Physical Experimental Data Research Articles

Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data

When computational models (either physics-based or data-driven) are used for the sensitivity analysis of engineering systems, the sensitivity estimate is affected by the accuracy and uncertainty of the model. This paper considers global sensitivity analysis (GSA) for situations where both a physics-based model and experimental observations are available, and investigates physics-informed machine learning strategies to effectively combine the two sources of information in order to maximize the accuracy of the sensitivity estimate. Two representative machine learning (ML) techniques are considered, namely, deep neural networks (DNN) and Gaussian process (GP) modeling, and two strategies for incorporating physics knowledge within these techniques are investigated, namely: (i) incorporating loss functions in the ML models to enforce physics constraints, and (ii) pre-training and updating the ML model using simulation and experimental data respectively. Four different models are built for each type (DNN and GP), and the uncertainties in these models are included in the Sobol’ indices computation. The DNN-based models, with many degrees of freedom in terms of model parameters and training options, are found to result in smaller bounds on the sensitivity estimates when compared to the GP-based models. The proposed methods are illustrated for additive manufacturing and lake temperature modeling examples.

Shepard scale produced and analyzed with mobile devices

Contributions in this column describe how smartphones and tablets can be used to analyze experimental data in physics experiments. This is possible in nearly all areas of physics—from classical mechanics, to thermodynamics, electrics, and radioactivity. While many examples already showed the analysis of acoustic experiments with mobile devices, no psychoacoustic phenomenon has been studied with the smartphone yet. Nevertheless, acoustic illusions are great starting points for an intrinsic motivation of learners. One great acoustic illusion, often found in science centers, is the so-called Shepard scale illusion. This paper shows how to use mobile devices to analyze and produce Shepard tones to get a deeper understanding of the acoustic concepts like tone, sound, pitch and frequency.

Concerning the Data of Numerical and Physical Experiments in Studying the Adsorption of Gas and Vapors in Micropores of Active Carbons

Concerning the Data of Numerical and Physical Experiments in Studying the Adsorption of Gas and Vapors in Micropores of Active Carbons

LED gates for measuring kinematic parameters using the ambient light sensor of a smartphone

Nowadays, electronic devices (especially smartphones) are developed to use as an alternative tool for recording experimental data in physics experiments. This is because of the embedded sensors in a smartphone such as the accelerometer, gyroscope, magnetometer, camera, microphone, and speaker. These sensors were used in physics experiments, such as the simple pendulum, spring pendulum, coupled pendulum, circular motion, Doppler effect, and spectrum of light, as well as video motion analysis. This paper describes a method for measuring the position-time of a moving object using the ambient light sensor of a smartphone. In the experiment, light-emitting diodes (LEDs), called LED gates, are arranged with equal distance on an inclined plane. A smartphone is attached to a cart and acts as the moving object on the inclined plane. With the appropriate application, the ambient light sensor of the smartphone measures the intensity of the LEDs with respect to time while the smartphone moves along the inclined plane. The results obtained using the LED gates method are in good agreement with that obtained using the video analysis method.

Correction and laboratory investigation for energy loss coefficient of square-edged orifice plate.

A lot of studies have shown that the hydraulic characteristics of orifice plate are mainly controlled by its contraction ratio, but the thickness of square-edged orifice plate also has many impacts on energy loss characteristics. The primary objective of this study was to investigated the effects of square-edged orifice plate thickness on energy loss characteristics. In this paper, the effects of square-edged orifice plate thickness on energy loss characteristics are investigated by numerical simulation using CFD. Orifice plate discharge tunnel is axial symmetric, two dimensional numerical simulations of orifice plate discharge tunnel flow was used. The equation (9) for calculating energy loss coefficient of square-edged orifice plate energy dissipater considering the influence of thickness is proposed. The results of the present research demonstrate that energy loss coefficient decreases with increase of the orifice plate thickness. The results of model experiment are consistence with the results calculated by using rectified equation in present paper. The CFD simulations and Model experiment for the flow through an orifice plate are carried out. For square-edged orifice plate energy dissipater, the relative orifice plate thickness T/D has remarkable impacts on its energy loss coefficient ξ. The Traditional equation (8) is corrected by numerical results. The equation (9) for calculating energy loss coefficient of square-edged orifice plate energy dissipater considering the influence of thickness is proposed and this equation is available in the condition of d/D = 0.4-0.8, T/D = 0.05-0.25, and Re > 105(Re is Reynolds number). Comparing with the physical model experimental data, the relative errors of equation (9) is smaller than 15%.

