Large-Eddy Simulations of a Reactive Solid Rocket Motor Plume

Paoli, Roberto; Poubeau, Adèle; Cariolle, D.

doi:10.2514/1.j058601

Cited by 6 publications

(5 citation statements)

References 37 publications

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“…Local start time AVBP is widely used to resolve non-reactive or reactive unsteady flows in simple or complex geometry Gicquel et al (2011). It is also relevant to predict pollutant formation and atmospheric dispersion (Poubeau et al 2016;Paoli et al 2020).…”

Section: Trialmentioning

confidence: 99%

Assessing the Internal Variability of Large-Eddy Simulations for Microscale Pollutant Dispersion Prediction in an Idealized Urban Environment

Lumet,

Jaravel,

Rochoux

et al. 2024

Boundary-Layer Meteorol

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This study aims at estimating the inherent variability of microscale boundarylayer flows and its impact on the air pollutant dispersion in urban environment. For this purpose, we present a methodology combining a high-fidelity Large-Eddy Simulation (LES) model and a stationary bootstrap algorithm, to estimate the internal variability of time-averaged quantities over a given analysis period thanks to sub-averages samples. A detailed validation of a LES microscale air pollutant dispersion model in the framework of the Mock Urban Setting Test (MUST) field-scale experiment is performed. We show that the LES results are in overall good agreement with the experimental measurements of wind velocity and tracer concentration, especially in terms of fluctuations and peaks of concentrations. We also show that both LES estimates and the MUST experimental measurements are subject to a significant internal variability, which is therefore essential to take it into account in the model validation exercise. Moreover, we demonstrate that the LES model is able to accurately reproduce the observed internal variability. This study only accounts for the microscale wind fluctuations, and is therefore a first step in measuring the complete effect of the atmospheric boundary-layer (ABL) internal variability on pollutant dispersion.

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Section: Trialmentioning

confidence: 99%

Assessing the Internal Variability of Large-Eddy Simulations for Microscale Pollutant Dispersion Prediction in an Idealized Urban Environment

Lumet,

Jaravel,

Rochoux

et al. 2024

Boundary-Layer Meteorol

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“…Additional advection-diffusion equations are solved for passive scalar dispersion. It is widely used to predict non-reactive and reacting unsteady flows in simple or complex geometry, and is applicable to pollutant formation and atmospheric dispersion [Paoli et al, 2020].…”

Section: Numerical Solvermentioning

confidence: 99%

“…Accidental short-term pollutant emissions (e.g. 2011 Fukushima power plant explosion - Tsuruta et al, 2014;/2020Australian bushfires -Graham et al, 2021 can significantly degrade air quality and impact public health. In urban areas, peak pollutant concentrations are difficult to track due to the complex interactions between meteorology and urban topography [Philips et al, 2013].…”

Section: Introductionmentioning

confidence: 99%

Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion

Nony¹,

Rochoux²,

Jaravel³

et al. 2022

Preprint

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Mapping near-field pollutant concentration is essential to track accidental toxic plume dispersion in urban areas. By solving a large part of the turbulence spectrum, large-eddy simulations (LES) have the potential to accurately represent pollutant concentration spatial variability. Finding a way to synthesize this large amount of information to improve the accuracy of lower-fidelity operational models (e.g. providing better turbulence closure terms) is particularly appealing. This is a challenge in multi-query contexts, where LES become prohibitively costly to deploy to understand how plume flow and tracer dispersion change with various atmospheric and source parameters. To overcome this issue, we propose a non-intrusive reduced-order model combining proper orthogonal decomposition (POD) and Gaussian process regression (GPR) to predict LES field statistics of interest associated with tracer concentrations. GPR hyperpararameters are optimized component-by-component through a maximum a posteriori (MAP) procedure informed by POD. We provide a detailed analysis of the reducedorder model performance on a two-dimensional case study corresponding to a turbulent atmospheric boundary-layer flow over a surface-mounted obstacle. We show that near-source concentration heterogeneities upstream of the obstacle require a large number of POD modes to be well captured. We also show that the component-by-component optimization allows to capture the range of spatial scales in the POD modes, especially the shorter concentration patterns in the high-order modes. The reduced-order model predictions remain acceptable if the learning database is made of at least fifty to hundred LES snapshot providing a first estimation of the required budget to move towards more realistic atmospheric dispersion applications.

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“…Various methods (Burkhardt & Kärcher, 2009; Georgopoulos & Seinfeld, 1986; Paoli, 2020; Paoli et al., 2011, 2020) have been developed to better simulate subgrid plumes in the stratosphere. Cariolle et al.…”

Section: Introductionmentioning

confidence: 99%

“…Various methods (Burkhardt & Kärcher, 2009;Georgopoulos & Seinfeld, 1986;Paoli, 2020;Paoli et al, 2011Paoli et al, , 2020 have been developed to better simulate subgrid plumes in the stratosphere. Cariolle et al (2009) used an effective reaction rate (ERR) method to parameterize the plume-scale chemistry into large-scale atmospheric models.…”

mentioning

confidence: 99%

Developing a Plume‐in‐Grid Model for Plume Evolution in the Stratosphere

Hong-wei

Eastham

Keith

2022

J Adv Model Earth Syst

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Stratospheric emissions from aircraft or rockets are important sources of chemical perturbations. Small‐radius high‐aspect‐ratio plumes from stratospheric emissions are smaller than global Eulerian models' grid cells. To help global Eulerian models resolve subgrid plumes in the stratosphere, a Lagrangian plume model, comprising a Lagrangian trajectory model and an adaptive‐grid plume model with a sequence of plume cross‐section representations (from a highly resolved 2‐D grid to a simplified 1‐D grid based on a tradeoff between the accuracy and computational cost), is created and embedded into a global Eulerian (i.e., GEOS‐Chem) model to establish a multiscale Plume‐in‐Grid (PiG) model. We compare this PiG model to the GEOS‐Chem model based on a 1‐month simulation of continuous inert tracer emissions by aircraft in the stratosphere. In the PiG results, the final injected tracer is more concentrated and approximately 1/3 of the tracer is at concentrations 2–4 orders of magnitude larger compared to the GEOS‐Chem results. The entropy of injected tracer in the PiG results is 6% lower than the GEOS‐Chem results, indicating less tracer mixing. The total product mass from a hypothetical second‐order process (applied to the injected tracer) in the PiG results is 2 orders of magnitude larger than the GEOS‐Chem results. Increasing the GEOS‐Chem model's horizontal resolution 4‐fold is insufficient to resolve this product difference, while requiring over seven times the computational resources of the PiG model. This paper describes the PiG model framework and parameterization of plume physical processes. Chemical and aerosol processes will be introduced in the future.

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Large-Eddy Simulations of a Reactive Solid Rocket Motor Plume

Cited by 6 publications

References 37 publications

Assessing the Internal Variability of Large-Eddy Simulations for Microscale Pollutant Dispersion Prediction in an Idealized Urban Environment

Assessing the Internal Variability of Large-Eddy Simulations for Microscale Pollutant Dispersion Prediction in an Idealized Urban Environment

Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion

Developing a Plume‐in‐Grid Model for Plume Evolution in the Stratosphere

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