Numerical Test Bed Research Articles

FE-aided Kalman Filter for nonlinear state estimation with unknown input

A Kalman Filter-Finite Element (KF-FE) framework for joint input-state estimation of nonlinear systems is proposed in the current study. The KF-FE framework has been developed aiming to estimate the nonlinear structural behavior induced by excessive loading such as earthquake-induced ground motions or strong waves, during which a limited number of responses can be measured. The proposed framework involves the use of a Bayesian recursive filter, namely the Kalman Filter, in conjunction with a nonlinear Finite Element (FE) model, which allows to update the state space model formulation at each time step. The proposed KF-FE framework builds its novelty for nonlinear systems on the basis of existing methods for online state estimation of linear systems. The application of such a framework can be suitable for nonlinear state estimation when only output measurements are available. A numerical testbed is adopted herein consisting of a 2D steel moment resisting frame structure subjected to earthquake ground motions. The KF-FE results from the nonlinear numerical analysis scheme indicate estimation performance of high accuracy for both global (e.g., nodal displacements and rotations) and local (moment-curvature relationships) demand parameters while the computational cost is kept low.

Grace: Low-cost time-synchronized GPIO tracing for IoT testbeds

Testbeds have become a vital tool for evaluating and benchmarking applications and algorithms in the Internet of Things (IoT). IoT testbeds commonly consist of low-power IoT devices augmented with observer nodes providing control, debugging, logging, and often also power-profiling capabilities. Today, the research community operates numerous testbeds, sometimes with hundreds of IoT nodes, to allow for detailed and large-scale evaluation. Most testbeds, however, lack opportunities for tracing distributed program execution with high accuracy in time, for example, via minimally invasive, distributed GPIO tracing. And the ones that do, like Flocklab, are built from custom hardware, which is often too complex, inflexible, or expensive to use for other research groups.This paper closes this gap and introduces Grace, a low-cost, retrofittable, distributed, and time-synchronized GPIO tracing system built from off-the-shelf components, costing less than €20 per node. Grace extends observer nodes in a testbed with (1) time-synchronization via wireless sub-GHz transceivers and (2) logic analyzers for GPIO tracing and logging, enabling time-synchronized GPIO tracing at a frequency of up to 8 MHz. We deploy Grace in a testbed and evaluate it, showing that it achieves an average time synchronization error between nodes of 1.53 μs using a single time source, and 15.3 μs between nodes using different time sources, sufficient for most IoT applications.

Nonlinear pinning control of stochastic network systems

We propose a constructive method to design a pinning control law that synchronizes a network of stochastic dynamical systems. Different from traditional pinning control, we add to the standard proportional controller a nonlinear feedback term that, in the absence of coupling, would minimize a given cost functional. We then derive analytic guarantees that the proposed control law can effectively synchronize coupled noisy systems described by stochastic differential equations. Building on our theoretical results, we provide an algorithm for control design, whose effectiveness is illustrated on numerical testbeds. Finally, we perform extensive Monte Carlo simulations to evaluate the performance of the proposed nonlinear pinning strategy and compare it against the traditional proportional law.

A Decision-Making Model for Predicting Technology Adoption Success

Advanced manufacturing technology (AMT) has the potential to significantly improve manufacturing performance and boost competitiveness in the global market. Investment in AMT remains a promising but potentially risky venture due to the numerous factors that must be considered before the full benefits of implementing a new technology can be realized. To respond to the reported risks and uncertainties, such as those revealed in the recent industrial revolution, it is very important to identify and classify the critical factors that can influence the success of AMT adoption early in the planning stage. Based on an extensive review of relevant literature, 32 critical factors are identified and classified into ten categories in this paper. A new multiple-input single-output (MISO) model is developed by combining the fuzzy Delphi method (FDM) and the fuzzy inference system (FIS) based on the objectives defined. The FDM is used to determine the critical factors, and the FIS addresses the general fuzzy multi-attribute decision-making (MADM) problem in order to evaluate and predict the percentage of AMT adoption success with an existing system. The model is validated using a numerical test bed, and the results show that the model is a proper tool for risk management in AMT implementation.

