Floating point exception fluent ошибка

Floating point exception (core dumped) is an error that arises when your application tries to do something that is not allowed with a floating point number. In this article, we’ll teach you why the floating point exception occurs and how to fix it. Also, we’ll give you other information about this error. Keep on reading to gain in-depth knowledge on this error and how to prevent it in the future.

Floating point exception occurs because of several reasons such as invalid operation, division by zero, overflow, underflow, or inexact. In this section, we’ll be going through these reasons one by one.

– Invalid Operation

An invalid operation occurs when an operation cannot be represented or has no mathematical value. For example, the square root of a negative number or the logarithm of a negative number falls into this category. We know that in the context of complex numbers you can take the square root of a negative number, but there is no way to represent this in computers.

Also, if your program performs a floating-point operation on a location meant for integer, this will cause an invalid operation. That is because there is a discrepancy between the stored data (integer) and the operation you are trying to perform on the data (floating-point operation).

– Division by Zero

When you try to divide a number by zero, you’ll get a floating-point exception. The same happens when you try to divide by infinity or NaN. The following are examples:

• -1/0
• log(0)

– Overflow

An overflow exception occurs when an operation produces a value that is outside of its range. This means that the value is either higher or lower than the smallest representable value.

– Underflow

When the result of a calculation is smaller than what can be stored in a data type, underflow occurs.

– Inexact

An inexact exception occurs when the result of an operation is not equal to the expected value. This happens when you do the operation with exponent range and unbound precision.

How to Fix Floating Point Exception (Core Dumped)

You can fix the floating-point exception by avoiding invalid operations, avoiding computations that lead to division by zero, avoiding overflow, and avoiding underflow. The following steps will go into detail on how to solve floating point exception (core dumped):

– Avoid Invalid Operation

When performing computations in your code, it’s very easy to run into an invalid operation. However, if you are on the lookout, you can avoid such situations. For example, you can implement a check that’ll stop a calculation with a negative number. Another example is an attempt to compute a string and a number.

Also, if you are using OpenFOAM, ensure your mesh is correct and your courant number is not too high. Otherwise, you’ll get a floating point exception (core dumped) OpenFOAM error. In addition, when you are using Gromacs, ensure your simulation is correct. Otherwise, you can get a floating point exception (core dumped) gromacs error.

What’s more, you can run into a floating point exception (core dumped) ubuntu error when you are using Packet Tracer or BRM client in Ubuntu. In both cases, you will need to install the right software to get rid of the error. As a final note, avoid using negatives with conditional checks in your code.

– Avoid Computations That Lead to Division by Zero

You can run into computations that lead to division by zero when you are working on an array using a for loop. One of the best ways to do this is to check if the denominator is zero before you divide. This will avoid the floating-point exception error. Another option is to use try-catch block or try-except block in supported programming languages.

In Python, the try-except block will prevent floating point exception (core dumped) python error. What’s more, there have been reported cases where division by zero leads to floating point exception (core dumped) pytorch error. Meanwhile, in Assembly language, division by zero can lead to floating point exception (core dumped) assembly error.

The same happens when using the Netwide Assembler (NASM), where you’ll get floating point exception (core dumped) nasm error when you divide a number by zero.

– Avoid Overflow

You can avoid overflow by making sure you do not go above what a particular data type can store. Code examples will help you better understand what we are talking about, so let’s see some code examples.

In the C code detailed below, we have an unsigned char that can take a maximum of 256 characters. However, we set its initial value to 250 and then we perform a loop that requests for the next 10. This means we are asking for 260 characters from 256, hence an overflow occurs. We show the overflow by printing the hexadecimal equivalent of the numbers.

#include <stdio.h>
int main(int argc, char* argv[]) {
// Declare an unsigned char
unsigned char n = 250;
int i;
// Print the numbers in Hexadecimal
// and ordinary number
for (i = 0; i < 10; i++) {
printf(“%hhu | %hhXn”, n, n);
n++;
}
return 0;
}

The following is the output of the code above:

250 | FA
251 | FB
252 | FC
253 | FD
254 | FE
255 | FF
0 | 0
1 | 1
2 | 2
3 | 3

– Avoid Underflow

You can prevent underflow by not taking away from a data type more than what it can provide. If you don’t, an underflow will happen.

In the code example below, we have an unsigned char whose value is five. Afterwards, we loop over it 10 times, decrementing the loop counter at every iteration. In the end, only the first five counters will output the correct result. The rest will produce an underflow.

