The effects of thermal radiation in a heated jet of water vapor are studied with a direct numerical simulation coupled to a Monte-Carlo solver. The adequacy of the numerical setup is first demonstrated in the uncoupled isothermal and heated turbulent plane jets with comparisons to experimental and numerical data. Radiative energy transfer is then accounted for with spectral dependency of the radiative properties described by the Correlated-k (ck) method. Between the direct impact through modification of the temperature field by the additional radiative transfer and the indirect one where the varied flow density changes the turbulent mixing, the present study is able to clearly identify the second one in the jet developed region by considering conditions where effects of thermal radiation are moderate. When using standard jet scaling laws, the different studied cases without radiation and with small-to-moderate radiative heat transfer yield different profiles even when thermal radiation becomes locally negligible. By deriving another scaling law for the decay of the temperature profile, self-similarity is obtained for the different turbulent jets. The results of the study allow for distinguishing whether thermal radiation modifies the nature of heat transfer mechanisms in the jet developed region or not while removing the indirect effects of modified density.
Simplified chemistry models are commonly used in reactive computational fluid dynamics (CFD) simulations to alleviate the computational cost. Uncertainties associated with the calibration of such simplified models have been characterized in some works, but to our knowledge, there is a lack of studies analyzing the subsequent propagation through CFD simulation of combustion processes.This work propagates the uncertainties -arising in the calibration of a global chemistry model -through direct numerical simulations (DNS) of flame-vortex interactions. Calibration uncertainties are derived by inferring the parameters of a two-step reaction mechanism for methane, using synthetic observations of one-dimensional laminar premixed flames based on a detailed mechanism. To assist the inference, independent surrogate models for estimating flame speed and thermal thickness are built taking advantage of the Principal Component Analysis (PCA) and the Polynomial Chaos (PC) expansion. Using the Markov Chain Monte Carlo (MCMC) sampling method, a discussion on how push-forward posterior densities behave with respect to the detailed mechanism is provided based on three different calibrations relying (i) only on flame speed, (ii) only on thermal thickness, and (iii) on both quantities simultaneously.The model parameter uncertainties characterized in the latter calibration are propagated to two-dimensional flamevortex interactions using 60 independent samples. Posterior predictive densities for the time evolution of the heat release and flame surface are consistent, being that the confidence intervals contain the reference simulation. However, the twostep mechanism fails to reproduce the flame response to stretch as it was not considered in the calibration. This study highlights the capabilities and limitations of propagating chemistry-models uncertainties to CFD applications to fully quantify posterior uncertainties on target quantities.
Abstract. Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with model output statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate the extent to which AQ forecasts can be improved using a variety of MOS methods, including moving average, quantile mapping, Kalman filter, analogs and gradient boosting machine methods, and consider as well the persistence method as a reference. We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a comprehensive set of continuous and categorical metrics at various timescales, along different lead times and using different meteorological input datasets. Our results show that O3 forecasts can be substantially improved using such MOS corrections and that improvements go well beyond the correction of the systematic bias. Depending on the timescale and lead time, root mean square errors decreased from 20 %–40 % to 10 %–30 %, while Pearson correlation coefficients increased from 0.7–0.8 to 0.8–0.9. Although the improvement typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. The MOS methods relying on meteorological data were found to provide relatively similar performance with two different meteorological inputs. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with the lowest errors and highest correlations. However, they are not necessarily the best in predicting the peak O3 episodes, for which simpler MOS methods can achieve better results. Although the complex impact of MOS methods on the distribution of and variability in raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.
