Prompt location of indoor instantaneous air contaminant source through multi-zone model-based probability method by utilizing airflow data from coarse-grid CFD model

2021 ◽  
pp. 1420326X2110460
Author(s):  
Qianru Chen ◽  
Haidong Wang ◽  
Yuwei Dai ◽  
Yibing Hu
Keyword(s):  
Velocity Field ◽  
Joint Probability ◽  
Coarse Grid ◽  
Probability Method ◽  
Zone Model ◽  
Large Space ◽  
Time Saving ◽  
Calculation Time ◽  
Contaminant Source ◽  
Contaminant Sources

In order to ensure indoor air quality safety, locating the airborne contaminant sources accurately and quickly is extremely important so that timely measures can be taken to undermine the spread of pollutant and even eliminate the negative effects. Previous studies have shown that the multi-zone model can greatly reduce the inverse calculation time. However, the multi-zone model cannot describe the details of the indoor velocity field, which limits its application in complex multi-zone or large space buildings. On the premise of the accuracy and computational speed, based on the joint probability method, this study adopted the coarse-grid CFD method to speed up the process of acquiring the indoor airflow field, together with the multi-zone model method, to perform the inverse calculation of indoor airborne contaminant source location. In the backward calculation process, we conducted the ‘transpose' of the velocity field to obtain adjoint matrix, instead of computing ‘negative' of the velocity vector to save the calculation time. A two-dimensional ventilation model was utilized to validate the method, which proved the accuracy and time-saving potential of it. This study provides theoretical and practical prospect for the real-time inverse calculation of locating the indoor airborne contaminant sources.

An Investigation of the Transverse Velocity Profile in the Case of Internal Viscous Aerodynamic Problems

1984 ◽  
Author(s):  
P. Kotidis ◽  
P. Chaviaropoulos ◽  
K. D. Papailiou
Keyword(s):  
Velocity Field ◽  
Flow Field ◽  
Velocity Profile ◽  
Shear Layer ◽  
Transverse Velocity ◽  
Transverse Plane ◽  
Zone Model ◽  
Viscous Shear ◽  
Rotational Part ◽  
Confined Flows

The development of transverse velocity profile is directly related to the development of secondary vorticity. In the internal aerodynamics case with potential external flow, although vorticity remains confined inside the viscous shear layer, secondary vorticity induced velocities exist outside of it. If the secondary vorticity field is known, the induced secondary velocity field is well approximated following Hawthorne’s classical analysis. In the present work, the above analysis is used to separate the velocity field in the transverse plane into a potential and a rotational part. In the case of confined flows, the rotational part is confined inside the viscous shear layer, while the potential part occupies the whole flow field. This last part is the consequence of the “displacement” effects of the shear layer in the transverse plane. Therefore, the present work allows a re-examination of the flow two-zone model (separation of the flow field in a viscous and an inviscid part) in confined flows. On the other hand, the limitations of Hawthorne’s theory are examined, while a parallel analysis is presented for the case where the secondary vorticity distribution varies not only along the blade height, but also circumferentially.

scholarly journals An objective and efficient method for estimating probabilistic coastal inundation hazards

2019 ◽  
Vol 99 (2) ◽  
pp. 1105-1130 ◽  
Author(s):  
Kun Yang ◽  
Vladimir Paramygin ◽  
Y. Peter Sheng
Keyword(s):  
High Resolution ◽  
Storm Surge ◽  
Computational Cost ◽  
Joint Probability ◽  
Water Levels ◽  
Computational Time ◽  
Probability Method ◽  
Highly Efficient ◽  
Inundation Maps ◽  
Flood Elevation

