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Groundwater extraction from aquifers is a common practice for human use, and variations in groundwater levels can provide valuable information on the hydrogeological properties of the aquifer. However, reliable data on pumping rates and distribution are often lacking due to unsupervised groundwater pumping activities. This study presents a new mathematical model for transfer function modeling that depicts the drawdown response caused by pumping in an unconfined aquifer system. To account for the dense and unsupervised pumping events, the uniform pumping approach was used to estimate these effects. To more accurately represent unconfined flow, the model first integrates lagging theory into a response function derived from the Boussinesq equation. The lagging theory accounts for the effects of both inertial force and capillary suction. Furthermore, the model has been used to derive both specific yield and transmissivity along with two lagging parameters simultaneously using only groundwater level information from the Choshui River region in Taiwan. The estimated results suggest that this approach provides reliable estimates of hydrogeological parameters, demonstrating its usefulness for water resource management and water budget evaluation. Plain Language Summary This study examines the process of extracting water from underground sources, known as aquifers, and how monitoring changes in water levels can provide valuable insights into the characteristics of the aquifer. It can be challenging to obtain accurate information on the amount of water being pumped out due to inadequate monitoring. The researchers have developed a novel approach to comprehending the impact of water extraction on the water levels in these subterranean regions. This method considers the general pattern of water use to pump out water without close monitoring. It also incorporates realistic ideas about how water moves through the ground, taking into account factors such as the delay in water movement and the role of different forces in the soil. The study applied this method to data from Taiwan's Choshui River area and was able to determine important details about the aquifer using only water level information. These findings hold promise for effective water resource management and the wise use of water. A novel mathematical model for groundwater extraction in unconfined aquifer systems. The model captures drawdown response due to pumping, integrates insights from uniform pumping approach for reflecting heavy pumping activities, the Boussinesq equation for formulating the unconfined flow, and lagging theory for capturing the effect from capillary fringe.

This paper focuses on the modeling and analysis of a high-voltage layer heater (HVLH) designed for environmentally friendly vehicles, including electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), through multiphysics simulations that cover electrical, thermal, and fluid dynamics aspects. Due to the significant expenses and extensive time needed for producing and experimentally characterizing HVLHs, simulation and physical modeling methods are favored in the development stage. This research pioneers the separate modeling of thermal boundary conditions for the heating element (TFE) within the electrical domain, enabling the calculation of Joule heating and the analysis of transient conjugate heat transfer. Moreover, this research initiates the application of transfer function modeling for the HVLH component, expanding its use to the broader context of heating, ventilation, and air conditioning (HVAC) systems. The simulation results, which include calculations for Joule heating and temperature fields based on input voltage and flow conditions, closely follow experimental data. The derived transfer function, along with the regression parameters, precisely predicts the dynamic behavior of the system. The simulation-based modeling approach presented in this study significantly advances the design and control of environmentally friendly electric heating systems, providing a sustainable and cost-effective solution.

The uterine electromyogram, also called electrohysterogram (EHG), is the electrical signal generated by uterine contractile activity. The EHG has been considered an expanding technique for pregnancy monitoring and preterm risk evaluation. Data were collected on the abdominal surface. It has been speculated the effect of the placenta location on the characteristics of the EHG. In this work, a preliminary exploration method is proposed using the average spectra of Alvarez waves contractions of subjects with anterior and non-anterior placental position as a basis for the triple-dispersion Cole model that provides a best fit for these two cases. This leads to the uterine impedance estimation for these two study cases. Non-linear least square fitting (NLSF) was applied for this modelling process, which produces electric circuit fractional models’ representations. A triple-dispersion Cole-impedance model was used to obtain the uterine impedance curve in a frequency band between 0.1 and 1 Hz. A proposal for the interpretation relating the model parameters and the placental influence on the myometrial contractile action is provided. This is the first report regarding in silico estimation of the uterine impedance for cases involving anterior or non-anterior placental positions.

