). The results obtained showed that the unit discharge is the key driving factor for the transition from laminar to transitional flow. Furthermore, the relationship between the resistance coefficient (f) and the Reynolds number (Re) was not monotonically increasing or decreasing, but behaviours observed were specifically linked to each vegetation coverage. However, a critical coverage threshold was identified, and it corresponded to 2.72% when the slope gradient tested was 3.49%. This threshold decreased with the increase of the slope gradient. In addition, when the vegetation coverage was less than the critical threshold, the f was negatively correlated with Re, otherwise if the vegetation coverage was higher than the critical threshold identified the f–Re relation was positively correlated. The total flow resistance under synthetic grass and stem cover was unequal to the linear superposition of grain resistance and form resistance caused by synthetic stem and grass, which meant that the linear superposition approach is not applicable to overland flows. Finally, a model was developed to predict the flow resistance by applying the π‐theorem and the multiple nonlinear regression analysis and it has been validated against the experimental results confirming its accuracy and high performance (adj.R2 = 0.99, NSE = 0.94).

Nitrogen and phosphorus concentration zoning control method of surface water in the Haihe River Basin

Various uncertain influencing factors and incomprehensible mechanisms have posed daunting challenges to the management and treatment of regional surface water environment quality. The unified measures and the “one-size-fits-all” management approach limit the treatment effectiveness. Therefore, considering natural and human activities which are major factors affecting the surface water environment quality, the present study proposed a set of zoning management and control schemes for nitrogen and phosphorus concentrations in surface water based on its natural attributes. Selecting DEM, rainfall, vegetation type, soil type, and land use, and employing “grid transformation”, “data extraction”, “attribute superposition” of GIS software and “correlation analysis”, “cluster analysis”, and “principal component analysis” of SPSS software, the Haihe River Basin was divided into the prevention zone, the control zone, and the non-control zone with different natural attribute sets by the correlation coefficient R2 and the nitrogen and phosphorus pollution therein. Based on the nitrogen and phosphorus data of 276 surface water quality monitoring sites, the multiple nonlinear stepwise regression analysis was conducted to construct the relationship between a single water quality indicator and its natural attributes in the three zones. The results are of essential practical significance to surface water environment quality zoning management and survey in the Haihe River Basin. Meanwhile, it provides innovative insights into environmental zoning management in other regions.

Standoff Detection and Identification of Liquid Chemicals on a Reflective Substrate Using a Wavelength-Tunable Quantum Cascade Laser.

Standoff chemical detection and identification techniques are necessary for ensuring safe exposure to dangerous substances. Molecular fingerprints of unknown chemicals can be measured using wavelength-tunable quantum cascade lasers operating in long-wavelength infrared. In this work, we present a method that can identify liquid chemicals on a reflective substrate via diffuse reflection spectra measurement from 50 cm away and multiple nonlinear regression analysis. Experimental measurements and numerical analyses were conducted for different chemical surface densities and angles of light incidence using diethyl phthalate (DEP) and dimethyl methylphosphonate (DMMP). Candidate substances can be classified using a deep learning model to reduce analysis time.

Degree of bending (DoB) in jack-up unit tubular KK-joints under the axial, in-plane bending (IPB), and out-of-plane bending (OPB) loads

ABSTRACT The determination of degree of bending (DoB) is critical to improve the accuracy of estimating fatigue life using stress life (S-N) method. Based on the truss structure of jack-up unit, the numerical model of tubular KK-joints including rack plate (thickness is 180mm) is established. In the present research, the data extracted from 378 FE analyses on 108 tubular KK-joints models were used to study the effect of geometric parameters on DoB values of KK-joints under six different types of axial loading, IPB and OPB loadings. The geometric parameters affecting the DoB of tubular KK-joints are analyzed by multiple nonlinear regression analysis, and 21 parameter design formulas are obtained to calculate the DoB values of tubular KK-joints under axial loadings, IPB and OPB loadings. The established geometric parameter equation can effectively evaluate the fatigue characteristics of truss legs with rack plate of jackup unit.

