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Boundary Layer Wind Tunnel (BLWT) facilities are commonly used for assessing wind loads on structures. Although BLWT facilities routinely match 1st and 2nd-order wind profile models, evidence suggests that turbulence in the roughness sublayer and the inertial sublayer exhibit non-Gaussian higher-order properties. These non-Gaussian properties can influence peak wind pressures, which govern certain structural limit states and play an important role in design. In the first part of this project, Machine learning (ML) methods are employed to identify relationships between roughness element configurations and higher-order statistical properties of the wind field. A semi-automated framework with an active learning portion and a wind tunnel experimental procedure is developed. The learning framework adaptively selects roughness profiles and launches new experiments to identify differing profiles with second-order equivalent flow as quantified by turbulence intensity. The premise is that second-order equivalent wind fields can differ in higher-order properties and therefore extreme value derived peak loads may differ. Over the course of this project, the turbulence profiles from hundreds of different Terraformer roughness element configurations were collected, providing a very rich dataset of boundary layer flow as a function of upwind fetch. Experiment 1 provides the metadata to describe and interpret measured wind profiles at the UFBLWT for a data set collected for the Benchmark experiments and 3 different phases: 1) Sinusoidal waves experiments, 2) Shape study experiments and, 3) Random field experiments. Experiment 2 of this dataset presents the results of experiments conducted in the UFBLWT, with a focus on measuring turbulence characteristics and pressure coefficients on a bluff body under varying terrain roughness configurations. The dataset provides valuable insights into the influence of upwind fetch and surface roughness on wind-induced forces, contributing to improved modeling and prediction of wind loads on structures. Based on the Terraformer configurations in experiment 1, select configurations (Benchmark and Phase 1 Terraformer configurations only) were chosen for bluff body experiments, along with additional approach turbulence measurements at a lateral location to the model. This dataset includes three key components for Benchmark and Phase 1 Terraformer configurations: reference wind velocity (uRef), lateral approach flow profiles (LatFlow), and pressure coefficients (Cpdata) on the bluff body. 

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. 

BackgroundThe occupational burnout epidemic is a growing issue, and in the United States, up to 60% of medical students, residents, physicians, and registered nurses experience symptoms. Wearable technologies may provide an opportunity to predict the onset of burnout and other forms of distress using physiological markers. ObjectiveThis study aims to identify physiological biomarkers of burnout, and establish what gaps are currently present in the use of wearable technologies for burnout prediction among health care professionals (HCPs). MethodsA comprehensive search of several databases was performed on June 7, 2022. No date limits were set for the search. The databases were Ovid: MEDLINE(R), Embase, Healthstar, APA PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science Core Collection via Clarivate Analytics, Scopus via Elsevier, EBSCOhost: Academic Search Premier, CINAHL with Full Text, and Business Source Premier. Studies observing anxiety, burnout, stress, and depression using a wearable device worn by an HCP were included, with HCP defined as medical students, residents, physicians, and nurses. Bias was assessed using the Newcastle Ottawa Quality Assessment Form for Cohort Studies. ResultsThe initial search yielded 505 papers, from which 10 (1.95%) studies were included in this review. The majority (n=9) used wrist-worn biosensors and described observational cohort studies (n=8), with a low risk of bias. While no physiological measures were reliably associated with burnout or anxiety, step count and time in bed were associated with depressive symptoms, and heart rate and heart rate variability were associated with acute stress. Studies were limited with long-term observations (eg, ≥12 months) and large sample sizes, with limited integration of wearable data with system-level information (eg, acuity) to predict burnout. Reporting standards were also insufficient, particularly in device adherence and sampling frequency used for physiological measurements. ConclusionsWith wearables offering promise for digital health assessments of human functioning, it is possible to see wearables as a frontier for predicting burnout. Future digital health studies exploring the utility of wearable technologies for burnout prediction should address the limitations of data standardization and strategies to improve adherence and inclusivity in study participation. 

Over the course of this project, the turbulence profiles from hundreds of different Terraformer roughness element configurations were collected, providing a very rich dataset of boundary layer flow as a function of upwind fetch. Vertically aligned turbulence measuring Cobra probes vertically separated by 160 mm are mounted on an automated articulating gantry programmed to move in three dimensions. In this manner, three vertical turbulence profiles in different lateral locations are measured during each experiment. The flow is driven by the eight vane axial fans and conditioned by the Irwin Spires through the inlet after which the boundary layer flow is developed over the Terraformer roughness element section before arriving at the measuring plane. The x direction is defined as the upstream from the vane axial fans, y direction is perpendicular to the x direction and represents the lateral locations of the measured vertical profiles. Finally, z represents the vertical height of the cobra probes from the floor of the UFBLWT.