Asymmetric network information cache based on mobile traffic in software-defined network

Software-defined network is an encouraging research area that realizes updates throughout an entire network, and a wireless network is ubiquitous in an up-to-date world. The combination of these techniques, which is referred to as a wireless software-defined network, has been a significant development in numerous testbeds. The extensive use of distributed controllers that achieved elastic extension and are fault tolerant in large-scale wireless networks is hopeful. Despite their profits, they generate significant overhead in synchronizing network information. Thus, power-hungry wireless software-defined network devices limit the usage of distributed controllers in wireless networks. The patterns of storing network information have a crucial effect on controller performances, as they determine the probability of processing requests based on local data. To address this problem, we identify a better pattern for controllers to retain proper network information to balance costs in terms of both synchronous traffic and response time. We analyze the characteristics of distributed controllers and propose an overhead model of a distributed controller in a wireless software-defined network. Based on this model, we design an asymmetric network information cache to make trade-offs among different controller architectures. The asymmetric network information cache measures real-time network traffic and computes the traffic among sub-networks controlled by controller nodes. Then, the controller asymmetrically caches suitable network data to substantially busier nodes. The experimental results indicate that the asymmetric network information cache achieves a trade-off between synchronous traffic and the average response time.

Experimental research testbed for internet of things: A survey from security services perspectives

IoT testbed has been continuously on the rise in line with the significant advances of technology in sensor network to provide real-world interconnection. Researchers have developed numerous testbeds for IoT applications with many unique innovations. However, there is still a missing element in the testbed analysis on the security services and its methodologies that can lead to secure IoT application systems. This paper provides a framework to evolving IoT testbeds from the security services perspective, methodology and competency in IoT security which supports and enable multidisciplinary experimentation. Through a comprehensive literature survey of existing IoT testbeds, a set of core security design requirement for IoT security testbed is identified by examining vulnerabilities and attacks at every layer of architecture to get a deep understanding of the security assessment and best practices to conduct IoT security services.

Adaptive interpolation techniques for structural acoustic system response

In recent years, several new techniques for interpolating the response of structural acoustic systems have been developed. Besides the various underlying approximation schemes that utilize concepts such as Padé approximants or implicitly interpolatory subspaces, the main advantage of these methods is that their structure is adaptively determined, meaning that they select interpolation points and, when appropriate, interpolation order as the algorithm proceeds. This allows users to have little to no a priori knowledge of the system response and yet still achieve approximations of a desired accuracy. Furthermore, the interpolation is generated at minimal cost giving rise to approximations that are efficient as well as accurate. A variety of these techniques are introduced and discussed with emphasis on the trade-offs between the methods. Then, a numerical test bed is developed to evaluate the performance of the interpolation schemes giving each access to the same computational resources. Recommendations are made based on these simulations concerning the appropriate choice of adaptive interpolation for various scenarios.

A mixing timescale model for TPDF simulations of turbulent premixed flames

Transported probability density function (TPDF) methods are an attractive modeling approach for turbulent flames as chemical reactions appear in closed form. However, molecular micro-mixing needs to be modeled and this modeling is considered a primary challenge for TPDF methods. In the present study, a new algebraic mixing rate model for TPDF simulations of turbulent premixed flames is proposed, which is a key ingredient in commonly used molecular mixing models. The new model aims to properly account for the transition in reactive scalar mixing rate behavior from the limit of turbulence-dominated mixing to molecular mixing behavior in flamelets. An a priori assessment of the new model is performed using direct numerical simulation (DNS) data of a lean premixed hydrogen–air jet flame. The new model accurately captures the mixing timescale behavior in the DNS and is found to be a significant improvement over the commonly used constant mechanical-to-scalar mixing timescale ratio model. An a posteriori TPDF study is then performed using the same DNS data as a numerical test bed. The DNS provides the initial conditions and time-varying input quantities, including the mean velocity, turbulent diffusion coefficient, and modeled scalar mixing rate for the TPDF simulations, thus allowing an exclusive focus on the mixing model. The new mixing timescale model is compared with the constant mechanical-to-scalar mixing timescale ratio coupled with the Euclidean Minimum Spanning Tree (EMST) mixing model, as well as a laminar flamelet closure by Pope and Anand (1984). It is found that the laminar flamelet closure is unable to properly capture the mixing behavior in the thin reaction zones regime while the constant mechanical-to-scalar mixing timescale model under-predicts the flame speed. The EMST model coupled with the new mixing timescale model provides the best prediction of the flame structure and flame propagation among the models tested, as the dynamics of reactive scalar mixing across different flame regimes are appropriately accounted for.

