Pollutant dispersion in a large indoor space. Part 2: Computational fluid dynamics predictions and comparison with a scale model experiment for isothermal flow

Finlayson, Elizabeth U.; Gadgil, Ashok; Thatcher, Tracy L.; Sextro, R.G.

doi:10.1111/j.1600-0668.2004.00243.x

Cited by 37 publications

(27 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to transient nature of contaminant dispersion and low resolution in the experiment, it is difficult to compare CFD predictions with experimental measurements. Only recently there have been studies for isothermal flow conditions where small scale experimental models with high spatial and temporal resolution concentration maps have been validated with CFD predictions 31 . Initial studies demonstrate that Reynolds Averaged Navier-Stokes (RANS) turbulence model provides good predictions for both transient and steady state pollutant concentrations for isothermal flows.…”

Section: Research Approachmentioning

confidence: 99%

Influence of cabin conditions on placement and response of contaminant detection sensors in a commercial aircraft

Mazumdar

Chen

2008

J. Environ. Monit.

View full text Add to dashboard Cite

show abstract

Section: Research Approachmentioning

confidence: 99%

Influence of cabin conditions on placement and response of contaminant detection sensors in a commercial aircraft

Mazumdar

Chen

2008

J. Environ. Monit.

View full text Add to dashboard Cite

show abstract

“…Consequently, a minimum Reynolds number is usually prescribed for model tests. Model Reynolds numbers in excess of the critical Reynolds number are preferable, as the range of turbulent length scales (the turbulence cascade) increases with increasing Reynolds number (Finlayson et al, 2004).…”

Section: Reynolds Number Scalingmentioning

confidence: 99%

Validation data for models of contaminant dispersal : scaling laws and data needs.

O’Hern¹,

Ceccio

2004

View full text Add to dashboard Cite

Contaminant dispersal models for use at scales ranging from meters to miles are widely used for planning sensor locations, first-responder actions for release scenarios, etc. and are constantly being improved. Applications range from urban contaminant dispersal to locating buried targets from an exhaust signature. However, these models need detailed data for model improvement and validation. A small Sandia National Laboratories Laboratory Directed Research and Development (LDRD) program was funded in FY04 to examine the feasibility and usefulness of a scale-model capability for quantitative characterization of flow and contaminant dispersal in complex environments. This report summarizes the work performed in that LDRD. The basics of atmospheric dispersion and dispersion modeling are reviewed. We examine the need for model scale data, and the capability of existing model test methods. Currently, both full-scale and model scale experiments are performed in order to collect validation data for numerical models. Full-scale experiments are expensive, are difficult to repeat, and usually produce relatively sparse data fields. Model scale tests often employ wind tunnels, and the data collected is, in many cases, derived from single point measurements. We review the scaling assumptions and methods that are used to relate model and full scale flows. In particular, we examine how liquid flows may be used to examine the process of atmospheric dispersion. The scaling between liquid and gas flows is presented. Use of liquid as the test fluid has some advantages in terms of achieving fully turbulent Reynolds numbers and in seeding the flow with neutrally buoyant tracer particles. In general, using a liquid flow instead of a gas flow somewhat simplifies the use of full field diagnostics, such as Particle Image Velocimetry and Laser Induced Fluorescence. It is also possible to create stratified flows through mixtures of fluids (e.g., water, alcohol, and brine). Lastly, we describe our plan to create a small prototype water flume for the modeling of stratified atmospheric flows around complex objects. The incoming velocity profile could be 3 tailored to produce a realistic atmospheric boundary layer for flow-in-urban-canyon measurements. The water tunnel would allow control of stratification to produce, for example, stable and unstable atmospheric conditions. Models ranging from a few buildings to cityscapes would be used as the test section. Existing noninvasive diagnostics would be applied, including particle image velocimetry for detailed full-field velocity measurement, and laser induced fluorescence for noninvasive concentration measurement. This scale-model facility will also be used as a test-bed for data acquisition and model testing related to the inverse problem, i.e., determination of source location from distributed, sparse measurement locations. In these experiments the velocity field would again be measured and data from single or multiple concentration monitors would be used to locate the continuous or tr...

show abstract

“…For example, CFD models can provide detailed within-room profiles of velocities and contaminant concentrations (Fan, 1995;Nielsen, 2004). Such information can be useful in large spaces where the well-mixed assumption and uniform temperature profiles are commonly inappropriate (Finlayson et al, 2004;Gan and Riffat, 2004;Jayaraman et al, 2006;Yang et al, 2001).…”

Section: Computational Fluid Dynamicsmentioning

confidence: 99%

Bayesian based design of real-time sensor systems for high-risk indoor contaminants

Sreedharan¹

2007

View full text Add to dashboard Cite

The sudden release of toxic contaminants that reach indoor spaces can be hazardous to building occupants. To respond effectively, the contaminant release must be quickly detected and characterized to determine unobserved parameters, such as release location and strength. Characterizing the release requires solving an inverse problem. Designing a robust real-time sensor system that solves the inverse problem is challenging because the fate and transport of contaminants is complex, sensor information is limited and imperfect, and real-time estimation is computationally constrained.This dissertation uses a system-level approach, based on a Bayes Monte Carlo framework, to develop sensor-system design concepts and methods. I describe three investigations that explore complex relationships among sensors, network architecture, interpretation algorithms, and system performance. The investigations use data obtained from tracer gas experiments conducted in a real building.The influence of individual sensor characteristics on the sensor-system performance for binary-type contaminant sensors is analyzed. Performance tradeoffs among sensor 2 accuracy, threshold level and response time are identified; these attributes could not be inferred without a system-level analysis. For example, more accurate but slower sensors are found to outperform less accurate but faster sensors.Secondly, I investigate how the sensor-system performance can be understood in terms of contaminant transport processes and the model representation that is used to solve the inverse problem. The determination of release location and mass are shown to be related to and constrained by transport and mixing time scales. These time scales explain performance differences among different sensor networks. For example, the effect of longer sensor response times is comparably less for releases with longer mixing time scales.The third investigation explores how information fusion from heterogeneous sensors may improve the sensor-system performance and offset the need for more contaminant sensors. Physics-and algorithm-based frameworks are presented for selecting and fusing information from noncontaminant sensors. The frameworks are demonstrated with door-position sensors, which are found to be more useful in natural airflow conditions, but which cannot compensate for poor placement of contaminant sensors.The concepts and empirical findings have the potential to help in the design of sensor systems for more complex building systems. The research has broader relevance to additional environmental monitoring problems, fault detection and diagnostics, and system design. i

show abstract

Pollutant dispersion in a large indoor space. Part 2: Computational fluid dynamics predictions and comparison with a scale model experiment for isothermal flow

Cited by 37 publications

References 16 publications

Influence of cabin conditions on placement and response of contaminant detection sensors in a commercial aircraft

Influence of cabin conditions on placement and response of contaminant detection sensors in a commercial aircraft

Validation data for models of contaminant dispersal : scaling laws and data needs.

Bayesian based design of real-time sensor systems for high-risk indoor contaminants

Contact Info

Product

Resources

About