Resistance-based Model Research Articles

Integrating temperature gradient-based 3T and resistance-based models for simulating evapotranspiration and its components

The accurate estimation of evapotranspiration (ET) and its major components [e.g., transpiration (Tr) and soil evaporation (Ev)] requires an understanding of complex soil–plant-atmosphere interactions. The temperature gradient-based three temperature (3T) model for simulating the dynamics of Ev and Tr has no requirement for resistance terms and its use of simple input variables (i.e., temperature and radiation) allows its application to both field studies and remote sensing. However, reference temperature is a key requirement for the 3T model and approaches for obtaining accurate reference temperature and the implications of different reference temperature choices on ET estimates remain poorly understood. This study improved a resistance-based model applicable to a wide range of climatic/ecosystem conditions and integrated it with the 3T model to predict ET and Tr. A novel numerical approach was used to estimate the reference temperature under dry land surface scenarios. The two models provided similar predictions of Ev, Tr, and ET at the four sites provided that the available energy for the canopy (e.g., net radiation) and ground soil (e.g., net radiation and ground heat flux) was constrained within the framework of the energy balance. Alternatively, the accuracy of simulations of Ev and ET by the 3T model significantly declined due to systematic mismatching of input drivers (temperature and radiation) under dry and wet scenarios. We emphasize that 3T is working better when input temperature and radiation under dry and wet land conditions were systematically matched within the framework of energy balance. The results of this study provide a promising novel temperature-gradient method for detecting canopy water stress.

Atmospheric deposition of reactive nitrogen to a deciduous forest in the southern Appalachian Mountains.

Assessing nutrient critical load exceedances requires complete and accurate atmospheric deposition budgets for reactive nitrogen (Nr). The exceedance is the total amount of Nr deposited to the ecosystem in excess of the critical load, which is the amount of Nr input below which harmful effects do not occur. Total deposition includes all forms of Nr (i.e., organic and inorganic) deposited to the ecosystem by wet and dry pathways. Here we present results from the Southern Appalachian Nitrogen Deposition Study (SANDS), in which a combination of measurements and field-scale modeling was used to develop a complete annual Nr deposition budget for a deciduous forest at the Coweeta Hydrologic Laboratory. Wet deposition of ammonium, nitrate, nitrite, and bulk organic N were measured directly. The dry deposited Nr fraction was estimated using a bidirectional resistance-based model driven with speciated measurements of Nr air concentrations (e.g., ammonia, ammonium aerosol, nitric acid, nitrate aerosol, bulk organic N in aerosol, total alkyl nitrates, and total peroxy nitrates), micrometeorology, canopy structure, and biogeochemistry. Total annual deposition was ~6.7 kg N ha-1 yr-1, which is on the upper end of Nr critical load estimates recently developed for similar ecosystems in the nearby Great Smoky Mountains National Park. Of the total (wet + dry) budget, 51.1% was contributed by reduced forms of Nr , with oxidized and organic forms contributing ~41.3% and 7.6%, respectively. Our results indicate that reductions in deposition would be needed to achieve the lowest estimates (~3.0 kg N ha-1 yr-1) of Nr critical loads in southern Appalachian forests.

Development of g-functions for large diameter shallow bore helical ground heat exchangers

In this paper, a new approach to calculate the transfer functions (g-functions) for simulating the thermal performance of large diameter, shallow bore helical Ground Heat Exchangers (He-GHE) is proposed using mean fluid temperature rather than borewall temperature. The g-functions are generated using a validated numerical Capacitance Resistance Model-Helical GHE (CaRM-He) for different bore diameters, bore depth and helical pipe pitch. Mathematical formulation is presented which allows calculation of the combined g-function for multiple bores using the g-functions for individual cases. A simplified resistance-based model which enables the calculation of traditional borewall temperature-based g-functions using the mean fluid g-functions for different mass flowrates is also presented. Finally, mean fluid temperature is calculated for an array of eight He-GHEs for Sacramento climate zone using (a) CaRM-He model, (b) mean fluid temperature-based g-function and (c) borewall temperature-based g-functions for maximum (0.128 kg s−1), reference (0.063 kg s−1) and minimum (0.057 kg s−1) mass flowrates. For the reference mass flowrate case, the predicted mean fluid temperature Root Mean Square Deviation (RMSD) between the CaRM-He simulation and the g-function approaches is less than 5% and 6% of the yearly average temperature difference between the inlet and outlet for the mean fluid and borewall based g-functions respectively. For the other mass flowrates, the RMSD in mean fluid temperature varies between 9% and 17% of the yearly average temperature difference between the inlet and outlet temperature. Overall, the proposed g-function approach can be used effectively to estimate the performance of large diameter helical GHEs.

