Introductory university-level calculus, linear algebra, abstract algebra, probability, statistics, and stochastic processes.
Applications of integrals (1): Length of a curve
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As an application of integrals, we consider the length of a curve. Specifically, we consider curves in the 2-dimensional space defined parametrically.
Let \(x(t)\) and \(y(t)\) be \(C^1\) functions defined on an interval containing the closed interval \([a,b]\). If \(t\) moves in \([a,b]\), the point \((x(t), y(t))\) on \(\mathbb{R}^2\) moves smoothly, drawing a curve. Let us denote this curve by \(C\). Let \(P = (x(a), y(a))\) and \(Q = (x(b), y(b))\) be the end points of the curve \(C\). We want to measure the ``length'' of the curve \(C\). But what is the length of a \emph{curve}, anyway? We do know how to calculate the length of a line segment (Pythagorean theorem). So, let us approximate the curve by line segments. Consider the partition of the closed interval \([a,b]\):
Then \(P_0 = (x(t_0), y(t_0)) = P\), \(P_1 = (x(t_1), y(t_1))\), \(P_2 = (x(t_2), y(t_2))\), \(\cdots\), \(P_{n-1} = (x(t_{n-1}), y(t_{n-1}))\), \(P_n = (x(t_n), y(t_n)) = Q\) are all on \(C\). We can approximate the curve \(C\) by connecting the line segments \(P_0P_1\), \(P_1P_2\), \(\cdots\), \(P_{n-1}P_{n}\). By adding the lengths of these segments, we can approximate the length \(l_{\Delta}\) of the curve \(C\). The length of \(P_{i}P_{i+1}\) is given by
Since \(y(t)\) is a \(C^1\) function, \(\frac{d}{dt}y(t)\) is continuous so the difference between \(\frac{d}{dt}y(s_i')\) and \(\frac{d}{dt}y(s_i)\) becomes smaller as \(\Delta\) is more refined. Thus, we have the approximation
As we refine \(\Delta\), both sides of {eq:mMlen} converge to the same value that is \(\int_{a}^{b}h(t)dt\). Thus, it is natural to define the length \(l(C)\) of the curve \(C\) by
We can use multiple integrals to compute areas and volumes of various shapes. Area of a planar region Definition (Area) Let \(D\) be a bounded closed region in \(\mathbb{R}^2\). \(D\) is said to have an area if the multiple integral of the constant function 1 over \(D\), \(\iint_Ddxdy\), exists. Its value is denoted by \(\mu(D)\): \[\mu(D) = \iint_Ddxdy.\] Example . Let us calculate the area of the disk \(D = \{(x,y)\mid x^2 + y^2 \leq a^2\}\). Using the polar coordinates, \(x = r\cos\theta, y = r\sin\theta\), \(dxdy = rdrd\theta\), and the ranges of \(r\) and \(\theta\) are \([0, a]\) and \([0, 2\pi]\), respectively. Thus, \[\begin{eqnarray*} \mu(D) &=& \iint_Ddxdy\\ &=&\int_0^a\left(\int_0^{2\pi}rd\theta\right)dr\\ &=&2\pi\int_0^a rdr\\ &=&2\pi\left[\frac{r^2}{2}\right]_0^a = \pi a^2. \end{eqnarray*}\] □ Volume of a solid figure Definition (Volume) Let \(V\) be a solid figure in the \((x,y,z)\) space \(\mathbb{R}^3\). \(V\) is...
Defining the birth process Consider a colony of bacteria that never dies. We study the following process known as the birth process , also known as the Yule process . The colony starts with \(n_0\) cells at time \(t = 0\). Assume that the probability that any individual cell divides in the time interval \((t, t + \delta t)\) is proportional to \(\delta t\) for small \(\delta t\). Further assume that each cell division is independent of others. Let \(\lambda\) be the birth rate. The probability of a cell division for a population of \(n\) cells during \(\delta t\) is \(\lambda n \delta t\). We assume that the probability that two or more births take place in the time interval \(\delta t\) is \(o(\delta t)\). That is, it can be ignored. Consequently, the probability that no cell divides during \(\delta t\) is \(1 - \lambda n \delta t - o(\delta t)\). Note that this process is an example of the Markov chain with states \({n_0}, {n_0 + 1}, {n_0 + 2}...
Consider integrating a function \(f(x,y)\) over a region \(D\) which may not be bounded or closed. In the case of a univariate function, this corresponds to the improper integral where we took the limits of the endpoints of a closed interval. In the case of multiple integrals, we adopt the notion of a "sequence of regions." Consider a sequence of regions \(\{K_n\}\) where each \(K_n\) is a subset of \(\mathbb{R}^2\) that satisfies the following conditions: (a) \(K_1 \subset K_2\)\(\subset \cdots \subset\) \(K_n \subset K_{n+1} \subset \cdots\). (b) For all \(n\in \mathbb{N}\), \(K_n \subset D\). (c) For all \(n \in\mathbb{N}\), \(K_n\) is bounded and closed. (d) For any bounded closed set \(F\) that is included in \(D\) (i.e., \(F \subset D\)), if \(n\) is sufficiently large, then \(F \subset K_n\). In other words: for all bounded closed \(F \subset D\), there exists some \(N\in \mathbb{N}\) such that, for all \(n\in \mathbb{N}\), if \(n \geq N\) then \(F \subset K_...
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