We don't help with homework but if you are working on your own, you are welcome to post questions here. I can't guarantee that any of us can help if the passages are very technical.
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Can I post some technical reading passages ( related to Mechanical engineering ) here ? They sometimes drive me crazy because the questions that are made up are hard. They are basically made up according to the given passages. However, nailing all of them is beyond my ability very often. Is it possible in this site or not. I want to get the permission from you .
Thanks
We don't help with homework but if you are working on your own, you are welcome to post questions here. I can't guarantee that any of us can help if the passages are very technical.
Last edited by emsr2d2; 08-Nov-2013 at 00:52. Reason: typo
They are not my homework. I'm an English teacher. no homework. I will post them with the given questions.
Thanks
Probabilistic design is a discipline within engineering design. It deals primarily with the consideration of the effects of random variability upon the performance of an engineering system during the design phase. Typically, these effects are related to quality and reliability. Thus, probabilistic design is a tool that is mostly used in areas that are concerned with quality and reliability. For example, product design, quality control, systems engineering, machine design, civil engineering (particularly useful in limit state design) and manufacturing. It differs from the classical approach to design by assuming a small probability of failure instead of using the safety factor.
When using a probabilistic approach to design, the designer no longer thinks of each variable as a single value or number. Instead, each variable is viewed as a [probability distribution]. From this perspective, probabilistic design predicts the flow of variability (or distributions) through a system. By considering this flow, a designer can make adjustments to reduce the flow of random variability, and improve quality. Proponents of the approach contend that many quality problems can be predicted and rectified during the early design stages and at a much reduced cost.
Typically, the goal of probabilistic design is to identify the design that will exhibit the smallest effects of random variability. This could be the one design option out of several that is found to be most robust. Alternatively, it could be the only design option available, but with the optimum combination of input variables and parameters. This second approach is sometimes referred to as robustification, parameter design or design for six sigma.
Q1: Which one has NOT stated as final results of probabilistic design ?
1. robustness
2. reliability
3. optimization
4. productivity
Q2: How does probabilistic design differ from classical one?
1. It's useful in limit state design
2. It considers safety factor for failure
3. It considers random inconsistency in a system.
4. It denies chance of failure.
How did you find reliability in Q1? I found optimization and robustness but not reliability and productivity as you pointed out.
How did you infer inconsistency in Q2?
Third sentence. "Typically, these effects are related to quality and reliability."
The second paragraph talks about viewing each variable as a distribution. A distribution is a range of possible values. So, it is variability.
It also talks about reducing the flow of random variability. Variability is inconsistency.
Turbulence is flow characterized by recirculation, eddies, and apparent randomness. Flow in which turbulence is not exhibited is called laminar. It should be noted, however, that the presence of eddies or recirculation alone does not necessarily indicate turbulent flow—these phenomena may be present in laminar flow as well. Mathematically, turbulent flow is often represented via a Reynolds decomposition, in which the flow is broken down into the sum of an average component and a perturbation component.
It is believed that turbulent flows can be described well through the use of the Navier–Stokes equations. Direct numerical simulation (DNS), based on the Navier–Stokes equations, makes it possible to simulate turbulent flows at moderate Reynolds numbers. Restrictions depend on the power of the computer used and the efficiency of the solution algorithm. The results of DNS have been found to agree well with experimental data for some flows.
Most flows of interest have Reynolds numbers much too high for DNS to be a viable option, given the state of computational power for the next few decades. Any flight vehicle large enough to carry a human (L > 3 m), moving faster than 72 km/h (20 m/s) is well beyond the limit of DNS simulation (Re = 4 million). Transport aircraft wings (such as on an Airbus A300 or Boeing 747) have Reynolds numbers of 40 million (based on the wing chord). In order to solve these real-life flow problems, turbulence models will be a necessity for the foreseeable future. Reynolds-averaged Navier–Stokes equations (RANS) combined with turbulence modelling provides a model of the effects of the turbulent flow. Such a modelling mainly provides the additional momentum transfer by the Reynolds stresses, although the turbulence also enhances the heat and mass transfer. Another promising methodology is large eddy simulation (LES), especially in the guise of detached eddy simulation (DES)—which is a combination of RANS turbulence modelling and large eddy simulation.
Which one is TRUE about Reynolds decomposition?
1. It's a physical symbol for turbulence flow.
2. It's a mathematical phenomena presents in laminar flow
3. It's turbulence flow presents in a variable part around a constant value.
4. It's average part of turbulence plus perturbed laminar flow.
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