scholarly journals Empirical prediction models for Vaccinium myrtillus and V. vitis-idaea berry yields in North Karelia, Finland

Silva Fennica ◽  
2003 ◽  
Vol 37 (1) ◽  
Author(s):  
Marjut Ihalainen ◽  
Kauko Salo ◽  
Timo Pukkala
Keyword(s):  
Prediction Models ◽  
Vaccinium Myrtillus ◽  
Empirical Prediction

The Quantification of Internal Noise Levels and Wall Pressure Spectra in Industrial Gas Pipelines

1991 ◽  
Author(s):  
M. P. Norton ◽  
A. Pruiti
Keyword(s):  
Prediction Models ◽  
Transmission Loss ◽  
Pressure Fluctuations ◽  
Internal Noise ◽  
Wall Pressure ◽  
Gas Pipelines ◽  
Noise Levels ◽  
Empirical Prediction ◽  
Wall Pressure Fluctuations ◽  
Semi Empirical

Abstract This paper addresses the issue of quantifying the internal noise levels/wall pressure fluctuations in industrial gas pipelines. This quantification of internal noise levels/wall pressure fluctuations allows for external noise radiation from pipelines to be specified in absolute levels via appropriate noise prediction models. Semi-empirical prediction models based upon (i) estimated vibration levels and radiation ratios, (ii) semi-empirical transmission loss models, and (iii) statistical energy analysis models have already been reported on by Norton and Pruiti 1,3 and are not reported on here.

Modeling and Analysis of Surface Roughness with Statistical and Soft Computing Approach

2021 ◽  
Vol 106 ◽  
pp. 109-115
Author(s):  
L.B. Abhang ◽  
M. Hameedullah
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Prediction Models ◽  
Tool Geometry ◽  
Modeling And Analysis ◽  
Empirical Prediction ◽  
Lathe Machine ◽  
Carbide Inserts ◽  
Computing Approach

The objective of this study focuses on developing empirical prediction models using response regression analysis and fuzzy-logic. These models latter can be used to predict surface roughness according to technological variables. The values of surface roughness produced by these models are compared with experimental results. Experimental investigation has been carried out by using scientific composite factorial design on precision lathe machine with tungsten carbide inserts. Surface roughness measured at end of each experimental trial (three times), to get the effect of machining conditions and tool geometry on the surface finish values. Research showed that soft computing fuzzy logic model developed produces smaller error and has satisfactory results as compared to response regression model during machining.

scholarly journals Empirical Prediction Models of 1-min Rain Rate Distribution for Various Integration Time

2008 ◽  
Vol 8 (2) ◽  
pp. 84-89 ◽  
Author(s):  
Myoung-Won Jung ◽  
Il-Tak Han ◽  
Moon-Young Choi ◽  
Joo-Hwan Lee ◽  
Jeong-Ki Pack
Keyword(s):  
Rain Rate ◽  
Prediction Models ◽  
Integration Time ◽  
Rate Distribution ◽  
Empirical Prediction

scholarly journals Measurement and Prediction of Short-Range Path Loss between 27 and 40 GHz in University Campus Scenarios

2020 ◽  
Author(s):  
Glaucio Ramos ◽  
Carlos Vargas ◽  
Luiz Mello ◽  
Paulo Pereira ◽  
Sandro Gonçalves ◽  
...  
Keyword(s):  
Short Range ◽  
Prediction Models ◽  
Path Loss ◽  
Prediction Method ◽  
University Campus ◽  
Millimetre Wave ◽  
Empirical Prediction ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Path Loss Measurements

Abstract In this paper, we present the results of short-range path loss measurements in the microwave and millimetre wave bands, at frequencies between 27 and 40 GHz, obtained in a campaign inside a university campus in Rio de Janeiro, Brazil. Existing empirical path loss prediction models, including the alpha-beta-gamma (ABG) model and the close-in free space reference distance with frequency dependent path loss exponent (CIF) model are tested against the measured data, and an improved prediction method that includes the path loss dependence on the height di erence between transmitter and receiver is proposed. A fuzzy technique is also applied to predict the path loss and the results are compared with those obtained with the empirical prediction models.