Severity of drought in California (U.S.) varies from year-to-year and is highly influenced by precipitation in winter months, causing billion-dollar events in single drought years. Improved understanding of the variability of drought on decadal and longer timescales is essential to support regional water resources planning and management. This paper presents a soft-computing approach to forecast the Palmer Drought Severity Index (PDSI) in California. A time-series of yearly data covering more than two centuries (1801–2014) was used for the design of ensemble projections to understand and quantify the uncertainty associated with interannual-to-interdecadal predictability. With a predictable structure elaborated by exponential smoothing, the projections indicate for the horizon 2015–2054 a weak increase of drought, followed by almost the same pace as in previous decades, presenting remarkable wavelike variations with durations of more than one year. Results were compared with a linear transfer function model approach where Pacific Decadal Oscillation and El Niño Southern Oscillation indices were both used as input time series. The forecasted pattern shows that variations attributed to such internal climate modes may not provide more reliable predictions than the one provided by purely internal variability of drought persistence cycles, as present in the PDSI time series.

The industrial processes that require the use of the web require a control system which allows for preserving the properties of the web unaltered, avoiding the risk of wrinkling, tearing, breakage and other defects. This control generally takes place by detecting the tension and the speed in certain points of the system since these variables determine the stress state on the web, which, if altered beyond certain ranges, can lead to the defects mentioned above. The problem of tension and web speed control is very demanding because the system’s dynamic is a function of many process variables that often vary over a wide range. In this study, an experimental system consisting of 12 rollers, four motorised, was analysed. This system was divided into four subsystems according to the logic of decentralised control. The tension of the initial and final subsystems and the speeds of the two central subsystems were monitored. This study proposes estimating continuous-time transfer functions using experimental time-domain data. A nonlinear least-squares search-based method minimises a weighted prediction error norm for directly identifying the mathematical model used to describe the web transport system. To test the performance of the proposed strategy, experimental data were collected in an open-loop configuration with constant voltage given to the four servo motors. The collected data were subsequently processed to define an extremely simple system model composed of a very limited number of parameters representing the system through transfer functions. The model was further validated by comparing the results obtained through simulations with the experimental data obtained with different inputs, and was also validated with some closed-loop tests.

The signal of climate through the North Atlantic Oscillation (NAO) extends to westerly weather and to the Baltic Sea river runoff (BSRR) and further to the salinity and the marine fauna in the Baltic Sea. Our working hypothesis was that increased BSRR should also lead to increasing nutrient concentrations in the seawater. In rivers, transfer function (TF) models of the loading were constructed by time series of BSRR and tot-P concentrations. Based on the loading time series, we modelled, to our knowledge, first time, seawater tot-P concentrations in both the Northern Baltic Proper and in the Gulf of Bothnia, both on the surface (0–20 m) and deeper (21–70 m) waters. Our results further suggest a unifying mechanism by the BSRR that could explain most prominent ecological changes observed in the Baltic Sea during and after the 1970s. Such changes are eutrophication (as in this paper) and decreasing salinity and growth and reproduction of marine fauna, all of which have been separately described as due to different causes. BSRR is crucial when possible future developments of the Baltic Sea environment are considered because a general opinion exists that the rainfall (and the BSRR) is expected to increase in pace with proceeding climate change.

Changes in groundwater level have been recognized by the earthquakes at various epicentral distances. The M9 Sumatra earthquake resulted in changes in the groundwater level, electrical conductivity, and temperature in monitoring wells on Jeju Island, South Korea. In regions of different groundwater type (basal, lower parabasal, upper parabasal, and high-level groundwater), the changes in the groundwater levels at 25 monitoring wells ranged between 4.0 and 49.5 cm; changes in the electrical conductivity at six monitoring wells ranged between 1 and 27,975 μS/cm; and the changes in water temperature at three wells ranged between 0.02 and 1.37 °C. The irregular groundwater level changes at different locations on the island due to the earthquake reflect various interactions between hydrological properties and seismological processes. The impact of the earthquake was successfully recognized via transfer function modeling between the time series of groundwater level and the tidal oscillation. On the basis of the theoretical aquifer response to the earthquake, storage coefficient estimates for aquifers, which could not be determined from the single-well pumping tests, were determined to be within the range of 1.22·10 −4 -3.51·10 −6 .

