Analytical Model of the Soil Temperature Distribution Based on Weather Data

2020 ◽  
Author(s):  
M. Naser Reda ◽  
Markus Spinnler ◽  
Rajib Mahamud ◽  
Thomas Sattelmayer
Keyword(s):  
Temperature Distribution ◽  
Soil Temperature ◽  
Analytical Model ◽  
Numerical Models ◽  
Computational Cost ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Data Sets ◽  
Temperature Profiles

Abstract The measurement of soil temperature profiles for different locations or climates is essential for calculating the thermal performance of applications connected with the soil, e.g., underground heat storage systems. Estimating soil temperature profiles is identified as crucial knowledge for plant and crop growth as well as for germination in all agricultural tasks. The ground temperature depends on weather conditions (ambient temperature, solar irradiation, wind velocity, sky radiation, etc.) that contribute to the resulting temperature distribution within the soil close to the surface. In literature, several approaches have been discussed to predict soil temperature in different climates and locations, such as data-driven models, wavelet transform artificial neural networks, statistical models, etc. However, these models require extensive data sets from literature and high computational efforts. In the present study, a one-dimensional analytical model will be presented, which is based on the Green’s Function (GF) method. The model can estimate the daily and annual variation of the soil temperature distribution at different depths from real-time weather data sets. The model was experimentally validated with an accuracy of more than 96%. The significant advantage of the presented analytical method is the low computational cost, which is lower than that of numerical models by approximately two orders of magnitude.

Analytical Model of the Soil Temperature Distribution Based on Weather Data

2021 ◽  
Author(s):  
M. Naser Reda ◽  
M. Spinnler ◽  
R. Mahamud ◽  
Thomas Sattelmayer
Keyword(s):  
Temperature Distribution ◽  
Soil Temperature ◽  
Analytical Model ◽  
Weather Data

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

scholarly journals Wave Propagation in Thin-Walled Aortic Analogues

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
C. G. Giannopapa ◽  
J. M. B. Kroot ◽  
A. S. Tijsseling ◽  
M. C. M. Rutten ◽  
F. N. van de Vosse
Keyword(s):  
Experimental Data ◽  
Wave Propagation ◽  
Frequency Domain ◽  
Analytical Model ◽  
Viscoelastic Properties ◽  
Numerical Models ◽  
Computational Cost ◽  
One Dimensional ◽  
Arterial Blood

Research on wave propagation in liquid filled vessels is often motivated by the need to understand arterial blood flows. Theoretical and experimental investigation of the propagation of waves in flexible tubes has been studied by many researchers. The analytical one-dimensional frequency domain wave theory has a great advantage of providing accurate results without the additional computational cost related to the modern time domain simulation models. For assessing the validity of analytical and numerical models, well defined in vitro experiments are of great importance. The objective of this paper is to present a frequency domain analytical model based on the one-dimensional wave propagation theory and validate it against experimental data obtained for aortic analogs. The elastic and viscoelastic properties of the wall are included in the analytical model. The pressure, volumetric flow rate, and wall distention obtained from the analytical model are compared with experimental data in two straight tubes with aortic relevance. The analytical results and the experimental measurements were found to be in good agreement when the viscoelastic properties of the wall are taken into account.

scholarly journals Seamless climate change projections and seasonal predictions for bushfires in Australia

2020 ◽  
Vol 70 (1) ◽  
pp. 120
Author(s):  
Andrew J. Dowdy
Keyword(s):  
Climate Change ◽  
Long Range ◽  
Time Scales ◽  
Weather Conditions ◽  
Weather Data ◽  
Future Climate Change ◽  
Data Sets ◽  
Fire Weather ◽  
Climate Change Projections ◽  
Different Time Scales

Spatio-temporal variations in fire weather conditions are presented based on various data sets, with consistent approaches applied to help enable seamless services over different time scales. Recent research on this is shown here, covering climate change projections for future years throughout this century, predictions at multi-week to seasonal lead times and historical climate records based on observations. Climate projections are presented based on extreme metrics with results shown for individual seasons. A seasonal prediction system for fire weather conditions is demonstrated here as a new capability development for Australia. To produce a more seamless set of predictions, the data sets are calibrated based on quantile-quantile matching for consistency with observations-based data sets, including to help provide details around extreme values for the model predictions (demonstrating the quantile matching for extremes method). Factors influencing the predictability of conditions are discussed, including pre-existing fuel moisture, large-scale modes of variability, sudden stratospheric warmings and climate trends. The extreme 2019–2020 summer fire season is discussed, with examples provided on how this suite of calibrated fire weather data sets was used, including long-range predictions several months ahead provided to fire agencies. These fire weather data sets are now available in a consistent form covering historical records back to 1950, long-range predictions out to several months ahead and future climate change projections throughout this century. A seamless service across different time scales is intended to enhance long-range planning capabilities and climate adaptation efforts, leading to enhanced resilience and disaster risk reduction in relation to natural hazards.

scholarly journals Wave Propagation in Thin-Walled Aortic Analogues

2008 ◽  
Author(s):  
C. G. Giannopapa ◽  
J. M. B. Kroot
Keyword(s):  
Experimental Data ◽  
Wave Propagation ◽  
Frequency Domain ◽  
Analytical Model ◽  
Viscoelastic Properties ◽  
Numerical Models ◽  
Computational Cost ◽  
Arterial Blood Flow ◽  
One Dimensional ◽  
Arterial Blood

Research wave propagation in liquid filled vessels is often motivated by the need to understand arterial blood flow. Theoretical and experimental investigation of the propagation of waves in flexible tubes has been studied by many researchers. The analytical one dimensional frequency domain wave theory has a great advantage of providing accurate results without the additional computational cost related to the modern time domain simulation models. For assessing the validity of analytical and numerical models well defined in-vitro experiments are of great importance. The objective of this paper is to present a frequency domain transmission line analytical model based on one-dimensional wave propagation theory and validate it against experimental data obtained for aortic analogues. The elastic and viscoelastic properties of the wall are included in the analytical model. The pressure, flow and wall distention results obtained from the analytical model are compared with experimental data in two straight tubes with aortic relevance. The analytical models and the experimental measurements were found to be in good agreement when the viscoelastic properties of the wall are taken into account.

