Proposed Modeling Technique Research Articles

Accurate modeling and safety evaluation of dented pipeline with internal pressure based on reverse modeling technique

ABSTRACT As a common form for pipeline geometric defects, dents can induce the local stress concentration of the pipeline, which would result in the debilitation of the bearing capacity and bring huge safety hazards. In this paper, the accurate surface profiles of dented pipeline are acquired based on the 3D scanning technology and deformation detection technology, respectively. Then, the dented pipeline models are constructed by the reverse modeling technique, and the stress distribution characteristics of the dented pipeline in the operation condition are simulated using ABAQUS. The maximum von Mises stresses of a dented pipeline obtained from two proposed modeling techniques are basically consistent. However, the deformation detection technology can effectively avoid the excavation work for buried pipelines. Therefore, the reverse modeling technique based on the deformation detection technology has strong feasibility in practical projects and significant application value in the safety assessment of submarine pipelines, where excavation work is impractical.

A Graph-Based Technique for the Automated Control-Oriented Modeling of District Heating Networks

Abstract Advanced control strategies for delivering heat to users in a district heating network have the potential to improve performance and reduce wasted energy. To enable the design of such controllers, this paper proposes an automated plant modeling framework that captures the relevant system dynamics, while being adaptable to any network configuration. Starting from the network topology and system parameters, the developed algorithm generates a state-space model of the system, relying on a graph-based technique to facilitate the combination of component models into a full network model. The accuracy of the approach is validated against experimental data collected from a laboratory-scale district heating network. The verification shows an acceptable average normalized root-mean-square error of 0.39 in the mass flow rates delivered to the buildings, and 0.15 in the network return temperature. Furthermore, the ability of the proposed modeling technique to rapidly generate models characterizing different network configurations is demonstrated through its application to topology optimization. The optimal design, obtained via a branch and bound algorithm, reduces network heat losses by 15% as compared to the conventional length-minimized topology.

Efficient Prototyping of a Field-Programmable Gate Array-Based Real-Time Model of a Modular Multilevel Converter

Field-programmable gate array (FPGA)-based real-time simulation plays a crucial role in testing power–electronic dominated systems with the formation of controller hardware-in-the-loop (CHIL) or power hardware-in-the-loop (PHIL). This work describes an efficient implementation of computation time and resource usage in the FPGA-based study of a modular multilevel converter (MMC) with detailed electromagnetic transients. The proposed modeling technique can be used in continuous control mode (CCM) and discontinuous control mode (DCM) for high-switching frequency semiconductor technologies. An FPGA-based designed solver structure is also presented to take advantage of the parallel features of FPGAs to achieve an ultra-fast calculation speed. In addition, two different switch modeling techniques are discussed with a five-level MMC case study. Experimental results on the NI PXIe platform show the feasibility of the proposed implementation, and a time step of 100 nanoseconds is achieved.

A new method for handling heterogeneous data in bioinformatics

Heterogeneous data, especially a mixture of numerical and categorical data, widely exist in bioinformatics. Most of works focus on defining new distance metrics rather than learning discriminative metrics for mixed data. Here, we create a new support vector heterogeneous metric learning framework for mixed data. A heterogeneous sample pair kernel is defined for mixed data and metric learning is then converted to a sample pair classification problem. The suggested approach lends itself well to effective resolution through conventional support vector machine solvers. Empirical assessments conducted on mixed data benchmarks and cancer datasets affirm the exceptional efficacy demonstrated by the proposed modeling technique.

Experimental-based numerical simulation of interacting suspended ceiling-sprinkler piping systems

Suspended ceiling and fire sprinkler piping (CP) systems are two of the most common interacting nonstructural elements inside the buildings. While each of these elements individually is prone to losses during the earthquakes, their interaction can even more intensify their associated damage. This article aims to integrate system-level modeling methodology by using existing subsystem-level models in OpenSees platform to simulate the interacting behavior of CP systems. To do so, the numerical model of the CP systems is developed by using a series of previously developed component-level nonlinear models. Experimental results from a shake table study of CP systems installed in a five-story building (fully scaled) are used for the validation of the proposed methodology. Experimental acceleration and displacement responses of CP systems at different locations as well as the damage-progression pattern in the suspended ceiling system are predicted well through the use of the proposed modeling technique.