Reliability analysis of a multi-stack solid oxide fuel cell from a systems engineering perspective

A reliability analysis methodology for a multi-stack solid oxide fuel cell (SOFC) system is presented based on physical modeling and experimental data. The constructed failure probability function comprises three operation phases: (i) as-fabricated, (ii) start-up, and (iii) constant power generation. In-house experimental data are used to capture the behavior during start-up and normal operation, including drifts of the operating point due to degradation. The physics-based model provides a theoretical structure, but alternatives are discussed as well which are particularly suitable for experimentalists. The failure probability function can be used to calculate the reliability of a multi-stack SOFC system. The effect of operating stacks under over-load conditions in terms of degradation is discussed. It is shown that the customized failure probability function better explains the dependency on operating conditions compared to standard parametric distribution functions. The originality of this work is mainly twofold: first, the construction of the failure probability function of a multi-stack SOFC using experimental data for degradation as well as start-up on a system level; second, the focus on technological implications covered by the methodology. Hence, this systems engineering methodology aims to provide a process for reliability analysis in which the strength of both approaches are utilized, namely keeping sufficient parameter-dependency by means of the physics-based approach together with the simplicity of stochastic methods. This methodology is not limited to SOFC but may also be applicable to other modular (electrochemical) systems. Besides references on SOFC from a theoretical and experimental perspective, the extended bibliography also includes references on reliability analysis and engineering design, which may act as a starting point for related system analyses.

Dynamic measurement of the elastic constant of an helicoidal spring by a smartphone

We describe an educational activity that can be done by using smartphones to collect data in physics experiments aimed to measure the oscillating period of a spring-mass system and the elastic constant of the helicoidal spring by the dynamic method. Results for the oscillating period and for the elastic constant of the spring agree very well with measurements obtained by different methods. We also discuss the error analysis that can be done in an introductory physics laboratory at undergraduate level.

Numerical Modeling of Venturi Flume

In order to measure flow rate in open channels, including irrigation channels, hydraulic structures are used with a relatively high degree of reliance. Venturi flumes are among the most common and efficient type, and they can measure discharge using only the water level at a specific point within the converging section and an empirical discharge relationship. There have been a limited number of attempts to simulate a venturi flume using computational fluid dynamics (CFD) tools to improve the accuracy of the readings and empirical formula. In this study, simulations on different flumes were carried out using a total of seven different models, including the standard k–ε, RNG k–ε, realizable k–ε, k–ω, and k–ω SST models. Furthermore, large-eddy simulation (LES) and detached eddy simulation (DES) were performed. Comparison of the simulated results with physical test data shows that among the turbulence models, the k–ε model provides the most accurate results, followed by the dynamic k LES model when compared to the physical experimental data. The overall margin of error was around 2–3%, meaning that the simulation model can be reliably used to estimate the discharge in the channel. In different cross-sections within the flume, the k–ε model provides the lowest percentage of error, i.e., 1.93%. This shows that the water surface data are well calculated by the model, as the water surface profiles also follow the same vertical curvilinear path as the experimental data.

