The extended transfer function model for the simulation of pesticides transport along the unsaturated zone

2021 ◽  
Author(s):  
Marialaura Bancheri ◽  
Antonio Coppola ◽  
Annachiara Colombi ◽  
Angelo Basile
Keyword(s):  
Transfer Function ◽  
Travel Time ◽  
Unsaturated Zone ◽  
Transfer Functions ◽  
Temporal Distribution ◽  
Absolute Error ◽  
Breakthrough Curves ◽  
Retardation Factor ◽  
Transfer Function Model ◽  
Function Model

<p><span lang="EN-US">The scope of this work is to present the extended transfer function model (TFM-ext) that allows to simulate the spatio-temporal distribution of nonpoint-source pollutants, e.g., pesticides, along the unsaturated zone, till the groundwater table depth.</span></p> <p><span lang="EN-US">The model is based on the transfer functions approach, i.e., on the travel time probability density functions (TT pdfs), which describe the leaching behavior in a given soil profile. The strength of the model, despite the important assumptions on time-invariant TT pdfs and steady-state input fluxes, is that it derives the TT pdfs from a physical quantity, i.e., the unsaturated hydraulic conductivity function k(</span>θ<span lang="EN-US">).  Moreover, the model extends the transport process to the generic depth z, where information on hydraulic properties could not be available, assuming a lognormal travel time pdf, whose parameters are scaled according to the generalized transfer function model. </span><span lang="EN-GB">In the case of reactive solutes, the model considers both the mass decay and the retardation factor.</span></p> <p><span lang="EN-US">The TFM-ext was validated in Valle Telesina, a hilly area of around 200 km<sup>2</sup><span class="apple-converted-space"> </span>in Italy. Forty-six soil profiles, completely characterized from the hydrological point of view, were used to evaluate the mean travel times and the breakthrough curves at the groundwater depth and then compared with the results of a physically based model, Hydrus 1D. Results gave very high correlation coefficients (above 0.8), a mean absolute error of around 40 days and a percent bias of -16%.</span></p> <p><span lang="EN-US">Moreover, a comprehensive sensitivity analysis to evaluate to which parameters the TFM-ext is more sensitive, was performed. Results shown that </span>τ <span lang="EN-US">anf θ<sub>s</sub> parameters related to the slope of the k(θ) are those affecting more the travel time. </span></p> <p><span lang="EN-US">The model was implemented as an operative tool for the specific groundwater vulnerability assessment within the geospatial Decision Support System developed for LANDSUPPORT H2020 project.</span></p>

Dynamic Observer Method Based on Modified Green’s Functions for Robust and More Stable Inverse Algorithms

2008 ◽  
Author(s):  
Priscila F. B. Sousa ◽  
Ana P. Fernandes ◽  
Vale´rio Luiz Borges ◽  
George S. Dulikravich ◽  
Gilmar Guimara˜es
Keyword(s):  
Heat Transfer ◽  
Transfer Function ◽  
Green's Functions ◽  
Transfer Functions ◽  
Green’S Functions ◽  
Three Dimensional ◽  
Original Method ◽  
Identification Algorithm ◽  
Transfer Function Model ◽  
Function Model

This work presents a modified procedure to use the concept of dynamic observers based on Green’s functions to solve inverse problems. The original method can be divided in two distinct steps: i) obtaining a transfer function model GH and; ii) obtaining heat transfer functions GQ and GN and building an identification algorithm. The transfer function model, GH, is obtained from the equivalent dynamic systems theory using Green’s functions. The modification presented here proposes two different improvements in the original technique: i) A different method of obtaining the transfer function model, GH, using analytical functions instead of numerical procedures, and ii) Definition of a new concept of GH to allow the use of more than one response temperature. Obtaining the heat transfer functions represents an important role in the observer method and is crucial to allow the technique to be directly applied to two or three-dimensional heat conduction problems. The idea of defining the new GH function is to improve the robustness and stability of the algorithm. A new dynamic equivalent system for the thermal model is then defined in order to allow the use of two or more temperature measurements. Heat transfer function, GH can be obtained numerically or analytically using Green’s function method. The great advantage of deriving GH analytically is to simplify the procedure and minimize the estimative errors.

