A Modified Directional Flame Thermometer: Development, Calibration, and Uncertainty Quantification

The technique for identification (determination of the parameters) of the model of high-pressure discharge lamp by comparing the simulated and experimental data was developed. Searching for the model parameters is divided into two main stages: 1) determining the degree of influence of each parameter change on the model output; 2) directly searching for the minimum functional divergence. Divergence evaluation is carried out by determining the value of the functional - the sum of the squares of divergences during the studied time period. When searching for minimum divergence, a corresponding set of model parameters is determined. The technique for determining the parameters of the mathematical model of high-pressure discharge lamp was designed to determine such parameters of the model, which allow to describe it with the necessary precision. Trial computational experiment showed the performance of the model parameter determination program, developed using the technique. It was found that the models that are based on the current-voltage characteristics of lamps are promising. The need to determine the model parameters is caused by the requirement for the accuracy of the research, and the availability of regular deviations in characteris­tics of lamps of the same type from different manufacturers, batches and even between units in batches from that of manufacturers and general. Ensuring the model accuracy is also important in developing and creating complex control systems that can be realized with microprocessor control, where the model is used for calculating the control object behavior and selecting control signals.

The paper deals with determination of parameters of a dispersion model used for describing the flow of liquid on a plate with mobile packing in the region of gas velocities up to 1.5 m s-1. The parameters of the model - the diffusion Peclet number and mean residence time of liquid - were determined from the nonideal input impulse of tracer concentration and its response by the method of numerical integration of differential equation with subsequent optimization of parameters. The results of measurements are presented graphically and in the form of criterion equations.

Validation approaches can determine the degree of accuracy of simulated models representing real structures. Therefore these approaches should deal with concepts concerning fidelity-to-data, the uncertainty quantification and the comparative metrics i.e. measures that quantify the level of agreement between simulation and experimental outcomes. In the context of nonlinear systems associated with nonlinear models additional care should be taken when attempting to perform a validation scheme, in order to evaluate the examined models, due to bifurcations that may occur.In this paper, experimental datasets from a laboratory based three-story building structure are used in order to calibrate the parameters of a physics-based model using system identification methods. The structure described here is linear; however, the work is a necessary precursor to the investigation of the behaviour of the structure when nonlinearity is introduced in the form a cantilever beam impacting the highest story of the building for a specific range of excitations. In this study the linear model is identified using experimental data and the sensitivity of model predictions is examined when the parameters of the model are varied.KeywordSystem identification

micrOMEGAs: Version 1.3

Quantitative Raman spectroscopic investigation of geo-fluids high-pressure phase equilibria: Part II. Accurate determination of CH4 solubility in water from 273 to 603 K and from 5 to 140 MPa and refining the parameters of the thermodynamic model

Description Eleven peer-reviewed papers address the significant challenges associated with performing thermal measurements as part of fire standards development, testing and analysis. The testing environment and the results of fire standards tests are almost always based on one or more thermal measurements. Measurements of importance include temperature, heat flux, calorimetry, and gas species concentrations. These measurements are also of primary importance to the experimental validation of computer models of fire growth and material response. Topics cover: Temperature--despite the frequency of temperature measurement to characterize test environments and material response, challenges remain in consistently performing measurements with quantified uncertainty. This section addresses temperature measurement of conditions ranging from thermal fields in furnace environments to thermal response of engulfed objects in large pool fires and thermal protection effectiveness of firefighter’s clothing. Thermocouples, while straightforward in use and operation, are illustrated as deserving consideration of the uncertainty in measurements for each specific application. Heat Flux--measurements of heat flux are useful to define the fire thermal field for purposes of evaluating material thermal response. Several established gauges have been extensively in fire standards. As with temperature measurements, the resulting uncertainty varies with the gauge design and the environment. The magnitude of this uncertainty, and the need to perform cost-effective experiments and tests, has yielded some new designs and application techniques. Significant progress associated with existing methods address calibration, angular sensitivity, and uncertainty quantification under large fire conditions. Calorimetry and Ignition Energy--although not as common as heat flux and temperature measurements, these parameters often play the key role in fire standards for the role they play in the initiation, growth, and spread of fire environments. Heat release rate measurements and the evaluation of oxygen are also discussed. In addition, uncertainty in the measurement of ignition energy is also explored. Modern diagnostics and tools allow a closer look at legacy methods and techniques for performing these measurements. Audience: This new publication is a valuable reference guide for researchers and engineers who need to measure temperature, radiant flux, and heat transfer; scientists and engineers involved in testing flammability limits of vapor phrase mixtures; testing laboratory personnel; and university professors and students.

Thermodynamic modeling and uncertainty quantification of CO2-loaded aqueous MEA solutions

This study focuses mainly on observing urban development in Taiwan's Wu‐Tu watershed from the perspective of urban hydrological theory. An approach is proposed for developing a method for incorporating available meteorological data to define the degree of change in a runoff hydrograph for urbanizing basins. The mean rainfall was estimated using the Kriging method. For calibration, two methods of calculating the effective rainfall (the Φ‐index method and the non‐linear‐programming (NLP) method) were used as model inputs, and the optimal global parameters of the linear reservoir model were then obtained from the shuffled complex evolution (SCE) algorithm. Twenty‐six (1966–1991) and eight (1994–1997) rainfall–runoff events were used for calibration and verification, respectively. The NLP method yielded better results than the Φ‐index method, especially for multipeak rainfall–runoff events. The regression equation determined the relationship between the parameters of the model and impervious areas. A comparison based on the results of the instantaneous unit hydrograph of the study area revealed that three decades of urbanization had increased the peak flow by 27%, and the time to peak was decreased by 4 h. The study simply describes the results of the impact of imperviousness on hydrological modelling. Copyright © 2002 John Wiley & Sons, Ltd.