#include <stdio.h>
int main(int argc, char* argv[]) {
// Declare an unsigned char
unsigned char n = 5;
int i;
// Print the numbers with for-loop
for (i = 0; i < 10; i++) {
printf(“%hhu | %hhXn”, n, n);
n–;
}
return 0;
}

The output of the code:

5 | 5
4 | 4
3 | 3
2 | 2
1 | 1
0 | 0
255 | FF
254 | FE
253 | FD
252 | FC

Useful Information About the Floating-point Exception Error

In this section, we’ll discuss related information about the floating-point exception. This will be more like a question-and-answer conversation. We’ve outlined the most commonly-asked questions below, with expert answers following.

– What Is Floating-point Exception Sigfpe?

Floating-point exception SIGFPE is a signal raised by floating-point exception which results in the process termination and production of a core file. Meanwhile, the former and the latter will occur in the absence of a signal-handler subroutine. However, if there is a signal handler subroutine, the process calls it instead.

– What Is Floating-point Numbers Represented As?

Floating-point numbers are represented as scientific notation, so the scientific notation can be written as F multiplied by two raised to an Exponent, where F is the Fraction, E is the Exponent and the number two is the Radix. Meanwhile, both the Fraction and Exponent can be positive or negative numbers.

Also, modern computers use the IEEE 754 standard to represent floating-point numbers. For example, 2.6, 0.28, and negative 11.34 are all floating-point numbers. However, numbers like 80 and 70 are not floating-point numbers.

– How Can We Avoid Floating-point Exception in Fluent?

You can avoid floating-point exceptions in Fluent by setting your mesh to CFD. Here is a more detailed step-by-step process to do this:

1. Set your mesh to CFD.
2. The mesh should have an orthogonal array value with good skewness.
3. Your mesh should be unstructured and arranged in triangles arrangement.
4. Show all the bodies of your geometry in your mesh without cuts.
5. If there is improper initialization, reset and start again.
6. If you are doing transient apps, select Pressure-Implicit with Splitting of Operator (PISO).
7. Set your timestamps to less than 5 multiply by 10 exponential negative 3.
8. Activate Implicit Update Settings.

– How Do You Stop a Floating Error in Python?

You cannot stop floating errors, however, you can manage them by using another representation for the values for precise computation. For example, if you need exact decimal rounding, you can represent the values as binary-coded decimals.

– How Are Floating-point Numbers Stored?

Floating-point numbers are stored as the significand and the exponent with a sign bit. The high-order bit indicates a sign. Meanwhile, zero indicates a positive value, while one indicates a negative value. As a result, the exponent is the remaining eight bits.

Conclusion

This article explained floating-point exception and how to fix floating-point exception (core dumped); we also learned about other information that related to this error. The following points were tackled in this guide:

• Floating-point exception when there is an error in floating-point arithmetic.
• Causes of floating-point exception include invalid operation and overflow.
• You cannot avoid floating-point errors, but you can manage them.
• SIGFPE is a signal raised by the floating-point exception.
• Floating-point exception can occur in applications like OpenFoam, Gromacs, and Fluent by Ansys.

With everything that we’ve taught you in this article, you are now well-prepared to tackle floating-point exception confidently.

Здравствуйте! Моделирую процесс горения в камере сгорания ГПА. Работаю с флюентом и ансисом в целом вообще в первый раз в жизни. Модель турбулентности ставлю k-e, в Species задаю Non-Premixed Combustion и Steady Diffusion Flamelet. В Methods ставлю всё Default. Когда запускаю расчёт, то на 15-16 итерации вылазит ошибка Floating point exception. В чём может быть проблема? Плохая сетка или же проблема в задаче граничных условий и методов решения?

Очень вероятно, что проблемы возникают из-за недостаточных знаний в программе и теории в целом, но всё же кнопки вроде все правильно нажимаю, хотя судя по всему нет, может найдутся знающие люди? Причём пытался сделать расчёт в CFX — там всё выполнилось успешно, но нужно сделать во флюенте, так как в планах пытаться играть с топливными композициями, а в сфх такое не представляется возможным(насколько я знаю)

Вот что выдаёт флюент :

Reversed flow on 3357 faces (18.8% area) of pressure-inlet 34.

Reversed flow on 50 faces (0.1% area) of pressure-inlet 30.

Minimum PDF table enthalpy exceeded in 1091207 cells on zone 40881.

Maximum PDF table enthalpy exceeded in 174481 cells on zone 40881.

turbulent viscosity limited to viscosity ratio of 1.000000e+05 in 181 cells

15 1.9921e+00 1.4142e-02 1.6948e-02 8.4086e-03 4.9934e-01 4.7837e-02 1.1709e-01 1.7435e-02 9.0172e-02 0:43:50 85

Error at Node 0: floating point exception

Reversed flow on 1042 faces (4.9% area) of pressure-inlet 34.

Reversed flow on 199 faces (0.2% area) of pressure-inlet 30.

Stabilizing enthalpy to enhance linear solver robustness.

Stabilizing enthalpy using GMRES to enhance linear solver robustness.