Visibility classification and influencing-factors analysis of airport: A deep learning approach

This paper discusses the visibility classification and influencing-factor analysis of an airport, which were carried out through a data-driven deep learning approach and multiple nonlinear regression analysis. First, a deep integrated model for airport visibility classification was developed, and the relationship between runway visual range (RVR) and surface meteorological elements for different reasons was analyzed. To extract more low-level features from nighttime images, the original images were transformed into pseudo-color images, and the image pairs were input into the classification model. The proposed deep integrated model combined two popular convolutional neural network (CNN) models: VGG-16 and Xception. The results showed that the accuracy of the proposed model was 87.64%, with the F1-score being 88.58%; it achieved the best performance among other well-known experimental CNNs. Afterward, the relationships between RVR and meteorological optical range (MOR), source light intensity (I), and background light (BL) were analyzed, and the results showed that RVR increased with increasing I under the same MOR and BL conditions; however, it decreased with increasing log (BL) values under the same I and MOR conditions. RVR increased as MOR increased when other conditions remained unchanged. Subsequently, the multivariate regression analysis was conducted to fit the relationship between MOR and surface meteorological elements, and the results showed that the cubic curve was the best-fit curve with R-square values of 0.937 and 0.860 in December 2019 and March 2020, respectively. Finally, combined with the analysis of the factors influencing the RVR, a significant negative correlation was observed with relative humidity, along with a significant positive correlation with temperature and a positive correlation with average wind speed in the winter of 2019 and spring of 2020. However, the pressure factor showed the opposite correlation, possibly because the arrival of cold air in winter increases the ground pressure, which is conducive for the dissipation of pollutants. Meanwhile, the increase in temperature and decrease in pressure in spring increases the chance of disturbances in the atmosphere, thereby increasing the likelihood of adverse visibility factors. ◆ Pseudo-color images were found beneficial for improving the accuracy of visibility identification. ◆ An integrated model combined VGG-16 and Xception achieved the best performance compared to that of other popular models. ◆ The relationship between RVR and MOR, I, and BL was found. ◆ The multivariate regression analysis between MOR and surface meteorological elements in different seasons was conducted. ◆ There is a correlational relationship between RVR and surface meteorological elements.

In-plane vibration analysis of horizontally curved beams resting on visco-elastic foundation subjected to a moving mass

This paper deals with the in-plane dynamics of Horizontally Curved Beams (HCBs) supported by a visco-elastic foundation under the excitation induced by a moving mass. What has been included as worthy of detailed exposition is the inevitable contribution of the inertial actions of the mass object into the problem formulation. By taking into account the effect of Coriolis acceleration, centrifugal force and rotary inertia, the governing coupled non-linear differential equations of equilibrium for a simply supported HCB are derived.In the proposed solution, a new system of linear ordinary differential equations is distilled from the governing differential equations of motion, which can be solved utilizing standard numerical techniques. The capability as well as the reliability of the proposed semi-analytical solution is also examined through an extensive parametric study.On that account, non-dimensional parameters are defined to advantage as input data in response calculations and to better interpret the outcomes. To this aim, the Dynamic Amplification Factor (DAF) spectra are introduced with particular attention to the influential parameters such as subtended (central) angle and radius of the HCB, mass and speed of the moving object along with the number of contributing modes.The significance of allowing for the contribution of moving mass inertia is demonstrated in the dynamic response of HCBs, focusing on highly curved elements under the action of a heavy mass at high speeds.It is shown that, in a wide range of design parameters, the simplified moving-force solution would not work to satisfaction if utilized in the analysis of HCB-moving mass dynamics. However, the moving mass framework takes care of all the essential effects induced due to a moving object running on an HCB.In addition, on the basis of the multiple nonlinear regression analysis, simplified expressions have been established which are advantageous in that they supply a means to predict the DAFs in a more practical manner. The findings of the present investigation imply that the influence of additional inertial terms is of fundamental importance in a wide range of design parameters. Therefore, emphasis is placed on implementing the moving mass method, as a more rigorous and reliable representative of the real-world dynamic response of HCBs in comparison with the unsafe results obtained from the moving force approach.Attention is also accorded to the influence of cross-ties in conjunction with the torsional stiffness of rail-tie connection on the in-plane dynamics of HCBs. Also, the results indicate that sharply-curved HCBs, especially at high velocities, are apt to higher DAFs under the action of an in-plane moving inertial load.

