/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 17 Crack Velocity An article in the... [FREE SOLUTION] | 91Ó°ÊÓ

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Crack Velocity An article in the May \(23,1996,\) issue of Nature addresses the interest some physicists have in studying cracks to answer the question, "How fast do things break, and why?" Entries in the table are estimated from a graph in this article, showing velocity of a crack during a 60 -microsecond experiment. Crack Velocity during Breakage $$ \begin{array}{|c|c|} \hline \begin{array}{c} \text { Time } \\ \text { (microseconds) } \end{array} & \begin{array}{c} \text { Velocity } \\ \text { (meters per second) } \end{array} \\ \hline 10 & 148.2 \\ \hline 20 & 159.3 \\ \hline 30 & 169.5 \\ \hline 40 & 180.7 \\ \hline 50 & 189.8 \\ \hline 60 & 200.0 \\ \hline \end{array} $$ a. Find a model for the data. b. What is the average speed at which a crack travels between 10 and 60 microseconds?

Short Answer

Expert verified
Linear model: \( v(t) = 1.036t + 137.84 \). Average speed: 174.1 m/s.

Step by step solution

01

Analyze Data for Model Fitting

We are given time and velocity data and are tasked with finding a model. Observing the data, the relationship between time and velocity appears linear because the change in velocity is approximately constant as time increases.
02

Use Linear Regression for Model

To find a linear model, we can use the formula for the equation of a line: \( y = mx + b \). Here, \( y \) is velocity, \( x \) is time, \( m \) is the slope, and \( b \) is the y-intercept. Compute \( m \) by determining the change in velocity over the change in time from the data provided.
03

Calculate the Slope (m)

Using the first and last data points: \((10, 148.2)\) and \((60, 200.0)\). Slope \( m = \frac{200.0 - 148.2}{60 - 10} = \frac{51.8}{50} = 1.036 \).
04

Determine the y-intercept (b)

Use the slope from Step 3 and any data point to find \( b \). Using point \((10, 148.2)\), substitute into \( y = mx + b \): \( 148.2 = 1.036 \times 10 + b \). Solving for \( b \), we get \( b = 148.2 - 10.36 = 137.84 \).
05

Formulate the Linear Model

Substitute \( m \) and \( b \) into the line equation to get the model: \( v(t) = 1.036t + 137.84 \).
06

Calculate Average Speed

Average speed is calculated by integrating the velocity function over the given time interval (10 to 60 microseconds) and dividing by the interval length. Here, calculate average speed as arithmetic mean because data is linear. \( \text{Average speed} = \frac{v(60) + v(10)}{2} = \frac{200 + 148.2}{2} = 174.1 \text{ m/s}. \)

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Linear Regression
Linear regression is a fundamental method in statistics and calculus modeling to approximate the relationship between variables using a linear equation. It helps predict outcomes based on data inputs, by fitting the best straight line - often called the regression line - through the data points.

In the context of crack velocity, linear regression is used to model the relationship between time, measured in microseconds, and velocity, in meters per second. Since the change in velocity appears uniform as time progresses, this suggests a linear relationship needs to be fitted. By applying linear regression, we determine the slope, which represents how much velocity changes per microsecond, and the y-intercept, which indicates the estimated starting velocity when time is zero.
  • To find the slope (\( m \)), observe the change in velocity divided by the change in time.
  • The y-intercept (\( b \)) is found by rearranging the line equation (\( y = mx + b \)) with a known velocity-time point.
Ultimately, the task is to formulate the line equation that best predicts velocity based on time, such as in this exercise where the model is \( v(t) = 1.036t + 137.84 \).

This model helps understand how velocity increases over the experiment's time duration.
Average Speed Calculation
Average speed provides a single value that represents the velocity over a period, allowing us to understand how fast something, like a crack, travels on average over a specific time frame. To calculate the average speed, we usually integrate the velocity function over the time interval and then divide by the interval's length. However, with linear data, a simpler calculation can be used:

We take the arithmetic mean of the initial and final velocities. In this exercise, the velocity at the beginning of 10 microseconds is 148.2 meters per second, and by 60 microseconds, it reaches 200 meters per second.
  • The arithmetic mean formula utilized here is \( \text{Average speed} = \frac{v(60) + v(10)}{2} \).
  • Plug in the values to get \( \text{Average speed} = \frac{200 + 148.2}{2} = 174.1 \text{ m/s}\).
This mean provides a concise view of the crack's travelling pace over the time interval, encapsulating the overall behavior of the velocity pattern.
Velocity-Time Relationship
The velocity-time relationship in this exercise describes how velocity changes with time during the crack formation. Understanding this relationship helps physicists and engineers analyze the behavior of materials when exposed to stress and breakage. The velocity represents how fast the crack propagates through the material, whereas time marks the sequence of measurement.

A key insight from studying these variables is identifying whether the velocity remains constant, increases linearly, or exhibits more complex behavior. In linear relationships, as shown in this exercise, the velocity changes at a constant rate with respect to time.
  • This can be visualized as a straight-line graph, where the slope indicates the rate of change of velocity.
  • Understanding it enables better prediction and control over the material's response to external stressors.
By establishing a clear velocity-time relationship, scientists can improve material design and performance by foreseeing potential breakage scenarios. This understanding is crucial in many applications such as construction, aerospace, and even electronics.

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