Experimental-based Model Research Articles

A machine learning and experimental-based model for prediction of soil sorption capacity toward phenanthrene

Sorption by soil is the fundamental basis for environment fate of hydrophobic organic contaminants (HOCs), which varies significantly depending on diverse properties of soils. Therefore, a generalized approach to predict HOC sorption by soils is required. In this study, 488 data points were extracted from references and adopted to develop models for estimating the sorption capacities of phenanthrene in soils using six different machine learning (ML) approaches. The extreme gradient boosting (XGBT) model demonstrated the most favorable performance, achieving a coefficient of determination of 0.91 and root-mean-square errors of 0.24 for the testing dataset. The XGBT model's performance was further demonstrated by comparing with experimental data from batch sorption tests conducted on 20 soil samples collected from 17 provinces of China. The differences between the predicted values and the experimental values were statistically equal to zero (p = 0.14). Leveraging the XBGT model together with soil properties from the Harmonized World Soil Database, the distribution of sorption capacities in Chinese soils was successfully depicted on a national scale. This research is expected to contribute to a deeper understanding of the migration of persistent organic pollutants in terrestrial system. Furthermore, the established model holds implications for more precise and scientific soil environmental management.

Determination and validation of polarization losses parameters to predict current/voltage-characteristics for SOFC button cell

Solid Oxide Fuel Cell represents a valid and clean alternative to conventional combined heat and power systems. These devices can convert different kinds of fuels into electricity with high efficiencies (up to 75–80 %) and low CO2 or hazardous emissions. In order to understand and validate Solid Oxide Fuel Cell behavior at different operating conditions, investigations are currently focusing their attention on modeling. In this work, an experimental-based model to study the effect of different hydrogen and temperature conditions on both the polarization processes and its effect on Solid Oxide Fuel Cell performance is presented. Experimental parametric campaigns were carried out using a button anode supported Solid Oxide Fuel Cell with an active area of about 2 cm2. A practical zero-dimensional mathematical model based on low and high voltage approximations of the Butler-Volmer equations was used starting from an experimental current–voltage dataset to obtain polarization curve parameters representing different physical phenomena associated with the cell. To validate the obtained results and gain additional information, electrochemical impedance spectroscopy measurements and distribution of relaxation times analysis were performed both in open circuit voltage and underload at 0.5 A/cm2. Finally, from the fitted parameters, it was possible to derive dimensionless charge transfer coefficients related to the most probable reaction mechanisms occurring in the cell, equal to 2 for the anodic process and 3.5 for the cathodic process. Moreover, from the results the obtained exchange current densities range from 0.024 A/cm2 to 0.048 A/cm2 for O2 reduction and from 0.012 A/cm2 to 0.073 A/cm2 for H2 oxidation and subsequently, the activation energies equal to 100 kJ/mol and 66 kJ/mol for anodic and cathodic electrochemical processes, respectively.

Estimation of biomolecule amount by analyzing magnetic nanoparticle cluster distributions from alternating current magnetization spectra for magnetic biosensing

The hydrodynamic size distribution of magnetic nanoparticle (MNP) clusters is crucial for determining the amounts of biomolecules by analyzing the distributions of the Brownian relaxation time in the corresponding alternating current (AC) magnetization spectra. This study proposes a method for estimating the distribution of MNP clusters from recorded AC magnetization spectra using an experimental-based model. The proposed model is used to form a matrix equation that comprises the distribution and AC magnetization spectra of MNP clusters, and the MNP–biomolecule cluster distribution is obtained by solving an inverse problem. The results show that the variations in the MNP clusters distribution estimated from the model correspond to those measured by dynamic light scattering. The MNP cluster distributions estimated using the experimental model were used to estimate streptavidin concentrations in the range of 0.19–3.38 μM. The estimated values agree accurately with the true values, demonstrating the feasibility of the proposed method for biosensing.