Utilizing Buckingham Pi theorem and multiple regression analysis in scaling up direct contact membrane distillation processes

Predicting the performance of a full-scale direct contact membrane distillation (DCMD) module based on experimental lab-scale results is rather difficult, since the DCMD performance is dependent on many different process parameters. Hence, there is a need for a methodology to perform DCMD system up-scaling based on lab-scale experimental results. In this study, we devise an approach to scale up the performance of DCMD systems by using the Buckingham's Pi theorem to group the DCMD process parameters into eight relevant dimensionless groups. Experimental data obtained from literature at various module dimensions were used to evaluate the developed dimensionless groups. An experimentally validated computational fluid dynamics (CFD) model was also developed and used to extend the coverage of operational parameters beyond the available experimental data. Then, two empirical dimensionless correlations were created, using multiple nonlinear regression analysis, and then validated, to enable the prediction of flux and pressure drop in DCMD systems at any scale.

Urbanization of energy poverty? The case of Mozambique

What is the impact of the unprecedented scale and pace of urbanization in the global South on the evolution of energy poverty incidence? Remarkably enough, urban population growth and energy poverty are mainly studied separately from each other in different strands of the literature. We argue that there is much to be gained in studying these phenomena in their interdependence. To illustrate this, we analyze for the case of Mozambique the relationship between spatial variation in population density and three commonly used indicators of energy poverty over time. We think that the case of Mozambique exemplifies the future development of Africa's energy poverty situation in many respects. Our analysis makes use of two newly constructed datasets with household level information that originate from national household welfare surveys as well as an own survey for Mozambique's capital city Maputo. The data show that over time, and especially in urban areas, energy poverty is decreasing in terms of consumption quantities and access to modern energy fuels, but increasing in terms of energy expenditure shares. Also, we show that by largely ignoring transport energy use, the national welfare surveys typically overestimate energy consumption poverty while underestimating energy expenditure poverty to a substantial degree. Finally, based on a multiple non-linear regression analysis we find, after controlling for spatial sorting of households, that if population densities rise, energy poverty levels in terms of modern fuel use first fall and then increase – thus following a U-shaped pattern. In contrast, the relationship between population density and energy expenditure poverty follows an inverted N-shaped curve. This suggests that urbanization processes in poor countries like Mozambique may not lead to an unequivocal reduction of energy poverty rates, in line with existing evidence on urbanization of poverty in Africa.

Manufacturing and Modeling of Polypropylene-based Hybrid Composites by Using Multiple-Nonlinear Regression Analysis

In this research, artichoke stem particles (AS) and wollastonite (W) were used as an organic and inorganic fillers in order to improve the mechanical properties of polypropylene (PP). In this regard, PP-matrix composites containing AS and W were produced as non-hybrid and hybrid using a high speed thermo-kinetic mixer. Mechanical properties of polymer composites were investigated by the tensile test. Experimental results reveal that the highest modulus of elasticity was obtained in PP-W and the highest tensile strength was obtained in raw PP while the lowest ultimate strain value was obtained in PP-W-AS. Then, multiple-nonlinear regression analysis was employed to determine the effect of weight ratios of W and AS in PP on modulus of elasticity, tensile strength and ultimate strain. Experimental results were expressed with polynomial, rational and trigonometric models. The results show that the proposed models have well fitted with the experimental results. The coefficient of determination (R2) values were found between 0.95 and 1 in all models. Also, boundedness check control of the proposed models which gives information about whether models are realistic or not was carried out by calculating the maximum and minimum values produced by the relevant model.

Application of Soft Computing Techniques to Estimate Cutter Life Index Using Mechanical Properties of Rocks

The wear of cutting tools is critical for any engineering applications dealing with mechanical rock excavations, as it directly affects the cost and time of project completion as well as the utilization rate of excavators in various rock masses. The cutting tool wear could be expressed in terms of the life of the tool used to excavate rocks in hours or cutter per unit volume of excavated materials. The aim of this study is to estimate disc cutter wear as a function of common mechanical rock properties including uniaxial compressive strength, Brazilian tensile strength, brittleness, and density. To achieve this goal, a database of cutter life was established by analyzing data from 80 tunneling projects. The data were then utilized for evaluating the relationship between rock properties and cutter consumption by means of cutter life index. The analysis was based on artificial intelligence techniques, namely artificial neural networks (ANN) and fuzzy logic (FL). Furthermore, linear and non-linear regression methods were also used to investigate the relationship between these parameters using a statistical software package. Several alternative models are introduced with different input variables for each model, to identify the best model with the highest accuracy. To develop these models, 70% of the dataset was used for training and the rest, for testing. The estimated cutter life by various models was compared with each other to identify the most reliable model. It appears that the ANN and FL techniques are superior to standard linear and non-linear multiple regression analysis, based on the higher correlation coefficient (R2) and lower Mean square error (MSE).

