Predicting failure after a certain time

In summary, the speaker is looking for a material that will fail within a week under 2.5 giga pascals of pressure. The material should also be able to withstand temperatures between 1 to 3 degrees celcius and be in a non-oxidising environment. The structure is spherical and the speaker is seeking advice on how to predict when a certain material will fail. They are open to suggestions, but are also considering using chemistry or melting as a means of failure. The speaker appreciates the input and finds it useful for their project.
  • #1
kateman
114
0
Hello,

Iam trying to find a material which will fail after a week or so under roughly 2.5 giga pascals. The load will vary slightly but not overly significantly considering the magnitute of pressure. It stays between about 1 to 3 degrees celcius and stays in a non-oxidising environment. The shape of the structure is spherical. Any suggestions of material would be fantastic but that's not what iam here to ask:

Basically under the above set conditions, iam looking for some way to predict when a certain material will fail. I know this will depend on the material, how its formed, etc but those are deliberate variables which I intend to change - Iam wondering if someone could direct me to some way of being able to make an informed decision on how I could go about selecting these variables with an approximate time frame in mind of when the structure will fail.

I'd appreciate any ideas/input, thank-you!
 
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  • #2
I would go another way and make the failing not rely on the pressure. Use chemistry or melting. For most materials the break down is a run away effect. A small crack or disturbance is making it's way through the material collecting more and more pressure on the weak point and getting faster and faster, so I would guess it can go quite some time before the process starts and once it is going, it is very fast. I doubt that you will be able to hit your mark with an accuracy of a few days unless you use an active trigger, or at least a chemical process with no run away effects.
 
  • #3
wow, actually you have given me something important to think about. Since then I had gone a different way but I do like what your getting at with melting. Thanks for your help, it was useful to my project =)
 

1. How do scientists predict failure after a certain time?

Predicting failure after a certain time involves using scientific methods and data analysis techniques to make educated predictions about the likelihood of failure based on past patterns and trends. This can include conducting experiments, analyzing data, and creating mathematical models to make predictions.

2. What factors are taken into consideration when predicting failure after a certain time?

There are several factors that can be taken into consideration when predicting failure after a certain time. These can include the type of material being studied, environmental factors, usage patterns, and any known defects or weaknesses in the material or system being studied.

3. Can predicting failure after a certain time be 100% accurate?

No, predicting failure after a certain time cannot be 100% accurate. While scientific methods and data analysis can provide valuable insights and predictions, there are always uncertainties and variables that cannot be accounted for. Additionally, unforeseen events or changes in conditions can impact the accuracy of the prediction.

4. How can predicting failure after a certain time be beneficial?

Predicting failure after a certain time can be beneficial in several ways. It can help identify potential problems and allow for preventative measures to be taken, saving time and resources. It can also inform decision-making processes, such as when to replace or repair equipment, and help improve safety and reliability.

5. What are some common methods used for predicting failure after a certain time?

Some common methods used for predicting failure after a certain time include statistical analysis, reliability testing, and predictive modeling. These methods involve analyzing data and patterns to make educated predictions about the likelihood of failure, and are often used in combination with each other to provide a more comprehensive understanding of the situation.

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