27 research outputs found
์ฐ์ํ ์ธก์ถ๋ ฅ๊ธฐ๋ฅผ ์ฌ์ฉํ๋ ๋ก์ผ์ ๊ณต๋ ฅ ์ ํธ ๊ฐ์ญ๋ ฅ ์์น๋ถ์ ๋ฐ ๋์ฒด ๋ชจ๋ธ๋ง
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2017. 8. ์ด์๊ฐ.The supersonic jet interaction generated by a continuous type side-jet thruster of the missile was considered.
Firstly, the jet interaction flow field was investigated using numerical simulations. The simulation was made use of the three-dimensional unstructured-based computational fluid dynamics (CFD) solver.
The numerical simulation method was validated through comparison with wind tunnel test results. Flow fields investigation and jet interaction effects for various flow conditions, jet magnitude, and jet direction conditions were performed.
Secondly, the jet interaction aerodynamic database based on CFD data was developed and assessed.
The generation of the jet interaction aerodynamic database for the continuous type side-jet requires a large amount of simulation data owing to the complex nature of jet interaction. To reduce the required number of simulations, seven jet operating conditions were selected using geometrical symmetry at firstthen, three-dimensional numerical simulations were conducted to build the jet interaction aerodynamic database in the reduced design space. Two modeling approaches were used in developing the jet interaction aerodynamic database. One is CFD-based modeling with a full factorial sampling, and the other is surrogate modeling, based on the Latin hypercube sampling and Kriging method, for the interim database. The resulting two aerodynamic databases were assessed through comparison with flight test results. Based on the comparison, both models showed a suitable representation of the aerodynamic coefficients within 10\% error during the jet operation period. This assessment confirms that the jet interaction aerodynamic database for missiles with continuous type side-jet thruster can be constructed using the CFD-based modeling approach. The surrogate model was found to perform well compared with the CFD-based model within an acceptable error level.Chapter 1 Introduction 1
1.1 Research Background 1
1.1.1 Side-jet control of missile 1
1.1.2 Continuous type side-jet 3
1.1.3 Jet interaction aerodynamic database 4
1.2 Literature Review and Scope of Works 5
1.3 Objective of Research 7
1.4 Outline 7
Chapter 2 Numerical Method 9
2.1 Governing Equations 9
2.2 Gas Modeling 11
2.2.1 Calorically perfect gas 11
2.2.2 Thermally perfect gas, Multiple gases 12
2.3 Spatial Discretization 13
2.3.1 Convective uxes 14
2.3.2 Viscous uxes 16
2.4 Temporal Discretization 18
2.5 Turbulence Modeling 18
Chapter 3 Numerical Investigation of Continuous Type side-jet 21
3.1 Conguration and Computational Grid 21
3.2 Jet Interaction Parameters and Evaluation 23
3.3 Jet Direction and Scale of Continuous Type Side-jet 24
3.4 Simulation Conditions 25
3.5 Wind Tunnel Test and Validation of Numerical Method 27
3.5.1 Jet interaction similitude parameter 27
3.5.2 Jet-o cases 29
3.5.3 Jet-on cases 29
3.6 Investigation of Jet Interaction for Continuous Type Side-jet 43
3.6.1 Simulation results of continuous type side-jet 43
3.6.2 Flow Features of Jet Interaction for Continuous Type side-jet 49
3.6.3 Eect of jet interaction parameters 60
Chapter 4 Surrogate Modeling of Jet Interaction Aerodynamic Database for Continuous Type side-jet 66
4.1 Jet interaction aerodynamic database of continuous type side-jet 66
