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Abstract Active machine learning is widely used in computational studies where repeated numerical simulations can be conducted on high performance computers without human intervention. But translation of these active learning methods to physical systems has proven more difficult and the accelerated pace of discoveries aided by these methods remains as yet unrealized. Through the presentation of a general active learning framework and its application to large-scale boundary layer wind tunnel experiments, we demonstrate that the active learning framework used so successfully in computational studies is directly applicable to the investigation of physical experimental systems and the corresponding improvements in the rate of discovery can be transformative. We specifically show that, for our wind tunnel experiments, we are able to achieve in approximately 300 experiments a learning objective that would be impossible using traditional methods.

Abstract Following the first detection in the United Kingdom of Usutu virus (USUV) in wild birds in 2020, we undertook a multidisciplinary investigation that combined screening host and vector populations with interrogation of national citizen science monitoring datasets to assess the potential for population impacts on avian hosts. Pathological findings from six USUV-positive wild passerines were non-specific, highlighting the need for molecular and immunohistochemical examinations to confirm infection. Mosquito surveillance at the index site identified USUV RNA in Culex pipiens s.l. following the outbreak. Although the Eurasian blackbird (Turdus merula) is most frequently impacted by USUV in Europe, national syndromic surveillance failed to detect any increase in occurrence of clinical signs consistent with USUV infection in this species. Furthermore, there was no increase in recoveries of dead blackbirds marked by the national ringing scheme. However, there was regional clustering of blackbird disease incident reports centred near the index site in 2020 and a contemporaneous marked reduction in the frequency with which blackbirds were recorded in gardens in this area, consistent with a hypothesis of disease-mediated population decline. Combining results from multidisciplinary schemes, as we have done, in real-time offers a model for the detection and impact assessment of future disease emergence events.

This study applies computationally efficient shallow neural networks to predict topographic effect multipliers directly from digital elevation data obtained from complex terrain, such as mountainous areas. Data were obtained from boundary layer wind tunnel (BLWT) modeling of surface wind flow over six regions in mainland Puerto Rico and its municipal islands. The results demonstrate an improvement over linear regression models, even for computationally efficient low neuron count and single hidden layer models. The paper proposes the development of a global BLWT data atlas to inform development of methods to predict topographic wind speedup for a diverse range of topography and surface roughness conditions. It also identifies knowledge gaps that could prevent standardization of data collected from different BLWT experimental designs.

Abstract Background Culicoides biting midges are biological vectors of arboviruses including bluetongue virus (BTV), Schmallenberg virus (SBV) and African horse sickness virus (AHSV). Zoos are home to a wide range of ‘at risk’ exotic and native species of animals. These animals have a high value both in monetary terms, conservation significance and breeding potential. To understand the risk these viruses pose to zoo animals, it is necessary to characterise the Culicoides fauna at zoos and determine which potential vector species are feeding on which hosts. Methods Light-suction traps were used at two UK zoos: the Zoological Society of London (ZSL) London Zoo (LZ) and ZSL Whipsnade Zoo (WZ). Traps were run one night each week from June 2014 to June 2015. Culicoides were morphologically identified to the species level and any blood-fed Culicoides were processed for blood-meal analysis. DNA from blood meals was extracted and amplified using previously published primers. Sequencing was then carried out to determine the host species. Results A total of 11,648 Culicoides were trapped and identified (n = 5880 from ZSL WZ; n = 5768 from ZSL LZ), constituting 25 different species. The six putative vectors of BTV, SBV and AHSV in northern Europe were found at both zoos and made up the majority of the total catch (n = 10,701). A total of 31 host sequences were obtained from blood-fed Culicoides. Culicoides obsoletus/C. scoticus, Culicoides dewulfi, Culicoides parroti and Culicoides punctatus were found to be biting a wide range of mammals including Bactrian camels, Indian rhinoceros, Asian elephants and humans, with Culicoides obsoletus/C. scoticus also biting Darwin’s rhea. The bird-biting species, Culicoides achrayi, was found to be feeding on blackbirds, blue tits, magpies and carrion crows. Conclusions To our knowledge, this is the first study to directly confirm blood-feeding of Culicoides on exotic zoo animals in the UK and shows that they are able to utilise a wide range of exotic as well as native host species. Due to the susceptibility of some zoo animals to Culicoides-borne arboviruses, this study demonstrates that in the event of an outbreak of one of these viruses in the UK, preventative and mitigating measures would need to be taken.

