Database for Research Projects to Solve the Inverse Heat Conduction Problem
To achieve the optimal performance of an object to be heat treated, it is necessary to know the value of the Heat Transfer Coefficient (HTC) describing the amount of heat exchange between the work piece and the cooling medium. The prediction of the HTC is a typical Inverse Heat Transfer Problem (IHC...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-06-01
|
| Series: | Data |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5729/4/3/90 |
| _version_ | 1811306850730115072 |
|---|---|
| author | Sándor Szénási Imre Felde |
| author_facet | Sándor Szénási Imre Felde |
| author_sort | Sándor Szénási |
| collection | DOAJ |
| description | To achieve the optimal performance of an object to be heat treated, it is necessary to know the value of the Heat Transfer Coefficient (HTC) describing the amount of heat exchange between the work piece and the cooling medium. The prediction of the HTC is a typical Inverse Heat Transfer Problem (IHCP), which cannot be solved by direct numerical methods. Numerous techniques are used to solve the IHCP based on heuristic search algorithms having very high computational demand. As another approach, it would be possible to use machine-learning methods for the same purpose, which are capable of giving prompt estimations about the main characteristics of the HTC function. As known, a key requirement for all successful machine-learning projects is the availability of high quality training data. In this case, the amount of real-world measurements is far from satisfactory because of the high cost of these tests. As an alternative, it is possible to generate the necessary databases using simulations. This paper presents a novel model for random HTC function generation based on control points and additional parameters defining the shape of curve segments. As an additional step, a GPU accelerated finite-element method was used to simulate the cooling process resulting in the required temporary data records. These datasets make it possible for researchers to develop and test their IHCP solver algorithms. |
| first_indexed | 2024-04-13T08:52:56Z |
| format | Article |
| id | doaj.art-186be2259db14998a86b2c816e35799b |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-04-13T08:52:56Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-186be2259db14998a86b2c816e35799b2022-12-22T02:53:25ZengMDPI AGData2306-57292019-06-01439010.3390/data4030090data4030090Database for Research Projects to Solve the Inverse Heat Conduction ProblemSándor Szénási0Imre Felde1John von Neumann Faculty of Informatics, Óbuda University, Bécsi út 96/b., H-1034 Budapest, HungaryJohn von Neumann Faculty of Informatics, Óbuda University, Bécsi út 96/b., H-1034 Budapest, HungaryTo achieve the optimal performance of an object to be heat treated, it is necessary to know the value of the Heat Transfer Coefficient (HTC) describing the amount of heat exchange between the work piece and the cooling medium. The prediction of the HTC is a typical Inverse Heat Transfer Problem (IHCP), which cannot be solved by direct numerical methods. Numerous techniques are used to solve the IHCP based on heuristic search algorithms having very high computational demand. As another approach, it would be possible to use machine-learning methods for the same purpose, which are capable of giving prompt estimations about the main characteristics of the HTC function. As known, a key requirement for all successful machine-learning projects is the availability of high quality training data. In this case, the amount of real-world measurements is far from satisfactory because of the high cost of these tests. As an alternative, it is possible to generate the necessary databases using simulations. This paper presents a novel model for random HTC function generation based on control points and additional parameters defining the shape of curve segments. As an additional step, a GPU accelerated finite-element method was used to simulate the cooling process resulting in the required temporary data records. These datasets make it possible for researchers to develop and test their IHCP solver algorithms.https://www.mdpi.com/2306-5729/4/3/90Inverse Heat Conduction Problemheat transfer coefficientGPUmachine learning |
| spellingShingle | Sándor Szénási Imre Felde Database for Research Projects to Solve the Inverse Heat Conduction Problem Data Inverse Heat Conduction Problem heat transfer coefficient GPU machine learning |
| title | Database for Research Projects to Solve the Inverse Heat Conduction Problem |
| title_full | Database for Research Projects to Solve the Inverse Heat Conduction Problem |
| title_fullStr | Database for Research Projects to Solve the Inverse Heat Conduction Problem |
| title_full_unstemmed | Database for Research Projects to Solve the Inverse Heat Conduction Problem |
| title_short | Database for Research Projects to Solve the Inverse Heat Conduction Problem |
| title_sort | database for research projects to solve the inverse heat conduction problem |
| topic | Inverse Heat Conduction Problem heat transfer coefficient GPU machine learning |
| url | https://www.mdpi.com/2306-5729/4/3/90 |
| work_keys_str_mv | AT sandorszenasi databaseforresearchprojectstosolvetheinverseheatconductionproblem AT imrefelde databaseforresearchprojectstosolvetheinverseheatconductionproblem |