4.2 Dened Jet Direction Conditions 67
4.3 Jet interaction modeling strategy 70
4.4 CFD-Based Modeling of Jet Interaction 73
4.4.1 Numerical simulation for jet interaction modeling 73
4.4.2 CFD-based jet interaction modeling results 74
4.5 Surrogate Modeling Method 77
4.5.1 Design of experiments 77
4.5.2 Kriging predictor 78
4.6 Surrogate Modeling of Jet Interaction 81
4.6.1 Jet interaction modeling and evaluation 81
4.6.2 Surrogate modeling of jet interaction results 83
Chapter 5 Assessment of Jet Interaction Modeling Results 95
5.1 Post Flight Analysis for Jet Interaction Database Identication 95
5.2 Assessment of Jet Interaction Database 99
Chapter 6 Conclusion 106
Appendix Chapter A Extension Rules of Jet Directions 109
Bibliography 113
๊ตญ๋ฌธ์ด๋ก 120Docto
Prefetching Framework for General Workloads Using Breakpoint
ํ๋ก๊ทธ๋จ์ ๋ก๋ฉ ์๋๋ ํ๋ก๊ทธ๋จ์ด ์์ฒญํ๋ ๋์คํฌ ๋ธ๋ก์ ๋ฏธ๋ฆฌ ์ฝ์ด ๋ค์์ผ๋ก์จ(ํ๋ฆฌํ์นญ) ํฅ์์ํฌ ์ ์๋ค. ๊ทธ๋ฌ๋ ๊ธฐ์กด์ ํ๋ฆฌํ์นญ ๊ด๋ จ ๊ธฐ๋ฒ๋ค์ ํน์ ํ๋ก๊ทธ๋จ์ ์ต์ ํ๋ ๊ฒฝ์ฐ๋ฅผ ์ ์ธํ๋ฉด ์๋นํ ์ค๋ฒํค๋๋ฅผ ๋ณด์ฌ์ฃผ์๋ค. ํนํ ์์ฒญ๋ธ๋ก์ ์ ํํ ์ถ์ ํ๋๋ฐ ์ด๋ ค์์ด ์์๋ค. ์ด๋ค ๋ธ๋ก๋ค์ ์ฌ๋ฌ ์ํ์ค(๋จ์์๊ฐ ๋ด์ ์ถ์ ๋ ๋ธ๋ก๋ค)์ ๋ํ๋ ์ ์๊ณ ๋ ์ ๊ทผ ์ํ์ค๊ฐ ๋์ผ ํ๋๋ผ๋ ๋ฒํผ ์บ์์ ์ํด์ ์ ๊ทผ ์๊ฐ๊ณผ ์์ง๋๋ ๋ธ๋ก ์ ๋ณด๊ฐ ๋ค๋ฅผ ์ ์๊ธฐ ๋๋ฌธ์ ๋ถ์์ด ๊น๋ค๋กญ๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์ํํธ์จ์ด์ ์ ๊ทผ ๋ฐฉ๋ฒ์ผ๋ก ์๋ก์ด ๋ฒ์ฉ ์ํฌ๋ก๋ ํ๋ฆฌํ์นญ ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ์ ์ํ๋ ํ๋ฆฌํ์นญ ๊ธฐ๋ฒ์ ๋ธ๋ ์ดํฌํฌ์ธํธ๋ฅผ ํ๋ก๊ทธ๋จ์ ์ ์ฌ ์ ์์ ๋ฐฐ์นํจ์ผ๋ก์จ ์์ฒญ ๋ธ๋ก์ ์๊ด๊ด๊ณ ์ ๋ณด๋ฅผ ์์งํ๊ณ , ์ด๋ฅผ ๋ฐํ์ผ๋ก ํ๋ฆฌํ์นญ์ ์ํํ๋ค. ์์ฉ ํ๋๋์คํฌ๋ฅผ ์ด์ฉํ ์คํ ๊ฒฐ๊ณผ, ๋ถํ์ํ ์ค๋ฒํค๋๊ฐ ๊ฐ์๋์์ผ๋ฉฐ ๊ธฐ๋ ์๊ฐ์ ํ๊ท 30%, ๋ก๋ฉ์ ํ๊ท 15% ๋จ์ถ๋์์์ ํ์ธํ์๋ค.Application loading speed can be improved by timely prefetching disk blocks likely to be needed by an application. However, existing prefetchers if they are not specialized to a particular application incur high overheads and are poor at identifying the blocks that will actually be required. There are many sequences in which blocks may be needed and, even if two access sequences are identical, block tracing and access timings can be affected significantly by the state of the buffer cache. We propose a new application independent software based prefetching technique, in which breakpoints are inserted at appropriate places in an application to collect the information on correlations between the blocks and to prefetch the potential blocks ahead of their schedule based on it. Experiments on an HDD based desktop PC demonstrated an average 30% reduction in application launch time and 15% in general I/O, while reducing the wasted overhead.OAIID:oai:osos.snu.ac.kr:snu2014-01/102/0000001265/3SEQ:3PERF_CD:SNU2014-01EVAL_ITEM_CD:102USER_ID:0000001265ADJUST_YN:YEMP_ID:A002514DEPT_CD:4190FILENAME:๊ณ ๊ด์ง๋
ผ๋ฌธ_09(833-838) csts14-01.pdfDEPT_NM:์ปดํจํฐ๊ณตํ๋ถSCOPUS_YN:NCONFIRM:
๋ด์ฅํ ์์คํ ์ ์ํ ๋ฆฌ๋ ์ค ๊ฐ๋ฐ ๋ฐ ์ฌ๋ก ๋ถ์
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ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์ ๊ธฐ.์ปดํจํฐ๊ณตํ๋ถ,2001.Maste
ไบๅๆๅปบ็ฉ์ ์ฝ์ ๏งๅ์ ๋ฐ๋ฅธ FCU์์คํ ่ฎๆต้ๅถๅพก ๆนๆณ์ ็ถๆฟๆง์ ๊ดํ ็ก็ฉถ
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ผ๋ฌธ(์์ฌ)--์์ธๅคงๅญธๆ ก ๅคงๅญธ้ข :ๅปบ็ฏๅญธ็ง,1995.Maste
3์ธ๋ ๋ฐฉ์ก๋ง์์์ FGS ๋น๋์ค ์ ์ก์ ์ํ ํจ์จ์ ์ธ ์๋ฌ์ฒ๋ฆฌ ๋ฐ ์ค์ผ์ค๋ง
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ผ๋ฌธ(๋ฐ์ฌ) --์์ธ๋ํ๊ต ๋ํ์ :์ ๊ธฐ. ์ปดํจํฐ๊ณตํ๋ถ,2007.Docto
Intentionality-related Deep Learning Method in Web Prefetching
