Air pollutant dispersion from a large semi-enclosed stadium in an urban area: high-resolution CFD modeling versus full-scale measurements

Abstract: High-resolution CFD simulations and full-scale measurements have been performed to assess the dispersion of air pollutants (CO2) from the large semi-enclosed Amsterdam ArenA football stadium. The dispersion process is driven by natural ventilation by the urban wind flow and by buoyancy, and by the interaction between outdoor wind flow and indoor airflow which are only connected by the relatively small ventilation openings in the stadium facade. The CFD simulations are performed with the 3D Reynolds-averaged Navier-Stokes equations supplemented with the realizable k-e model to provide closure. The full-scale measurements include reference wind speed, wind direction, and outdoor and indoor air temperature, water vapor and indoor CO2 concentration. In particular, the focus is on CFD simulations and measurements for the few hours immediately after a concert, when the stadium roof remains closed and when indoor air temperature,water vapor and CO2 concentration have reached a maximum level due to the attendants. The removal of the sources/attendants allows an assessment of the natural ventilation rate using the concentration decay method. The CFD simulations compare favorably with the measurements in terms of mean wind velocity in the main ventilation openings and in terms of the CO2 concentration decay after the concerts. The validated CFD model will in the future be used for a detailed evaluation of indoor concentration gradients and the interaction between wind-induced and buoyancy-induced natural ventilation

Low-Reynolds number mixing ventilation flows:impact of physical and numerical diffusion on flow and dispersion

\u3cp\u3eQuality assurance in computational fluid dynamics (CFD) is essential for an accurate and reliable assessment of complex indoor airflow. Two important aspects are the limitation of numerical diffusion and the appropriate choice of inlet conditions to ensure the correct amount of physical diffusion. This paper presents an assessment of the impact of both numerical and physical diffusion on the predicted flow patterns and contaminant distribution in steady Reynolds-averaged Navier–Stokes (RANS) CFD simulations of mixing ventilation at a low slot Reynolds number (Re≈2,500). The simulations are performed on five different grids and with three different spatial discretization schemes; i.e. first-order upwind (FOU), second-order upwind (SOU) and QUICK. The impact of physical diffusion is assessed by varying the inlet turbulence intensity (TI) that is often less known in practice. The analysis shows that: (1) excessive numerical and physical diffusion leads to erroneous results in terms of delayed detachment of the wall jet and locally decreased velocity gradients; (2) excessive numerical diffusion by FOU schemes leads to deviations (up to 100%) in mean velocity and concentration, even on very high-resolution grids; (3) difference between SOU and FOU on the coarsest grid is larger than difference between SOU on coarsest grid and SOU on 22 times finer grid; (4) imposing TI values from 1% to 100% at the inlet results in very different flow patterns (enhanced or delayed detachment of wall jet) and different contaminant concentrations (deviations up to 40%); (5) impact of physical diffusion on contaminant transport can markedly differ from that of numerical diffusion.\u3c/p\u3

Dry run

Simulating wind and rain around a stadium determines the best design for keeping spectators dry. Results can be used to improve the design of future stadiums as well as to diagnose and correct problems with existing stadiums

Optimization of air curtain performance by particle image velocimetry measurements and computational fluid dynamics simulations:turbulence model validation

Air curtains can be applied to aerodynamically separate two environments. Air curtains are plane impinging jets at high-Reynolds numbers, preventing the transfer of heat and mass from one environment to another. The performance of an air curtain is called the separation efficiency, which depends on a wide range of jet and environmental parameters, such as jet velocity and turbulence intensity, jet thickness, air temperature differences and pressure differences over the air curtain. This study presents the first results of ongoing research on the optimization of air curtain performance. The first results consist of reduced-scale experiments in a water channel using Particle Image Velocimetry (PIV), and of steady Reynolds-averaged Navier-Stokes Computational Fluid Dynamics (CFD) simulations. The PIV measurements are used to validate the CFD model. Comparison of the experimental results with the results obtained with steady RANS CFD simulations in combination with three different turbulence models showed a fairly accurate agreement