The identification of a reactive distillation system for the production of n-butyl acetate from the esterification reaction between acetic acid and n-butanol has been carried out in this research work. In order to achieve the aim of the research work, a prototype plant of the process was developed using ChemCAD from which dynamics data were generated upon applications of step changes to the reboiler duty and the reflux ratio, which were the input variables of the system. Thereafter, the transfer function of the process, later represented in Simulink environment, was formulated using the dynamics data and with the aid of MATLAB. The simulation of the transfer function model of the system was also carried out for open loop by applying step changes unto the input variables using the developed Simulink model of the system. Thereafter, the closed-loop control system developed also in Simulink environment was simulated by applying step changes to the set-point variable, which was the bottom mole fraction of n-butyl acetate. The results obtained from the simulation of the prototype plant of the reactive distillation process showed ChemCAD to be a powerful tool for steady state and dynamics prototype plant development. Furthermore, good representation and stability were also observed to exist in the system from the formulation and the simulation of the transfer function model of the process, which were carried out with the aid of MATLAB/Simulink. Moreover, the selection of appropriate closed-loop time constant contained in the tuning parameter formulas of IMC-based control system showed that the value suggested by Rivera et al. [1] was very good for this system, compared to those of Chien and Fruehauf [2] and Skogestad [3], because it could give closed-loop dynamic response with comparatively very low values of integral squared error (ISE), integral absolute error (IAE) and integral time absolute error (ITAE) for both proportional-integral (PI) and proportional-integral-derivative (PID) control systems. In addition, the comparison made between the IMC-based tuning approach and other ones (Cohen-Coon, Tyreus-Luyben and Ziegler-Nichols) considered in this work made it known that IMC-based tuning technique was the best among all those considered because its ISE, IAE and ITAE were found to be the lowest for both PI-and PID-controlled cases simulated.

Groundwater is a vital resource for various water users in the Netherlands. However, due to a changing climate, increasing water demand and changes in the water system, the country is increasingly exposed to groundwater droughts. Water managers use various indicators and statistics to identify groundwater droughts. These indicators characterise the drought for example in terms of intensity, duration and probability of occurrence. Often, these indicators require information on long-term average groundwater conditions and extreme situations that can occur over long periods. However, the availability of long-term groundwater observations of more than ten years in length is limited. Particularly, extreme groundwater drought events are ill-described and subject to large uncertainty in their characterisation. This study explores a novel method for obtaining long-term phreatic groundwater levels by combining a data-driven time series model using transfer function-noise modelling with detrended historical meteorological time series representing the current climate. The method is applied to an area in the Netherlands to generate groundwater levels for the period 1910–2022. Our results reveal differences in the characterisation of groundwater droughts when the extended groundwater time series are compared with observations of a limited duration (eight years). Using the 2018 summer drought event as an example, we find that the probability of this groundwater drought occurring is approximately once every twelve years, based on the eight-year observation period. However, this probability reduces to a once every 24-year event when using historically generated groundwater time series to characterise the groundwater drought. We conclude that characterising droughts with the extended groundwater time series based on historical meteorological data can provide a more comprehensive insight into the intensity and frequency of groundwater droughts, as well as the probability of occurrence of current groundwater levels. Hence, the proposed method supports water managers in establishing return period-based criteria for measures, such as deciding when to implement irrigation bans.

Understanding the temporal variability in the concentration of airborne PM10 can be of benefit as it would lead to more reliable models that can inform the monitoring and control of air pollution. Established forecasting approaches are generally data driven and offer little in terms of furthering the understanding of the dynamics of data. A variability decomposition (VD) based transfer function model can be used to decompose variability in time series data into inherent and external, thus concentrating on modelling only the external variability as a function of the model inputs. The VD approach was used to model the multi-year maximum daily PM10 concentration recorded in St Leonards, Edinburgh using historic values and PM10 concentrations recorded at the Grangemouth monitoring station situated 19 miles to the North West using two established approaches as benchmarks. The results indicate that the transfer function models using the Grangemouth data were superior to the univariate model in terms of the RMSE and MAPE. The performance of the VD transfer function model was comparable to the benchmark in terms of forecast accuracy, but superior in providing improved physical interpretation of the model components