Flat versus Curved Contact Surfaces Effect on Consumer P-Metric and LT Tire Operating Temperatures3

2008 ◽  
Vol 36 (2) ◽  
pp. 129-145
Author(s):  
L. Spadone ◽  
J. Bokar
Keyword(s):  
Regression Models ◽  
Flat Surface ◽  
Operating Temperature ◽  
Step Test ◽  
Curved Surfaces ◽  
Statistical Regression ◽  
Load Pressure ◽  
Test Conditions ◽  
Endurance Testing ◽  
Contact Surfaces

Abstract Rolling stress and resultant operating temperature is critical to the endurance of a tire. There are fundamental differences between the tire stresses when operating on a flat surface, as experienced in normal highway use, and on a cylindrical laboratory test wheel. Even though there are substantially higher tire stresses and temperatures on a curved test wheel, nonetheless, cylindrical road wheels are widely used with the industry for tire endurance testing. Therefore, it is important to consider the severity of test conditions intended for a flat surface and the equivalent severity on the curved surface in order to avoid subjecting the tire to unrealistic stresses and temperatures. In this study, temperature measurements were made at the tire belt edge and centerline, for both flat and curved surfaces, under designed ranges of load, pressure, and speed conditions. Statistical regression models that predict the temperatures at the tire centerline and belt edge locations are developed and discussed. A simple yet consistent conversion from flat to curved conditions is provided so that equivalent tire temperatures are obtained. Flat highway conditions are derived that are temperature equivalent solutions to each of the FMVSS139 step test conditions.

Truck Tire Operating Temperatures on Flat and Curved Test Surfaces

2005 ◽  
Vol 33 (3) ◽  
pp. 156-178 ◽  
Author(s):  
T. J. LaClair ◽  
C. Zarak
Keyword(s):  
Flat Surface ◽  
Operating Temperature ◽  
Operating Conditions ◽  
Load Pressure ◽  
Truck Tire ◽  
Endurance Testing ◽  
Operating Temperatures ◽  
The Impact ◽  
Tread Rubber ◽  
Cylindrical Test

Abstract Operating temperature is critical to the endurance life of a tire. Fundamental differences between operations of a tire on a flat surface, as experienced in normal highway use, and on a cylindrical test drum may result in a substantially higher tire temperature in the latter case. Nonetheless, cylindrical road wheels are widely used in the industry for tire endurance testing. This paper discusses the important effects of surface curvature on truck tire endurance testing and highlights the impact that curvature has on tire operating temperature. Temperature measurements made during testing on flat and curved surfaces under a range of load, pressure and speed conditions are presented. New tires and re-treaded tires of the same casing construction were evaluated to determine the effect that the tread rubber and pattern have on operating temperatures on the flat and curved test surfaces. The results of this study are used to suggest conditions on a road wheel that provide highway-equivalent operating conditions for truck tire endurance testing.

Regression Modelling for Prediction of Construction Cost and Duration

2016 ◽  
Vol 857 ◽  
pp. 195-199 ◽  
Author(s):  
Nivea Thomas ◽  
Anu V. Thomas
Keyword(s):  
Regression Models ◽  
Critical Path ◽  
Real Data ◽  
Project Managers ◽  
Statistical Regression ◽  
Statistical Process ◽  
Project Cost ◽  
Cost Overruns ◽  
Building Projects ◽  
The Relationship

Construction investments are sensitive to time and cost overruns. Delay and cost escalation are considered two threats to project success. The project objective is to develop a model to predict project cost and duration based on historical data of similar projects. Statistical regression models are developed using real data of building projects. The methodology is adopted in 3 steps: a) Data collection b) Statistical analysis using Statistical Package for Social Sciences (SPSS) software c) Interpretation of results. The real data of cost and duration of 51 building projects have been collected. In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modelling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. The analysis is done using SPSS developed by IBM Corporation. The Regression models have been developed using the data collected from Noel Builders, Kakkanad, Ernakulam to predict the project cost and duration. The developed models are validated using split sample approach. The model outputs can be used by project managers in the planning phase to validate the scheduled critical path time and project budget.

Development of statistical regression models for ventilation estimation

2009 ◽  
Author(s):  
Shaopeng Liu ◽  
R.X. Gao ◽  
Qingbo He ◽  
J. Staudenmayer ◽  
P. Freedson
Keyword(s):  
Regression Models ◽  
Statistical Regression

Experimenal analysis of in-plane strain on curved surfaces using transferring techniques

1978 ◽  
Vol 13 (2) ◽  
pp. 121-128 ◽  
Author(s):  
J Buitrago ◽  
A J Durelli ◽  
V J Parks
Keyword(s):  
Rigid Body ◽  
Finite Deformation ◽  
Flat Surface ◽  
Flexible Structures ◽  
Strain Analysis ◽  
Curved Surfaces ◽  
Double Curvature ◽  
Threshold Strain ◽  
Rigid Body Motions ◽  
Inside Out

