Verification and Validation of CFD Modeling for Low-Flow-Coefficient Centrifugal Compressor Stages

In this paper, the numerical model of a centrifugal compressor low-flow stage is verified. The gaps and labyrinth seals were simulated in the numerical model. The task was to determine the optimal settings for high-quality modeling of the low-flow stages. The intergrid interface application issues, turbulence and roughness models are considered. The obtained numerical model settings are used to validate seven model stages for the range of the optimal conditional flow coefficient with Φopt = 0.008–0.018 and the conditional Mach number Mu = 0.785–0.804. The simulation results are compared with the experimental data. The high pressure stage-7 (HPS-7) stage with Φopt = 0.010 and Mu = 0.60 at different inlet pressure of 4, 10 and 40 atm is considered separately. Acceptable validation results are obtained with the recommended numerical model settings; the modeling uncertainty for the polytropic pressure coefficient δη*pol < 4% for the efficiency coefficient δη*pol exceeds the limit of 4% only in the two most low-flow stages, U and V.

Integration of a Coupled Fire-Atmosphere Model Into a Regional Air Quality Forecasting System for Wildfire Events

The objective of this study was to assess feasibility of integrating a coupled fire-atmosphere model within an air-quality forecast system to create a multiscale air-quality modeling framework designed to simulate wildfire smoke. For this study, a coupled fire-atmosphere model, WRF-SFIRE, was integrated, one-way, with the AIRPACT air-quality modeling system. WRF-SFIRE resolved local meteorology, fire growth, the fire plume rise, and smoke dispersion, and provided AIRPACT with fire inputs. The WRF-SFIRE-forecasted fire area and the explicitly resolved vertical smoke distribution replaced the parameterized BlueSky fire inputs used by AIRPACT. The WRF-SFIRE/AIRPACT integrated framework was successfully tested for two separate wildfire events (2015 Cougar Creek and 2016 Pioneer fires). The execution time for the WRF-SFIRE simulations was &lt;3 h for a 48 h-long forecast, suggesting that integrating coupled fire-atmosphere simulations within the daily AIRPACT cycle is feasible. While the WRF-SFIRE forecasts realistically captured fire growth 2 days in advance, the largest improvements in the air quality simulations were associated with the wildfire plume rise. WRF-SFIRE-estimated plume tops were within 300-m of satellite-estimated plume top heights for both case studies analyzed in this study. Air quality simulations produced by AIRPACT with and without WRF-SFIRE inputs were evaluated with nearby PM2.5 measurement sites to assess the performance of our multiscale smoke modeling framework. The largest improvements when coupling WRF-SFIRE with AIRPACT were observed for the Cougar Creek Fire where model errors were reduced by ∼50%. For the second case (Pioneer fire), the most notable change with WRF-SFIRE coupling was that the probability of detection increased from 16 to 52%.

Comparison of Top-Down and Bottom-Up Road Transport Emissions through High-Resolution Air Quality Modeling in a City of Complex Orography

Vehicular emissions are a predominant source of pollution in urban environments. However, inherent complexities of vehicular behavior are sources of uncertainties in emission inventories (EIs). We compare bottom-up and top-down approaches for estimating road transport EIs in Manizales, Colombia. The EIs were estimated using a COPERT model, and results from both approaches were also compared with the official top-down EI (estimated from IVE methodology). The transportation model PTV-VISUM was used for obtaining specific activity information (traffic volumes, vehicular speed) in bottom-up estimation. Results from COPERT showed lower emissions from the top-down approach than from the bottom-up approach, mainly for NMVOC (−28%), PM10 (−26%), and CO (−23%). Comparisons showed that COPERT estimated lower emissions than IVE, with higher differences than 40% for species such as PM10, NOX, and CH4. Furthermore, the WRF–Chem model was used to test the sensitivity of CO, O3, PM10, and PM2.5 predictions to the different EIs evaluated. All studied pollutants exhibited a strong sensitivity to the emission factors implemented in EIs. The COPERT/top-down was the EI that produced more significant errors. This work shows the importance of performing bottom-up EI to reduce the uncertainty regarding top-down activity data.

DETERMINATION OF DEOXYGENATION RATE OF URBAN RIVER USING MODIFICATION METHODS FOR CITEPUS RIVER WATER, BANDUNG, INDONESIA

<span id="docs-internal-guid-9f43cecc-7fff-332e-09a3-ed0af9ff66fe"><span>River water quality modeling needs appropriate and suitable coefficients especially in application for specific river like urban river. </span><span>Aim:</span><span> This study aims to determine the value of the coefficient with a short term duration and a variable test time span. Several ways and methods of determining the rate of deoxygenation are developed according to the characteristics of the river and the environment. Modification method was applied in this research in which the test time span was unequal. The river chosen in this study is the Citepus River, Bandung, Indonesia representing an urban river in a tropical country. </span><span>Methodology and Results:</span><span> Sampling was carried out in the dry season. The laboratory analysis method used in determining the rate of deoxygenation uses the Slope Method of data from the short term incubation, which is ten days. The results showed that the Thomas Slope method's deoxygenation rate (K1) was 0.095 per day in the upstream segment, 0.917 per day in the middle segment, and 0.180 per day in the downstream segment. While the Ultimate BOD (La) value is 46.95 mg/l in the upstream segment, 38.70 mg/l in the middle segment, and 37.60 mg/l in the downstream segment. </span><span>Conclusion, significance, and impact of study:</span><span> The results of this study show that the value of the deoxygenation rate is similar to the theoretical surface water conditions. However, in the upstream segment, there is still a low deoxygenation rate value due to non-optimal activity of microorganisms. This findings will be very useful both in water quality modeling and river management.</span></span>