A node resistance-based probability model for resolving duplicate named entities

Duplicate entities tend to degrade the quality of data seriously. Despite recent remarkable achievement, existing methods still produce a large number of false positives (i.e., an entity determined to be a duplicate one when it is not) that are likely to impair the accuracy. Toward this challenge, we propose a novel node resistance-based probability model in which we view a given data set as a graph of entities that are linked each other via relationships, and then compute the probability value between two entities to see how similar the two entities are. Especially, in the graph, each node has its own resistance value equivalent to 1-confidence (normalized in 0–1) and resistance $$\cdot $$ probability value is filtered out per node during computing the probability value. To evaluate the proposed model, we performed intensive experiments with different data sets including ACM ( https://dl.acm.org ), DBLP ( https://dblp.uni-trier.de ), and IMDB ( https://imdb.com ). Our experimental results show that the proposed probability model outperforms the existing probability model, improving average F1 scores up to 14%, but never worsens them.

Understanding the Difference in Soft-Error Sensitivity of Back-Biased Thin-BOX SOI SRAMs to Space and Terrestrial Radiation

High tolerance against soft errors is regarded as one of the advantages of silicon-on-insulator (SOI) technology, as well as the reduction in power consumption by applying a back-bias from under the buried oxide layer (BOX). These advantages are appealing to Internet-of-Things (IoT) and space applications. Recently, it was found in a heavy-ion experiment that a static random-access memory (SRAM) fabricated with a 65-nm thin-BOX SOI technology exhibited a 100-fold soft-error sensitivity when it received a back-bias. This was due to long line-type formations of multiple cell upsets (MCUs) caused by radiation-induced potential perturbation under the BOX. However, devices fabricated with a similar technology tested with terrestrial neutrons did not show such a phenomenon in another previous study. To understand this difference, in the present work, a resistance-based model is adopted. Prediction of device soft-error sensitivity due to line-type MCUs under different radiation environment are also made. Then, characteristics of secondary ions generated by terrestrial neutrons in silicon devices are studied using simulation.

Resistance-Based Modeling for Soft Errors in SOI SRAMs Caused by Radiation-Induced Potential Perturbation Under the BOX

Silicon-on-insulator (SOI) technology has been considered capable of developing devices with high tolerance against soft errors. In addition, with a thin buried oxide (BOX) layer, reduction in power consumption can be further achieved by applying a back bias from under the BOX, which is one of SOI technology’s many advantages and is appealing to Internet-of-Things and space applications. Recently, it was found during a heavy ion experiment that a static random access memory fabricated with a thin-BOX SOI technology exhibited a 100-fold soft error sensitivity when it received a back-bias. This was due to long line-type formation of multiple cell upsets (MCUs) caused by radiation-induced potential perturbation under the BOX. In this paper, a resistance-based model is developed for the evaluation of potential perturbation, predicting the device soft error sensitivity. The predictions made are verified by simulation. The model also provides an explanation to why the line-type MCU only occurs in a certain radiation environment and an optimization method to reduce the potential perturbation.

Mixed matrix membranes applications: Development of a resistance-based model

In this study, a model for the prediction of the mass transport through ideal mixed matrix membranes (MMMs) for pervaporation and gas separation processes has been introduced. A Resistance-Based (RB) model was used in conjunction with a three-directional Finite Difference (FD) numerical solution to derive a semi-empirical model for calculating the effective permeability of ideal mixed matrix membranes. Predictions of the effective permeability obtained with the proposed model were compared with the estimated permeability for ideal MMMs using numerous analytical solutions such as Maxwell, Lewis-Nielsen, Bruggeman and Hennepe models. The extended RB model was theoretically able to predict accurately the effective permeability of ideal MMMs over a large range of filler volume fraction. Higher deviations were observed between the predictions of the extended RB model and the estimations of the majority of the other models in the case of MMMs containing higher permeable fillers and larger amount of filler material.

Modelling of mixed matrix membranes: Validation of the resistance-based model

In this study, the extended Resistance-Based (RB) model introduced in the preceding paper, was validated for the prediction of the steady state mass transport of species through various mixed matrix membranes (MMMs) used in pervaporation and gas separation applications. This validation was performed by using experimental data for the pervaporation separation of butanol from binary aqueous solutions using Polydimethylsiloxane (PDMS)/activated carbon (AC) mixed matrix membranes and the permeability data of various gases through Polyvinylidenefluoride (PVDF)/Zeolite A4 and PVDF/MCM-41 mixed matrix membranes obtained from the literature. These experimental data were compared with the predicted values obtained from the extended RB model as well as the estimations obtained by numerous analytical models for ideal MMMs under the identical conditions. Results show that the extended RB model is a very good predictive model to estimate the effective permeability of ideal MMMs used for pervaporation and gas separation.