Bilinear damage evolution in AA2011 wire drawing processes

This paper presents an experimental and numerical analysis of damage evolution in AA2011 aluminum alloy wires drawn under different scenarios. To this end, load-unload tensile tests were firstly carried out in order to characterize the degradation of the mechanical response in every cycle where the experimental results show a bilinear damage relationship in terms of the effective plastic strain. Therefore, a modification of the classical Lemaitre model is proposed in this work in order to reproduce bilinear paths of damage with the addition of only two parameters that can be directly obtained from the material characterization. Then, the damage predictive capability of this new experimental-based model is assessed in numerical simulations of the drawing process in one and two passes (considering for this last case the sequential and tandem configurations) where the computed predictions are compared with the corresponding experimental data showing a good agreement between them.

Experimental and Modelling Study of Gravity Drainage in a Three-Block System

Gravity drainage is known as the controlling mechanism of oil recovery in naturally fractured reservoirs. The efficiency of this mechanism is controlled by block-to-block interactions through capillary continuity and/or reinfiltration processes. In this study, at first, several free-fall gravity drainage experiments were conducted on a well-designed three-block apparatus and the role of tilt angle, spacers’ permeability, wettability and effective contact area (representing a different status of the block-to-block interactions between matrix blocks) on the recovery efficiency were investigated. Then, an experimental-based numerical model of free-fall gravity drainage process was developed, validated and used for monitoring the saturation profiles along with the matrix blocks. Results showed that gas wetting condition of horizontal fracture weakens the capillary continuity and in consequence decreases the recovery factor in comparison with the original liquid wetting condition. Moreover, higher spacers’ permeability increases oil recovery at early times, while it decreases the ultimate recovery factor. Tilt angle from the vertical axis decreases recovery factor, due to greater connectivity of matrix blocks to vertical fracture and consequent channelling. Decreasing horizontal fracture aperture decreases recovery at early times but increases the ultimate recovery due to a greater extent of capillary continuity between the adjacent blocks. Well match observed between the numerical model results and the experimental data of oil recovery makes the COMSOL multiphysics model attractive for application in multi-blocks fractured systems considering block-to-block interactions. The findings of this research improve our understanding of the role of different fracture properties on the block-to-block interactions and how they change the ultimate recovery of a multi-block system.

Uncertainty analysis of cutting parameters during grinding based on RSM optimization and Monte Carlo simulation

AbstractDue to the importance of high surface quality of machined parts, regarding its functional requirements, it is necessary to determine an appropriate set of grinding parameters. According to the uncertainty of the machining process, the statistical techniques have recently been used to set up an experimental-based model for estimating the performance of machining parameters and optimizing them. The purpose of this study is to demonstrate the modeling and optimization of the grinding process using three approaches. First, multi non-linear regression (MNLR) based on central composite design (CCD) was used to determine the process model. Then the grinding parameters were optimized considering response surface methodology (RSM). Finally, the probabilistic uncertainty analysis was applied by using Monte Carlo simulation as a function of wheel speed and feed rate. The surface roughness value, which was named the response variable, was estimated by fitting the MNLR model with a predicted regression coefficient (R2pred) of 84.69 %. Wheel speed of 4205.6 rpm and feed rate of 2.969 mm × min−1 were calculated as RSM-optimized conditions with a surface roughness of 2.26326 μm. The verification experiments were performed with three replications to verify the predicted surface roughness value obtained with the derived model, and 2.263 ± 2 % μm of surface roughness was calculated using RSM optimized conditions. Monte Carlo simulations were found to be quite effective for identification of the uncertainties in surface roughness that could not be identified by deterministic ways.

Bulk power system frequency stability assessment in presence of microgrids

An attempt is made in this paper to propose an analytical approach to mathematically assess the impact of MGs penetration level on power system frequency stability. Firstly, experimental-based model of constitute DGs of the University of Kurdistan-MG (UOK-MG) in islanded mode may be derived. Afterwards, realistic model of the MG in grid connected mode may be derived to realize penetrated system for future studies. Finally, some simple algebraic equations are derived to predict frequency dynamics of interest in penetrated system. The method interprets the frequency dynamic behavior of the penetrated system based on the conventional system in terms of frequency nadir, Rate of Change of Frequency (RoCoF), steady state deviation and a rolling window. Simulation results demonstrate high efficiency of the proposed approach for dynamical modeling and stability analysis of penetrated power systems.

Microgrids Impact on Power System Frequency Response

Microgrid (MG), as a cluster of Distributed Generations (DGs) and local loads, may be characterized with zero or small inertia constant. Reduction of system rotational inertia, in response to increasing MGs penetration level, renders frequency dynamics faster. This paper presents an attempt to propose a new analytical approach to mathematically assess the impact of MGs penetration level on power system frequency stability. In this way, an experimental-based model is employed to make the simulation results realistic. The method interprets the frequency dynamic behavior of the penetrated system based on the conventional system in terms of frequency nadir, steady state deviation and a 15-second rolling window.

An experimental-based model for the assessment of the mechanical properties of road pavements using ground-penetrating radar

This work proposes an experimental-based model for the assessment of the stiffness of a road flexible pavement using ground-penetrating radar (GPR – 2 GHz horn antenna) and light falling weight deflectometer (LFWD) non-destructive testing (NDT) methods. It is known that the identification of early decay and loss of bearing capacity is a major challenge for effective maintenance of roads and the implementation of pavement management systems (PMSs). To this effect, a time-efficient methodology based on quantitative and qualitative modelling of road stiffness is developed. The viability of using a GPR system in combination with LFWD equipment is also proven.

Modelling of autonomic regulation in cardiovascular system based on baroreflex mechanism and overwhelming controller

The control of cardiovascular system by autonomic nervous system using model-based analysis is very useful to predict blood flow and blood pressure at different locations of human body. The regulation of cardiovascular system by autonomic nervous system is a very complex mechanism, so, an experimental-based model analysis may prove very helpful in studying and analysing its working properly. In this article, a very basic model of cardiovascular system is presented with blood pressure dynamics studied throughout the body. A new bond graph model of the autonomic nervous system embedded with baroreflex system is also presented. Autonomic regulation of ventricular contractility is represented by means of transfer functions. The results in terms of maximum ventricular elastance ([Formula: see text]) and end systolic pressure are shown for two experiments. Another alternative modelling approach to represent the function of nervous system action in blood pressure regulation in terms of an overwhelming controller has been proposed. This controller overwhelms the system properties and therefore helps in managing the unmodelled parts or properties of a system and makes it impeccable for controlling complex systems. All the results and simulations are obtained using Symbols Shakti® software (Bond graph software).

Two dimensional simulation and modeling of the electrical characteristics of the a-SiC/c-Si(p) based, thyristor-like, switches

The electrical characteristics of the Al/a-SiC/c-Si(p)/c-Si(n+)/Al switches were successfully simulated here for the first time. Forward breakover voltage VBF, forward voltage drop VF and anode current simulated values of the device showed very good agreement with the experimental results. Electric field and impact generation rate across the switches are also simulated for different anode current conditions extending to second breakdown region of the device, showing a shifting of the phenomena caused by electric field and impact generation rate, from c-Si(p) region to a-SiC film as anode voltage values increase from VBF up to second breakdown region of the device. A both simulation and experimental based model describing the device behavior, is also presented here. Two critical facts leading to switching transition are proved to be at first a-SiC/c-Si(p) heterojunction breakdown dominated by impact generation rate and second subsequent trap filling in the amorphous film. Al/a-SiC/c-Si(p+)/c-Si(p)/c-Si(n+)/Al switches with reduced VBF and VF values, were also fabricated and successfully simulated here, enhancing the validity of our simulation procedure and making the switches candidates for ESD protection devices, also due to their advantages of high anode current value density of 5A/mm2 before reaching second breakdown conditions in conjunction with cheap and easy fabrication procedure mainly due to r.f. sputtering technique used for a-SiC film fabrication.

