IV. Transport Phenomena Lecture 18: Forced Convection in Fuel Cells II

Similar documents
III. Transport Phenomena

Lecture 29: Forced Convection II

Transport by convection. Coupling convection-diffusion

V. Electrostatics Lecture 24: Diffuse Charge in Electrolytes

CONVECTIVE HEAT TRANSFER

Consequences of Orthogonality

1.061 / 1.61 Transport Processes in the Environment

Lecture IX. Definition 1 A non-singular Sturm 1 -Liouville 2 problem consists of a second order linear differential equation of the form.

IV. Transport Phenomena. Lecture 23: Ion Concentration Polarization

Lecture 16 : Fully Developed Pipe flow with Constant Wall temperature and Heat Flux

Midterm Solution

Mathematical Modeling using Partial Differential Equations (PDE s)

Problem set 3: Solutions Math 207B, Winter Suppose that u(x) is a non-zero solution of the eigenvalue problem. (u ) 2 dx, u 2 dx.

v(x, 0) = g(x) where g(x) = f(x) U(x). The solution is where b n = 2 g(x) sin(nπx) dx. (c) As t, we have v(x, t) 0 and u(x, t) U(x).

Class Meeting # 2: The Diffusion (aka Heat) Equation

1.061 / 1.61 Transport Processes in the Environment

Lecture 4: Dynamics of Equivalent Circuits

The Generalized Boundary Layer Equations

MATH34032 Mid-term Test 10.00am 10.50am, 26th March 2010 Answer all six question [20% of the total mark for this course]

7.2 Sublimation. The following assumptions are made in order to solve the problem: Sublimation Over a Flat Plate in a Parallel Flow

3 Green s functions in 2 and 3D

Problem 4.3. Problem 4.4

Supporting Information

13 PDEs on spatially bounded domains: initial boundary value problems (IBVPs)

10.34 Numerical Methods Applied to Chemical Engineering. Quiz 2

v ( ) x (b) Using Navier-Stokes equations, derive an expression


VI. Porous Media. Lecture 34: Transport in Porous Media

Boundary-value Problems in Rectangular Coordinates

TRANSPORT IN POROUS MEDIA

Math 46, Applied Math (Spring 2009): Final

4.7 Dispersion in an oscillatory shear flow

MECH 375, Heat Transfer Handout #5: Unsteady Conduction

AMS 212A Applied Mathematical Methods I Appendices of Lecture 06 Copyright by Hongyun Wang, UCSC. ( ) cos2

VII. Porous Media Lecture 36: Electrochemical Supercapacitors

Numerical Solutions for the Lévêque Problem of Boundary Layer Mass or Heat Flux

Diffusion - The Heat Equation

Physics 250 Green s functions for ordinary differential equations

In this worksheet we will use the eigenfunction expansion to solve nonhomogeneous equation.

Hilbert Spaces. Hilbert space is a vector space with some extra structure. We start with formal (axiomatic) definition of a vector space.

1D heat conduction problems

Lecture 17 The Bipolar Junction Transistor (I) Forward Active Regime

Elementary Non-Steady Phenomena

Process of whiskey maturation & Current Efforts to improve Comparison Goals Modeling nonlinear Diffusion

Separation of Variables in Linear PDE: One-Dimensional Problems

Math 353, Fall 2017 Study guide for the final

Second-Order Linear ODEs (Textbook, Chap 2)

lim = F F = F x x + F y y + F z

Vector Spaces. Vector space, ν, over the field of complex numbers, C, is a set of elements a, b,..., satisfying the following axioms.

