flow-induced vibrations an engineering guide下载

《流动诱导振动工程指南》是一本关于流动诱导振动现象的工程指南，可以帮助工程师更好地理解和解决相关问题。流动诱导振动是指当物体或结构暴露在流体中时，由于流体流动产生的振动现象。这种振动可能导致结构疲劳、动态失稳或噪声问题，对工程项目的安全性和可靠性产生重要影响。

这本指南主要介绍了流动诱导振动的机理、影响因素、分析方法以及控制和减震措施。它涵盖了流动诱导振动的基本概念、相关数学模型和理论，以及实际工程中的应用案例和实践经验。读者可以从中学习如何评估流动诱导振动风险，进行结构设计和优化，以最大程度地减少振动带来的负面影响。

此外，指南还提供了丰富的图表和示意图，帮助读者更直观地理解和应用相关内容。它也包括了一些常见的工程振动问题，并提供了解决方案和优化建议。无论是从事机械、航空、土木、水利等领域的工程师，还是对流动诱导振动感兴趣的研究人员，都可以从这本指南中获得实用的知识和工作指导。

总之，《流动诱导振动工程指南》是一本对于工程师和研究人员来说非常有价值的参考书，可以帮助他们深入了解流动诱导振动的机理和应用，提升工程项目的质量和可靠性。

相关问题

Vacuum-Induced Traps in Microscale Gases

我可以解释一下真空诱导陷阱在微观气体中的作用。真空诱导陷阱是一种物理学现象，它可以将物质从一个区域转移到另一个区域，由于气体的低压，这种转移是在一个负压区域。在这种情况下，气体中的分子会被吸入到一个小尺寸的腔室或容器内，从而形成一个真空诱导陷阱。

Implement a Matlab function which completes the following task: Give the best approximation of a given function f on a closed interval with respect to 2-norm (or more generally, a norm induced by an inner product).

Here is a possible implementation of the function:

function [x, y] = best_approximation(f, a, b, n)
% Computes the best approximation of the function f on the interval [a,b]
% using n terms of the Fourier series expansion with respect to the 2-norm.
% Returns the approximation function and its values at n+1 equidistant points.

% Define the inner product on the interval [a,b]
ip = @(u, v) integral(@(x) u(x).*v(x), a, b);

% Compute the Fourier coefficients of f
a0 = 1/(b-a) * integral(f, a, b);
an = @(k) 1/(b-a) * integral(@(x) f(x).*cos(k*pi*(x-a)/(b-a)), a, b);
bn = @(k) 1/(b-a) * integral(@(x) f(x).*sin(k*pi*(x-a)/(b-a)), a, b);

% Compute the approximation function and its values
x = linspace(a, b, n+1)';
y = zeros(n+1, 1);
y(1) = a0/2;
for k = 1:n
    y = y + an(k)*cos(k*pi*(x-a)/(b-a)) + bn(k)*sin(k*pi*(x-a)/(b-a));
end

To use this function, you need to provide a function handle to the function you want to approximate, the endpoints of the interval, and the number of terms you want to use in the Fourier series expansion. For example, to approximate the function `f(x) = x^2` on the interval `[0,1]` with 10 terms, you can call the function like this:

f = @(x) x.^2;
a = 0;
b = 1;
n = 10;
[x, y] = best_approximation(f, a, b, n);

The output `x` contains the `n+1` equidistant points at which the approximation function is evaluated, and `y` contains the corresponding function values. You can plot the original function and its approximation using the following code:

xx = linspace(a, b, 1000)';
yy = f(xx);
yyh = interp1(x, y, xx);
plot(xx, yy, 'b-', xx, yyh, 'r--');
legend('f(x)', 'approximation');

This will produce a plot showing the original function in blue and its approximation in red. You can adjust the number of terms in the Fourier series to see how the approximation improves with more terms.