Many prediction models have been proposed to improve the effectiveness of web prefetching for reducing the response time perceived by users when browsing the web. Most of these models are based on structure learning and are applied at the client side. Currently, considerable attention is being paid to proxy-based prefetching because it is more effective and accurate in predicting the correlated pages of many websites of similar interest for more homogeneous users. Compared with client-based prefetching, more complex prediction tasks must run in the proxy, which implies that a more powerful prediction model is required. Thus, based on the time-series characteristics of browsing records, we proposed the intentionality-related long short-term memory (Ir-LSTM) model, which combines both the Skip-Gram embedding method and the LSTM model while expanding the input features with user information. We also propose a novel dynamic allocation module for detecting real-time traffic bursts and correspondingly adjusting the correlation coefficient of the model's output to achieve higher server-side resource utilization while fully maximizing hit ratio
Waveguide-type strongly coupled oscillator์ ๊ดํ ์ฐ๊ตฌ
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ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ,2002.Maste
ํ๋ ฅ ๋ฐ์ ํ์ ์ต์ ๊ณต๋ ฅ ์ต์ ํ ์ค๊ณ์ ๊ดํ ์ฐ๊ตฌ
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ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ,2003.Maste
Efficiency analysis by the change of public service management method
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ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ธฐ์
์ ์ฑ
ํ๊ณผ, 2011.8. ๊น์ค๊ธฐ.Maste
File-System-Level SSD Caching for Improving Application Launch Time
์์ฉํ๋ก๊ทธ๋จ์ ๊ธฐ๋ ์๊ฐ์ ๊ธฐ๊ธฐ์ ๋ํ ์ฌ์ฉ์ ์ฒดํ์ ์ํฅ์ ์ฃผ๋ ์ค์ํ ์งํ๋ก ๋ณด์กฐ ๊ธฐ์ต ์ฅ์น์ ์ฑ๋ฅ์ ์ํด ํฐ ์ํฅ์ ๋ฐ๋๋ค. ํ๋๋์คํฌ ๋์ SSD๋ฅผ ์ฌ์ฉํ๊ฒ ๋๋ฉด ๊ธฐ๋ ์๊ฐ์ ํฌ๊ฒ ๋ฎ์ถ์ ์์ง๋ง ๋น์ฉ ๋๋น ์ฑ๋ฅ์ ๊ณ ๋ คํ๋ฉด ์์ ์ฉ๋์ SSD๋ฅผ ํ๋๋์คํฌ์ ์บ์๋ก ์ฐ๋ ๊ฒ์ด ํ์ค์ ์ธ ๋์์ด ๋ ์ ์๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ํ์ผ์์คํ
์์ค์์ ํ๋๋์คํฌ ์์ ๋ธ๋ก์ SSD๋ก ์ด์ฃผ์ํค๋ ๊ธฐ๋ฒ์์ ์ํ๋ค. ์ ์ํ ๊ธฐ๋ฒ์ ๊ธฐ์กด์ SSD ์บ์ฑ ๊ธฐ๋ฒ๋ค์์ ์๊ตฌ๋๋ ์บ์ ๋ฐ์ดํฐ์ ์ฌ์์ ํ์ํ ์ฃผ ๋ฉ๋ชจ๋ฆฌ, CPU, ๊ทธ๋ฆฌ๊ณ ์ฌ์ ์ ๋ณด์ ์ ์ง๋ฅผ ์ํ SSD ๊ณต๊ฐ ์ฌ์ฉ์ ๋ถ๊ฐ์ ์ธ ์ค๋ฒํค๋๊ฐ ์๋ค. 8๊ฐ์ ์์ฉํ๋ก๊ทธ๋จ์ ์ด์ฉํ ์คํ์์ ๋ฉํ๋ฐ์ดํฐ์ ๋ฐ์ดํฐ ๋ธ๋ก์ ๋ชจ๋ SSD์ ์บ์ฑํ ๊ฒฝ์ฐ์ ๊ธฐ๋์๊ฐ์ด ํ๊ท 56% ๋จ์ถ๋จ์ ํ์ธํ์๋ค.
Application launch time is an important performance metric to user experience in desktop and laptop environment, which mostly depends on the performance of secondary storage.
Application launch times can be reduced by utilizing solid-state drive (SSD) instead of hard disk drive (HDD). However, considering a cost-performance trade-off, utilizing SSDs as caches for slow HDDs is a practicable alternative in reducing the application launch times. We propose a new SSD caching scheme which migrates data blocks from HDDs to SSDs. Our scheme operates entirely in the file system level and does not require an extra layer for mapping SSD-cached data that is essential in most other schemes. In particular, our scheme does not incur mapping overheads that cause significant burdens on the main memory, CPU, and SSD space for mapping table. Experimental results conducted with 8 popular applications demonstrate our scheme yields 56% of performance gain in application launch, when data blocks along with metadata are migrated.N