Duty-cycle optimization for IEEE 802.15.4 wireless sensor networks

Most applications of wireless sensor networks require reliable and timely data communication with maximum possible network lifetime under low traffic regime. These requirements are very critical especially for the stability of wireless sensor and actuator networks. Designing a protocol that satisfies these requirements in a network consisting of sensor nodes with traffic pattern and location varying over time and space is a challenging task. We propose an adaptive optimal duty-cycle algorithm running on top of the IEEE 802.15.4 medium access control to minimize power consumption while meeting the reliability and delay requirements. Such a problem is complicated because simple and accurate models of the effects of the duty cycle on reliability, delay, and power consumption are not available. Moreover, the scarce computational resources of the devices and the lack of prior information about the topology make it impossible to compute the optimal parameters of the protocols. Based on an experimental implementation, we propose simple experimental models to expose the dependency of reliability, delay, and power consumption on the duty cycle at the node and validate it through extensive experiments. The coefficients of the experimental-based models can be easily computed on existing IEEE 802.15.4 hardware platforms by introducing a learning phase without any explicit information about data traffic, network topology, and medium access control parameters. The experimental-based model is then used to derive a distributed adaptive algorithm for minimizing the power consumption while meeting the reliability and delay requirements in the packet transmission. The algorithm is easily implementable on top of the IEEE 802.15.4 medium access control without any modifications of the protocol. An experimental implementation of the distributed adaptive algorithm on a test bed with off-the-shelf wireless sensor devices is presented. The experimental performance of the algorithms is compared to the existing solutions from the literature. The experimental results show that the experimental-based model is accurate and that the proposed adaptive algorithm attains the optimal value of the duty cycle, maximizing the lifetime of the network while meeting the reliability and delay constraints under both stationary and transient conditions. Specifically, even if the number of devices and their traffic configuration change sharply, the proposed adaptive algorithm allows the network to operate close to its optimal value. Furthermore, for Poisson arrivals, the duty-cycle protocol is modeled as a finite capacity queuing system in a star network. This simple analytical model provides insights into the performance metrics, including the reliability, average delay, and average power consumption of the duty-cycle protocol.

How oligoclonal are germinal centers? A new method for estimating clonal diversity from immunohistological sections

BackgroundThe germinal center (GC) reaction leads to antibody affinity maturation and generation of memory B cells, but its underlying mechanisms are poorly understood. To assemble this puzzle, several key pieces of information are needed, one in particular being the number of participating B cell clones. Since this clonal diversity cannot be observed directly, earlier studies resorted to interpreting two types of available experimental data: Immunohistology of GCs containing two phenotypically distinct B-cell populations, and antibody gene sequences of small B-cell samples from GCs. Based on a simple model, investigators concluded that a typical GC was seeded by 2-8 B cells, endorsing the current notion that GCs are oligoclonal from the onset.ResultsA re-evaluation of these data showed that the used simple model is not statistically consistent with the original data. From an analysis of the experimental system, we propose a new model for estimating GC clonal diversity, including the initially neglected sampling and measurement errors, and making more general assumptions. Consistency analysis with the new model yielded an estimation of sampling and measurement errors in the experimental data of 10-11% for one B-cell population and 62-64% for the other population, and an average number of 19-23 seeder B cells. An independent analysis of antibody gene sequences of small B-cell samples from GCs, using an adapted Yule estimator of diversity, yielded a minimum estimation of 20-30 GC founder B cells, confirming the previous results.ConclusionsOur new experimental-based model provides a highly improved method to estimate the clonal diversity of GCs from inmunohistochemistry data of chimeric animals. Calculations based on this model, and validated by an independent approach, indicate that GCs most likely contain broadly varying numbers of different B cell clones, averaging 5- to 10-fold more clones than previously estimated. These findings, in line with recent results showing that GC sizes and life times are also subject to high variability, dramatically change the picture of GC dynamics.