A piecewise spline approach for modeling of ECG signals

This paper presents a new spline-based modeling method of electrocardiogram (ECG) signal that can reproduce normal as well as abnormal ECG beats. Large volume ECG data is required for automatic machine learning diagnostic systems, medical education, research and testing purposes but due to privacy issues, access to this medical data is very difficult. Given this, modeling an ECG signal is a very challenging task in the field of biomedical signal processing. Spline-based modeling is the latest and one of the most efficient methods with very low computational complexity in the domain of ECG signal generation. In this paper, healthy ECG and arrhythmia conditions have been considered for the synthetic generation, (namely Atrial fibrillation and Congestive heart failure ECG beats) because these are the leading causes of death globally. To validate the performance of the presented modeling method, it is tested on 100 signals, also the percentage root mean square difference (PRD) and the root mean square error (RMSE) have been determined. These calculated values are analyzed and the results are found to be very promising and show that the presented method is one of the best methods in the field of synthetic ECG signal generation. A comparison amongst relevant existing techniques and the proposed method is also presented. The performance merit values PRD and RMSE, for the proposed method obtained are 38.99 and 0.10092, respectively, which are lower than the values obtained in other compared methods. To ensure fidelity of the proposed modeling technique with respect to IEC60601 standard, few Conformance Testing Services (CTS)database signals have also been modelled with a very close resemblance with the standard signals.

A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods

Fault prognosis is an important area of research that aims to predict and diagnose faults in complex systems. The sudden failure of industrial components can have adverse consequences for an organisation in terms of time, cost and workflow. It is, therefore, critical to ensure the maintenance of equipment components in optimal condition in order to avoid downtime that may cause significant disruption. Due to this reason, in recent years, there has been an increasing interest in creating innovative methods for fault prognosis that can increase system reliability and reduce maintenance costs. Gear pumps are widely utilised in a variety of industrial applications, and their reliability and effectiveness are crucial for achieving optimal system performance. Gear pumps, on the other hand, are prone to malfunctions and failures, which can result in substantial downtime and maintenance costs. The challenge is to develop a fault prognosis approach that is reliable and accurate enough to detect and diagnose defects in a gear pump before they cause system failures. This paper proposes a novel computational strategy for the fault prognosis of an external gear pump using Machine Learning (ML) approaches. Due to the unavailability of sufficient experimental data in the vicinity of failure mechanisms, a novel approach to generating a high-fidelity in-silico dataset via a Computational Fluid Dynamic (CFD) model of the gear pump in healthy and faulty working conditions is presented. However, considering the computational demand for rerunning the same CFD simulations, novel synthetic data generation techniques are implemented by perturbing the frequency content of the time series to recreate other working conditions and constructing degradation behaviour using linear and cubic interpolation methods for run-to-failure scenarios. The synthetically created datasets are used to train the underlying ML metamodel for fault prognosis. Two types of ML algorithms are employed for fault prognosis: Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms. A series of numerical examples are shown, allowing us to infer that the proposed modelling technique is feasible in an industrial setting and that employing the MLP algorithm delivers superior fault prognosis results when compared to SVM. Furthermore, datasets generated using the cubic interpolation method have lower prediction errors than datasets generated using the linear interpolation method due to the smoother degradation behaviour in the data.