The Natural Hazards Engineering Research Infrastructure (NHERI) experimental facility at the University of Florida provides a diverse suite of experimental resources to support wind hazard research. The 40-m long Boundary Layer Wind Tunnel (BLWT) simulates boundary layer flows to characterize wind loading on rigid structural models and assess the response of aeroelastic structures. The use of experimental automation tools provides researchers unparalleled flexibility in their test configurations while supporting high throughput testing and data collection. The Terraformer, an array of 1116 roughness elements, can be rapidly reconfigured to generate terrain conditions in less than 90 s, the test section turntable can move automatically through a range of wind approach angles, and the instrumentation gantry can traverse preset paths to collect wind field measurements anywhere in the tunnel cross-section. These test automation tools, along with mechatronic structural models and real-time data transfer and processing, provide new opportunities in experimental wind tunnel testing. Recent cyber-physical wind tunnel testing projects highlight the benefits of these experimental automation tools. This paper will also discuss the most recent addition to the BLWT, the Flow Field Modulator (FFM). It consists of a 2D array of 319 individually controlled ducted fans driven by electronic speed controllers. The FFM expands the BLWT capabilities by supporting the simulation of non-monotonic profiles and non-stationary events such as gust fronts and downbursts, where mean velocity and turbulence distributions change over short spatial scales. The ability to simulate these flow conditions in a wind tunnel enables the investigation of a wide range of damaging conditions and the solutions for mitigating their impact on structures.

This paper presents a generalized approach for predicting (i.e., interpolating) the magnitude and distribution of roof pressures near separated flow regions on a low-rise structure based on freestream turbulent flow conditions. A feed-forward multilayer artificial neural network (ANN) using a backpropagation (BP) training algorithm is employed to predict the mean, root-mean-square (RMS), and peak pressure coefficients on three geometrically scaled (1:50, 1:30, and 1:20) low-rise building models for a family of upwind approach flow conditions. A comprehensive dataset of recently published boundary layer wind tunnel (BLWT) pressure measurements was utilized for training, validation, and evaluation of the ANN model. On average, predicted ANN peak pressure coefficients for a group of pressure taps located near the roof corner were within 5.1, 6.9, and 7.7% of BLWT observations for the 1:50, 1:30, and 1:20 models, respectively. Further, very good agreement was found between predicted ANN mean and RMS pressure coefficients and BLWT data.

This paper presents the experimental design and subsequent findings from a series of experiments in a large boundary layer wind tunnel to investigate the variation of surface pressures with increasing upwind terrain roughness on low-rise buildings. Geometrically scaled models of the Wind Engineering Research Field Laboratory experimental building were subjected to a wide range of turbulent boundary layer flows, through precise adjustment of a computer control terrain generator called the Terraformer. The study offers an in-depth examination of the effects of freestream turbulence on extreme pressures under the separation “bubble” for the case of the wind traveling perpendicular to wall surfaces, independently confirming previous findings that the spatial distribution of the peaks is heavily influenced by the mean reattachment length. Further, the study shows that the observed peak pressures collapse if data are normalized by the mean reattachment length and a non-Gaussian estimator for peak velocity pressure.

This paper explores the use of a cyber-physical systems (CPS) “loop-in-the-model” approach to optimally design the envelope and structural system of low-rise buildings subject to wind loads. Both the components and cladding (C&C) and the main wind force resisting system (MWFRS) are considered through multi-objective optimization. The CPS approach combines the physical accuracy of wind tunnel testing and efficiency of numerical optimization algorithms to obtain an optimal design. The approach is autonomous: experiments are executed in a boundary layer wind tunnel (BLWT), sensor feedback is monitored and analyzed by a computer, and optimization algorithms dictate physical changes to the structural model in the BLWT through actuators. To explore a CPS approach to multi-objective optimization, a low-rise building with a parapet wall of variable height is considered. In the BLWT, servo-motors are used to adjust the parapet to a particular height. Parapet walls alter the location of the roof corner vortices, reducing suction loads on the windward facing roof corners and edges, a C&C design load. At the same time, parapet walls increase the surface area of the building, leading to an increase in demand on the MWFRS. A combination of non-stochastic and stochastic optimization algorithms were implemented to minimize the magnitude of suction and positive pressures on the roof of a low-rise building model, followed by stochastic multi-objective optimization to simultaneously minimize the magnitude of suction pressures and base shear. Experiments were conducted at the University of Florida Experimental Facility (UFEF) of the National Science Foundation’s (NSF) Natural Hazard Engineering Research Infrastructure (NHERI) program.

