Capturing Real Life Variability in Virtual Testing

2006 ◽  
Author(s):  
D. M. Bardot ◽  
A. F. Emery
Keyword(s):  
Probability Distribution ◽  
Real Life ◽  
Operating Conditions ◽  
Engineering System ◽  
Endothermic Reaction ◽  
Hostile Environment ◽  
Deterministic Analysis ◽  
Virtual Testing ◽  
Specific Goal ◽  
Computationally Expensive

The motivation for this work is the need to analyze the behavior of an engineering system subjected to the hostile environment of a fire. The specific goal is to estimate the survivability of a component potted in insulating foam. The foam undergoes an endothermic reaction and through this heat sink protects the component. Because the operating conditions are stochastic and because the properties of the foam can only be estimated, the usual deterministic analysis cannot be used. Instead, Bayesian inference is used to estimate the critical foam parameters and the operating conditions are described in terms of probabilities. The survivability is then expressed in terms of a probability distribution. Because the computations are very computationally expensive, recourse was made by expressing the computed results as a response surface defined in terms of a Gaussian process.

scholarly journals Modeling of a Real-Life Industrial Reactor for Hydrogenation of Benzene Process

Catalysts ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 412
Author(s):  
Mirosław K. Szukiewicz ◽  
Krzysztof Kaczmarski
Keyword(s):  
Catalyst Activity ◽  
Real Life ◽  
Operating Conditions ◽  
Reactor Operation ◽  
Reactor Model ◽  
High Scale ◽  
Industrial Reactor ◽  
Knowledge Based ◽  
Operation Conditions ◽  
Insight Into

A dynamic model of the hydrogenation of benzene to cyclohexane reaction in a real-life industrial reactor is elaborated. Transformations of the model leading to satisfactory results are presented and discussed. Operating conditions accepted in the simulations are identical to those observed in the chemical plant. Under those conditions, some components of the reaction mixture vanish, and the diffusion coefficients of the components vary along the reactor (they are strongly concentration-dependent). We came up with a final reactor model predicting with reasonable accuracy the reaction mixture’s outlet composition and temperature profile throughout the process. Additionally, the model enables the anticipation of catalyst activity and the remaining deactivated catalyst lifetime. Conclusions concerning reactor operation conditions resulting from the simulations are presented as well. Since the model provides deep insight into the process of simulating, it allows us to make knowledge-based decisions. It should be pointed out that improvements in the process run, related to operating conditions, or catalyst application, or both on account of the high scale of the process and its expected growth, will remarkably influence both the profits and environmental protection.

A Solution Framework for Response Surface-Based Multiple Quality Characteristics Optimization

2018 ◽  
Vol 25 (05) ◽  
pp. 1850025
Author(s):  
Sasadhar Bera ◽  
Indrajit Mukherjee
Keyword(s):  
Response Surface ◽  
Optimization Problems ◽  
Real Life ◽  
Quality Characteristics ◽  
Operating Conditions ◽  
Simultaneous Optimization ◽  
Trade Off ◽  
Multiple Quality Characteristics ◽  
Pin On Disc ◽  
Application Potential

A common problem generally encountered during manufacturing process improvement involves simultaneous optimization of multiple ‘quality characteristics’ or so-called ‘responses’ and determining the best process operating conditions. Such a problem is also referred to as ‘multiple response optimization (MRO) problem’. The presence of interaction between the responses calls for trade-off solution. The term ‘trade-off’ is an explicit compromised solution considering the bias and variability of the responses around the specified targets. The global exact solution in such types of nonlinear optimization problems is usually unknown, and various trade-off solution approaches (based on process response surface (RS) models or without using process RS models) had been proposed by researchers over the years. Considering the prevalent and preferred solution approaches, the scope of this paper is limited to RS-based solution approaches and similar closely related solution framework for MRO problems. This paper contributes by providing a detailed step-by-step RS-based MRO solution framework. The applicability and steps of the solution framework are also illustrated using a real life in-house pin-on-disc design of experiment study. A critical review on solution approaches with details on inherent characteristic features, assumptions, limitations, application potential in manufacturing and selection norms (indicative of the application potential) of suggested techniques/methods to be adopted for implementation of framework is also provided. To instigate research in this field, scopes for future work are also highlighted at the end.

