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Liouville and Cloud Models of Randomly Forced Particle-laden Flow
Liouville and Cloud Models of Randomly Forced Particle-laden Flow
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151342
- ISBN
- 9798383124512
- DDC
- 620
- 서명/저자
- Liouville and Cloud Models of Randomly Forced Particle-laden Flow
- 발행사항
- [Sl] : University of California, San Diego, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 304 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Jacobs, Gustaaf B.;Coimbra, Carlos F.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- 초록/해제
- 요약Eulerian-Lagrangian (EL) models are developed that account for stochasticity and randomness in tracers of inertial particles forced by a carrier flow phase. Central to the novelty of the models is a forcing formulation that uses a series expansion with random coefficients to account for epistemic and aleatoric uncertainties, in lieu of commonly used stochastic, random-walk processes.Starting from randomly forced ordinary differential equations that govern the Lagrangian inertial point-particle tracer dynamics, Lagrangian cloud and Liouville models are derived. Both cloud and Liouville models are closed and are shown to more accurately and computationally efficiently predict the propagation of the forcing randomness into confidence intervals of the particle phase solution as compared to Monte Carlo sampling methods.The closed and predictive particle cloud tracer models the mean motion and deformation of a cloud of inertial particles at a singular point in space and along its Lagrangian trajectory in time. The tracer builds upon the Subgrid Particle-Averaged Reynolds Stress Equivalent (SPARSE) formulation first introduced in Davis et al. (2017) for the tracing of particle clouds. Using a combination of the forcing models, averaging and a truncated Taylor series expansion to estimate the statistical correlations in the cloud region, the SPARSE model is closed and achieves a third convergence for the confidence interval with respect the number of samples.The Liouville models are rigorously derived with the method of distributions and do not require truncation or ad-hoc assumptions. The deterministic PDF models are described by hyperbolic partial differential equations (PDEs). In Eulerian form, the PDEs are solved with grid-based spectral methods. To recover the Lagrangian character of the disperse phase, the method of characteristics is employed to derive a PDF formulation based on the computation of flow maps, circumventing difficulties of solving high-dimensional PDE equations. This formulation is local, does not require grid based methods nor sampling, and offers a complete statistical description. It is shown that the Liouville PDF models may generalize Langevin and Fokker-Planck descriptions of particle statistics to non-Gaussian noise of the random walk.An inverse model to infer stochastic descriptions of particle forcings from noisy trajectory data using an adjoint formulation is also introduced using a point-particle approach.
- 일반주제명
- Fluid mechanics
- 일반주제명
- Aerospace engineering
- 일반주제명
- Applied physics
- 키워드
- Liouville model
- 키워드
- Random forcing
- 기타저자
- University of California, San Diego Mechanical and Aerospace Engineering (Joint Doctoral with SDSU)
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211151342
■006m o d
■007cr#unu||||||||
■020 ▼a9798383124512
■035 ▼a(MiAaPQ)AAI31242311
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a620
■1001 ▼aDominguez-Vazquez, Daniel.
■24510▼aLiouville and Cloud Models of Randomly Forced Particle-laden Flow
■260 ▼a[Sl]▼bUniversity of California, San Diego▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a304 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Jacobs, Gustaaf B.;Coimbra, Carlos F.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2024.
■520 ▼aEulerian-Lagrangian (EL) models are developed that account for stochasticity and randomness in tracers of inertial particles forced by a carrier flow phase. Central to the novelty of the models is a forcing formulation that uses a series expansion with random coefficients to account for epistemic and aleatoric uncertainties, in lieu of commonly used stochastic, random-walk processes.Starting from randomly forced ordinary differential equations that govern the Lagrangian inertial point-particle tracer dynamics, Lagrangian cloud and Liouville models are derived. Both cloud and Liouville models are closed and are shown to more accurately and computationally efficiently predict the propagation of the forcing randomness into confidence intervals of the particle phase solution as compared to Monte Carlo sampling methods.The closed and predictive particle cloud tracer models the mean motion and deformation of a cloud of inertial particles at a singular point in space and along its Lagrangian trajectory in time. The tracer builds upon the Subgrid Particle-Averaged Reynolds Stress Equivalent (SPARSE) formulation first introduced in Davis et al. (2017) for the tracing of particle clouds. Using a combination of the forcing models, averaging and a truncated Taylor series expansion to estimate the statistical correlations in the cloud region, the SPARSE model is closed and achieves a third convergence for the confidence interval with respect the number of samples.The Liouville models are rigorously derived with the method of distributions and do not require truncation or ad-hoc assumptions. The deterministic PDF models are described by hyperbolic partial differential equations (PDEs). In Eulerian form, the PDEs are solved with grid-based spectral methods. To recover the Lagrangian character of the disperse phase, the method of characteristics is employed to derive a PDF formulation based on the computation of flow maps, circumventing difficulties of solving high-dimensional PDE equations. This formulation is local, does not require grid based methods nor sampling, and offers a complete statistical description. It is shown that the Liouville PDF models may generalize Langevin and Fokker-Planck descriptions of particle statistics to non-Gaussian noise of the random walk.An inverse model to infer stochastic descriptions of particle forcings from noisy trajectory data using an adjoint formulation is also introduced using a point-particle approach.
■590 ▼aSchool code: 0033.
■650 4▼aFluid mechanics
■650 4▼aEnvironmental engineering
■650 4▼aAerospace engineering
■650 4▼aApplied physics
■653 ▼aEulerian-Lagrangian models
■653 ▼aLiouville model
■653 ▼aParticle-laden flow
■653 ▼aPoint-cloud model
■653 ▼aRandom forcing
■690 ▼a0204
■690 ▼a0775
■690 ▼a0538
■690 ▼a0215
■71020▼aUniversity of California, San Diego▼bMechanical and Aerospace Engineering (Joint Doctoral with SDSU).
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161339▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