A Brief Review of Network and Sensor Virtualization for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are more vigorously being used with applications relating to the Internet of Things (IoT). They are used for a variety of applications, but are frequently deployed keeping in view a specific objective. Sensor network virtualization may be classified as network and sensor virtualization. Virtualization comes with numerous challenges in the form of segregating various networks, hardware and associated software in the system. This research paper touches upon the past and current research directions associated with virtualization, various design targets, layouts and numerous testbeds have also been looked into. The paper also highlights the current challenges and future avenues associated to expedite research on virtualization. Keywords: Internet of things (IoT), Network virtualization, Sensor virtualization, Virtualization, Wireless sensor network (WSN), WSN testbeds.

The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection

In today's highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evalua- tions prior to suppliers' ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker's assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including ''extremely preferred'', ''moder- ately preferred'', and ''weakly preferred''. In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.

Nonglobal Parameter Estimation Using Local Ensemble Kalman Filtering

Abstract The authors study parameter estimation for nonglobal parameters in a low-dimensional chaotic model using the local ensemble transform Kalman filter (LETKF). By modifying existing techniques for using observational data to estimate global parameters, they present a methodology whereby spatially varying parameters can be estimated using observations only within a localized region of space. Taking a low-dimensional nonlinear chaotic conceptual model for atmospheric dynamics as a numerical test bed, the authors show that this parameter estimation methodology accurately estimates parameters that vary in both space and time, as well as parameters representing physics absent from the model.

Use of Vehicle Power-Train Simulation with AMT for Fuel Economy and Performance

Frequent stops and starts for the traffic lights as well as acceleration/deceleration, rather than at constant speed, are the most common transient operating conditions. Consequently, understanding the dynamic characteristics of the powertrain system is vitally important in designing an efficient and fresh vehicle. To investigate the dynamic characteristics of the road vehicle performance, apart from a real road testing, it is necessary to set up a numerical test bed for dynamic performance simulation. The latter is even more convenient, costless and data repeatable. However, the aim of the present paper is to establish a computer simulation package by which the road vehicle power-train system design is developed for a small passenger vehicle based on fuel economy and performance. Furthermore, a proposed professional computer program of Automated Manual Transmission (AMT) system related to organizer algorithm of attractive gearshift requests is also introduced. The result of the proposed AMT ensures that the drive wheels force balance in order to keep gear shift, to kick down or up not only shift feel but also the fuel consumption, and to alarm by near a specific failure or to get back system performance.

A cross-sectional analysis of composite beams based on asymptotic framework

This paper presents the accurate prediction of static behavior of composite beams with arbitrary cross-sections. The asymptotic recursive formulation is reviewed first, where the initial three-dimensional problems are split into the macroscopic 1D problems and the microscopic 2D problems. The finite element formulation for the microscopic 2D problems is then presented in order to find the crosssectional warping solutions. The warping solutions obtained contribute the cross-sectional properties to the macroscopic 1D problems. The end effect of the 1D beam problem is also considered via the kinematic correction for a displacement prescribed boundary. The approach presented is applied to the beams with relatively complicated material distributions and cross-sectional geometry. As numerical test-beds, a three-layered sandwich beam and a composite beam with the multi-cell cross-section are taken to analyze the local deformation. A parametric study is also carried out to investigate the significance of shear deformation due to the cross-sectional orthotropic characteristics. The cross-sectional deformation is predicted based on the asymptotic framework. The accuracy of the present approach is assessed by comparing the results obtained with the 3D FEM solutions obtained by ANSYS.

Dielectric Characterization of PCL-Based Thermoplastic Materials for Microwave Diagnostic and Therapeutic Applications

We propose the use of a polycaprolactone (PCL)-based thermoplastic mesh as a tissue-immobilization interface for microwave imaging and microwave hyperthermia treatment. An investigation of the dielectric properties of two PCL-based thermoplastic materials in the frequency range of 0.5-3.5 GHz is presented. The frequency-dependent dielectric constant and effective conductivity of the PCL-based thermoplastics are characterized using measurements of microstrip transmission lines fabricated on substrates comprised of the thermoplastic meshes. We also examine the impact of the presence of a PCL-based thermoplastic mesh on microwave breast imaging. We use a numerical test bed comprised of a previously reported 3-D anatomically realistic breast phantom and a multi-frequency microwave inverse scattering algorithm. We demonstrate that the PCL-based thermoplastic material and the assumed biocompatible medium of vegetable oil are sufficiently well matched such that the PCL layer may be neglected by the imaging solution without sacrificing imaging quality. Our results suggest that PCL-based thermoplastics are promising materials as tissue immobilization structures for microwave diagnostic and therapeutic applications.