scholarly journals Kernel density estimation and its application

2018 ◽  
Vol 23 ◽  
pp. 00037 ◽  
Author(s):  
Stanisław Węglarczyk
Keyword(s):  
Probability Density Function ◽  
Probability Distribution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Real Life ◽  
Kernel Estimation ◽  
Kernel Density ◽  
Two Dimensional ◽  
2D Histogram ◽  
Function Shape

Kernel density estimation is a technique for estimation of probability density function that is a must-have enabling the user to better analyse the studied probability distribution than when using a traditional histogram. Unlike the histogram, the kernel technique produces smooth estimate of the pdf, uses all sample points' locations and more convincingly suggest multimodality. In its two-dimensional applications, kernel estimation is even better as the 2D histogram requires additionally to define the orientation of 2D bins. Two concepts play fundamental role in kernel estimation: kernel function shape and coefficient of smoothness, of which the latter is crucial to the method. Several real-life examples, both for univariate and bivariate applications, are shown.

scholarly journals Simulation Study of the Process of Friction in the Working Elements of a Car Braking System at Different Degrees of Wear

2018 ◽  
Vol 12 (3) ◽  
pp. 221-226 ◽  
Author(s):  
Andrzej Borawski
Keyword(s):  
Simulation Study ◽  
Friction Pair ◽  
Experimental Studies ◽  
Real Life ◽  
Operating Conditions ◽  
Heating Process ◽  
Passenger Cars ◽  
The Road ◽  
Emergency Braking ◽  
Braking System

Abstract Among the many elements of a modern vehicle, the braking system is definitely among the most important ones. Health, and, frequently, life, may rest upon the design and reliability of brakes. The most common friction pair used in passenger cars today is a disc which rotates with the road wheel and a cooperating pair of brake pads. The composite material of the pad results in changing tribological properties as the pad wears, which was demonstrated in experimental studies. The change is also facilitated by the harsh operating conditions of brakes (high and rapid temperature changes, water, etc.). This paper looks into how changing tribology reflects on the heating process of disc and pads during braking. And so a simulation study was conducted, as this method makes it possible to measure temperature in any given point and at any time, which is either impossible or extremely difficult in real life conditions. Finite element method analyses were performed for emergency braking events at various initial speeds of the vehicle reflecting the current road speed limits.

scholarly journals Automatic Measurement of Magnetron Rieke Diagrams

2019 ◽  
Author(s):  
Vladimir Bilik
Keyword(s):  
Reflection Coefficient ◽  
Anode Voltage ◽  
Block Diagram ◽  
Impedance Matching ◽  
Real Life ◽  
Automatic Measurement ◽  
Operating Conditions ◽  
Polar Diagram ◽  
Coupling Structure ◽  
Fully Automatic

A Rieke diagram [1] is a magnetron characteristic that visualizes the dependence of the generated frequency fg and the net delivered power PL on the load reflection coefficient GR. GR is defined in a specific magnetron-to-waveguide coupling structure called the standard or reference launcher (Fig. 1). The diagram is plotted as a family of isolines of constant fg and of constant PL in the polar diagram of GR. Rieke diagrams are essential in the design of applications without isolators, such as domestic or professional microwave ovens. Constructing Rieke diagrams is tedious, time-consuming and equipment-demanding [2], [3], preventing systematic studies of their dependence on operating conditions, such as anode voltage and its ripple, filament current, mounting repeatability, etc. We have devised a procedure, centering around a high-power automatic impedance matching device (autotuner), which enables fully automatic measurement and plotting of the stated dependences. A block diagram of the setup is shown in Fig. 1. The autotuner, when terminated in a match (waterload), can accomplish a task inverse to impedance matching: realizing any desired reflection coefficient GR. The measurement consists of stepping through a grid of n suitably chosen reflection coefficients GR = xR + jyR, covering a desired area of the polar diagram. Each GR is measured accurately by the autotuner, along with the corresponding fg and PL. Thus, raw data for constructing a Rieke diagram are obtained, the data consisting of a collection of n points {GR, fg, PL}i, i = 1…n, with GR, in general, irregularly scattered in the complex plane. A dedicated MATLAB routine then reads the data, sorts them out to create tabulated functions fg = f(xR, yR), PL = f(xR, yR), approximates these by a 2D spline, and uses the splines to plot smoothed isocontours for chosen constant values of fg and PL, completing thus the desired Rieke diagram construction. We will present details of this procedure as well as real-life examples. Fig. 1. Rieke diagram measurement setup. References Meredith, R. J., Engineers' Handbook of Industrial Microwave Heating, London: The IEE, 1998, 250–270. Takahashi, H., I. Namba, K. Akiyama, J. Microwave Power, 1979, 14, 261–267.Yixue, W., Z. Zhaotang, Proc. ICMMT'98, 1998, 795–798.