Finite-element simulation of multi-axial fatigue loading in metals based on a novel experimentally-validated microplastic hysteresis-tracking method

We propose a new approach to the prediction of multiaxial fatigue with proportional and non-proportional loading based on a recently developed three-dimensional small-strain microplasticity-based constitutive theory. The core idea of the theory is to incorporate pre-full-yield microplastic deformations in a computationally efficient way. Fatigue life is then correlated to the accumulated (micro)plastic work, which is obtained from the total plastic dissipation. The constitutive parameters required are calibrated using just a monotonic stress-strain curve determined from a simple compression test experiment together with a low cycle uniaxial fatigue experiment. The resulting three-dimensional constitutive model has also been implemented into the Abaqus/Explicit finite element program through a vectorized user-material subroutine interface with a fully-implicit, unconditionally-stable and robust time integration scheme. Using the suitably-calibrated constitutive model, a series of uniaxial and multiaxial stress- and strain-based, constant-amplitude fatigue finite element simulations have been conducted to compare with the physical experiment data from the literature. It is shown that the developed theory and its finite-element implementation (which have fewer parameters than cycle counting based methods) are able to better predict the experimental fatigue life under multiaxial proportional or non-proportional loading conditions.

Multicarrier Dynamics in Quantum Dots.

Multicarrier dynamics play an essential role in quantum dot photophysics and photochemistry, and they are primarily governed by nonradiative Auger processes. Auger recombination affects the performance of lasers, light-emitting diodes, and photodetectors, and it has been implicated in fluorescence intermittency phenomena which are relevant in microscopy and biological tagging. Auger cooling is an important mechanism of rapid electron thermalization. Inverse Auger recombination, known as impact ionization, results in carrier multiplication which can enhance the efficiencies of solar cells. This article first reviews the physical picture, theoretical framework and experimental data for Auger processes in bulk crystalline semiconductors. With this context these aspects are then reexamined for nanocrystal quantum dots, and we first consider fundamental features of Auger recombination in these systems. Methods for the chemical control of Auger recombination and Auger cooling are then discussed in the context of how they illuminate the underlying mechanisms, and we also examine the current understanding of carrier multiplication in quantum dots. Manifestations of Auger recombination in quantum dot devices are finally considered, and we conclude the article with a perspective on remaining unknowns in quantum dot multicarrier physics.

Investigation of light non-aqueous phase liquid penetration in double-porosity using physical experiment and computer simulation

Groundwater resources benefits to human activity for developing country. Groundwater contamination is crucial, particularly due to the amount of leakage and spillage of hydrocarbon liquids such as light non-aqueous phase liquids (LNAPLs), resulting in contaminated the groundwater and unsafe for domestic and agriculture activities. Penetration of hydrocarbon liquids into groundwater can be seen through double-porosity soil. Therefore, this paper investigates the penetration of LNAPLs in double-porosity soil using computer modelling to calibrate and validate from physical experiment data. These computer modelling and physical experiment studies discuss the pattern and rate of LNAPL penetrations by employing PetraSim commercial software and digital image processing technique (DIPT) by using acrylic glass cylinder, mirror and Nikon D90 digital camera. The LNAPL volume of 70 ml and 150 ml were poured instantaneously onto the surface of soil sample for calibration and validation purposes. The penetration pattern in double-porosity were monitored and recorded using digital camera at pre-determine time intervals. The images were processed using Surfer software and Matlab routine to plot the LNAPL penetration pattern. PetraSim simulation was used to calibrate and validate the penetration of LNAPL through double-porosity soil with physical experiment data. As a result, the PetraSim results valid with the physical experiment results. The Nash-Sutcliffe efficiency results more than 0.50 with percentage of differences for calibration and validation are 1.34% and 5.47% between physical experiment and PetraSim simulation. As a conclusion, PetraSim simulation can be used for further investigation on LNAPL penetration through subsurface soil.

