The design and control of turbomachinery require a wealth of spatiotemporal data. Thus, the low-cost and robust estimation of global aerodynamics from extremely limited data and noisy measurements is an important problem. This paper describes a data-driven approach to estimate the full-field pressure distribution of a turbine cascade flow in combination with sparse-distributed sensor measurements. For the offline library building and online reconstructing, the reduced-order model based on standard proper orthogonal decomposition (POD) and least squares approximation, and sparse representation based on overcomplete dictionary and L1 norm minimization are leveraged. To enhance the reconstruction accuracy and robustness with noisy measurements and varied sensor selections, a novel blocked K-means clustering strategy is developed to reconstruct the global flow field through the superposition of multiple local clusters. The statistical results indicate that sparse representation outperforms gappy POD in high-noise measurement environments due to its superior noise robustness and effective feature selection. By applying the proposed blocked clustering strategy, the accuracy and robustness of sparse estimation are significantly improved. The mean square error of gappy POD can be reduced by 9.86% for pressure reconstruction at 90% span of the turbine blade. Sparse representation produces excellent robustness enhancement when the noise intensity exceeds 0.3. Overall, the local reconstruction framework developed in this paper exhibits outstanding advantages in reconstruction accuracy and robustness.

Mounting interest in modeling outdoor diffusion and transmission of bioaerosols due to the prevalence of COVID-19 in the urban environment has led to better knowledge of the issues concerning exposure risk and evacuation planning. In this study, the dispersion and deposition dynamics of bioaerosols around a vaccine factory were numerically investigated under various thermal conditions and leakage rates. To assess infection risk at the pedestrian level, the improved Wells-Riley equation was used. To predict the evacuation path, Dijkstra’s algorithm, a derived greedy algorithm based on the improved Wells-Riley equation, was applied. The results show that, driven by buoyancy force, the deposition of bioaerosols can reach 80 m on the windward sidewall of high-rise buildings. Compared with stable thermal stratification, the infection risk of unstable thermal stratification in the upstream portion of the study area can increase by 5.53% and 9.92% under a low and high leakage rate, respectively. A greater leakage rate leads to higher infection risk but a similar distribution of high-risk regions. The present work provides a promising approach for infection risk assessment and evacuation planning for the emergency response to urban bioaerosol leakage.

Considering the significance of multipath transmissions of respiratory pathogens in the post-epidemic era, surprisingly little is acknowledged regarding the susceptibility, short-term aerodynamics, and exposure risk of children in indoor environments. Here, experimental and computational investigations were conducted to evaluate the exposure risks associated with respiratory pathogens. The dominant effect of recirculation structure originating from indoor ventilation, including air supply modes and air change rate, on aerosol dispersion was quantitatively proved. A large proportion of deposited aerosol particles was captured by the human body, and deposited particles may further increase under high air change rate, which required a balanced ventilation strategy. Little discrepancies existed between adults and children in exposure risk by airborne transmission. The infection probability by contact transmission for children, however, may be surprisingly high due to high frequency in interactive activities, deposition on upper and lower limbs of accompanying parents, and the wall within arm span.