10.34 Numerical Methods Applied to Chemical Engineering Fall Homework #6: Boundary Value Problems (BVPs)

Lecture 12: Detailed balance and Eigenfunction methods

6 Non-homogeneous Heat Problems

Review and problem list for Applied Math I

Section 12.6: Non-homogeneous Problems

Homework for Math , Fall 2016

Chapter 5 Time-Dependent Conduction

Principles of Convection

LECTURE 33: NONHOMOGENEOUS HEAT CONDUCTION PROBLEM

Chemical Reaction Engineering Prof. Jayant Modak Department of Chemical Engineering Indian Institute of Science, Bangalore

Introduction to Heat and Mass Transfer. Week 7

6. Continuous Release - Point Source

Inner products. Theorem (basic properties): Given vectors u, v, w in an inner product space V, and a scalar k, the following properties hold:

Chapter 4 TRANSIENT HEAT CONDUCTION

VIII. Phase Transformations. Lecture 38: Nucleation and Spinodal Decomposition

Lecture 7. Sums of random variables

Hilbert Spaces: Infinite-Dimensional Vector Spaces

Overview of Convection Heat Transfer

Vectors in Function Spaces

Index. C 2 ( ), 447 C k [a,b], 37 C0 ( ), 618 ( ), 447 CD 2 CN 2

Lecture 4.6: Some special orthogonal functions

5. Advection and Diffusion of an Instantaneous, Point Source

(L, t) = 0, t > 0. (iii)

HEAT CONDUCTION USING GREEN S FUNCTIONS

Differential equations, comprehensive exam topics and sample questions

Partial Differential Equations Summary

Method of Separation of Variables

Physics 6303 Lecture 15 October 10, Reminder of general solution in 3-dimensional cylindrical coordinates. sinh. sin

A First Course on Kinetics and Reaction Engineering Unit 33. Axial Dispersion Model

Heat and Mass Transfer Prof. S.P. Sukhatme Department of Mechanical Engineering Indian Institute of Technology, Bombay

Existence Theory: Green s Functions

MAE502/MSE502 Partial Differential Equations in Engineering. Spring 2012 Mon/Wed 5:00-6:15 PM

Weighted Residual Methods

23 Elements of analytic ODE theory. Bessel s functions

Partial Differential Equations

Game Physics. Game and Media Technology Master Program - Utrecht University. Dr. Nicolas Pronost

INTRODUCTION TO CATALYTIC COMBUSTION

2.29 Numerical Fluid Mechanics Spring 2015 Lecture 9

Study of Forced and Free convection in Lid driven cavity problem

1 Constant Pressure Inner Boundary Condition

Diffusion on the half-line. The Dirichlet problem

Parallel Plate Heat Exchanger

Some Explicit Solutions of the Cable Equation

INTRODUCTION TO FLUID MECHANICS June 27, 2013

Fall f(x)g(x) dx. The starting place for the theory of Fourier series is that the family of functions {e inx } n= is orthonormal, that is

Linear Operators, Eigenvalues, and Green s Operator

Lecture 19: Heat conduction with distributed sources/sinks

Chapter 3: Transient Heat Conduction

Soft Bodies. Good approximation for hard ones. approximation breaks when objects break, or deform. Generalization: soft (deformable) bodies

12.009/ Problem Set 2

u = (u, v) = y The velocity field described by ψ automatically satisfies the incompressibility condition, and it should be noted that

Transcription:

IV. Transport Phenomena Lecture 18: Forced Convection in Fuel Cells II MIT Student (and MZB) As discussed in the previous lecture, we are interested in forcing fluid to flow in a fuel cell in order to increase the limiting current, and thus the power output of the device. The model problem considered has a steady uniform flow of fluid carrying the reactant at a constant concentration, c 0, into a 2-D channel of height H. In this model we will assume that the fluid velocity profile across the channel is uniform (i.e. plug flow). We are interested in analyzing the transport of the reactant carried into the system by the convective fluid stream to the active membrane located at y = 0, which has a length of L. y Wall y = H Uniform Flow c = c 0 δ(x) Diffusion Layer x = 0 x = l 1 c(y=0) = 0 Membrane x = l 2 Entrance Length + Figure 1: Model problem of forced convection in a fuel cell x = L x From conservation of mass, there is a balance between convection and diffusion terms: [1] Since the flow is steady, the transient term can be neglected. In the region when x >> l 1, convection dominates over axial diffusion: [2] From scaling analysis, we can define l 1 as the point where convection occurs at the same scale as axial diffusion. That is: [3] Nondimensionalizing the position variables as follows leads us to the Péclet Number, Pe. Let: 1