A new grid-transferring technique is introduced that allows the strain analysis of flat or curved surfaces of single or double curvature. The technique consists of transferring a grid from the structure to a flat surface by means of a thin, adhesive, transparent ribbon. Information is obtained along a strip, or point-by-point when circles or rosettes are used. The technique is specially suitable for the solution of problems of finite deformation of flexible structures, and its threshold strain is about 0.004. As a verification of the new method, strains obtained on a disc under diametral compression are compared with results already given in the literature. As a general example of application, strains on the anticlastic surface of tubes with and without perforation, and turned inside out, are determined. The method is not influenced by rigid-body motions.

scholarly journals Evolution of populations expanding on curved surfaces

2018 ◽  
Author(s):  
Daniel A. Beller ◽  
Kim M. J. Alards ◽  
Francesca Tesser ◽  
Ricardo A. Mosna ◽  
Federico Toschi ◽  
...  
Keyword(s):  
Range Expansion ◽  
Evolutionary Dynamics ◽  
Flat Surface ◽  
Analytical Description ◽  
Relative Increase ◽  
Curved Surfaces ◽  
Complex Environments ◽  
Neutral Genetic Diversity ◽  
Expanding Population ◽  
Dynamics Of Populations

AbstractThe expansion of a population into new habitat is a transient process that leaves its footprints in the genetic composition of the expanding population. How the structure: of the environment shapes the population front and the evolutionary dynamics during such a range expansion is little understood. Here, we investigate the evolutionary dynamics of populations consisting of many selectively neutral genotypes expanding on curved surfaces. Using a combination of individual-based off-lattice simulations, geometrical arguments, and lattice-based stepping-stone simulations, we characterise the effect of individual bumps on an otherwise flat surface. Compared to the case of a range expansion on a flat surface:, we observe a transient relative increase, followed by a decrease, in neutral genetic diversity at the population front. Ill addition, we find that individuals at the sides of the bump have a dramatically increased expected number of descendants, while their neighbours closer to the bump’s centre are far less lucky. Both observations can be explained using an analytical description of straight paths (geodesics) on the curved surface, Complementing previous studies of heterogeneous flat environments, the findings here build our understanding of how complex environments shape the evolutionary dynamics of expanding populations.

scholarly journals Weather prediction using random forest machine learning model

2021 ◽  
Vol 22 (2) ◽  
pp. 1208
Author(s):  
R. Meenal ◽  
Prawin Angel Michael ◽  
D. Pamela ◽  
E. Rajasekaran
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Random Forest ◽  
Solar Radiation ◽  
Regression Models ◽  
Tamil Nadu ◽  
Weather Prediction ◽  
Learning Model ◽  
Statistical Regression ◽  
Machine Learning Model

The complex numerical climate models pose a big challenge for scientists in weather predictions, especially for tropical system. This paper is focused on presenting the importance of weather prediction using machine learning (ML) technique. Recently many researchers recommended that the machine learning models can produce sensible weather predictions in spite of having no precise knowledge of atmospheric physics. In this work, global solar radiation (GSR) in MJ/m2/day and wind speed in m/s is predicted for Tamil Nadu, India using a random forest ML model. The random forest ML model is validated with measured wind and solar radiation data collected from IMD, Pune. The prediction results based on the random forest ML model are compared with statistical regression models and SVM ML model. Overall, random forest machine learning model has minimum error values of 0.750 MSE and R2 score of 0.97. Compared to regression models and SVM ML model, the prediction results of random forest ML model are more accurate. Thus, this study neglects the need for an expensive measuring instrument in all potential locations to acquire the solar radiation and wind speed data.

scholarly journals Investigation of Functional Dependency between the Characteristics of the Machining Process and Flatness Error Measured on a CMM

2021 ◽  
Vol 21 (6) ◽  
pp. 158-167
Author(s):  
Branko Štrbac ◽  
Dragan Rodić ◽  
Milan Delić ◽  
Borislav Savković ◽  
Miodrag Hadžistević
Keyword(s):  
Surface Roughness ◽  
Regression Models ◽  
Machining Process ◽  
Error Assessment ◽  
Statistical Regression ◽  
Form Error ◽  
Neuro Fuzzy ◽  
Input Variables ◽  
Flatness Error ◽  
The Ideal

Abstract Numerous studies have shown that the choice of measurement strategy (number and position of measurement points) when measuring form error on a coordinate-measuring machine (CMM) depends on the characteristics of the machining process which was used to machine the examined surface. The accuracy of form error assessment is the primary goal of verification procedures and accuracy is considered perfect only in the case of the ideal verification operator. Since the ideal verification operator in the “point-by-point” measuring mode is almost never used in practice, the aim of this study was to examine a relationship which had not been examined in earlier studies, namely how the machining process, surface roughness and a reduced number of points in the measurement strategy affect the accuracy of flatness error assessment. The research included four most common cutting processes applied to flat surfaces divided into nine different classes of roughness. In order to determine functional dependency between the observed input variables and the output, statistical regression models and neuro-fuzzy logic (artificial intelligence tool) were used. The analyses confirmed the significance of all three input parameters, with surface roughness being the most significant one. Both the statistical regression models and neuro-fuzzy models proved to be adequate, matching the experimental results. The use of these models makes it possible to determine flatness error measured on a CMM if input variables considered in the paper are known.

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