An analytical model for estimating soil temperature profiles on the Qinghai-Tibet Plateau of China

2015 ◽  
Vol 8 (2) ◽  
pp. 232-240 ◽  
Author(s):  
Guojie Hu ◽  
Lin Zhao ◽  
Xiaodong Wu ◽  
Ren Li ◽  
Tonghua Wu ◽  
...  
Keyword(s):  
Soil Temperature ◽  
Analytical Model ◽  
Tibet Plateau ◽  
Temperature Profiles ◽  
Qinghai Tibet Plateau

Multiple Reflection and Transmission Theory for Wave Propagation in the Aorta

2009 ◽  
Author(s):  
C. G. Giannopapa ◽  
J. M. B. Kroot
Keyword(s):  
Experimental Data ◽  
Wave Propagation ◽  
Frequency Domain ◽  
Analytical Model ◽  
Numerical Models ◽  
Computational Cost ◽  
Arterial Blood Flow ◽  
Transition Line ◽  
One Dimensional ◽  
Arterial Blood

Wave propagation in liquid filled vessels is often motivated by the need to understand arterial blood flow. Theoretical and experimental investigations of traveling waves in flexible tubes have been performed by many researchers. The analytical one dimensional frequency domain wave theory has a great advantage of providing accurate results without the additional computational cost involved in the modern time domain simulation models. Transition line theory allows including non uniformities of vessels by capturing them as several uniform segments. For assessing the validity of analytical and numerical models well defined in-vitro experiments are of great importance. The objective of this paper is to present a frequency domain transmission line analytical model based on one-dimensional wave propagation theory and validate it against experimental data obtained for aortic analogues. The analytical model is set up by multiple sections and a formulation is derived that incorporates the multiple reflections and transmissions of propagating waves through the interfaces of these sections. The aortic analogues include straight and tapered tubes. The pressure, flow and wall distention results obtained from the analytical model are compared with experimental data in two straight tubes and one tapered one with aortic relevance. The analytical models and the experimental measurements were found to be in good agreement for both the uniform and tapered tubes.

HOW TO USE CROP GROWTH MODEL WOFOST FOR FORECASTING GROWTH AND YIELD OF A CROP

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
LAL SINGH ◽  
PARMEET SINGH ◽  
RAIHANA HABIB KANTH ◽  
PURUSHOTAM SINGH ◽  
SABIA AKHTER ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Growth And Yield ◽  
Weather Data ◽  
Data Sets ◽  
Crop Growth Model ◽  
Control Center ◽  
Field Crops ◽  
Crop Calendar

WOFOST version 7.1.3 is a computer model that simulates the growth and production of annual field crops. All the run options are operational through a graphical user interface named WOFOST Control Center version 1.8 (WCC). WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. The files with crop, soil and weather data are explained, as well as the run files and the output files. A general overview is given of the development and the applications of the model. Its underlying concepts are discussed briefly.

High-speed isotachophoresis. Analytical aspects of the steady-state longitudinal temperature profiles

1979 ◽  
Vol 44 (3) ◽  
pp. 841-853 ◽  
Author(s):  
Zbyněk Ryšlavý ◽  
Petr Boček ◽  
Miroslav Deml ◽  
Jaroslav Janák
Keyword(s):  
Steady State ◽  
Temperature Distribution ◽  
Electric Field ◽  
Field Intensity ◽  
Electric Field Intensity ◽  
Minor Component ◽  
Physical Models ◽  
Temperature Profiles ◽  
Longitudinal Temperature ◽  
Continuous Character

The problem of the longitudinal temperature distribution was solved and the bearing of the temperature profiles on the qualitative characteristics of the zones and on the interpretation of the record of the separation obtained from a universal detector was considered. Two approximative physical models were applied to the solution: in the first model, the temperature dependences of the mobilities are taken into account, the continuous character of the electric field intensity at the boundary being neglected; in the other model, the continuous character of the electric field intensity is allowed for. From a comparison of the two models it follows that in practice, the variations of the mobilities with the temperature are the principal factor affecting the shape of the temperature profiles, the assumption of a discontinuous jump of the electric field intensity at the boundary being a good approximation to the reality. It was deduced theoretically and verified experimentally that the longitudinal profiles can appreciably affect the longitudinal variation of the effective mobilities in the zone, with an infavourable influence upon the qualitative interpretation of the record. Pronounced effects can appear during the analyses of the minor components, where in the corresponding short zone a temperature distribution occurs due to the influence of the temperatures of the neighbouring zones such that the temperature in the zone of interest in fact does not attain a constant value in axial direction. The minor component does not possess the steady-state mobility throughout the zone, which makes the identification of the zone rather difficult.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

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