scholarly journals Rational Transfer Function Model for a Double-Pipe Parallel-Flow Heat Exchanger

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1212
Author(s):  
Krzysztof Bartecki
Keyword(s):  
Heat Exchanger ◽  
Partial Differential Equations ◽  
Transfer Function ◽  
Differential Equations ◽  
Transfer Functions ◽  
Parallel Flow ◽  
Transfer Function Model ◽  
Function Model ◽  
Rational Transfer ◽  
Double Pipe

Transfer functions of typical heat exchangers, resulting from their partial differential equations, usually contain irrational functions which quite accurately describe the spatio-temporal nature of the processes occurring therein. However, such an accurate but complex mathematical representation is often not convenient from the practical point of view, and some approximation of the original model would be more useful. This paper discusses approximate rational transfer functions for a typical thick-walled double-pipe heat exchanger working in the parallel-flow configuration. Using the method of lines with the backward difference scheme, the original symmetric hyperbolic partial differential equations describing the heat transfer phenomena are transformed into a set of ordinary differential equations and expressed in the form of N subsystems representing spatial sections of the exchanger. Each section is described by a rational transfer function matrix and their cascade interconnection results in the overall approximation model expressed by a matrix of rational transfer functions of high order. Based on the rational transfer function representation, the frequency and steady-state responses of the approximate model are evaluated and compared with those resulting from its original irrational transfer function model. The presented results show better approximation quality for the “crossover” input–output channels where the in-domain heat conduction effects prevail as compared to the “straightforward” channels, where the transport delay associated with the heat convection dominates.

scholarly journals Fitting of Generic Process Models by an Asymmetric Short Relay Feedback Experiment—The n-Shifting Method

2021 ◽  
Vol 11 (4) ◽  
pp. 1651
Author(s):  
José Sánchez Moreno ◽  
Sebastián Dormido Bencomo ◽  
José Manuel Díaz Martínez
Keyword(s):  
Time Delay ◽  
Transfer Function ◽  
Dynamic Systems ◽  
Transfer Functions ◽  
Process Models ◽  
Transfer Function Model ◽  
Minimum Phase ◽  
Relay Feedback ◽  
Function Model ◽  
Generic Process

This paper presents the generalization of the shifting method for relay feedback identification of dynamic systems of any order. The original shifting method enables the fitting of a maximum of five parameters of a transfer function model from the information obtained from a short relay test and without prior knowledge of the process to identify. The generalization, known as n-shifting, allows the estimation of the parameters of transfer functions of any order by applying one short relay test to the process to identify. Without loss of generality, the n-shifting approach is applied to fit an n-order plus time delay (n-OPTD) model but the approach can be also developed to identify models with other structures (non-minimum phase, unstable, integrators). Some examples of estimations are presented.

An Instrumental Variable Method for Continuous-Time Transfer Function Model Identification With Application to Controller Auto-Tuning

2006 ◽  
Vol 129 (2) ◽  
pp. 154-162
Author(s):  
Patrick J. Cunningham ◽  
Matthew A. Franchek
Keyword(s):  
Transfer Function ◽  
Instrumental Variable ◽  
Transfer Functions ◽  
Spark Ignition Engine ◽  
Discrete Models ◽  
Parameter Estimates ◽  
Transfer Function Model ◽  
Function Model ◽  
Asymptotic Consistency ◽  
Auto Tuning

An instrumental variable algorithm is presented that estimates the coefficients of a continuous transfer function model directly from sampled data. The algorithm is based on instrumental variables extracted from an auxiliary model and input and output signal derivatives estimated by filtered difference equations. As a result, this method does not require any prior knowledge of the output noise. To ensure the validity of the filtered derivative estimates, a criterion based on the Nyquist frequency and the system bandwidth is established. Then the concept of asymptotic consistency is applied to the proposed instrumental variable algorithm to identify the conditions for convergence of the model parameter estimates. Specifically, the asymptotic consistency conditions impose a continuous and persistent exciting constraint on the input signal. This is analogous to the persistent excitation condition for identification of discrete models. The proposed instrumental variable algorithm is demonstrated within an auto-tuning algorithm for feedback controllers based on plant inversion. In this application, the algorithm is only suitable for lower-order transfer functions that are minimum-phase and stable. These types of systems are common in industrial applications for manufacturing and process control. Here, the algorithm is experimentally validated for automatic tuning of the idle speed controller on a 4.6L Ford V-8 spark ignition engine.

