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ZanzabumX [31]
3 years ago
9

A rectangular channel 3.0 m wide has a flow rate of 5.0 m3/s with a normal depth of 0.50 m. The flow then encounters a dam that

rises 0.25 m above the channel bottom. Will a hydraulic jump occur?
Engineering
1 answer:
Lina20 [59]3 years ago
8 0

Answer:

The hydraulic will jump since the flow is subcritical ( i.e. Y2 > Yc )

Explanation:

width of channel = 3.0 m

Flow rate = 5 m^3/s

Normal depth = 0.50 m

Flow encounters a dam rise of 0.25 m

To know if the hydraulic jump will occur

we will Determine the new normal depth

Y2 = 3.77m

Yc ( critical depth )= 0.66m

Attached below is the detailed solution

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1)What are the three previous manufacturing revolutions Mr. Scalabre mentions? When did these take place?
Ostrovityanka [42]

The three previous manufacturing revolutions that Mr. Scalabre mentioned and their year of occurrence are:

  1. The steam engine in the mid-19th Century
  2. The mass-production model in the early 20th Century
  3. The first automation wave in the 1970s

<h3>What is a Manufacturing Revolution?</h3>

This refers to the process of change from a handicraft economy to industry production-based production.

Hence, we can see that Mr. Scalabre believes we are not growing in productivity because there has not been enough automation to perform the tasks needed.

The effect of robotics is making an impact on productivity because a lot of complex, difficult tasks are done by machines.

3D printing has made an impact on productivity because there is a reduction in the pressing cycle and errors due to negligence are reduced.

The role the engineers have to play in the next revolution is that they would have to produce mathematical model that can be used to produce better AIs

Read more about manufacturing revolutions here:

brainly.com/question/14316656

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8 0
2 years ago
PythonA group of statisticians at a local college has asked you to create a set of functionsthat compute the median and mode of
skelet666 [1.2K]

Answer:

  1. def median(l):
  2.    if(len(l) == 0):
  3.       return 0
  4.    else:
  5.        l.sort()
  6.        if(len(l)%2 == 0):
  7.            index = int(len(l)/2)
  8.            mid = (l[index-1] + l[index]) / 2
  9.        else:
  10.            mid = l[len(l)//2]  
  11.        return mid  
  12. def mode(l):
  13.    if(len(l)==0):
  14.        return 0
  15.    mode = max(set(l), key=l.count)
  16.    return mode  
  17. def mean(l):
  18.    if(len(l)==0):
  19.        return 0
  20.    sum = 0
  21.    for x in l:
  22.        sum += x
  23.    mean = sum / len(l)
  24.    return mean
  25. lst = [5, 7, 10, 11, 12, 12, 13, 15, 25, 30, 45, 61]
  26. print(mean(lst))
  27. print(median(lst))
  28. print(mode(lst))

Explanation:

Firstly, we create a median function (Line 1). This function will check if the the length of list is zero and also if it is an even number. If the length is zero (empty list), it return zero (Line 2-3). If it is an even number, it will calculate the median by summing up two middle index values and divide them by two (Line 6-8). Or if the length is an odd, it will simply take the middle index value and return it as output (Line 9-10).

In mode function, after checking the length of list, we use the max function to estimate the maximum count of the item in list (Line 17) and use it as mode.

In mean function,  after checking the length of list,  we create a sum variable and then use a loop to add the item of list to sum (Line 23-25). After the loop, divide sum by the length of list to get the mean (Line 26).

In the main program, we test the three functions using a sample list and we shall get

20.5

12.5

12

3 0
3 years ago
A biotechnology company produced 225 doses of somatropin, including 11 which were defective. Quality control test 15 samples at
Radda [10]

Answer:

  • <u>0.59</u>

Explanation:

The <em>batch</em> is <em>rejected</em> if any of the <em>random samples are found defective</em>, or, what is the same, it will be accepted only if all 15 samples are good.

The probability that none be defective is the same probability that all the samples are good. Thus, start to calculate the probability that the batch is accepted.

The probability that the first sample is good is 214 /225, because there are 225 - 11 = 214 good samples in 225 doses.

The probability that the second samples is good too is 213/224, because there is 1 less good sample, in the 224 remaining samples.

By the same process, you conclude that the consecutive probabilities of selecting a good sample are: 212/223, 211/222, 210/221, . . . up to 199/211.

The joint probability of all the samples are good is the product of each probability:

\frac{214}{225}\cdot\frac{213}{224}\cdot\frac{212}{223}\cdot\frac{211}{222}\cdot\frac{210}{221}\cdot\frac{209}{220}\cdot\frac{208}{219}\cdot\frac{207}{218}\cdot\frac{206}{217}\cdot\frac{205}{216}\cdot\frac{204}{215}\cdot\frac{203}{214}\cdot\frac{202}{213}\cdot\frac{201}{212}\cdot\frac{200}{211}\cdot\frac{199}{210}

The result is: 0.41278 ≈ 0.41

The conclusion is that the probability that all the samples are good and the batch is accepted is 0.41.

Therefore, <em>the probability that the batch is rejected</em> is 1 - 0.41 = 0.59.

4 0
3 years ago
A ball A is thrown vertically upward from the top of a 30-m-high building with an initial velocity of 5 m&gt;s. At the same inst
expeople1 [14]

Answer:

s= 20.4 m  

Explanation:

First lets write down equations for each ball:  

s=so+vo*t+1/2a_c*t^2

for ball A:

s_a=30+5*t+1/2*9.81*t^2

for ball B:  

s_b=20*t-1/2*9.81*t^2

to find time deeded to pass we just put that

s_a = s_b  

30+5*t-4.91*t^2=20*t-4.9*t^2

t=2 s  

now we just have to put that time in any of those equations an get distance from the ground:  

s = 30 + 5*2 -1/2*9.81 *2^2  

s= 20.4 m  

6 0
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Write a function named "read_prices" that takes one parameter that is a list of ticker symbols that your company owns in their p
ohaa [14]

Answer:

import pandas pd

def read_prices(tickers):

price_dict = {}

# Read ingthe ticker data for all the tickers

for ticker in tickers:

# Read data for one ticker using pandas.read_csv  

# We assume no column names in csv file

ticker_data = pd.read_csv("./" + ticker + ".csv", names=['date', 'price', 'volume'])

# ticker_data is now a panda data frame

# Creating dictionary

# for the ticker

price_dict[ticker] = {}

for i in range(len(ticker_data)):

# Use pandas.iloc  to access data

date = ticker_data.iloc[i]['date']

price = ticker_data.iloc[i]['price']

price_dict[ticker][date] = price

return price_dict  

7 0
3 years ago
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