A NONLINEAR REGRESSION MODEL TO ESTIMATE THE SIZE OF WEB APPS CREATED USING THE CAKEPHP FRAMEWORK

Context. The problem of estimating the software size in the early stage of a software project is important because a software size estimate is used for predicting the software development effort, including Web apps created using the CakePHP framework. The object of the study is the process of estimating the size of Web apps created using the CakePHP framework. The subject of the study is the nonlinear regression models to estimate the size of Web apps created using the CakePHP framework.
 Objective. The goal of the work is the building the nonlinear regression model with three predictors for estimating the size of Web apps created using the CakePHP framework on the basis of the Box-Cox four-variate normalizing transformation to increase the confidence in early size estimation of these apps.
 Method. The model, confidence and prediction intervals of multiply nonlinear regression to estimate the size of Web apps created using the CakePHP framework are constructed based on the Box-Cox multivariate normalizing transformation for non-Gaussian data with the help of appropriate techniques. The techniques to build the models, confidence, and prediction intervals of nonlinear regressions are based on the multiple nonlinear regression analysis using the multivariate normalizing transformations. The techniques allow taking into account the correlation between dependent and independent variables in the case of normalization of multivariate non-Gaussian data. In general, this leads to a reduction of the mean magnitude of relative error, the widths of the confidence, and prediction intervals in comparison with nonlinear models constructed using univariate normalizing transformations.
 Results. Comparison of the constructed model with the nonlinear regression models based on the decimal logarithm and the BoxCox univariate transformation has been performed.
 Conclusions. The nonlinear regression model with three predictors to estimate the size of Web apps created using the CakePHP framework is constructed on the basis of the Box-Cox four-variate transformation. This model, in comparison with other nonlinear regression models, has a larger multiple coefficient of determination, a smaller value of the mean magnitude of relative error and smaller widths of the confidence and prediction intervals. The prospects for further research may include the application of other multivariate normalizing transformations and data sets to construct the nonlinear regression model to estimate the size of Web apps created using the other frameworks.

Propagation characteristics of the overpressure waves and flame fronts of methane explosions in complex pipeline networks

The propagation characteristics of the overpressure waves and flame fronts of methane explosions in complex pipe networks under realistic conditions were studied. A custom-designed experimental platform was used, the propagation characteristics of overpressure wave and flame wave in complex pipe network were characterized by overpressure attenuation coefficient and flame mutation coefficient. The results showed that the overpressure wave propagation characteristics in complex pipe networks were more complicated and disordered than those in straight or simple branched pipes. The overpressure wave attenuation and superposition occurred many times in the propagation process, the explosion shock wave attenuation in the beveled branch pipe was faster. When the flame wave passed through all the branch tubes, the flame wave velocity increased significantly. When the flame wave passed through the same tube at different times, the flame wave mutation coefficient was different, the change of flame wave temperature had a certain lag compared with the change of flame velocity, resulting in the peak of flame wave temperature and flame wave velocity appearing in different branch tubes. The experimentally obtained data were fit to functional relationships using nonlinear multiple regression analysis, and good agreement between the functional relationships and the data was obtained.

Performance Characterization of Unsaturated Granular Soils Using Static and Dynamic Plate Load Test

Abstract Determination of the modulus of subgrade reaction (ks) is an essential geotechnical design parameter that describes the relationship between stress and the associated settlement of soils. The ks is typically obtained using a laborious and costly method called the plate load test (PLT), and thus, to overcome the limitations related to the PLT, determining an alternative testing technique that can easily predict the ks is a priority. The feasibility of using a light weight deflectometer (LWD), also known as the dynamic PLT, to determine the ks of unbound pavement layers was thus investigated in this research. Eighteen laboratory testing models of granular soils were prepared and tested using the dynamic PLT (LWD) in conjunction with the static PLT (PLT). The soils’ characteristics examined under both dynamic and static loading conditions were analyzed and correlated. A series of multiple nonlinear regression analyses were conducted using three groups of independent variables: (1) LWD measurements, (2) basic soil properties, and (3) LWD data plus basic soil properties. Several nonlinear regression models were developed to predict the modulus of subgrade reaction on the basis of the LWD measurements and selected soil characteristics. The results revealed that certain LWD measurements, including dynamic modulus (Ed), surface deflection (δ), and degree of compatibility (Dc), are strongly correlated with the results from PLT. Additionally, good correlations were identified between ks and maximum dry density, suggesting that using LWD measurements along with basic soil properties yields correlations with adequate prediction quality.