Experimental Evaluation of Vibrational Stability of SOPs in Aerospace Industry Using PCB Strain Effectiveness of a PCB-Strain-Based Design Methodology

Steinberg’s theory, which is based on the fatigue failure theory, has been widely used for predicting the structural safety of solder joints in aerospace electronic units under vibration during launches. However, theoretical limitations are encountered when evaluating the structural safety of highly integrated electronic packages mounted on printed circuit boards (PCBs) under various boundary conditions. Therefore, in our previous study, a PCB-strain-based methodology was proposed to overcome the technical limitations of the conventional Steinberg theory, and its effectiveness was validated by conducting fatigue life tests on various types of specimens, such as the ball grid array, column grid array, and quad flat package. In this study, the aim was to increase its completeness and reliability by targeting small outline packages (SOPs) that have not yet been considered. The finite element (FE) model of the SOP was proposed to guarantee the reliable prediction of the structural safety of the solder joints used in the PCB-strain-based methodology. The proposed modeling technique contributes to enable the rapid construction of an FE model for the electronic unit because it was greatly simplified into a zero-dimensional lumped mass and rigid link element to simulate the package mass and solder joint, respectively. The effectiveness of the methodology was validated by performing fatigue life tests on PCB specimens under various boundary conditions. Those experimental and analytical results indicated that the proposed methodology was much more effective in predicting the structural safety of a solder joint for various cases of tested specimens compared with the Steinberg’s theory. The simplified FE model of SOP with the rigid link element connected to six points on the package mounting area of the PCB was effective for estimating margin of safety of solder joint. The results of this study would contribute to increase the availability of the proposed methodology for rapid mechanical design of electronic units in aerospace industries.

New formulations for the Scheduled Service Network Design Problem

We propose a new approach to formulating the Scheduled Service Network Design Problem (SSNDP) that involves modeling with enumerated consolidations of shipments routed on the physical network. This is in contrast to the classical approach of capturing the synchronization of vehicles and shipments needed for consolidation with a time-expanded network. The proposed formulation has both a stronger linear relaxation and is less symmetric. We present multiple speed-up techniques and with an extensive computational study illustrate that the consolidation-based formulation is much easier to solve with an off-the-shelf solver than the classical formulation based on a time-expanded network.To avoid instances involving large numbers of consolidations we propose a hybrid formulation that combines ideas from the consolidation and time-expanded network-based approaches to formulating the SSNDP. We show that instances of the hybrid formulation are much easier to solve than both instances of the pure consolidation-based formulation and those based on a time-expanded network formulation. Finally, we discuss how formulating with consolidations facilitates modeling issues that have not yet been addressed in the literature, such as bin-packing considerations when computing vehicle capacity needs. In addition, the proposed modeling technique for bin-packing considerations in a consolidation-based formulation yields instances that are easier to solve than those wherein capacity is modeled in an aggregate sense.

Utilizing XFEM model to predict the flexural strength of woven fabric Kenaf FRP plate strengthened on plain concrete beam

The evolution of advancing computing technology has encouraged the use of finite element analysis to require constitutive models, mostly adopted extensive experimental datasets. Nevertheless, fewer material properties are required within the bilinear traction-separation relationship incorporated with the XFEM technique and require fewer computation efforts due to the energetic approach simulation. This study used ABAQUS CAE 2021 to predict the flexural strength of plain concrete beams strengthened with Kenaf Fibre Reinforced Polymer (KFRP) plates under a four-point bending test, later validated with experimental datasets. The varying parameters, including woven fabric architectures, lengths, widths, and plate thickness, were considered. The results demonstrated that the consistency proposed modelling technique in this study well-captured failure modes and crack development as experimental observations. All models demonstrated an increase in peak load, and a comparison of load-deflection profiles was made between the numerical FEA modelling with the experimental works. The peak load discrepancies between the numerical outputs and the experimental datasets were found to be less than 12% in all testing series. Despite promising findings, stress analysis on peel and shear stresses associated with failure modes exhibited was performed. It was found that KFRP ruptures occurred due to peel stress peak at plate mid-span, while shear mode failure demonstrates concentrated peel stress (to lesser extent shear stress) at KFRP edge tip. Hence, a more unified approach is promoted to require only two material properties (preferably independently measured values). This approach enables a designer to choose the optimum FRPs size using the current modelling tool, substantially reducing laborious and expensive experimental setup.