Molecular simulation of methane adsorption in micro- and mesoporous carbons with applications to coal and gas shale systems

Methane adsorption in porous carbon systems such as coal and the organic matrix of gas shales is an important factor in determining the feasibility of CO2 injection for enhanced natural gas recovery and possible sequestration of CO2. Methane and CO2 adsorb competitively on carbon surfaces and an understanding of each gas individually is important for determining a model to predict the feasibility of this approach for permanent CO2 storage. Coal and gas shales have a very heterogeneous pore system, ranging from the micro, meso, and macro-scales, with the pore size strongly affecting the adsorption behavior. In micropores, the force fields of opposing pore walls are close enough that they will overlap and significantly influence the adsorption behavior, which affects adsorbate packing and density. To determine the size at which these effects become non-negligible and to determine the magnitude of this impact, grand canonical Monte Carlo simulations have been carried out to estimate the adsorption isotherms of methane across a range of pore sizes and at various temperature and pressure conditions characteristic of subsurface conditions. These isotherms have been calculated on graphitic surfaces as an initial model of coal and kerogen of gas shales. The general trend within pore sizes is that larger pores exhibit lower excess density compared to smaller pores. However, at pressure above 1MPa, the adsorption capacities of 0.6-nm pores drop below those of the wider pores, ultimately decreasing below that of the 1.2-nm pore at 18MPa. The density of adsorbed methane changes non-monotonically with increasing pore width, and drops to a minimum in 1.2-nm pores at 12MPa. The isotherms have been compared with experimental data to gauge their accuracy, and the behavior of the adsorbed layer has been examined in detail. At pressures less than 2.5MPa, the molecular simulation estimates underpredict the excess adsorption, while at pressures greater than 2.5MPa up to 20MPa, the simulation estimates overpredict the excess adsorption. This discrepancy is likely due to the limitation of the experimental-based model that was used to generate the pore size distribution and the surface functionalities of the porous media that were ignored in the molecular simulation investigations, but likely play an important role in determining accurate capacities under confinement at the nanoscale.

Experimental-based model for predicting the skid resistance of asphalt pavements

The skid resistance of asphalt pavement is a major characteristic of road safety. This study aimed at the development of a model that expresses skid resistance as a function of mixture gradation, aggregate texture and traffic level. The model was developed based on comprehensive measurements and analysis of asphalt mixture surface characteristics (friction, texture and skid resistance) in the laboratory and the field. In addition, aggregates were characterised using the aggregate imaging system after different polishing intervals in the Micro-Deval. The aggregate gradation was described using the two-parameter cumulative Weibull distribution function. The developed model provides an estimate of the skid number in the field. Consequently, this model can be used by engineers to select different combinations of aggregate types and mixture designs such that the desired level of skid resistance at a given traffic level is achieved.

Development and Implementation of an Experimental-Based Reduced-Order Model for Feedback Control of Subsonic Cavity Flows

This work is focused on the development of a reduced-order model based on experimental data for the design of feedback control for subsonic cavity flows. The model is derived by applying the proper orthogonal decomposition (POD) in conjunction with the Galerkin projection of the Navier-Stokes equations onto the resulting spatial eigenfunctions. The experimental data consist of sets of 1000 simultaneous particle image velocimetry (PIV) images and surface pressure measurements taken in the Gas Dynamics and Turbulent Laboratory (GDTL) subsonic cavity flow facility at the Ohio State University. Models are derived for various individual flow conditions as well as for their combinations. The POD modes of the combined cases show some of the characteristics of the sets used. Flow reconstructions with 30 modes show good agreement with experimental PIV data. For control design, four modes capture the main features of the flow. The reduced-order model consists of a system of nonlinear ordinary differential equations for the modal amplitudes where the control input appears explicitly. Linear and quadratic stochastic estimation methods are used for real-time estimation of the modal amplitudes from real-time surface pressure measurements.