The Value of Multiproxy Reconstruction of Past Climate

Understanding the dynamics of climate change in its full richness requires the knowledge of long temperature time series. Although long-term, widely distributed temperature observations are not available, there are other forms of data, known as climate proxies, that can have a statistical relationship with temperatures and have been used to infer temperatures in the past before direct measurements. We propose a Bayesian hierarchical model to reconstruct past temperatures that integrates information from different sources, such as proxies with different temporal resolution and forcings acting as the external drivers of large scale temperature evolution. Additionally, this method allows us to quantify the uncertainty of the reconstruction in a rigorous manner. The reconstruction method is assessed, using a global climate model as the true climate system and with synthetic proxy data derived from the simulation. The target is to reconstruct Northern Hemisphere temperature from proxies that mimic the sampling and errors from tree ring measurements, pollen indices, and borehole temperatures. The forcing series used as covariates are solar irradiance, volcanic aerosols, and greenhouse gas concentrations. The Bayesian model was successful in integrating these different sources of information in creating a coherent reconstruction. Within the context of this numerical testbed, a statistical process model that includes the external forcings can improve the quality of a hemispheric reconstruction when long time scale proxy information is not available. This article has supplementary material online.

Evaluation and Modification of a Robust Path Following Controller for Marine Surface Vessels in Wave Fields

This paper evaluates a novel robust path following controller for marine surface vessels in wave fields. Because of complex wave structure interactions, most path following control designs neglect the wave impact at the design stage and rely on simulation or experiment to ensure the satisfactory performance in wave fields. In this paper, we first introduce a numerical test bed that combines the ship dynamics and wave effects on vessels to facilitate the model-base performance evaluation of path following control systems in wave fields. Then, a novel robust path following controller is described and its key features are summarized. Simulation results of the path following controller in the wave field are presented. Several issues, such as steady-state errors and rudder oscillations, have been identified, thereby motivating controller modification and gain retuning. Mitigating strategies for improving the controller performance are proposed and numerically evaluated. The simulation results show that the performance of the modified controller can be substantially improved in wave fields.

High strain-rate bulge forming of sheet metals using a solid bulging medium

The biaxial bulge-forming of sheet metals is a standard test to evaluate the formability and mechanical behaviour of materials. A new dynamic bulge-testing method is simulated and analysed in this study that can be performed on a conventional split Hopkinson pressure bar (SHPB) system. The aim of this work is to develop a numerical and theoretical technique for the analysis of materials and structures, which are dynamically loaded and subjected to the different levels of strain rates. The commercial finite element code ABAQUS/Explicit has been selected as the numerical test bed for the simulation of the dynamic bulging test. In the new bulge-forming technique, flexible rubber is used as the pressure-carrying medium instead of hydraulic fluid. The theoretical analysis is based on conventional hydraulic bulge test principles and SHPB relations. To verify the accuracy of the developed technique, analytical and finite element methods are compared and show good correlation with each other.

Relativistic hydrodynamics in the presence of puncture black holes

Many of the recent numerical simulations of binary black holes in vacuum adopt the moving puncture approach. This successful approach avoids the need to impose numerical excision of the black hole interior and is easy to implement. Here we wish to explore how well the same approach can be applied to moving black hole punctures in the presence of relativistic hydrodynamic matter. First, we evolve single black hole punctures in vacuum to calibrate our Baumgarte-Shapiro-Shibata-Nakamura implementation and to confirm that the numerical solution for the exterior spacetime is invariant to any junk (i.e., constraint-violating) initial data employed in the black hole interior. Then we focus on relativistic Bondi accretion onto a moving puncture Schwarzschild black hole as a numerical test bed for our high-resolution shock-capturing relativistic hydrodynamics scheme. We find that the hydrodynamical equations can be evolved successfully in the interior without imposing numerical excision. These results help motivate the adoption of the moving puncture approach to treat the binary black hole--neutron star problem using conformal thin-sandwich initial data.