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G-convergence and Homogenization of Viscoelastic Flows
Published in Robert P. Gilbert, Ana Vasilic, Sandra Klinge, Alex Panchenko, Klaus Hackl, Applications of Homogenization Theory to the Study of Mineralized Tissue, 2020
Robert P. Gilbert, Ana Vasilic, Sandra Klinge, Alex Panchenko, Klaus Hackl
Interface conditions. There are two interface conditions: the first is continuity of v across the interface (which is the actual moving interface governed by (9.4.2)), and the second is the equality of tractions Tsε−PsεIνs on the frozen interface. Here νs denotes the exterior (to the phase s) unit normal to the frozen interface.
Microscopic physical quantities derived from void ratio and probability distributions
Published in Ryosuke Kitamura, Kazunari Sako, Unsaturated Soil Mechanics with Probability and Statistics, 2019
Ryosuke Kitamura, Kazunari Sako
The diameter and the interval corresponding to the i-th interval of vertical axis are respectively denoted by Ds,i and ΔDs,i. The mass of soil in the i-th interval is denoted by Ms,unit,i, and then the following equation is obtained: Ms,unit=∑i=1nMs,unit,i
Fast Motion Control Using TDOF Control Structure and Optimal Feedforward Input
Published in Takashi Yamaguchi, Mitsuo Chee, Khiang Pang Chee, Advances in High-Performance Motion Control of Mechatronic Systems, 2017
The output responses obtained by experiments are shown in Figure 2.31. In Figure 2.31, a large residual vibration is observed in the output response of the FSC input and the performance degradation is significant. The positioning time is also extended from 1.33 s/unit to 1.94 s/unit. The FSC input has poor robustness to the frequency changes of the vibration modes.
Scheduling policies analysis for matching operations in Bernoulli selective assembly lines
Published in International Journal of Production Research, 2022
In such a selective assembly system, the main performance measurement index is the expected total revenue () of the finished assemblies in a cycle at a steady state, which is defined as follows: where represents the of category i product assembled with components with a gap of i quality grades, and represents the unit price of category i product. We assume the matched product's unit price . By introducing a discount factor , based on category i product's unit price, category product's unit price is off. The goal of this research is under different system parameter configurations to compare the performance of the three scheduling policies and conduct a performance analysis of WCQMP to maximise the total revenue.
Quality assessment of on-campus student housing facilities through a holistic post-occupancy evaluation (A case study of Iran)
Published in Architectural Engineering and Design Management, 2023
Mohammad Ali Nemati, Zahra Rastaghi
The Satisfaction Index () value was then calculated using the following formula (Dominowski, 1980; Sanni-Anibire & Hassanain, 2016). In this formula, () is the answer given to () indicator on the Likert scale, and () is the number of responses on the scale of (). This was used to determine the satisfaction level of the investigated performance indicators and dimensions. This formula's unit of measurement is the percentage. As a result, students are highly dissatisfied if their is between 20 and 39. Other scores range from 40 to 59, indicating dissatisfaction, from 60 to 79, expressing satisfaction. Students are extremely satisfied if the SI is between 80 and 100. Excel 2013 was used to conduct all calculations. The Satisfaction Index is a descriptive parameter that shows how satisfied the occupants are with each of the indicators, dimensions, and finally, the overview of the entire housing condition. However, due to its descriptive nature, this parameter could not directly guide the authors in prioritizing improvement actions. Therefore, the authors use the arithmetic mean of the answers given to each indicator as the Mean Satisfaction Index () to measure more accurately. This value was employed in forming the importance-satisfaction matrix (Chen, Pai, & Yeh, 2020).
Design and analysis of computer experiment via dimensional analysis
Published in Quality Engineering, 2018
Weijie Shen, Dennis K. J. Lin, Chia-Jung Chang
Suppose the input t = (x1, …, xk), where xj are the observed physical quantities with unit Uj, j = 1, …, k. The physical response we intend to model is y with unit U0. Among xj’s, select bbasis quantities, and denote them as x1, …, xb with corresponding units U1, …, Ub, such that they have the following two properties: (a) representativity: they can express all other units U0, Ub + 1, …, Uk, i.e., Uj = fj(U1, …, Ub), j = 0, b + 1, …, k; (b) independence: they cannot represent each other, ∄fj such that Uj = fj(U1, …, Ui ≠ j, …, Ub), j = 1, …, b. For example, suppose y has dimension length and unit meter, x1 has dimension length and unit meter, x2 has dimension time and unit second, x3 has dimension speed and unit meter/second. Then we can choose x1 and x2 to be basis quantities because their units (meter and second) have (a) representativity: can express y’s unit meter and x3’s unit meter/second; (b) independence: meter and second cannot represent each other, ∄f such that meter = f(second). Notice the set of basis quantities cannot include x3 in addition to {x1, x2} because that violates independence. It cannot be just {x1} because that violates representativity. But it can be {x1, x3} or {x2, x3}. The set is not unique but the size b is.