The Development of an Experimental Research Plan Based on Local Strain-Life Method for an Air Handling Unit Subjected to Seismic Loads

2014 ◽  
Vol 224 ◽  
pp. 226-231
Author(s):  
Michał Burak ◽  
Dariusz Skibicki ◽  
Michał Stopel
Keyword(s):  
Experimental Research ◽  
Real Life ◽  
Local Strain ◽  
Operating Conditions ◽  
Seismic Loads ◽  
Random Loading ◽  
Air Handling Unit ◽  
Research Plan

This paper presents procedures to be used for development of an experiment plan which is supposed to provide the tested structure with operating conditions most similar to those in real life. The research object is an air handling unit subjected to random loading such as earthquakes.

Dynamic Positioning System: Systematic Weight Assignment for DP Sub-Systems Using Multi-Criteria Evaluation Technique Analytic Hierarchy Process and Validation Using DP-RI Tool With Deep Learning Algorithm

2019 ◽  
Author(s):  
Charles Fernandez ◽  
Arun Kr. Dev ◽  
Rose Norman ◽  
Wai Lok Woo ◽  
Shashi Bhushan Kumar
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Learning Algorithm ◽  
Real Life ◽  
Operating Conditions ◽  
Dynamic Positioning ◽  
Analytic Hierarchy ◽  
Dynamic Positioning System ◽  
Weight Assignment ◽  
Hierarchy Process

Abstract The Dynamic Positioning (DP) System of a vessel involves complex interactions between a large number of sub-systems. Each sub-system plays a unique role in the continuous overall DP function for safe and reliable operation of the vessel. Rating the significance or assigning weightings to the DP sub-systems in different operating conditions is a complex task that requires input from many stakeholders. The weighting assignment is a critical step in determining the reliability of the DP system during complex marine and offshore operations. Thus, an accurate weighting assignment is crucial as it, in turn, influences the decision-making of the operator concerning the DP system functionality execution. Often DP operators prefer to rely on intuition in assigning the weightings. However, it introduces an inherent uncertainty and level of inconsistency in the decision making. The systematic assignment of weightings requires a clear definition of criteria and objectives and data collection with the DP system operating continuously in different environmental conditions. The sub-systems of the overall DP system are characterized by multi-attributes resulting in a high number of comparisons thereby making weighting distribution complicated. If the weighting distribution was performed by simplifying the attributes, making the decision by excluding part of them or compromising the cognitive efforts, then this could lead to inaccurate decision making. Multi-Criteria Decision Making (MCDM) methods have evolved over several decades and have been used in various applications within the Maritime and Oil and Gas industries. DP, being a complex system, naturally lends itself to the implementation of MCDM techniques to assign weight distribution among its sub-systems. In this paper, the Analytic Hierarchy Process (AHP) methodology is used for weight assignment among the DP sub-systems. An AHP model is effective in obtaining the domain knowledge from numerous experts and representing knowledge-guided indexing. The approach involved examination of several criteria in terms of both quantitative and qualitative variables. A state-of-the-art advisory decision-making tool, Dynamic Positioning Reliability Index (DP-RI), is used to validate the results from AHP. The weighting assignments from AHP are close to the reality and verified using the tool through real-life scenarios.

scholarly journals Student’s-t Mixture Regression-Based Robust Soft Sensor Development for Multimode Industrial Processes