<p>Awareness of air pollution impacts on public health is quickly increasing, especially in urban areas where legal air quality (AQ) limits are often exceeded. This awareness has driven policymakers to minimize citizens' exposure not only by direct legislative control in emissions (i.e., the application of a Low Emission Zone), but also by applying mobility restrictions to modify traffic patterns, and by the use of forecasted warnings to alert citizens of air pollution episodes. The European AQ directives encourage the use of numerical models to support the design and evaluation of such strategies.</p><p>In this framework, we present a versatile AQ model, CALIOPE-Urban (Benavides et al., 2019), able to address the threefold objectives to (i) compute urban air quality forecast at the street-scale resolution; (ii) to perform reanalysis studies of historical periods using a bias correction methodology that preserves the model spatial variability; and (iii) to simulate the traffic flow response to the application of different traffic restrictions and their effect on urban AQ.</p><p>In this contribution, we discuss two specific applications. On the one hand, CALIOPE-Urban is used to estimate the NO2 levels in the city of Barcelona (Spain) during the entire year of 2019. To do so, we report accurate maps of NO2 levels during the whole year by consistently integrating the AQ model data with publicly available observations from the official monitoring network in Catalonia (XVPCA) available in Barcelona by means of a bias correction method. On the other hand, the macroscopic traffic simulator BCN-VML (Rodriguez-Rey et al. 2021) coupled with CALIOPE-Urban is used to assess the AQ impact of the traffic flow-induced changes after the application of a traffic restriction policy.&#160;</p><p><strong>References</strong></p><p>Benavides, J., Snyder, M., Guevara, M., Soret, A., P&#233;rez Garc&#237;a-Pando, C., Amato, F., Querol, X., and Jorba, O.: CALIOPE-Urban v1.0: coupling R-LINE with a mesoscale air quality modelling system for urban air quality forecasts over Barcelona city (Spain), Geosci. Model Dev., 12, 2811&#8211;2835, https://doi.org/10.5194/gmd-12-2811-2019, 2019.</p><p>Rodriguez-Rey, D., Guevara, M., Linares, MP., Casanovas, J., Salmer&#243;n, J., Soret, A., Jorba, O., Tena, C., P&#233;rez Garc&#237;a-Pando, C.: A coupled macroscopic traffic and pollutant emission modelling system for Barcelona, Transportation Research Part D, accepted for publication.</p>
<p>Air pollution affects the economy, the environment, and public health. This is particularly relevant in dense urban areas due to their urban built, high traffic activity, and near-the-source population exposure. In the city of Barcelona, the 40 ug/m<sup>3</sup> nitrogen dioxide NO<sub>2</sub> annual limit value set up by the 2008/50/EC European Air Quality Directive (AQD) is systematically exceeded in traffic stations mainly due to the contribution of road transport. In the last Urban Mobility Plan (2019-2024), the city hall of Barcelona presented several traffic management strategies aiming to reduce on-road traffic emissions by both renewing and reducing the private motorized transport in the city. These measures include the application of tactical urban actions, green corridors and superblocks along with a Low Emission Zone, which together are expected to reduce the number of private vehicles circulating throughout the city by -25%. In parallel, the Port of Barcelona has recently announced a plan to electrify the docks and reduce emission from hotelling activities by -38%. To properly assess the impact of such measures, the AQD recommends the application of numerical models in combination with monitoring data. Following AQD recommendations, our study runs a coupled transport-emission model able to characterize traffic movement along the city and produce multiple scenarios that quantify the impact of the aforementioned measures on primary emissions. The resulting scenarios are then used to feed a multi-scale air quality modeling system to estimate NO<sub>2</sub> concentration values at very high resolution (20m, hourly). To reduce the uncertainty typically associated with modeling results, the estimated values are corrected with a data-fusion methodology using observations from the official monitoring network and several measurement campaigns. Our results show that the implementation of all mobility restrictions and electrification of the Port will allow Barcelona to comply with the current legislated NO<sub>2</sub> air quality standards at the traffic monitoring stations, with reductions up to -24.7% and -12 ug/m<sup>3</sup>. However, the resulting NO<sub>2</sub> levels achieved at these locations would still fail to meet the new 2021 WHO guideline (10 ug/m<sup>3</sup>) and the recent proposal for a revision of the EU AQD (20 ug/m<sup>3</sup>). Also, despite the estimated NO<sub>2</sub> reductions, several areas in the city would still be above the current legal limit of 40 ug/m<sup>3</sup>, including 16.7% of schools and 19.7% of hospitals and healthcare facilities. All in all, our results suggest the planned measures are steps in the right direction, yet still insufficient to ensure healthy AQ values across the entire city.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.