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The most efficient heat engine cycle is the Carnot cycle, consisting of two isothermal processes and two adiabatic processes (see Figure 02). The Carnot cycle can be thought of as the most efficient heat engine cycle allowed by physical laws. When the second law of thermodynamics states that not all the supplied heat in a heat engine can be used to do work, the Carnot efficiency sets the limiting value on the fraction of the heat which can be so used. In order to approach the Carnot efficiency, the processes involved in the heat engine cycle |
Figure 01 Heat Engine [view large image] |
Figure 02 Carnot Engine Cycle |
must be reversible and involve no change in entropy. This means that the Carnot cycle is an idealization, since no real engine processes are reversible and all real physical processes involve some increase in entropy. |
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The p-V diagrams for the more realistic cases are shown in Figure 03, 04, and 05 for the gasoline, diesel, and steam engines respectively. While the gasoline and diesel engines operate at about 50% efficiency, the steam engine runs at only about 30%. A brief description of the processes can be found in each of the diagram. |
Figure 03 Gasoline Engine [view large image] |
Figure 04 Diesel Engine [view large image] |
Figure 05 Steam Engine [view large image] |
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---------- (7) |
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This is known as the Boltzmann equation. It is very useful as mathematic tool in treating the process of fluid flow. By expanding the distribution funciton in terms of the power of the velocity - v0, v1, and v2, the equilibrium distribution in the form of Maxwell-Boltzmann distribution, the continuity equation, and the Navier-Stokes equations in fluid dynamics can be derived directly from Eq.(7). Analytical solutions of the Boltzmann equation are possible only under very restrictive assumptions. Direct numerical methods for computer simulation have been limited by the complexity of the equation, which in the complete 3-D time-dependent form requires seven independent variables for time, space and velocity. A 2-dimensional animation of a flow process is presented by clicking Figure 06. It shows the development of a clump of gas molecules initially |
Figure 06 Boltzmann Equation Simulation [view animation] |
released from the left. The particles flow to the right, reflected by the wall at the other end, then established an equilibrium configuration after some 4000 collisions between the particles. |
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---------- (8) |
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---------- (10) |
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---------- (11) |
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temperature, most of the bosons occupy the same state with E ~ 0. This is the Bose-Einstein condensate first discovered in 1995. Another example of Bose-Einstein distribution is the black-body radiation. In Fermi-Dirac distribution, the normalization constant A can be re-defined as A = e-Ef, where Ef is known as the Fermi energy, which has a value of a few ev for the electron gas in many metals. Note that f (E) = 1/2 at E = Ef for all temperatures. At low temperature most of the low energy states with E < Ef are filled. At high temperature with kT >> (E - Ef), |
Figure 07 Distribution Functions |
the distribution function becomes f (E) ~ (1/2) (1 - (E - Ef) / 2kT). Thus in this case, the energy states with E < Ef are more than half-filler; while for E > Ef they are less than half-filled. |
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In classical statistic, the velocity distribution of the ideal gas is given by the Maxwell distribution as shown in Figure 08. A relationship between the root-mean-square velocity vrms and the temperature T can be derived from such distribution function: m vrms2 = 3 k T or M vrms2 = 3 R T ---------- (12) where m denotes the mass of the molecule, M = mN0 is the molecular weight/mole, N0 is the Avogadro's number, and k = R / N0 = 1.38x10-16 erg/Ko is the Boltzmann constant . |
Figure 08 Maxwell Distribution [view large image] |
The formula in Eq.(12) provides a link between the microscopic root-mean-square velocity vrms of the particles and the macroscopic property T. |
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The mean free path (Figure 09) can be expressed mathematically as: l = 1 / nA = (l1 + l2 + l3 + ... + lN) / N---------- (13) where n is the number density, A is the collision cross section, li is the path length between collisions, i.e., length of the free path, and N is the total number of collisions. The concept of mean free path may be visualized by thinking of a man shooting a rifle aimlessly into a forest. Most of the bullets will hit trees, but some bullets will travel much farther than others. The |
Figure 09 Mean Free Path [view large image] |
average distance traveled by the bullets will depend inversely on both the denseness of the woods and the size of the trees. |