In the region where x >> l 1 or, we can neglect axial diffusion and define a time variable t = x/u along the streamlines to obtain a 1D diffusion equation in terms of y only. This approach is valid because the fluid flow rate is invariant with location in our model. It is worth restating that the Péclet Number is a dimensionless value that represents the ratio of the rate of convection to the rate of diffusion in the system. We can redefine Equation [1] as: [4] [5] and the initial condition at the inlet becomes: c(x = 0, y) = c(t = 0, y) = c 0. We are now ready to calculate the limiting current I lim as a function of the fluid velocity, U, under the assumption that reactions are fast enough everywhere for c(t, y = 0) 0 along the entire membrane when I I lim. The problem has now become: y Wall y = H c = c 0 x = 0 c(y=0) = 0 Membrane x = L t = x/u Figure 2: 1D diffusion model for forced convection in a fuel cell The limiting current is found by integrating the transverse mass flux along the membrane: [6] Below we will obtain an exact solution as a Fourier Series, but it is more accurate and easier when x or t to is small to find a boundary-layer similarity solution (as if H ) in the entrance region. 1. Entrance Region Using scaling analysis, the boundary layer thickness, δ(x), can be found: [7] Now we can define x = l 2 to be the axial location where the boundary layer first spans the entire height of the channel, that is δ(l 2 ) ~ H as is illustrated in Figure 1. Therefore: 2

[8] For l 1 << x << l 2, the boundary layer is much thinner than the channel height. Thus, we can set H =. If we nondimensionalize l 2 and δ : we see that we need 1/Pe << << Pe in order to use the boundary layer approximation, which is only valid for small boundary layers, i.e. when Pe >> 1. With H = (i.e. the boundary layers are so thin compared to the channel height that we can solve using a semi-infinite channel instead of one with finite height), we have a similarity solution to the diffusion equation: [9] [10] Note that here y/δ(x) is the stretched or inner coordinate of the boundary layer and that the error function, erf, is defined as: [11] Figure 3: Isoconcentration Lines for Entrance Region Now that the concentration profile is known as a function of distance along the membrane, we can calculate the flux density, F, on the membrane: From above, it is evident that the largest flux occurs near the inlet when x 0. [12] 3

Another dimensionless number that is commonly used is the Sherwood Number, Sh, which represents the ratio of convective to diffusive mass transport. Here it is a dimensionless flux: [13] 2. Fully Developed Region For x > l 2 or = O(Pe), we can still neglect axial diffusion, however we must solve the full problem for a finite channel height, H. First, let us define more dimensionless variables: We can now redefine the diffusion problem as: [14] [15] with initial condition:, and boundary conditions: and. This problem can be solved with Fourier Series, or more generally by Finite Fourier Transform 1, as a general series expansion in separable solutions to the PDE: where is the transform ( FFT ) and are the basis functions, determined by the following general procedure. The series expansion is needed since the boundary conditions are inconsistent with a single separable solution. 2.1 Step 1 - Determine the Basis Functions Generally, to solve we want the basis functions,, to be eigenfuctions of the linear operator : [16] and satisfy the homogeneous boundary conditions: [17] [18] 1 See W. M. Deen, Analysis of Transport Phenomena. 4