Dependence of Electrodynamics on the Models of the Electromagnetic Medium

This article traces the influence of physical models of the electromagnetic medium on electrodynamics. The connection between the ability of the electrodynamic equations to describe the processes under study and the adequacy of the model of the electromagnetic medium used in their derivation to the known data is shown. It is revealed that the assumption of the absence of the existence of the electromagnetic medium leads to certain methodological difficulties in deriving the electrodynamic equations. The paper highlights the main milestones in the evolution of the physical model of electromagnetic medium. As a rule, the existence of electromagnetic medium in physics has been denied up to the present time due to the lack of its consistent model. A new physical model of the electromagnetic medium is developed and based on it a system of electrodynamic equations is derived that agree with the known data of physical experiments and astronomical observations. On the basis of the proposed physical model, a mathematical description of the method of detecting the electromagnetic medium is obtained. The developed physical model made it possible not only to explain the data of known electromagnetic phenomena and experiments, but also to propose the Galilean relativity principle within the framework of classical mechanics. Possible consequences of detection of the electromagnetic medium are discussed.

Сравнение энергетической эффективности воспламенения топливной смеси искровым и стримерным разрядами

An increase in fuel efficiency and efficiency of combustion processes in power plants is discussed based on the data of physical and computational experiments. Two systems for ignition of a fuel mixture are considered, one of which uses a multi-point pulsed spark discharge, and the other uses a multipoint streamer discharge. A comparative assessment of the energy efficiency of each approach to the ignition of the air/fuel mixture is carried out, and conclusions are drawn about their effectiveness and prospects for use.

MODELING OF THE OXIDATION OF NITROGEN OXIDES BY OZONE

A mathematical model of the process of ozone oxidation of nitrogen oxides in a chemical reactor is presented. The conformity of the model to the real process is checked by comparing the results of calculations with the data of physical experiments on a laboratory installation. The results of numerical modeling of the process in the reactor using the specified mathematical model are presented. The results of the calculations revealed the influence of the initial parameters of the process on its performance characteristics, in particular on the efficiency of conversion of nitrogen monoxide into dioxide. Based on the analysis of the calculated characteristics, the optimal operating parameters of the reactor at different modes are determined. Bibl. 5, fig. 6, table. 4.

Effective model calibration via sensible variable identification and adjustment with application to composite fuselage simulation

Estimation of model parameters of computer simulators, also known as calibration, is an important topic in many engineering applications. In this paper we consider the calibration of computer model parameters with the help of engineering design knowledge. We introduce the concept of sensible (calibration) variables. Sensible variables are model parameters, which are sensitive in the engineering modeling, and whose optimal values differ from the engineering design values. We propose an effective calibration method to identify and to determine appropriate levels for the sensible variables with limited physical experimental data. The methodology is applied to a composite fuselage simulation problem.

Random Matrices

Large complex systems tend to develop universal patterns that often represent their essential characteristics. For example, the cumulative effects of independent or weakly dependent random variables often yield the Gaussian universality class via the central limit theorem. For non-commutative random variables, e.g. matrices, the Gaussian behavior is often replaced by another universality class, commonly called random matrix statistics. Nearby eigenvalues are strongly correlated, and, remarkably, their correlation structure is universal, depending only on the symmetry type of the matrix. Even more surprisingly, this feature is not restricted to matrices; in fact Eugene Wigner, the pioneer of the field, discovered in the 1950s that distributions of the gaps between energy levels of complicated quantum systems universally follow the same random matrix statistics. This claim has never been rigorously proved for any realistic physical system but experimental data and extensive numerics leave no doubt as to its correctness. Since then random matrices have proved to be extremely useful phenomenological models in a wide range of applications beyond quantum physics that include number theory, statistics, neuroscience, population dynamics, wireless communication and mathematical finance. The ubiquity of random matrices in natural sciences is still a mystery, but recent years have witnessed a breakthrough in the mathematical description of the statistical structure of their spectrum. Random matrices and closely related areas such as log-gases have become an extremely active research area in probability theory. This workshop brought together outstanding researchers from a variety of mathematical backgrounds whose areas of research are linked to random matrices. While there are strong links between their motivations, the techniques used by these researchers span a large swath of mathematics, ranging from purely algebraic techniques to stochastic analysis, classical probability theory, operator algebra, supersymmetry, orthogonal polynomials, etc.