An experimental-based model for prediction of flow noise with drag reducing polymers

The drag reduction characteristics of certain high molecular weight polymers have been studied by various investigators. Because of the polymer’s ability to reduce turbulent shear stress and dependence of the boundary layer wall pressure spectral amplitude on the shear stress, polymer has the potential to suppress noise and vibration caused by the boundary layer unsteady pressures. Compared to its effect on drag reduction, polymer additive effects on turbulent boundary layer (TBL) wall pressure fluctuations have received little attention. Kadykov and Lyamshev [Sov. Phys. Acoust. 16 (1970) 59], Greshilor et al. [Sov. Phys. Acoust. 21 (1975) 247] showed that drag reducing polymer additives do indeed reduce wall pressure fluctuations, but they have not established any scaling relationship which effectively collapse data. Some effort has been made by Timothy et al. [JASA 108 (1) (2000) 71] at Penn State University to develop a scaling relationship for TBL wall pressure fluctuations that are modified by adding drag reducing polymer to pure water flow. This paper presents a theoretical model based on the work of the Timothy et al. team at ARL, Penn State University. Through this model one can estimate, reduction in TBL flow induced noise and vibration for rigid smooth surfaces due to release of drag reducing polymers in boundary layer region. Using this theoretical model, flow noise as experienced by a typical flush mounted hydrophone has been estimated for a smooth wall plate as a function of polymer additive concentration. Effect of non-dimensionalisation of the wall pressure fluctuations frequency spectra with traditional outer, inner and mixed flow variables will also be addressed in the paper. The paper also covers a model based on molecular relaxation time in polymer additives which not only reduce drag but also flow induced noise up to certain polymer concentration.

A molecular-field-based similarity study of non-nucleoside HIV-1 reverse transcriptase inhibitors.

This article describes a molecular-field-based similarity method for aligning molecules by matching their steric and electrostatic fields and an application of the method to the alignment of three structurally diverse non-nucleoside HIV-1 reverse transcriptase inhibitors. A brief description of the method, as implemented in the program MIMIC, is presented, including a discussion of pairwise and multi-molecule similarity-based matching. The application provides an example that illustrates how relative binding orientations of molecules can be determined in the absence of detailed structural information on their target protein. In the particular system studied here, availability of the X-ray crystal structures of the respective ligand-protein complexes provides a means for constructing an 'experimental model' of the relative binding orientations of the three inhibitors. The experimental model is derived by using MIMIC to align the steric fields of the three protein P66 subunit main chains, producing an overlay with a 1.41 A average rms distance between the corresponding C alpha's in the three chains. The inter-chain residue similarities for the backbone structures show that the main-chain conformations are conserved in the region of the inhibitor-binding site, with the major deviations located primarily in the 'finger' and RNase H regions. The resulting inhibitor structure overlay provides an experimental-based model that can be used to evaluate the quality of the direct a priori inhibitor alignment obtained using MIMIC. It is found that the 'best' pairwise alignments do not always correspond to the experimental model alignments. Therefore, simply combining the best pairwise alignments will not necessarily produce the optimal multi-molecule alignment. However, the best simultaneous three-molecule alignment was found to reproduce the experimental inhibitor alignment model. A pairwise consistency index has been derived which gauges the quality of combining the pairwise alignments and aids in efficiently forming the optimal multi-molecule alignment analysis. Two post-alignment procedures are described that provide information on feature-based and field-based pharmacophoric patterns. The former corresponds to traditional pharmacophore models and is derived from the contribution of individual atoms to the total similarity. The latter is based on molecular regions rather than atoms and is constructed by computing the percent contribution to the similarity of individual points in a regular lattice surrounding the molecules, which when contoured and colored visually depict regions of highly conserved similarity. A discussion of how the information provided by each of the procedures is useful in drug design is also presented.

Analysis of tissue and arterial blood temperatures in the resting human forearm. 1948.

Analysis of tissue and arterial blood temperatures in the resting human forearm. 1948.