Seismic fragility functions for RC As-built and eccentric steel brace frames

Quasi-static cyclic loading tests were performed on single-story reduced-scale reinforced concrete as-built (un-retrofitted) and eccentrically steel braced (retrofitted) frames. The frames damage mechanism, force–displacement hysteretic response and capacity backbone envelopes were studied and a damage scale was developed. A simplified numerical modeling technique was proposed in the finite element-based package SeismoStruct and the model was evaluated, calibrated and validated with the test data. The proposed modeling technique was employed further to perform incremental dynamic time history analysis on two-story as-built and eccentric steel braced frames. Fragility functions for the frames in terms of immediate occupancy, life safety and collapse prevention were developed using a probabilistic-based methodology. The as-built frame was more fragile in terms of immediate occupancy, life safety and collapse prevention damage states as opposed to the retrofitted frame.

A Novel Modeling Technique via Coupled Magnetic Equivalent Circuit With Vector Hysteresis Characteristics of Laminated Steels

This paper proposes a method to include the anisotropic hysteresis characteristics of soft-magnetic laminated steels in the magnetic equivalent circuit (MEC) modeling. The loop-based MEC formulation is improved to handle the nonlinearity of the anisotropic magnetic hysteresis, including the dynamic classical eddy-current and excess fields. The developed MEC model is coupled with both the single-valued <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$B$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H$</tex-math></inline-formula> curve (SVC) in magnetostatic and the dynamic vector hysteresis model (VHM) in transient analysis. Results with a single elementary MEC element show that an alternating magnetic field in a single direction with a peak value smaller than 300 A/m causes a discrepancy of more than 10% between the magnetic flux densities calculated by the VHM and SVC at 50 and 200 Hz excitation frequencies. Moreover, the proposed modeling technique is verified experimentally using the laminated transformer core of TEAM problem 32. The induced voltage calculated by the MEC model with the VHM demonstrates a good agreement with the measurements, while the MEC model with the SVC calculates inaccurate voltage waveforms. Lastly, the total iron loss dissipated in the transformer's iron core is investigated to verify the proposed technique under different excitation levels and frequencies up to 500 Hz. It is observed that the proposed MEC model with the vector hysteresis characteristics of laminated steels is able to calculate the iron loss accurately, while the conventional single-valued curve method fails to estimate the iron loss.

An Advanced 3-D Electromagnetic Analysis and Experimental Validation of a Bipolar Subnanosecond Pulse Generation System

This article presents an advanced 3-D electromagnetic co-simulation approach developed using the CST software to evaluate the characteristics of a bipolar subnanosecond pulse forming system for application in the biomedical and defense domains. To the best of the authors’ knowledge, the simulation technique presented in this article, which integrates transient switching devices in the 3-D electromagnetic model of the bipolar subnanosecond pulse forming system, was not presented previously in the open literature. The design of the bipolar subnanosecond pulse forming system is based on a thorough analytical calculation with predictions verified using the 3-D numerical modeling to generate a voltage of up to 500 kV peak-to-peak amplitude with a duration of around 1.8 ns when connected to a 50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega$</tex-math> </inline-formula> load. A good agreement between the experimental results and the CST predictions was obtained, providing the correctness of the proposed modeling technique. Future plans involve connecting the bipolar subnanosecond pulse forming system to an ultra-wideband (UWB) antenna.

Prediction of Ground Water Level using SVM-WOA Approach: A Case Study

Reliable and accurate estimation of Groundwater Level (GWL) fluctuations is essential and vital for sustainable water resources management. Due to uncertainties and interdependencies in hydro-geological processes, GWL prediction is complex by the fact that fluctuation of GWL is extremely nonlinear and non-stationary. Utilising novel methods for accurately predicting GWL is of vital significance in arid regions. In present work, Support Vector Machine (SVM), in combination with Whale Optimisation Algorithm (SVM-WOA), is applied to forecast GWL in Bhubaneswar region (Odisha University of Agricultural Technology). Three quantitative statistical performance assessment indices, coefficient of determination (R2), Mean Squared Error (MSE), and Wilmott Index (WI), is used to assess model performances. Based on the assessment with conventional SVM and RBFN models, the performance of hybrid SVM-WOA model is preeminent. SVM-WOA is capable of predicting nonlinear behavior of GWLs. Proposed modelling technique can be applied in different regions for proper management of groundwater resources and provides significant information, at a short time scale, to estimate variability in groundwater at local level.

