Double-Plume Integral Models for Near-Field Mixing in Multiphase Plumes

Socolofsky, Scott A.; Bhaumik, Tirtharaj; Seol, Dong-Guan

doi:10.1061/(asce)0733-9429(2008)134:6(772)

Cited by 75 publications

(88 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The methods section also highlights an effective entrainment algorithm for crossflows borrowed from Eulerian single-phase plumes and presents a detailed solution to particle tracking within the Lagrangian integral model in a crossflow. A thorough validation of the double-plume integral model was presented in Socolofsky et al [8]; here, we validate our crossflow model to laboratory and field data in single-and multiphase plumes in Sect. 3.…”

Section: Introductionmentioning

confidence: 59%

“…Finally, all of the entrainment models described in this section were derived for singlephase buoyant jets. Socolofsky et al [8] calibrated a double-plume integral model for a multiphase plume and found that the best-fit entrainment coefficient for the inner plume (a i ) was 0.055, about half of the single-phase value for the top-hat model 0:083 ffiffi ffi 2 p ¼ 0:12. Milgram [21] reported measured entrainment coefficient values for a wide range of bubble plume experiments, with values spanning 0.057-0.23 for top-hat models, the results depending on a bubble Froude number.…”

Section: Entrainment Modelsmentioning

confidence: 99%

“…The module for the Stratified Plume Model (SPM) follows the equations presented in Socolofsky et al [8] and uses an Eulerian framework to predict the steady-state solution for multiphase plumes in stratified and still, or nearstill, environments. This model is similar to Asaeda and Imberger [5] and McDougall [19], but with a continuous peeling equation, as introduced by Crounse et al [20].…”

Section: Stratified Plume Model: Multiphase Plume Model For Quiescentmentioning

confidence: 99%

“…The centerline dilution, plume trajectory, the locations of intrusions, and the pathways of rising particles are common metrics these models need to predict. Since the Deepwater Horizon accident, we have been developing a modeling system for subsea oil and gas plumes based on the discrete particle model [1,2] within an integral plume model framework [3][4][5] that synthesizes modeling approaches in single-and multiphase plumes across the literature [6][7][8]. In this paper, we present this general modeling system for multiphase plumes in stratification and crossflow, its validation to laboratory and field experiments, and we discuss its predictions for canonical test cases of accidental oil-well blowouts in deep water to illustrate the complex role that thermodynamics and mass transfer often plays in multiphase plumes.…”

Section: Introductionmentioning

confidence: 99%

“…The first class of models are the double-plume integral models, which apply to plumes in pure stratification and use an Eulerian framework, solving for an inner, upward-rising plume of dispersed phases and entrained water and a separate, annular outer plume of descending fluid [5,8,19,20]. The inner plume is based on the multiphase model for an unstratified, quiescent ambient by Milgram [21], and the various authors of double-plume models differ in their formulation for exchange fluxes between the inner and outer plumes and the detrainment algorithm that initializes the outer plume; Socolofsky et al [8] provides a summary and sensitivity analysis of these different approaches. Advantages of the doubleplume model are that it easily lends itself to predict multiple inner plume and intrusion layer sequences when the dispersed phase does not intrude and that it predicts exchange between the inner rising plume and the outer downdraught plume.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow

2018

Self Cite

View full text Add to dashboard Cite

We present the development and validation of a numerical modeling suite for bubble and droplet dynamics of multiphase plumes in the environment. This modeling suite includes real-fluid equations of state, Lagrangian particle tracking, and two different integral plume models: an Eulerian model for a double-plume integral model in quiescent stratification and a Lagrangian integral model for multiphase plumes in stratified crossflows. Here, we report a particle tracking algorithm for dispersed-phase particles within the Lagrangian integral plume model and a comprehensive validation of the Lagrangian plume model for single-and multiphase buoyant jets. The model utilizes literature values for all entrainment and spreading coefficients and has one remaining calibration parameter j, which reduces the buoyant force of dispersed phase particles as they approach the edge of a Lagrangian plume element, eventually separating from the plume as it bends over in a crossflow. We report the calibrated form j ¼ ½ðb À rÞ=b 4 , where b is the plume halfwidth, and r is the distance of a particle from the plume centerline. We apply the validated modeling suite to simulate two test cases of a subsea oil well blowout in a stratificationdominated crossflow. These tests confirm that errors from overlapping plume elements in the Lagrangian integral model during intrusion formation for a weak crossflow are negligible for predicting intrusion depth and the fate of oil droplets in the plume. The Lagrangian integral model has the added advantages of being able to account for entrainment from an arbitrary crossflow, predict the intrusion of small gas bubbles and oil droplets when appropriate, and track the pathways of individual bubbles and droplets after they separate from the main plume or intrusion layer.