scholarly journals Approach to Petiole Sap Nutritional Diagnosis Method by Empirical Model Based on Climatic and Growth Parameters

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 188
Author(s):  
Alfonso Llanderal ◽  
Pedro García-Caparrós ◽  
José Pérez-Alonso ◽  
Juana Isabel Contreras ◽  
María Luz Segura ◽  
...  
Keyword(s):  
Soil Solution ◽  
Nutrient Concentration ◽  
Prediction Models ◽  
Growth Parameters ◽  
Sampling Period ◽  
Tomato Crop ◽  
Area Index ◽  
Empirical Prediction ◽  
Diagnosis Method ◽  
Petiole Sap

The aim of this work is to evaluate the relationship between the nutrient concentration in petiole sap and different agronomic and climatic variables for a tomato crop grown in a greenhouse in Mediterranean conditions. In addition, the persistence of the nutrient concentration in petiole sap was investigated with the aim of determining the sampling period that implies the best trade-off nutritional information. The experiment consisted of the selection of 20 sampling points inside the greenhouse. The samples of petiole, fully expanded leaf, and soil solution samples were collected weekly from 86 to 163 days after transplanting. Chloride, NO3−-N, H2PO4−-P, Na+, K+, Ca2+, and Mg2+ concentrations were determined in petiole sap and soil solution obtained by suction cups. Nitrogen, P, K, Cl, and Na concentrations were also determined in leaf. Finally, the petiole sap nutritional diagnosis method is the highest sensitive nutritional diagnosis method which compares soil solution and nutrient leaf content related to yield, and the statistical analysis performed in this research demonstrates that crop evapotranspiration (ETc), vapor pressure deficit (DPV), and leaf area index (LAI) are considered the most significant variables that allow the development of these empirical prediction models regarding nutrient concentration in petiole sap.

Empirical Prediction Models for Strength Gain Properties of Fly Ash Based Concrete Subjected to Accelerated Curing

2018 ◽  
Vol 1150 ◽  
pp. 73-90
Author(s):  
Vallarasu Manoharan Sounthararajan
Keyword(s):  
Fly Ash ◽  
Prediction Models ◽  
Pulse Velocity ◽  
Hot Water ◽  
Experimental Investigations ◽  
Strength Gain ◽  
Steam Curing ◽  
Empirical Prediction ◽  
Accelerated Curing ◽  
Water Curing

Experimental investigations on the early age, strength gain properties of fly ash blended cement concretes containing low and high volume fly ash replacement were studied. Concrete mixes were prepared with two different fly ash contents and varying concrete ingredients with water to binder ratio (w/b), fine to coarse aggregate ratio (F/c) and accelerator dosage. Five different curing techniques, namely controlled humidity curing; hot air oven curing, steam curing, hot water curing and normal water curing were adopted for curing the fly ash based concretes. Test results showed evidence the influence of accelerating admixtures and accelerated curing for obtaining the high early strength properties in fly ash mixed concrete. Most notably a maximum 1 day compressive strength of 40.20 MPa and 34.60 MPa with low (25%) and high (50%) volume fly ash concretes were obtained respectively in this study. Experimental results clearly indicated that the improvements on the strength gain properties with the careful selection of mix ingredients; accelerator addition and accelerated curing in fly ash based concrete mixes. Also, significant improvements on the flexural strength, elastic modulus, dynamic modulus and the ultrasonic pulse velocity test were noticed.

3D Physics-Based Numerical Simulations of Ground Motion in Istanbul from Earthquakes along the Marmara Segment of the North Anatolian Fault

2020 ◽  
Vol 110 (5) ◽  
pp. 2559-2576 ◽  
Author(s):  
Maria Infantino ◽  
Ilario Mazzieri ◽  
Ali Güney Özcebe ◽  
Roberto Paolucci ◽  
Marco Stupazzini
Keyword(s):  
Numerical Simulations ◽  
Ground Motion ◽  
Prediction Models ◽  
Seismic Source ◽  
North Anatolian Fault ◽  
Empirical Prediction ◽  
Earthquake Scenarios ◽  
The North ◽  
Specific Geometry ◽  
Directivity Effects