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3968 ◽  
Author(s):  
Jingbo Wang ◽  
Weiming Shao ◽  
Zhihuan Song
Keyword(s):  
Learning Algorithm ◽  
Real Life ◽  
Industrial Process ◽  
Soft Sensor ◽  
Operating Conditions ◽  
Industrial Processes ◽  
Computationally Efficient ◽  
Mixture Regression ◽  
Student’S T ◽  
T Distribution

Because of multiple manufacturing phases or operating conditions, a great many industrial processes work with multiple modes. In addition, it is inevitable that some measurements of industrial variables obtained through hardware sensors are incorrectly observed, recorded or imported into databases, resulting in the dataset available for statistic analysis being contaminated by outliers. Unfortunately, these outliers are difficult to recognize and remove completely. These process characteristics and dataset imperfections impose challenges on developing high-accuracy soft sensors. To resolve this problem, the Student’s-t mixture regression (SMR) is proposed to develop a robust soft sensor for multimode industrial processes. In the SMR, for each mixing component, the Student’s-t distribution is used instead of the Gaussian distribution to model secondary variables, and the functional relationship between secondary and primary variables is explicitly considered. Based on the model structure of the SMR, a computationally efficient parameter-learning algorithm is also developed for SMR. Results conducted on two cases including a numerical example and a real-life industrial process demonstrate the effectiveness and feasibility of the proposed approach.

scholarly journals Changes of Insulation Properties of Photovoltaic Cables Caused by Ageing Treatment

2020 ◽  
Vol 6 (1) ◽  
pp. 6-9 ◽  
Author(s):  
Juraj Packa ◽  
Vladimir Kujan ◽  
Daniel Štrkula ◽  
Vladimír Šály ◽  
Milan Perný
Keyword(s):  
Power Plants ◽  
Real Life ◽  
Life Time ◽  
Operating Conditions ◽  
Solar Panels ◽  
Font Size ◽  
Long Term Stability ◽  
Cable Systems

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">An important part of the photovoltaic power plants are cable systems. The dielectric properties of cables, reliability and durability depend on quality of production processes, operating conditions and degradation factors, as well. Expected lifetime of cable systems is more than 20-30 years in general. Their failure free operation and long-term stability of properties has a direct impact on the economic return of the investments. According to our experiences the tests in compliance with valid standards are not adequate to verify real life time during operation. Photovoltaic cables intended for use in outdoor applications for the connection between the solar panels and possible connection between panels and inverter were chosen for our experiments. <span style="-ms-layout-grid-mode: line;">The changes </span>of insulation resistance and breakdown voltage caused by some degradation factors, mainly water, are presented. This research was inspired by real failure in operation.</span>

Uncertainty quantification of pressure-driven analysis for water distribution network modeling

2015 ◽  
Vol 16 (3) ◽  
pp. 599-610 ◽  
Author(s):  
Ho Min Lee ◽  
Do Guen Yoo ◽  
Doosun Kang ◽  
Hwandon Jun ◽  
Joong Hoon Kim
Keyword(s):  
Negative Pressure ◽  
Water Distribution ◽  
Real Life ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Operating Conditions ◽  
Service Reliability ◽  
High Degree ◽  
Pressure Driven Analysis ◽  
Pressure Driven

The hydraulic analysis of water distribution networks (WDNs) is divided into two approaches: namely, a demand-driven analysis (DDA) and a pressure-driven analysis (PDA). In the DDA, the basic assumption is that the nodal demand is fully supplied irrespective of the nodal pressure, which is mainly suitable for normal operating conditions. However, in abnormal conditions, such as pipe failures or unexpected increase in demand, the DDA approach may cause unrealistic results, such as negative pressure. To address the shortcomings of DDA, PDA has been considered in a number of studies. For PDA, however, the head-outflow relation (HOR) should be given, which is known to contain a high degree of uncertainty. Here, the DDA-based simulator, EPANET2 was modified to develop a PDA model simulating pressure deficient conditions and a Monte Carlo simulation (MCS) was performed to consider the quantitative uncertainty in HOR. The developed PDA model was applied to two networks (a well-known benchmark system and a real-life WDN) and the results showed that the proposed model is superior to other reported models when dealing with negative pressure under abnormal conditions. In addition, the MCS-based sensitivity analysis presents the ranges of pressure and available discharge, quantifying service reliability of water networks.

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