SPICE-Compatible Equivalent Circuit Models for Accurate Time-Domain Simulations of Passive Photonic Integrated Circuits

Passive photonic integrated circuits (PICs) can be easily characterized in the frequency-domain, but their accurate time-domain performance evaluation is a hurdle for system-level designers, especially when dealing with resonant circuits having highly dispersive behavior, such as ring resonators. In this paper, a new equivalent circuit modeling and simulation approach is proposed, based on the Complex Vector Fitting algorithm, able to perform accurate and robust time-domain simulations of passive PICs directly in standard SPICE simulators. The proposed modeling technique starts from scattering parameters of passive PICs, and is able to capture linear and high order dispersion, backscattering, and wavelength dependent effects. Considering the different nature of optical and electronic signals, a novel concept of equivalent voltage and current for optical waveguides is proposed to simplify the optical to electronic ports conversion and to make it possible to connect and terminate the equivalent circuit models as needed in SPICE simulators, natively supporting bidirectional signal propagation in a waveguide. This work provides a precise and reliable solution to evaluate time-domain characteristics of passive PICs and to access any internal nodes within a circuit, such as the signals inside a ring resonator. Three examples of time-domain simulations of passive PICs in commercial SPICE simulators are presented to demonstrate the flexibility and advantages of the proposed technique.

Electromagnetic Parametric Modeling Using Combined Neural Networks and RLGC-Based Eigenfunctions for Two-Port Microstrip Structures

This article proposes a novel electromagnetic (EM) parametric modeling technique using combined neural networks and resistance, inductance, conductance, and capacitance (RLGC)-based eigenfunctions (neuro-EF) for microwave components with two-port microstrip structures. In the proposed technique, neural networks are used to learn the unknown relationship between the parameters of RLGC of the eigenfunction and geometrical parameters. The generation of training data depends on obtaining the correct eigenvalues of different modes. However, for different geometrical parameter samples, there is no uniform correspondence between the calculated eigenvalues and the modes. The incorrect correspondence may cause the two modes to be swapped, defined as the mode-swap issue. We propose a mode-matching method based on eigenvectors to solve this mode-swap issue. After the training data of eigenfunction parameters is obtained, a preliminary training of the neural networks and a two-step refinement training of the neuro-EF model are proposed to develop the overall EM parametric model. By the proposed modeling technique, the trained model can provide a fast and accurate prediction of EM responses for two-port microstrip structures as geometrical parameters change. For the parametric modeling of microwave components with microstrip structures, the proposed technique can obtain better accuracy in larger geometrical variations compared with the existing methods. Two examples of microstrip structures are used to illustrate the proposed technique.

Analysis and Data-Based Modeling of the Photochemical Reaction Dynamics of the Induced Singlet Oxygen in Light Therapies.