Experimental study and analytical modeling of an alkaline water electrolysis cell

A traditional alkaline aqueous electrolyzer is investigated by using a 3-electode structure that enables the reaction resistance of each individual electrode to be accurately monitored. Combining experimental observations with resistance-based model analysis, we establish a quantitative relationship between current density and key voltage losses, including losses due to thermodynamics, kinetics, ohmic, and mass transport. These results demonstrate that the oxygen evolution reaction and bubble effects play crucial roles in determining electrolyzer performance. By varying the distance between electrodes, 2 effective OH− conductivities in 0.4M KOH are found to be 0.1333 and 0.9650 second cm−1, depending on bubble formation and release rate at the electrode interface. Moreover, bubble coverage on electrode surface achieves a steady state of 96% when current density is above 0.1 A cm−2. In the study of various electrolyte concentrations, all the model predictions show good agreement with experimental results, confirming its ability to capture actual cell performance. This newly presented empirical resistance-based model provides a practical framework to simulate complicated electrolysis reactions, serving as a comprehensive guide for continuous improvement of water electrolysis.

Investigation of blood flow in the external carotid artery and its branches with a new 0D peripheral model.

BackgroundPatient-specific modelling in clinical studies requires a realistic simulation to be performed within a reasonable computational time. The aim of this study was to develop simple but realistic outflow boundary conditions for patient-specific blood flow simulation which can be used to clarify the distribution of the anticancer agent in intra-arterial chemotherapy for oral cancer.MethodsIn this study, the boundary conditions are expressed as a zero dimension (0D) resistance model of the peripheral vessel network based on the fractal characteristics of branching arteries combined with knowledge of the circulatory system and the energy minimization principle. This resistance model was applied to four patient-specific blood flow simulations at the region where the common carotid artery bifurcates into the internal and external carotid arteries.ResultsResults of these simulations with the proposed boundary conditions were compared with the results of ultrasound measurements for the same patients. The pressure was found to be within the physiological range. The difference in velocity in the superficial temporal artery results in an error of 5.21 ± 0.78 % between the numerical results and the measurement data.ConclusionsThe proposed outflow boundary conditions, therefore, constitute a simple resistance-based model and can be used for performing accurate simulations with commercial fluid dynamics software.

A Comprehensive Approach for Resistance Spot Welding Quality Estimation Using Dynamic Resistance Based Model

On-line quality assessment becomes one of the most critical requirements for improving the efficiency of automatic resistance spot welding (RSW) processes. Accurate and efficient model to perform non-destructive quality estimation is an essential part of the assessment. Besides the usual welding parameters, various measured variables have been considered for quality estimation in RSW. Among these variables, dynamic resistance gives a relative clear picture of the welding nugget formation and presents a significant correlation withseveral RSW quality indicators. This paper presents a structuredand comprehensiveapproach developed to design an effective dynamic resistancebased model for on-line quality estimation in RSW. The proposed approach examines welding parameters and conditions known to have an influence on weld quality, and builds a quality estimation model step by step. The modeling procedure begins by examining, through a structured experimental design, the relationships between welding parameters, typical characteristics of the dynamic resistance curves and multiple welding quality indicators. Using these results and various statistical tools, different integrated quality estimation models combining an assortment of dynamic resistance attributes are developed and evaluated. The results demonstrate that the proposed approach can lead to a consistentmodel able to accurately and reliably provide an appropriate estimationof the weld quality under variable welding conditions.

Evaluation of a resistance‐based model for the quantification of pulmonary arterial hypertension using MR flow measurements

To establish an estimate for the mean pulmonary arterial pressure (mPAP) derived from noninvasive data acquired with magnetic resonance (MR) velocity-encoded sequences. In seven sedated pigs synchronous catheter-based invasive pressure measurements (IPM) and noninvasive MR were acquired in the main pulmonary artery (MPA) at different severities of pulmonary arterial hypertension (PAH) that were caused by infusion of thromboxane A2 (TxA2). The invasively measured mPAP was correlated with the noninvasive MR velocity data and linear combination equations (LCE) were computed. Intravenously applied TxA2 induced a dose dependent level of severity of PAH with an mPAP of up to 54 mmHg without systemic effects. The acceleration time (AT) measured with MR demonstrated the best correlation with the mPAP (r(2) = 0.75). The LCE with the highest correlation (R = 0.945, alpha < 0.01) between IPM and MR revealed a mean difference of 0, a SD of s = 4.66 and a maximal difference of 12.2 mmHg using the Bland-Altman analysis. Applying the identified LCE allowed the estimation of the mPAP in an acute and resistance-based model of PAH with high accuracy using noninvasive MR velocity-encoded sequences.

CATHODIC PROTECTION DESIGN FOR COMPLEX BURIED CONDUCTOR NETWORKS

This paper deals with the design of cathodic protection for complex buried conductor networks, whereas the use of classical methods like the attenuation equations method for calculation of the cathodic protection potentials distribution might be a problem. A suitable approach is based on a discrete net model that allows facing the problem through the traditional equations of linear electrical networks. This method, successfully tested in several situations, was initially proposed using a conductance-based net model. The present paper improves on this method, using simple numerical examples to demonstrate that the dual net representation through a resistance-based model allows an easier and more systematic solution of typical cathodic protection problems, offering to the designer direct insight into how control variables (such as placement and loading of protection feeders) affect the objective function (voltage profile or total current injection).