A model problem about singular limits

In summary, the linear differential equation εx'' + x' + x = 0 with initial conditions x(0) = 1 and x'(0) = 0 is solved analytically for all ε > 0. For ε ≪ 1, there are two widely separated time scales in the problem and the solution x(t) is graphed. The singular limit x' + x = 0 is not valid for ε≪1. Two physical analogs of this problem involving a mechanical system and an electrical circuit are given, with the corresponding dimensionless parameter ε and its physical interpretation in the limit ε≪1.
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Homework Statement

Consider the linear differential equation εx'' + x' + x = 0, subject to the initial conditions x(0) = 1, x'(0) = 0.

a) Solve the problem analytically for all ε > 0.
b) Now suppose ε≪1. Show that there are two widely separated time scales in the problem, and estimate them in terms of ε.
c) Graph the solution x(t) for ε≪1, and indicate the two time scales on the graph.
d) What do you conclude about the validity of replacing εx'' + x' + x = 0 with its singular limit x' + x =0?
e) Give two physical analogs of this problem, one involving a mechanical system, and another involving an electrical circuit. In each case, find the dimensionless combination of parameters corresponding to ε, and state the physical meaning of the limit ε≪1.

The attempt at a solution

The corresponding characteristic equation is ελ^2 + λ +1 = 0 and λ = (-1 ± √1 - 4ε)/2ε. Since ε > 0 we have 1 - 4ε < 0 and two complex roots.
So x = Ae^(r*t)*cos(ω*t) + Be^(r*t)*sin(ω*t), where r = -1/2ε and ω = (|√1-4ε|)/2ε.
Using x(0) = 1 and x'(0) we get A = 1 and B = - r/ω.
Thus x = e^(r*t)*cos(ω*t) - (r/ω)e^(r*t)*sin(ω*t).
 
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b) Since ε is small, we have r ≈ 0 and ω ≈ 1. Thus, two time scales are t ≈ 0 and t ≈ 1/ε.c) Graph of x(t) for ε≪1[graph]The two time scales can be seen as the points where the graph intersects the x-axis.d) The singular limit is not valid since x(t) does not remain constant in the limit ε≪1.e) Mechanical system: ε corresponds to the damping coefficient in the equation of motion, and ε ≪ 1 implies that the damping is negligible.Electrical circuit: ε corresponds to the ratio of the capacitance to the inductance, and ε ≪ 1 implies that the inductive reactance is much greater than the capacitive reactance.
 

1. What is a singular limit in a scientific model?

A singular limit in a scientific model refers to a situation where one or more variables in the model become infinitely large or small, causing the model to break down or become invalid.

2. How do singular limits impact the accuracy of a model?

Singular limits can greatly affect the accuracy of a model, as they can lead to large errors or inaccuracies in the predictions made by the model. This is because the model is not able to accurately capture the behavior of the system when one or more variables reach extreme values.

3. What causes singular limits in scientific models?

Singular limits can be caused by a variety of factors, such as simplifying assumptions made in the model, the behavior of the system being modeled, or the limitations of the mathematical techniques used to solve the model. They can also arise due to physical or mathematical constraints.

4. How can scientists address singular limits in their models?

There are several approaches that scientists can take to address singular limits in their models. One approach is to modify the model to account for the extreme values of the variables, such as by using a different mathematical formulation or adding additional terms to the model. Another approach is to use numerical methods that can handle singularities, such as adaptive mesh refinement or special integration techniques.

5. Can singular limits be avoided in scientific modeling?

In some cases, singular limits cannot be completely avoided in scientific modeling. However, scientists can minimize their impact by carefully selecting and validating the assumptions and simplifications made in the model, and by using appropriate mathematical techniques and numerical methods to handle singularities. Additionally, conducting sensitivity analyses and incorporating uncertainty into the model can help account for potential errors caused by singular limits.

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