After applying the BC s it is seen that: B n = 0 and λ n = (n+½)π. Therefore, we have the series: This method is most useful if this set of functions satisfy the definition of orthogonality, which is a generalization of perpendicular vectors to infinite dimensions. Two functions ϕ n and ϕ m are said to be orthogonal if: 1 0, m n ϕ n, ϕ m = ϕ y ( n ( )ϕ m y)dy = δ m,n = [20] 0 1, m = n Furthermore, we choose A n so that, which makes the set orthonormal. A crucial property of our linear operator L (including its boundary conditions) is that it is selfadjoint, meaning that ϕ 1, Lϕ 2 =, since this implies that the its eigenfunctions are orthogonal 2 Lϕ 1,ϕ 2 It is then found that: [19] [21] 2.2 Step 2 - Transform the PDE into an ODE Now let s substitute our assumed solution, Equation [16], into the PDE of Equation [15]: [22] A property of eigenfunctions is that both sides of the PDE must be equal to. (We are essentially performing separation of variables and setting both sides equal to the same constant, but we can only do it for each term involving a single basis function). Now that the basis functions are orthogonal, the coefficients must be equal: 2 More generally, all Strum-Liouville differential operators of the form 1 d du Lu = p(x) + q(x)u(x) with homogeneous Robin boundary conditions w(x) dx dx u + a du = 0 are self adjoint with respect to the inner product, ϕ 1,ϕ 2 = ϕ 1(x)ϕ 2 (x)w(x)dx, so dx the eigenfunctions of such operators (which come up often in convection-diffusion problems, e.g. Bessel functions in cylindrical coordinates) are automatically orthogonal and convenient for series expansions. 5

[23] which yields the solution: [24] 2.3 Step 3 Evaluate the Constants Using Initial and Boundary Conditions Because, we have: [25] To invert the transform, take the inner product with ϕ m : [26] Using orthonormality: [27] Now that the coefficients have been determined, we can put it all together to form the solution: [28] Figure 4: Isoconcentration Lines for Fully Developed Flow Region For >> 1 (or x >> l 2 ) in the fully developed region, the first term in the series dominates: 6

[29] And the flux is: [30] Finally, we can calculate the limiting current, I lim, by integrating the mass flux over the membrane: [31] where. The fuel utilization, γ lim, is defined as the ratio of the fuel consumed to the fuel input into the fuel cell: [32] and γ lim are related by: [33] At a given fuel utilization we can boost the limiting current and thus the power, P = IV, by having fast flow (Pe >> 1) and thick channels, which allow more fuel into the system. 2.4 Flow Regimes Let s examine the limiting current and fuel utilization in two flow regimes: 2.4.1 Fast Flow (l 2 >> L) In this regime the fluid is moving quickly, which means the boundary layer is thin compared to the channel height. This is equivalent to saying that l 2 is located beyond the axial extent of the membrane (0 < x <L). y y = H Uniform Flow c = c 0 δ(x)<<h x = 0 x x = L 7

Figure 5: Fast Flow Regime for a Fuel Cell If δ(x) << H, then DL/U << H 2 and (D/UL)(L/H) 2 << 1 Pe(H/L) 2 >> 1. From Equation [13]: [34] Therefore, the fuel utilization in this regime is: [35] Because the quantity is constant in Equation [35], it is obvious that in the fast flow regime there is an inherent tradeoff between power and fuel utilization. That is, although faster flows generate larger power due to an increased I lim, less of the fuel is able to diffuse to the membrane by the time it reaches an axial position of x=l. 2.4.2 Slow Flow (l 2 << L) In this regime the diffusion profile is fully developed and the boundary layer has reached the top of the channel for much of the length of the membrane. y y = H Uniform Flow c = c 0 δ(x) x = 0 x = l x = L x 2 Figure 6: Slow Flow Regime for a Fuel Cell Now, [36] When, Equation [34] becomes. The fuel utilization in this case is: [37] Therefore, for slow flow or long channels, all of the fuel is consumed, but the power is low. Note that by keeping only the first term in the Fourier series for the slow flow we found that 8

< 1. To see that 1 (i.e. all the fuel is utilized), we would need to keep all of the terms in the Fourier series and calculated the exact flux, F(x). Figure 7: Limiting Current of Fuel Cell Figure 8: Fuel Utilization of Fuel Cell 9

MIT OpenCourseWare http://ocw.mit.edu 10.626 Electrochemical Energy Systems Spring 2014 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.