Seasonal Variation and Trend of Intraocular Pressure Decrease Over a 20-Year Period in Normal-Tension Glaucoma Patients

To investigate the trend of seasonal variation of intraocular pressure (IOP) in patients with normal-tension glaucoma over a 20-year period by retrospectively analyzing the Kyoto Prefectural University of Medicine Glaucoma Registry database as real-world data. Retrospective cohort study. Data points (n=49,007) were extracted retrospectively from the medical records of 1774 patients with normal-tension glaucoma (665 male patients and 1109 female patients; mean ± SD age was 59.8 ± 14.4 years; and mean ± SD observation period was 5.6 ± 4.4 years) seen over the 20-year period. We first calculated the mean IOP from all available data of each month from January 1997 through December 2016. The data were then categorized into 5 groups of 4 consecutive years each (1997-2000, 2001-2004, 2005-2008, 2009-2012, and 2013-2016) and the mean IOP of each month within the group was calculated. Seasonal variations of IOP over the 20-year study period and in the 5 consecutive groups were then investigated via nonlinear multiple regression analysis. A continuous decrease of IOP was detected throughout the 20-year period (P < .001), with distinct seasonal variation. The annual mean ± SD IOP was highest (13.9 ± 2.7 mm Hg) in the oldest group (1997-2000), with a gradual decrease in each subsequent group, finally becoming lowest (12.3 ± 2.7 mm Hg) in the most recent group (2013-2016) (P < .001), and all of them were accompanied by distinct seasonal variation (P < .001). Based on the Kyoto Prefectural University of Medicine Glaucoma Registry real-world longitudinal data, our findings revealed a continuous decrease and distinct seasonal variation of IOP in patients with normal-tension glaucoma throughout the 20-year study period.

Blast behavior of steel-concrete-steel sandwich panel: Experiment and numerical simulation

As a lateral resisting element, the steel–concrete composite structure has been gradually employed in the anti-seismic and the anti-explosion design of structures due to the favorable capacity of energy dissipation and impact resistance. The blast capacities of steel–concrete-steel (SCS) sandwich, concrete-steel–concrete (CSC), and reinforced concrete (RC) slabs were investigated by experiment and numerical simulation. The effects of the explosive charge, concrete grade, steel plate thickness, and length of shear studs on the blast behaviors of the composite slabs were studied by parametric analysis. The failure modes and dynamic responses of the specimens were investigated and compared. Simultaneously, the empirical formulas for mid-span deflection of the SCS and CSC slabs were proposed by using a multiple nonlinear regression analysis method. The results indicated that the three specimens exhibit different failure modes, in terms of local damage, spallation of concrete, and punching failure. And the steel plate takes a dominant role against the blast loads, which can provide a robust protective capacity to prevent the slab from damage due to the high tensile strength of steel. The SCS sandwich slab has the smallest damaged area on the front steel plate with a smaller mid-span displacement, maintains the integrity, and owns bearing capacity after the blast test compared with the CSC and RC members. The proposed empirical formula can well fit the relation among the deflection of the composite slabs, explosive charge, and thickness of steel plate, which can reasonably predict the midspan displacements of the composite slabs.

Unified equations to predict residual flexural tensile strength of lightweight steel fiber‐reinforced concrete

AbstractIn this paper, novel unified equations for predicting the residual flexural tensile strength of normal‐weight, lightweight, and ultra‐lightweight steel fiber‐reinforced concrete (SFRC) are proposed. The unified equations were obtained by conducting multiple nonlinear regression analysis on the test results from 60 notched prism tests conducted according to EN 14651. The proposed unified equations account for fiber geometry, fiber content, concrete compressive strength, and concrete density. Unlike existing prediction equations, which can only predict the flexural tensile strength of normal‐weight SFRC, the proposed equations are applicable for SFRC with varying densities. Test results from the literature verified the accuracy of the proposed equations to within 10% of observed values, on average. Compared to the existing equations, the proposed unified equations were better at predicting residual flexural tensile strength of both normal‐weight and lightweight steel fiber reinforced concrete.