Divergence detected in AMG solver: enthalpy

Minimum PDF table enthalpy exceeded in 1091207 cells on zone 40881.

Maximum PDF table enthalpy exceeded in 173468 cells on zone 40881.

Error at host: floating point exception

===============Message from the Cortex Process================================

Compute processes interrupted. Processing can be resumed.

==============================================================================

Error: floating point exception

Error Object: #f

Registering Pdf, («D:\gorenje\fluent\123_files\dp0\FLTG\Fluent\FLTG.2.pdf»)

Сетку строил в Fluent Meshing, вот что выдаёт Mesh Quality:

Minimum Orthogonal Quality = 9.00149e-02

(To improve Orthogonal quality , use «Inverse Orthogonal Quality»

where Inverse Orthogonal Quality = 1 — Orthogonal Quality)

Maximum Aspect Ratio = 1.00464e+02

Ryan T

July 20, 2020 22:10

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Error: Floating Point Exception. How do I fix this?
 

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Guvennergiz

July 21, 2020 01:36

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Hello Ryan,
1st of all can you check your mesh quality with the figure below (for min ortho. quality > 0.1 or max. skewness <0.95) ?
Best regards,
G�ven

Ryan T

July 21, 2020 04:22

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Guvennergiz

July 21, 2020 04:40

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Hello Ryan,
0.12 ortho. quality is in the «Bad» section on sprectrum. You can try to improve your ortho. quality.
Did you setup «Fluent with meshing»? If it is, unfortunately i do not have enough information on it to help you. But if you set the mesh section differently, please open your mesh and view your skewness and orthogonal quality. When you select these sections, a graphic will appear below showing the distribution of the elements. When you click on the columns that showing low quality elements, it will show you their place on the existing mesh. After that, you need to use various method-sizing-inflation settings to improve the quality of those elements.
There are several tutorials on youtube about that topic.
Hope that it helps to you.
Best regards,
G�ven

Ryan T

July 21, 2020 04:46

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2 Attachment(s)

Guvennergiz

July 21, 2020 04:50

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Hello Ryan,
There is a huge difference between «ortho. quality in Fluent» and «ortho. quality in meshing» as you can see. Did you make polyhedra mesh in Fluent?
Best regards,
G�ven

Ryan T

July 21, 2020 04:51

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Guvennergiz

July 21, 2020 05:06

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Hello Ryan,
That difference usually show up when you make polyhedra mesh in fluent (e.g you’ve created your mesh then opened Fluent, you can convert your mesh to polyhedra with a single button in Fluent and it can not be converted back).
Can you try to improve your mesh quality just a bit? After updating mesh, please check quality in Fluent again.
Best regards,
G�ven

Ryan T

July 21, 2020 05:12

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2 Attachment(s)

Guvennergiz

July 21, 2020 06:08

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Hi Ryan,
So we can continue with 2nd part: time step (ofc if you certainly sure your IC and BC).
I do not know what your time step size right now. Can you decrease your time step size and try again?
Btw, you are getting error while calculating right? Not initializing?
Best regards,
G�ven

Ryan T

July 21, 2020 06:15

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Your max. Aspect Ratio is very high. Maybe a structured mesh would improve your overall mesh quality.

Ryan T

July 21, 2020 14:42

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You can do the same with a bias in the sizing option for a structured mesh. I think the overall AR should be below 5. For the cells in the boundary layer 30 is okay, but you have a maximum AR of 700 which is way too high.

AlexanderZ

July 22, 2020 02:13

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Ryan T

July 22, 2020 16:08

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Ryan T

July 30, 2020 18:52

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FLUENT — Two common errors when using the planar conduction model (floating point exception or MPI error) and tips to address the underlying issues

	Most of the issues with planar conduction in parallel mode are not actually due to parallel. There are two other possible causes: I) If one gets floating point exception in serial mode, then one should check that all walls that have planar conduction ON should NOT have zero thickness. It is easy to make such mistake. Fortunately, it is rather easy to check for such condition by examining the bc file. To perform such quick check: -Write out a bc file, by going to FLUENT’s Text User Interface (TUI) command line and typing: /file/write-bc/any_name -Use any text editor that has search/replace functionality and read the bc file. -Search for line in the bc that contains: (planar-conduction? . #t) The #t means that planar conduction is turned on. Eighth line above this one is thickness definition. Thickness definition line looks as such: (d . 0.001). Search through the file for all the walls and make sure the thickness is not zero — you should not see: (d . 0). If you find one with zero thickness, then you can either visit that wall in boundary condition panel and put a non-zero positive thickness or remove planar conduction. You can also make these changes in the bc file. If you want to turn OFF planar conduction in the bc file, then repalce (planar-conduction? . #t) with (planar-conduction? . #f). You can read the modified bc file by typing in the TUI: /file/read-bc bc_name.