Scale-resolved and stochastic approaches for noise prediction in duct singularities
Başlık çevirisi mevcut değil.
- Tez No: 597469
- Danışmanlar: PROF. WOLFGANG POLIFKE
- Tez Türü: Doktora
- Konular: Enerji, Energy
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2017
- Dil: İngilizce
- Üniversite: Technische Universität München
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 163
Özet
Özet yok.
Özet (Çeviri)
This study focuses on the numerical investigation of the broadband noise generated by a low- Mach-number flow passing through ducted diaphragms. The goal of the study is to develop a fast and accurate tool based on a stochastic noise prediction method. Various noise prediction techniques including a direct approach and different hybrid approaches were implemented and were compared against experimental data. The experimental data was acquired performing induct aeroacoustic measurements on the test campaign installed in the anechoic chamber of the von Karman Institute for Fluid Dynamics. The measured data was post-processed using a multiport method to identify the active source. The scale-resolved flow data is provided from compressible Large Eddy Simulation. The applicability and the accuracy of a hybrid approach that combines Lighthill's analogy and Green's function for sound generation and radiation, respectively, are investigated. A tailored Green's function is proposed using the mode-matching technique to account for the scattering of single and tandem diaphragms in cylindrical ducts. Unsteady flow data required for the noise prediction approach is provided using the LES data, and alternatively through a stochastic method. The latter, namely 'Stochastic Noise Generation and Radidation' (SNGR) method, synthesizes turbulent velocity field satisfying the two-point statistics of a target mean flow. A grouping scheme for the noise sources based on the octree structure is introduced to minimize the memory requirements and further to reduce the computational cost. Comparison of the SNGR results and the LES predictions and measured data revealed that promising noise predictions can be achieved using the SNGR method given a proper anisotropy model and the spectral decay rate.
Benzer Tezler
- Yatırım kararlarının değerlemesinde reel opsiyonları bilişim teknolojileri yatırım uygulaması
Valuation of investment decisions with real options: Information technologies investment practice
SELÇUK ALTAN ÖZOĞUL
- Toplam faktör verimliliğinin büyümesi, bileşenleri ve makroekonomik belirleyicileri: OECD ülkeleri için bir analiz
Growth, components and macroeconomic determinants of total factor productivity: An analysis for OECD countries
NADİDE YİĞİTELİ
- Altızgara Modellemede Rassal Döngüler
Stochastic Eddies in Subgrid Modelling
RUKİYE KARA
Doktora
Türkçe
2015
MatematikMimar Sinan Güzel Sanatlar ÜniversitesiMatematik Ana Bilim Dalı
DOÇ. DR. MİNE ÇAĞLAR
- Türkiye enerji sektörünün stratejik konumu ve yapay sinir ağı modelleriyle enerji tüketiminin tahmini
Turkish energy sector strategic position and neural network prediction of energy consumption models with
HATİCE GENÇ KAVAS
Doktora
Türkçe
2019
EnerjiSivas Cumhuriyet Üniversitesiİşletme Ana Bilim Dalı
PROF. DR. HÜDAVERDİ BİRCAN