The macroscopic singlet oxygen (MSO) model for quantifying the light-induced singlet oxygen ( 1O2) always contain a set of nonlinear dynamic equations and therefore are generally difficult to be applied. This work was devoted to analyze and simplify this dynamic model. Firstly, the nonlinearity of the MSO model was analyzed with control theory. The conditions, under which it can be simplified to a linear one, were derived. Secondly, in the case of ample triplet oxygen concentration, a closed-form exact solution of the 1O2 model was further derived, in a nonlinear algebraic form with only four parameters that can be easily fitted to experimental data. Finally, in vitro experiments of anti-fungal light therapies were conducted, where the fungi, Candida albicans, were irradiated respectively by the 385, 405, 415, and 450 nm wavelength light. The singlet oxygen concentration levels in the fungi were measured, and then used to fit the developed models. The parameters of the closed-form exact solution were estimated from both the simulated and the measured experimental data. Based on this model, a functional relationship between the photon energy, fluence rate and singlet oxygen concentration was also established. The fitting accuracy of this model to the data was satisfactory, which therefore demonstrates the effectiveness of the proposed modeling techniques. The results from simulating the closed-form model indicate that the photon energy within the range of either 2.7 ∼ 2.8 eV or 3.0 ∼ 3. 2 eV (388 ∼ 413 nm or 443 ∼ 459 nm in wavelength) is more effective in generating singlet oxygen in the fungi studied in this work. It is the first attempt of applying control theory to analyze the photochemical reaction dynamics of light therapies in terms of their nonlinearity. The proposed modeling techniques also offer opportunities for determining the light dosages in treating fungal infection diseases, especially those on the surface tissues of human body.

A quantum graph approach to metamaterial design

Since the turn of the century, metamaterials have gained a large amount of attention due to their potential for possessing highly nontrivial and exotic properties—such as cloaking or perfect lensing. There has been a great push to create reliable mathematical models that accurately describe the required material composition. Here, we consider a quantum graph approach to metamaterial design. An infinite square periodic quantum graph, constructed from vertices and edges, acts as a paradigm for a 2D metamaterial. Wave transport occurs along the edges with vertices acting as scatterers modelling sub-wavelength resonant elements. These resonant elements are constructed with the help of finite quantum graphs attached to each vertex of the lattice with customisable properties controlled by a unitary scattering matrix. The metamaterial properties are understood and engineered by manipulating the band diagram of the periodic structure. The engineered properties are then demonstrated in terms of the reflection and transmission behaviour of Gaussian beam solutions at an interface between two different metamaterials. We extend this treatment to N layered metamaterials using the Transfer Matrix Method. We demonstrate both positive and negative refraction and beam steering. Our proposed quantum graph modelling technique is very flexible and can be easily adjusted making it an ideal design tool for creating metamaterials with exotic band diagram properties or testing promising multi-layer set ups and wave steering effects.

Feature-based machine learning for the efficient design of nanophotonic structures

Electromagnetic solvers are of paramount importance to simulate the behavior of nanophotonic structures. These solvers can be computationally expensive and therefore their use for design tasks such as optimization can result unfeasible. Machine learning techniques have been proposed in the literature for the modeling and design of photonic devices. In this modeling area, deep neural networks have attracted a lot of attention recently. However, a large number of data samples can be needed to train and validate deep neural networks and therefore the potential speed-up in a design flow offered by these models can be drastically reduced.In this paper, a feature-based machine learning technique is proposed for the design of nanophotonic structures. An adaptive wavelength sampling allows reducing the computational cost of the electromagnetic simulations needed to collect the data samples to train and validate the neural networks that model design features as a function of the design parameters. Design features represent very valuable and meaningful information for designers. The complexity of the feature-based neural networks is significantly reduced with respect to neural networks that model the full electromagnetic response sampled over a grid of wavelength values as a function of the design parameters. Also, a significantly reduced number of data samples is achieved in the case of feature-based modeling. The proposed modeling technique is validated by pertinent numerical results related to a multiband absorber nanostructure.

A Simple Closed-Loop Test Based Control of Boost Converter Using Internal Model Control and Direct Synthesis Approach

In this article, a simple controller design method for output voltage control of dc–dc boost converter (BC) has been proposed. A simple closed-loop step test with proportional gain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k_{0}$ </tex-math></inline-formula> is performed. BC open-loop transfer function is obtained from the closed-loop step response test data, which does not require information of BC circuit parameters. Furthermore, the controller is designed from the open-loop model of the BC through the internal model control (IMC) scheme and direct synthesis (DS) approach. The robust stability analysis of both the control schemes has been carried out. The proposed modeling technique and controller design methods have been validated in the experimental setup. Both the controller design techniques work satisfactorily. Superior transient performance of the IMC scheme in comparison to the DS scheme has been observed.