On the Analysis of Pollen-Climate Canonical Transfer Functions

1974 ◽  
Vol 4 (2) ◽  
pp. 162-174 ◽  
Author(s):  
Reid A. Bryson ◽  
John E. Kutzbach
Keyword(s):  
Transfer Function ◽  
Canonical Correlation ◽  
Transfer Functions ◽  
Canonical Variate ◽  
Transfer Function Model ◽  
Climate Variables ◽  
Site Factors ◽  
Function Model ◽  
Ecological Relationships ◽  
Canonical Transfer

Canonical correlation analysis, as described by Webb and Bryson, Quaternary Research 1972, provides a means of reconstructing past climates quantitatively from fossil pollen using a pollen-climate transfer function. This paper presents a method for analysis of variance of the transfer function model. This method is used to identify ecological relationships among the pollen and climate variables, to select climatically sensitive taxa, and to investigate the importance of site factors. Several criteria are presented, in addition to those used by Webb and Bryson, for choosing canonical variate pairs to include in the transfer function model, namely: the variate pair relationships should be ecologically meaningful; the transfer function model should yield stable paleoclimatic estimates; and, the variate pair relationships should be statistically meaningful. The application of these criteria to the set of variate pairs used in the transfer function model of Webb and Bryson is described and modifications are suggested.

Model Fungsi Transfer Input Ganda untuk Pemodelan Jakarta Islamic Index

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Nur Laela Fitriani ◽  
Pika Silvianti ◽  
Rahma Anisa
Keyword(s):  
Exchange Rate ◽  
Transfer Function ◽  
Multivariate Time Series ◽  
Model Identification ◽  
Transfer Function Model ◽  
Function Model ◽  
Jones Index ◽  
Multiple Input ◽  
Identification Stage ◽  
Stage Estimation

Transfer function model with multiple input is a multivariate time series forecasting model that combines several characteristics of ARIMA models by utilizing some regression analysis properties. This model is used to determine the effect of output series towards input series so that the model can be used to analyze the factors that affect the Jakarta Islamic Index (JII). The USD exchange rate against rupiah and Dow Jones Index (DJI) were used as input series. The transfer function model was constructed through several stages: model identification stage, estimation of transfer function model, and model diagnostic test. Based on the transfer function model, the JII was influenced by JII at the period of one and two days before. JII was also affected by the USD exchange rate against rupiah at the same period and at one and two days before. In addition, the JII was influenced by DJI at the same period and also at period of one until five days ago. The Mean Absolute Prencentage Error (MAPE) value of forecasting result was 0.70% and the correlation between actual and forecast data was 0.77. This shows that the model was well performed for forecasting JII.

scholarly journals Administrative Costs and Tariff Rates in the Presence of Customs Evasion: Evidence from Ecuador

Economies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 21
Author(s):  
Jazmín González Aguirre ◽  
Alberto Del Villar
Keyword(s):  
International Trade ◽  
Transfer Function ◽  
Gross Domestic Product ◽  
Foreign Trade ◽  
Transfer Function Model ◽  
Administrative Cost ◽  
Function Model ◽  
Administrative Costs ◽  
Tariff Rates ◽  
Budgetary Expenditure

This paper seeks to assess the effectiveness of customs policies in increasing the resources devoted to controlling and inspection. Specifically, it seeks to analyze whether an increase in the administrative cost of collecting taxes on foreign trade in Ecuador contributes to reducing customs fraud. To this end, we identify and estimate a transfer function model (ARIMAX), considering information on foreign trade such as official international trade statistics report and tariff rates, as well as the execution of budgetary expenditure and Ecuador’s gross domestic product (GDP). The period under study includes quarterly series from 2006 to 2018. The results obtained by the model indicate that allocating greater material and budgetary resources to combat customs fraud does not always achieve the objective of reducing customs evasion.

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