This paper describes a new full-scale (FS) testing apparatus for conducting performance evaluations of FS building envelope systems. The simulator can generate spatially uniform, time-varying pressure conditions associated with Saffir–Simpson Hurricane Wind Scale Category 5 winds while compensating for large air leakage through the specimen and also operate a high-speed wind tunnel, both with dynamic control. This paper presents system details, operating characteristics, and an early case study on the performance of large sectional door systems under wind pressure loading. Failure mechanisms are discussed, and finite element modeling is validated for two specimens. It demonstrates successful dynamic load control for large component and cladding systems, as well as simulation of flows near the building surface. These capabilities serve to complement other FS wind tunnel facilities by offering tools to generate ultimate load conditions on portions of the building. Further, the paper successfully demonstrates the utility of combining physical testing and computational analysis as a matter of routine, which underscores the potential of evolving FS testing to encompass cyber–physical approaches.

BACKGROUND: How accurately do people perceive extreme water speeds and how does their perception affect perceived risk? Prior research has focused on the characteristics of moving water that can reduce human stability or balance. The current research presents the first experiment on people's perceptions of risk and moving water at different speeds and depths. METHODS: Using a randomized within-person 2 (water depth: 0.45, 0.90 m) ×3 (water speed: 0.4, 0.8, 1.2 m/s) experiment, we immersed 76 people in moving water and asked them to estimate water speed and the risk they felt. RESULTS: Multilevel modeling showed that people increasingly overestimated water speeds as actual water speeds increased or as water depth increased. Water speed perceptions mediated the direct positive relationship between actual water speeds and perceptions of risk; the faster the moving water, the greater the perceived risk. Participants' prior experience with rip currents and tropical cyclones moderated the strength of the actual-perceived water speed relationship; consequently, mediation was stronger for people who had experienced no rip currents or fewer storms. CONCLUSIONS: These findings provide a clearer understanding of water speed and risk perception, which may help communicate the risks associated with anticipated floods and tropical cyclones.

BACKGROUND: How accurately do people perceive extreme wind speeds and how does that perception affect the perceived risk? Prior research on human-wind interaction has focused on comfort levels in urban settings or knock-down thresholds. No systematic experimental research has attempted to assess people's ability to estimate extreme wind speeds and perceptions of their associated risks. METHOD: We exposed 76 people to 10, 20, 30, 40, 50, and 60 mph (4.5, 8.9, 13.4, 17.9, 22.3, and 26.8 m/s) winds in randomized orders and asked them to estimate wind speed and the corresponding risk they felt. RESULTS: Multilevel modeling showed that people were accurate at lower wind speeds but overestimated wind speeds at higher levels. Wind speed perceptions mediated the direct relationship between actual wind speeds and perceptions of risk (i.e., the greater the perceived wind speed, the greater the perceived risk). The number of tropical cyclones people had experienced moderated the strength of the actual-perceived wind speed relationship; consequently, mediation was stronger for people who had experienced fewer storms. CONCLUSION: These findings provide a clearer understanding of wind and risk perception, which can aid development of public policy solutions toward communicating the severity and risks associated with natural disasters.

We have calculated the direct correlation function, c(1, 2), via a high-order virial expansion, for systems of hard spheres and spheroids, in both the isotropic and nematic phases. For hard spheres, we find that truncation at sixth order in density gives good agreement with simulation data. Close to freezing, the virial series still appears to converge to the simulation results, but there are significant discrepancies, particularly at very small separations. In the non-overlap region, the virial theory begins to capture the features found from simulation. We also calculate the pair distribution function, g(1, 2), from our estimates of c(1, 2), and find good agreement with simulation data at all densities up to freezing. For hard, prolate spheroids of aspect ratio 3 : 1, we calculate c(1, 2) and g(1, 2) in the isotropic phase, again finding good agreement with simulation data at moderate densities. Finally, we present the result of our calculations on c(1, 2) for 3 : 1 spheroids in the nematic phase. [ABSTRACT FROM AUTHOR]