Taxi

The semantic differential is a widely applied measurement technique in the information systems field. As we demonstrate in this study, however, there is evidence that many of the applications of the semantic differential seem to be subject to common shortcomings. In this study, we address these shortcomings by creating awareness of the requirements underlying semantic differentiation. We discuss the requirements of semantic differentiation and use them as a foundation to introduce a framework to assist researchers in applying the semantic differential more adequately. The framework puts renewed emphasis on bipolar scale selection and dimensionality testing, introduces semantic bipolarity as new criterion, and proposes distinct stages for the testing of wording and contextual contamination. We exemplify the framework using an illustration exercise, which centers on the assessment of the meaning of the concept “electronic marketplace quality”. Using a mixture of qualitative and quantitative methods, the illustration exercise clarifies the prerequisites for semantic differentiation and provides suggestions for researchers. The paper concludes with a discussion of several methodological implications

Determination of the Oswestry Disability Index score equivalent to a "satisfactory symptom state" in patients undergoing surgery for degenerative disorders of the lumbar spine-a Spine Tango registry-based study.

BACKGROUND CONTEXT The achievement of a given change score on a valid outcome instrument is commonly used to indicate whether a clinically relevant change has occurred after spine surgery. However, the achievement of such a change score can be dependent on baseline values and does not necessarily indicate whether the patient is satisfied with the current state. The achievement of an absolute score equivalent to a patient acceptable symptom state (PASS) may be a more stringent measure to indicate treatment success. PURPOSE This study aimed to estimate the score on the Oswestry Disability Index (ODI, version 2.1a; 0-100) corresponding to a PASS in patients who had undergone surgery for degenerative disorders of the lumbar spine. STUDY DESIGN/SETTING This is a cross-sectional study of diagnostic accuracy using follow-up data from an international spine surgery registry. PATIENT SAMPLE The sample includes 1,288 patients with degenerative lumbar spine disorders who had undergone elective spine surgery, registered in the EUROSPINE Spine Tango Spine Surgery Registry. OUTCOME MEASURES The main outcome measure was the ODI (version 2.1a). METHODS Surgical data and data from the ODI and Core Outcome Measures Index (COMI) were included to determine the ODI threshold equivalent to PASS at 1 year (±1.5 months; n=780) and 2 years (±2 months; n=508) postoperatively. The symptom-specific well-being item of the COMI was used as the external criterion in the receiver operating characteristic (ROC) analysis to determine the ODI threshold equivalent to PASS. Separate sensitivity analyses were performed based on the different definitions of an "acceptable state" and for subgroups of patients. JF is a copyright holder of the ODI. RESULTS The ODI threshold for PASS was 22, irrespective of the time of follow-up (area under the curve [AUC]: 0.89 [sensitivity {Se}: 78.3%, specificity {Sp}: 82.1%] and AUC: 0.91 [Se: 80.7%, Sp: 85.6] for the 1- and 2-year follow-ups, respectively). Sensitivity analyses showed that the absolute ODI-22 threshold for the two follow-up time-points were robust. A stricter definition of PASS resulted in lower ODI thresholds, varying from 16 (AUC=0.89; Se: 80.2%, Sp: 82.0%) to 18 (AUC=0.90; Se: 82.4%, Sp: 80.4%) depending on the time of follow-up. CONCLUSIONS An ODI score ≤22 indicates the achievement of an acceptable symptom state and can hence be used as a criterion of treatment success alongside the commonly used change score measures. At the individual level, the threshold could be used to indicate whether or not a patient with a lumbar spine disorder is a "responder" after elective surgery

Modeling transient particle transport in transient indoor airflow by fast fluid dynamics with the Markov chain method

It is crucial to accurately and efficiently predict transient particle transport in indoor environments to improve air distribution design and reduce health risks. For steady-state indoor airflow, fast fluid dynamics (FFD) + Markov chain model increased the calculation speed by around seven times compared to computational fluid dynamics (CFD) + Eulerian model and CFD + Lagrangian model, while achieving the same level of accuracy. However, the indoor airflow could be transient, if there were human behaviors involved like coughing or sneezing and air was supplied periodically. Therefore, this study developed an FFD + Markov chain model solver for predicting transient particle transport in transient indoor airflow. This investigation used two cases, transient particle transport in a ventilated two-zone chamber and a chamber with periodic air supplies, for validation. Case 1 had experimental data for validation and the results showed that the predicted particle concentration by FFD + Markov chain model matched well with the experimental data. Besides, it had similar accuracy as the CFD + Eulerian model. In the second case, the prediction by large eddy simulation (LES) was used for validating the FFD. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The computational time of the FFD + Markov chain model was 7.8 times less than that of the CFD + Eulerian model