Distribution of reformed coke oven gas in a shaft furnace

In recent years, the reformed coke oven gas (COG) was proposed to be used as reducing gas in a shaft furnace. A mathematical model of gas flow based on the reformed COG was built. The effects of the pressure ratio of reducing gas to cooling gas (k) on the gas distribution in the shaft furnace were investigated. The calculation results show that k is an important operation parameter, which can obviously affect the gas distribution in the shaft furnace. The value of k should be compromised. Both too big and too small k values are not appropriate, and the most reasonable value for k is 1:1.33. Under this condition, the utilization coefficient of reducing gas, the utilization coefficient of cooling gas and the coefficient of upward gas are 0.94, 0.92 and 1.03, respectively. Based on the validation of physical experiments, the calculated values of the model agreed well with the physical experimental data. Thus, the established model can properly describe the reformed COG distribution in an actual shaft furnace.

Phase detection with neural networks: interpreting the black box

Neural networks (NNs) usually hinder any insight into the reasoning behind their predictions. We demonstrate how influence functions can unravel the black box of NN when trained to predict the phases of the one-dimensional extended spinless Fermi–Hubbard model at half-filling. Results provide strong evidence that the NN correctly learns an order parameter describing the quantum transition in this model. We demonstrate that influence functions allow to check that the network, trained to recognize known quantum phases, can predict new unknown ones within the data set. Moreover, we show they can guide physicists in understanding patterns responsible for the phase transition. This method requires no a priori knowledge on the order parameter, has no dependence on the NN’s architecture or the underlying physical model, and is therefore applicable to a broad class of physical models or experimental data.

Optimal Design of Maxwell-Viscous Coulomb Air Damper With a Modified Fixed Point Theory

Abstract Air damper dynamic vibration absorber (DVA) is modeled using Maxwell transformed element and coulomb element. This damper serves to minimize vibration at resonant and operation of constant speed machine. Its stiffness and damping factor are transformed from Maxwell to Voigt arrangement. Meanwhile, viscous equivalent Coulomb damping is expressed by absolute relative motion. System transmissibility contours are plotted by min–max approach. Its optimal parameters are determined using this approach. Contour operation minimization is obtained from minimum system transmissibility. Moreover, exact solution of fixed points and optimal natural frequency ratio are obtained by a modified fixed point theory. Optimal design curve is derived by Coulomb damping derivative and maximum condition. Operational vibration level is minimized by 7% at the operation minimization using minimum condition. On the experimental side, test platform of the air damper is constructed using linear slide block system. Computational model of the air damper is established by its physical details and experimental data. Linear relationship is obtained between viscous and Coulomb damping angles. Modified fixed points are validated by frequency response function resonant peaks. Experimental vibration level is minimized by 5%, which being close to the minimization result. The model is validated within 5% accuracy by its optimal experimental curve.

Modeling and Control of Swing Oscillation of Underactuated Indoor Miniature Autonomous Blimps

Swing oscillation is widely observed among indoor miniature autonomous blimps (MABs) due to their underactuated design and unique aerodynamic shape. This paper presents the modeling, identification and control system design that reduce the swing oscillation of an MAB during hovering flight. We establish a dynamic model to describe the swing motion of the MAB. The model parameters are identified from both physical measurements, computer modeling and experimental data captured during flight. A control system is designed to stabilize the swing motion with features including low latency and center-of-mass (CM) position estimation. The modeling and control methods are verified with the Georgia-Tech Miniature Autonomous Blimp (GT-MAB) during hovering flight. The experimental results show that the proposed methods can effectively reduce the swing oscillation of GT-MAB.