In this article we present a drastic dimension reduction method to link the biophysical parameters of an electromechanical model of the heart with a compact representation of cardiac motion. Our approach relies on a projection of the displacement fields along the whole cardiac motion to the space of reduced-polyaffine transformations. Using these transformations, not only we describe the motion using a very small number of parameters but we show that each of these parameters has a physiological meaning. Moreover, using a PLS regression on a learning set made of a large number of simulations, we are able to find which of the input parameters of the model most impact the motion and what are the main relations mapping the polyaffine representation to the parameters of the model. We illustrate the potential of this method for building a direct and very fast model characterized by a highly reduced number of parameters.

In this study, a new compressible flow model for small orifice openings in pneumatic proportional directional control valves has been proposed. It is crucial to precisely control pneumatic valves over all control ranges; yet, conventional flow models fail around the closed position of the valve. The main deficit of the existing studies in the literature is to assume constant values for the parameters of the flow model over changing operating conditions. It has been demonstrated that these rough assumptions are insufficient in precisely predicting the mass flow rate, particularly for small orifice openings. An enhanced experimental setup has been introduced to improve the effectiveness of the proposed model. The cracking pressure ratio and parameters of the model have been identified with experimental method. In the proposed model, new empirical coefficients have been established after a thorough investigation of the impact of supply pressure on the flow behavior of the valve. Validation studies of the model in both the filling and exhausting states of the valve have been carried out at various supply pressures and orifice openings, yielding rather promising modeling performances. In validation tests, the real pressure and the pressure produced by new model have been compared, and good agreement has been achieved with 0.0039% absolute error. According to the findings, the proposed improved flow model can be selected in precision pneumatic control applications.

Multitask learning and nonlinear optimal control of the COVID-19 outbreak: A geometric programming approach

Structured soils with earthworm burrows are locally heterogeneous due to coatings along these biopore walls that are superimposed on the inter- and intra-aggregate pore network of the soil matrix. The partially compacted finer-textured and organic matter-rich coatings can limit the flow exchange between the macropores and the soil matrix during preferential flow. Still unknown are dynamic hydro-mechanical interrelations in coating and matrix domains that affect stress–strain behaviour at the macroscopic scale. Such hydro-mechanic interactions may be described with the discrete element method (DEM) coupled with a two-phase pore finite volume (2PFV) approach if relevant pore structures are represented in the model. The objective was to develop a coupled DEM-2PFV model together with a parameterization procedure. Major task was to create a parameterization procedure to calibrate micro parameters of the model by macro properties quantified from drainage experiments of soil samples with earthworm burrow wall coatings.The solid phase was modelled by particle aggregation creating inter and intra-aggregate pore network for the soil matrix in a cube of about 5 cm edge length and one side with the coating structure. This DEM model was coupled with a 2PFV model to simulate hydro-mechanic effects during drainage. Sand box drainage experiments were carried out on soil matrix and biopore samples with laser surface elevation measurements to obtain the mechanical stress–strain macro parameters necessary for model calibration. The poly-dispersed DEM-2PFV model was able to describe effects of two-phase air–water flow on stress–strain macro parameters. The micro parameters (i.e., particle stiffness and bond strength) of the pore scale model were obtained from macro parameters of the primary and secondary stress–strain stages by training a random forest meta-estimator. The model was able to reproduce the pore network of coating material and the inter- and intra-aggregate pore network of the matrix that are dynamically changing with the effective stress. The machine learning model revealed that the bond strength among particles within aggregates governed the shrinkage of soil matrix, while the particle stiffness of the coating material reduced the susceptibility of aggregate breakage producing a more stable inter-aggregated pore network during the drainage process. This study confirmed that coating material present in biopore surface increases the horizontal soil hydro structural stability. The microscale hydro-mechanic modelling can be useful for finding flow exchange parameters inputs for upscaled models and correlating pore-scale parameters to experimentally determined stress–strain macro parameters.

We study the effect of misclassification of a binary covariate on the parameters of a logistic regression model. In particular we consider 2 × 2 × 2 tables. We assume that a binary covariate is subject to misclassification that may depend on the observed outcome. This type of misclassification is known as (outcome dependent) differential misclassification. We examine the resulting asymptotic bias on the parameters of the model and derive formulas for the biases and their approximations as a function of the odds and misclassification probabilities. Conditions for unbiased estimation are also discussed. The implications are illustrated numerically using a case control study. For completeness we briefly examine the effect of covariate dependent misclassification of exposures and of outcomes.

In the present study, a modeling approach to estimate the parameters of the MRE isolator model with respect to the frequency-response curve is presented. To concur the response of the isolator over wide frequency range, fractional order based Kelvin Voigt model comprised of three parameters and the fractional Zener model having four parameters are proposed. An isolator operating in shear-mode is developed, and its performance is evaluated through the transmissibility tests. The parameters of the model are identified by minimizing the error between the transmissibility response from the MRE isolator model and the experimental results. A polynomial function is used to generalize the variation of these parameters with respect to the input current. The response predicted by the MRE isolator models confirms that both fractional Kelvin Voigt and fractional Zener modeling approaches are effective in portraying the transmissibility response. The fractional Zener MRE isolator model is more accurate and can reproduce the experimentally determined magnitude and phase response of the transmissibility with an accuracy greater than 91.5% and 84.87% respectively. On the contrary, the fractional Kelvin Voigt model is simpler in form, and it effectively reproduced the magnitude of the transmissibility response with an accuracy higher than 86.35% and the phase response greater than 83.77%.

Quality strategies in supply chain alliances of disposable items