show abstract

Section: Introductionmentioning

confidence: 59%

Section: Entrainment Modelsmentioning

confidence: 99%

Section: Stratified Plume Model: Multiphase Plume Model For Quiescentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow

2018

Self Cite

View full text Add to dashboard Cite

show abstract

Oil plumes and dispersion in Langmuir, upper-ocean turbulence: Large-eddy simulations and K-profile parameterization

Yang

Chen

Chamecki

et al. 2015

J. Geophys. Res. Oceans

View full text Add to dashboard Cite

Once oil plumes such as those originating from underwater blowouts reach the ocean mixed layer (OML), their near-surface dispersion is influenced heavily by wind and wave-generated Langmuir turbulence. In this study, the complex oil spill dispersion process is modeled using large-eddy simulation (LES). The mean plume dispersion is characterized by performing statistical analysis of the resulting fields from the LES data. Although the instantaneous oil concentration exhibits high intermittency with complex spatial patterns such as Langmuir-induced striations, it is found that the time-averaged oil distribution can still be described quite well by smooth Gaussian-type plumes. LES results show that the competition between droplet rise velocity and vertical turbulent diffusion due to Langmuir turbulence is crucial in determining both the dilution rate and overall direction of transport of oil plumes in the OML. The smoothness of the mean plume makes it feasible to aim at modeling the oil dispersion using Reynolds-averaged type formulations, such as the K-profile parameterization (KPP) with sufficient vertical resolution to capture vertical profiles in the OML. Using LES data, we evaluate the eddy viscosity and eddy diffusivity following the KPP framework. We assess the performance of previous KPP models for pure shear turbulence and Langmuir turbulence by comparing them with the LES data. Based on the assessment a modified KPP model is proposed, which shows improved overall agreement with the LES results for both the eddy viscosity and the eddy diffusivity of the oil dispersion under a variety of flow conditions and droplet sizes.

show abstract

Propagation of uncertainty and sensitivity analysis in an integral oil‐gas plume model

Wang

Iskandarani

Srinivasan³

et al. 2016

JGR Oceans

View full text Add to dashboard Cite

Polynomial Chaos expansions are used to analyze uncertainties in an integral oil‐gas plume model simulating the Deepwater Horizon oil spill. The study focuses on six uncertain input parameters—two entrainment parameters, the gas to oil ratio, two parameters associated with the droplet‐size distribution, and the flow rate—that impact the model's estimates of the plume's trap and peel heights, and of its various gas fluxes. The ranges of the uncertain inputs were determined by experimental data. Ensemble calculations were performed to construct polynomial chaos‐based surrogates that describe the variations in the outputs due to variations in the uncertain inputs. The surrogates were then used to estimate reliably the statistics of the model outputs, and to perform an analysis of variance. Two experiments were performed to study the impacts of high and low flow rate uncertainties. The analysis shows that in the former case the flow rate is the largest contributor to output uncertainties, whereas in the latter case, with the uncertainty range constrained by aposteriori analyses, the flow rate's contribution becomes negligible. The trap and peel heights uncertainties are then mainly due to uncertainties in the 95% percentile of the droplet size and in the entrainment parameters.

show abstract

Double-Plume Integral Models for Near-Field Mixing in Multiphase Plumes

Cited by 75 publications

References 25 publications

Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow

Integral models for bubble, droplet, and multiphase plume dynamics in stratification and crossflow

Oil plumes and dispersion in Langmuir, upper-ocean turbulence: Large-eddy simulations and K-profile parameterization

Propagation of uncertainty and sensitivity analysis in an integral oil‐gas plume model

Contact Info

Product

Resources

About