Abstract The joint probability method (JPM) is the traditional way to determine the base flood elevation due to storm surge, and it usually requires simulation of storm surge response from tens of thousands of synthetic storms. The simulated storm surge is combined with probabilistic storm rates to create flood maps with various return periods. However, the map production requires enormous computational cost if state-of-the-art hydrodynamic models with high-resolution numerical grids are used; hence, optimal sampling (JPM-OS) with a small number of (~ 100–200) optimal (representative) storms is preferred. This paper presents a significantly improved JPM-OS, where a small number of optimal storms are objectively selected, and simulated storm surge responses of tens of thousands of storms are accurately interpolated from those for the optimal storms using a highly efficient kriging surrogate model. This study focuses on Southwest Florida and considers ~ 150 optimal storms that are selected based on simulations using either the low fidelity (with low resolution and simple physics) SLOSH model or the high fidelity (with high resolution and comprehensive physics) CH3D model. Surge responses to the optimal storms are simulated using both SLOSH and CH3D, and the flood elevations are calculated using JPM-OS with highly efficient kriging interpolations. For verification, the probabilistic inundation maps are compared to those obtained by the traditional JPM and variations of JPM-OS that employ different interpolation schemes, and computed probabilistic water levels are compared to those calculated by historical storm methods. The inundation maps obtained with the JPM-OS differ less than 10% from those obtained with JPM for 20,625 storms, with only 4% of the computational time.

Convection of a passive scalar by a quasi-uniform random straining field

1974 ◽  
Vol 64 (4) ◽  
pp. 737-762 ◽  
Author(s):  
Robert H. Kraichnan
Keyword(s):  
Velocity Field ◽  
Probability Distributions ◽  
Joint Probability ◽  
Passive Scalar ◽  
Joint Probability Distribution ◽  
Molecular Diffusivity ◽  
Velocity Reversal ◽  
Amplitude Profile ◽  
Line Elements ◽  
Rates Of Growth

The stretching of line elements, surface elements and wave vectors by a random, isotropic, solenoidal velocity field in D dimensions is studied. The rates of growth of line elements and (D – 1)-dimensional surface elements are found to be equal if the statistics are invariant to velocity reversal. The analysis is applied to convection of a sparse distribution of sheets of passive scalar in a random straining field whose correlation scale is large compared with the sheet size. This is Batchelor's (1959) κ−1 spectral regime. Some exact analytical solutions are found when the velocity field varies rapidly in time. These include the dissipation spectrum and a joint probability distribution that describes the simultaneous effect of Stretching and molecular diffusivity κ on the amplitude profile of a sheet. The latter leads to probability distributions of the scalar field and its space derivatives. For a growing κ−1 range at zero κ, these derivatives have essentially lognormal statistics. In the steady-state κ−1 regime at κ > 0, intermittencies measured by moment ratios are much smaller than for lognormal statistics, and they increase less rapidly with the order of the derivative than in the κ = 0 case. The κ > 0 distributions have singularities a t zero amplitude, due to a background of highly diffused sheets. The results do not depend strongly on D. But as D → ∞, temporal fluctuations in the stretching rates become negligible and Batchelor's (1959) constant-strain dissipation spectrum is recovered.

scholarly journals Solving Inverse Problems of Unknown Contaminant Source in Groundwater-River Integrated Systems Using a Surrogate Transport Model Based Optimization

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2415
Author(s):  
Azade Jamshidi ◽  
Jamal Mohammad Vali Samani ◽  
Hossein Mohammad Vali Samani ◽  
Andrea Zanini ◽  
Maria Giovanna Tanda ◽  
...  
Keyword(s):  
Inverse Method ◽  
Transport Model ◽  
Computation Time ◽  
Forward Model ◽  
New Approach ◽  
Contaminant Source ◽  
Contaminant Sources ◽  
Speed Up ◽  
River Cross Section

The paper presents a new approach to identify the unknown characteristics (release history and location) of contaminant sources in groundwater, starting from a few concentration observations at monitoring points. An inverse method that combines the forward model and an optimization algorithm is presented. To speed up the computation, the transfer function theory is applied to create a surrogate transport forward model. The performance of the developed approach is evaluated on two case studies (literature and a new one) under different scenarios and measurement error conditions. The literature case study regards a heterogeneous confined aquifer, while the proposed case study was never investigated before, it involves an aquifer-river integrated flow and transport system. In this case, the groundwater contaminant originated from a damaged tank, migrates to a river through the aquifer. The approach, starting from few concentration observations monitored at a downstream river cross-section, accurately estimates the release history at a groundwater contaminant source, even in presence of noise on observations. Moreover, the results show that the methodology is very fast, and can solve the inverse problem in much less computation time in comparison with other existing approaches.