ABSTRACT In this article, the outcomes of a research cooperation between Politecnico di Milano, Italy, and Munich RE, Germany, aiming to improve ground-motion estimation in the Istanbul area through 3D physics-based numerical simulations (PBSs), are illustrated. To this end, 66 PBSs were run, considering earthquake scenarios of magnitude ranging from Mw 7 to 7.4 along the North Anatolian fault (NAF; Turkey), offshore Istanbul. The present article focuses on the detailed introduction of the simulated scenarios comprising: (1) the setup of the 3D numerical model, (2) the validation of the model with recordings of a recent earthquake, (3) the PBSs results, (4) a parametric study on the effect of different features of the seismic source, and (5) a comparison with well-established ground-motion prediction equations to highlight the main differences resulting from the use of a standard empirical approach as opposed to physics-based “source-to-site” numerical simulations. As a main outcome of this study, we observed as, for magnitude Mw 7 and 7.2, PBSs are in agreement with empirical prediction models whereas, for magnitude Mw 7.4, PBSs provide higher ground-motion estimates, as a consequence of directivity effects, amplified by the specific geometry of the portion of the NAF facing Istanbul.

Thermal conductivity of base-course materials

2005 ◽  
Vol 42 (1) ◽  
pp. 61-78 ◽  
Author(s):  
Jean Côté ◽  
Jean-Marie Konrad
Keyword(s):  
Thermal Conductivity ◽  
Prediction Models ◽  
Geometric Mean ◽  
Coarse Sand ◽  
Solid Particles ◽  
Degree Of Saturation ◽  
Empirical Prediction ◽  
Course Materials ◽  
Base Course ◽  
Improved Model

This paper presents the results of a comprehensive laboratory study on the thermal conductivity of dense and broadly graded coarse base-course materials used in pavements. Materials were selected from eight quarries along the axis of the St. Lawrence River to include a variety of samples of different geological origins. Nearly 200 tests were performed in a thermal conductivity cell using Pyrex heat flux meters to characterize the relationships between the thermal conductivity of unfrozen and frozen samples and the water–ice content. Sixteen tests were also performed on solid rock cylinders to characterize the influence of mineralogy on the thermal conductivity of solid particles from the selected quarries. The most widely used empirical prediction models for thermal conductivity of soils from the literature were found inappropriate to estimate the thermal conductivity of base-course materials. An improved model using the geometric mean method to compute the thermal conductivity for the solid particles and the saturated materials, a modified form of the geometric mean method to predict the thermal conductivity of dry materials, and empirical relationships to assess the normalized thermal conductivity of unfrozen and frozen base-course materials are presented. This new model predicted well the thermal conductivity for more than 150 unfrozen and frozen coarse sand and gravel samples from the literature. A step by step methodology is proposed to assess the thermal conductivity of base-course materials.Key words: base course, porosity, degree of saturation, mineralogy, unfrozen–frozen, thermal conductivity.

Sound-Propagation Curves in Industrial Workrooms: Statistical Trends and Empirical Prediction Models

1996 ◽  
Vol 3 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Murray Hodgson
Keyword(s):  
Prediction Models ◽  
Sound Propagation ◽  
Power Level ◽  
Point Of View ◽  
Absolute Level ◽  
Line Segments ◽  
Empirical Prediction ◽  
Straight Line ◽  
Statistical Trends ◽  
Made In

Predictions of workroom noise levels are based on predictions of the workroom sound-propagation curve, SP( r) – the variation with distance r from an omnidirectional point source of the sound-pressure level Lp ( r) minus the source sound-power level Lw SP( r) = Lp ( r)- Lw. While more accurate approaches such as ray tracing exist, from a practical point of view there is considerable scope for developing simple empirical prediction methods. In fact, several such models exist. However, these have short-comings which warrant the development of a new model. The approach taken here was to predict the slope(s) and absolute level(s) of the sound-propagation curve, approximated by one or more straight-line segments. With this in mind, octave-band sound-propagation measurements were made in a number of empty and fitted workrooms. The curves were approximated by one or two straight-line segments. The intercepts and slopes of the segments were then determined, and their averages and standard deviations calculated. The statistical trends provide information on the behaviour of sound in industrial workrooms. The results are used to develop simple empirical prediction models.

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