Research on the Prediction Model of Shield Advancement Rate in Sandy Cobble Ground of Beijing

The long distance and tight construction period of the earth pressure balance shield tunneling in Beijing sandy cobble ground have a very positive impact on grasping the construction progress and improving the organization efficiency. Based on the real-time monitoring data of on-site earth pressure, cutter head speed, total thrust, screw conveyor speed, and cutter head torque, this study investigated the correlation between the shield advancement rate and the above parameters based on multiple nonlinear regression analysis method, which lay down the foundation of shield advancement rate prediction. The prediction model achieved satisfactory results and was further applied to an airport project. The results show that it is of guiding significance to use multiple regression method to establish shield prediction model and put forward the best advancement parameters to ensure the construction progress. The order in which the four operational parameters affect the advancement rate is total thrust, cutter head torque, screw conveyor speed, and cutter head speed. When the advancement time is greater than 57 min, the average advancement rate and daily progress are highly positively correlated. The research provides a reliable basis for the advancement rate prediction, safety control, and parameter optimization of shield advancement in similar ground.

Estimation of Pressuremeter Modulus and Limit Pressure in Weathered Granite Based on the SPT-N Value and Chemical Weathering Index: A Case Study in South Korea

A pressuremeter test (PMT) is a representative and highly reliable in situ test for assessing the stress–strain behavior of weathered granite. Its application, however, is restricted by its cost and time requirements. Many researchers have also investigated the correlations between the SPT-N value and the pressuremeter modulus (Em) and limit pressure (PL) of soils, but they have mostly focused on sand, silt, and clay and have employed simple regression analysis. In this study, equations for Em and PL were derived for weathered granite through multiple nonlinear regression analyses using a chemical weathering index that quantitatively represents the degree of weathering. Nonlinear multiple regression analyses were conducted by combining the allometric models that produced the optimal correlations between Em, PL, energy corrected SPT-N (SPT-N60), and normalized VR (Vogt’ ratio) with vertical effective stress. The obtained equations for Em and PL had higher R2 values (0.76 and 0.46, respectively) compared with the simple regression equations reported in previous studies. Because local characteristics are important determinants of the engineering properties of geo-materials, the Em and PL equations proposed in this paper are intended for use in geotechnical surveys of weathered granite in South Korea.

Development of a second-order dynamic model for quantifying impact of thermal mass on indoor thermal environment

Indoor thermal environment can be significantly influenced by thermal mass, which is not considered quantitatively in building thermal design due to the lack of a simple and accurate calculation model. For this reason, a temperature elastic coefficient (TEC) is proposed to characterize the dynamic heat transfer between inner surfaces of external walls (ISEW) and indoor air, and an effective thermal capacity (ETC) is used to characterize thermal mass. A second-order dynamic model is thus developed to efficiently evaluate the impact of thermal mass on indoor thermal environment by employing the TEC and ETC. The results show that both the TEC and the ETC are affected by the insulation position, wall thermal resistance, air change rate and window-wall ratio (WWR), but independent of the outdoor air temperature, indoor heat source intensity and solar radiation. Furthermore, the combination of the second-order dynamic model and the database models of ETC and the TEC are determined by using multiple nonlinear regression analysis, and can help policy makers and building thermal designers to estimate the impact of thermal mass on the indoor thermal environment quickly and accurately.

Distribution Law of the Temperature Inversion Layer in a Deep Open-Pit Mine

Using the methods of field experiment, numerical simulation, and theoretical analysis, a deep concave open-pit mine (DCOM) geometric model is established. Ansys Fluent software is used to simulate the change in the temperature field at night in the DCOM over time and analyze the probability of the spatial distribution of the temperature inversion layer. The results show that the location inside the stope close to the southwest edge is more likely to present temperature inversion, and the location close to the northeast edge is the least likely to have temperature inversion, and the height range where temperature inversion is likely to occur is 100–0 m. The simulated results reveal a greater probability of temperature inversion at the closed circle (+80 m), which is consistent with that obtained by field observation. Through multiple nonlinear regression analysis, a prediction model of the spatial distribution probability of the temperature inversion layer is established, which provides a theoretical basis for the prediction and control of atmospheric pollution in the DCOMs.