Applying POT methods to the Revised Joint Probability Method for determining extreme sea levels

2014 ◽  
Vol 91 ◽  
pp. 140-150 ◽  
Author(s):  
Franck Mazas ◽  
Xavier Kergadallan ◽  
Philippe Garat ◽  
Luc Hamm
Keyword(s):  
Joint Probability ◽  
Probability Method ◽  
Sea Levels ◽  
Extreme Sea Levels ◽  
Joint Probability Method

Applications of a Compound Distribution on Estimating Wind and Wave Parameters for Fixed Platforms Design

2006 ◽  
Author(s):  
Sheng Dong ◽  
Jinjin Ning
Keyword(s):  
Joint Probability ◽  
Structural Response ◽  
Extreme Value Distribution ◽  
Probability Method ◽  
Base Shear ◽  
Extreme Wind ◽  
Compound Distribution ◽  
Calculation Results ◽  
Extreme Wind Speed ◽  
Return Value

Based on the hindcast data of 21 storm processes, a Poisson bivariate Logistic extreme value distribution is proposed to estimate the joint probability of extreme wind speed and extreme significant wave height in the storms, the frequency of which can be described by a Poisson distribution. In order to calculate the structural response of an ocean platform, such as base shear, three methods are utilized, namely (I) traditional univariate frequency analysis method; (II) base shear return value method; (III) wind-wave joint probability method. Calculation results show that the proposed statistical model is suitable for the design of fixed platforms in the storm-influenced area.

scholarly journals CHARACTERIZATION OF SPATIAL VARIATION IN HURRICANE SURGE

2018 ◽  
pp. 51
Author(s):  
Jennifer L. Irish ◽  
Donald T. Resio ◽  
Taylor G. Asher ◽  
Yi Liu
Keyword(s):  
Spatial Interpolation ◽  
Joint Probability ◽  
Empirical Orthogonal Functions ◽  
Probability Method ◽  
Optimal Sampling ◽  
Natural Structure ◽  
Orthogonal Functions ◽  
Physically Based ◽  
Probability Densities ◽  
Hazard Assessments

Planning, engineering, and development along surgeprone coasts rely on probabilistic surge hazard assessments. Over the last decade, U.S. agencies have implemented the joint probability method with optimal sampling (JPM-OS) (e.g., Resio et al. 2009) to overcome shortcomings in probabilistic estimates developed from the limited set of observed surges alone. Here, optimal sampling is used to reduce the number of high-fidelity surge simulations needed, given computational resource limitations. In current practice, hazard assessments with the JPM-OS use discrete storm simulations (order of 200 to 1000 storms), where each is assigned a probability mass (e.g., Toro et al. 2010), rather than defining surges for the continuum of probability densities. Such an approach introduces uncertainty because it does not fully capture the natural structure inherent in surge response (meteorological and larger-scale bathymetric effects) (Resio et al. 2017). On the other hand, physically based surge response functions (SRFs) that capture natural structure in the surge response provide an accurate—0.2 to 0.5 m rootmean- square error depending on topographic and geographic complexity—and efficient means for continuously defining probability densities (e.g., Taylor et al. 2015). But, application of SRFs in JPM-OS (JPMOS- SRF) has not been widely used in practice due a lack of systematic methods for spatial interpolation along complex shorelines and throughout the floodplain. Herein, we explore the use of spatially derived empirical orthogonal functions (EOFs) to overcome this spatial interpolation challenge.

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