Numerical Simulation of Surface Pressure and Temperature Distribution Along a Cone at Supersonic Mach Numbers Using CFD Article Swipe
Javed Shoukat Shaikh
,
Khizar Ahmed Pathan
,
Sher Afghan Khan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.37934/arnht.28.1.126
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.37934/arnht.28.1.126
The primary focus of this study is to use numerical simulations to analyze the static temperature and surface pressure distribution along the slant length of a cone at different Mach numbers and a range of semi-cone angles. Computational fluid dynamics (CFD) analysis numerically simulates temperature and surface pressure distribution. This research considers parameters such as supersonic Mach numbers, semi-cone angles, and different locations along the slant length of a cone. The study examines Mach numbers of 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0, along with cone angles ranging from 3° to 21°. The static temperature and pressure (P/Pa) results are measured at different locations (x/L) along the slant length of the cone, ranging from 0.1 to 1. The results for static temperature and pressure distribution obtained by CFD analysis are compared with results obtained by regression model at various Mach numbers and constant semi-cone angle (θ) = 12°. The results from the CFD analysis and the findings of the regression methodology are in agreement. This study found that the Mach number, semi-cone angle, and the various locations along the cone's slant length significantly impact the variation of static temperature and surface pressure distribution. As the Mach number and the semi-cone angle increase, the temperature and pressure distribution along the slant length of the cone also increase.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.37934/arnht.28.1.126
- https://semarakilmu.com.my/journals/index.php/arnht/article/download/12871/10272
- OA Status
- hybrid
- Cited By
- 1
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405492905
All OpenAlex metadata
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https://openalex.org/W4405492905Canonical identifier for this work in OpenAlex
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https://doi.org/10.37934/arnht.28.1.126Digital Object Identifier
- Title
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Numerical Simulation of Surface Pressure and Temperature Distribution Along a Cone at Supersonic Mach Numbers Using CFDWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-18Full publication date if available
- Authors
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Javed Shoukat Shaikh, Khizar Ahmed Pathan, Sher Afghan KhanList of authors in order
- Landing page
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https://doi.org/10.37934/arnht.28.1.126Publisher landing page
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https://semarakilmu.com.my/journals/index.php/arnht/article/download/12871/10272Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://semarakilmu.com.my/journals/index.php/arnht/article/download/12871/10272Direct OA link when available
- Concepts
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Mach number, Computational fluid dynamics, Supersonic speed, Mechanics, Ligand cone angle, Cone (formal languages), Geometry, Physics, Mathematics, Algorithm, Conical surfaceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.obtained | 127, 135 |
| abstract_inverted_index.pressure | 18, 47, 98, 125, 193, 207 |
| abstract_inverted_index.research | 50 |
| abstract_inverted_index.considers | 51 |
| abstract_inverted_index.different | 28, 61, 104 |
| abstract_inverted_index.increase, | 203 |
| abstract_inverted_index.increase. | 217 |
| abstract_inverted_index.locations | 62, 105, 178 |
| abstract_inverted_index.numerical | 9 |
| abstract_inverted_index.semi-cone | 35, 58, 145, 173, 201 |
| abstract_inverted_index.simulates | 43 |
| abstract_inverted_index.variation | 187 |
| abstract_inverted_index.agreement. | 165 |
| abstract_inverted_index.parameters | 52 |
| abstract_inverted_index.regression | 137, 161 |
| abstract_inverted_index.supersonic | 55 |
| abstract_inverted_index.methodology | 162 |
| abstract_inverted_index.numerically | 42 |
| abstract_inverted_index.simulations | 10 |
| abstract_inverted_index.temperature | 15, 44, 96, 123, 190, 205 |
| abstract_inverted_index.distribution | 19, 126, 208 |
| abstract_inverted_index.Computational | 37 |
| abstract_inverted_index.distribution. | 48, 194 |
| abstract_inverted_index.significantly | 184 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.66977992 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |