PART A
What type of approach (Continuous or discrete)you will follow while simulating an aircraft system? Elaborate in detail.
The acquaintance capacity to Weaver's Requests of Discrete and Continuous Fourier Inquiry Speeches the notional and investigative features of Fourier inquiry, counting subjects frequently found only in more advanced treatises. Provides background information before going on to cover such topics as existence of the inner product, distribution theory, Fourier series representation of complex functions, properties and behaviour of the Fourier transform, Fourier transform of a distribution, physical interpretation of convolution, the fast Fourier transform, sampling a function, and much more. Includes exercises, problems, applications, over 150 illustrations, and a Fourier transform FORTRAN subroutine.
Differentiate between:
a) Digital - analog and hybrid simulators
The recent and rapid increase in the application of the airmobile concept within the U.S. Army is well known. Accompanying the fact of the airmobile division, brigade, etc., has been the concomitant requirement for the development and improvement of the airborne electronics (avionics) to support this major innovation in conventional warfare. This increased emphasis for the development of new and more sophisticated avionic equipment's and subsystems has led to the organization of an Avionics Laboratory within the U.S. Army Electronics Command. In the Avionics Laboratory, the problem of defining system performance characteristics for advanced avionics has in turn generated a requirement for analysing the tactical mission envelope of both existing and advanced Army aircraft.
b) Analog and hybrid computers.
There are two kinds of computers: analog and digital. (Also hybrid, meaning a combination.) Analog computers are so unimportant compared to digital computers that we will polish them off in a couple of paragraphs.
And so he did. For many years afterward, until 1986, the fossil remnants of analog computers-operational amplifiers-were found in special purpose processors used in military and aerospace applications and in top-of-the-line electronic synthesizers, such as those designed and built by Robert Moog. However, in 1986 Carver Mead revived interest in special-purpose analog computers to model neural systems. His work showed that analog designs could model the retina, the cochlea and other biological systems. The circuits designed by Mead and his associates easily solved tasks using continuous data that were difficult and time-consuming for digital computers that manipulated discrete data algorithmically.
Why do need feedback systems? Discuss the simulation of an autopilot by taking suitable examples?
The nonlinear response characteristics of an electrohydraulic servo system were successfully simulated using an electronic analogue computer (differential analyser). This servo system was the control unit of an autopilot used in the automatic stabilization and control of an aircraft. One element of the servo system, the amplifier, tended to saturate beyond certain voltage input magnitudes and was the cause of the nonlinear response. In obtaining a satisfactory simulation of the servo system it was necessary not only to take into account the nonlinear amplifier characteristics but also the accumulative effect of sometime lags of the servo system
PART B
Differentiate between discrete and continuous probability functions?
In first source, the person who figured out his distribution based it on his survey of heights and always rounded off to the nearest inch. The height is presented as a random variable X, and X takes on values 0, 1, 2, 3, and so on inches. So X is a DISCRETE random variable because it can only take on these values and not for example 1.5. (discrete sort of means separate and 1 ,2 ,3 etc. are separate from each other).The random variable comes along with a distribution. Meaning you have to specify the probability of each value occurring. For example P(X=0) = 0 because no adult will be 0 inches. And maybe P(X=72)=0.1, because maybe 1/10 of the adults are about 6 feet tall (so actually between 71.5 and 72.5 inches since heights were rounded off.)
How can we use stochastic variables in probability concepts of simulation. Give examples to support your answer?
This paper introduces a software, Stochastic Project Scheduling Simulation (SPSS), developed to measure the probability to complete a project in a certain time specified by the user. To deliver a project by a completion date committed to in a contract, a number of activities need to be carried out. The time that an entire project takes to complete and the activities that determine total project duration are always questionable because of the randomness and stochastic nature of the activities' durations. Predicting a project completion probability is valuable, particularly at the time of bidding. The SPSS finds the longest path in a network and runs the network a number of times specified by the user and calculates the stochastic probability to complete the project in the specified time. The SPSS can be used by a contractor: (1) to predict the probability to deliver the project in a given time frame and (2) to assess its capabilities to meet the contractual requirement before bidding. The SPSS can also be used by a construction owner to quantify and analyse the risks involved in the schedule. The benefits of the tool to researchers are: (1) to solve program evaluation and review technique problems; (2) to complement Monte Carlo simulation by applying the concept of project network modelling and scheduling with probabilistic and stochastic activities via a web based Java Simulation which is operate able over the Internet, and (3) to open a way to compare a project network having different distribution functions.
Elaborate the need of probability functions in system simulation? What are the various measures of probability functions?
A probability density function (PDF) method is extended to turbulent spray flows. The PDF transport equation of the mixture fraction for turbulent spray flows is deduced, modelled, and solved. The numerical results of the methanol vapour mass fraction for a non-reacting spray, which are obtained using the PDF method, are in good agreement with experimental data and improve the results from the moment closure method. Furthermore, the shapes of the probability density function of the mixture fraction at different positions, which are computed by the Monte Carlo method, are presented and analysed. It appears that the spray source changes the value of the mean mixture fraction, but it does not change the shape of its PDF. A comparison of the Monte Carlo PDF with the standard ß PDF shows that the ß function fails to describe the shape of the PDF. With the definition of appropriate local maximum and minimum values of the mixture fraction, a modified ß PDF is suitable to reflect the shape of the Monte Carlo PDF very well.
