Aerodynamic performance of biomimicry snake-shaped airfoil

The cross-section shape and proportionality between geometrical dimensions are the most important design parameters of any lifting surfaces. These parameters affect the amount of the aerodynamic forces that will be generated. In this study, the focus is placed on the snake-cross-section airfoil known as the S-airfoil. It is found that there is a lack of available researches on S-airfoil despite its important characteristics. A parametric study on empty model of the S-airfoil with a cross-section shape that is inspired by the Chrysopelea paradise snake is conducted through numerical simulation. Simulation using 2D-ANSYS FLUENT17 software is used to generate the lift and drag forces to determine the performance of airfoil aerodynamic. Based on the results, the S-airfoil can be improved in performance of aerodynamic by reducing the thickness at certain range, whereby changing the thickness-to-chord ratio from 0.037 to 0.011 results in the increment of lift-to-drag ratio from 2.629 to 3.257. On other hand, increasing the height-to-chord ratio of the S-airfoil will increase maximum lift coefficient but drawback is a wide range of angles of attack regarding maximum lift-to-drag ratio. Encouraging results obtained in this study draws attention to the importance of expanding the research on S-airfoil and its usage, especially in wind energy

Using home monitoring technology to study the effects of traumatic brain injury on older multimorbid adults: protocol for a feasibility study

Introduction The prevalence of traumatic brain injury (TBI) among older adults is increasing exponentially. The sequelae can be severe in older adults and interact with age-related conditions such as multimorbidity. Despite this, TBI research in older adults is sparse. Minder, an in-home monitoring system developed by the UK Dementia Research Institute Centre for Care Research and Technology, uses infrared sensors and a bed mat to passively collect sleep and activity data. Similar systems have been used to monitor the health of older adults living with dementia. We will assess the feasibility of using this system to study changes in the health status of older adults in the early period post-TBI.Methods and analysis The study will recruit 15 inpatients (>60 years) with a moderate-severe TBI, who will have their daily activity and sleep patterns monitored using passive and wearable sensors over 6 months. Participants will report on their health during weekly calls, which will be used to validate sensor data. Physical, functional and cognitive assessments will be conducted across the duration of the study. Activity levels and sleep patterns derived from sensor data will be calculated and visualised using activity maps. Within-participant analysis will be performed to determine if participants are deviating from their own routines. We will apply machine learning approaches to activity and sleep data to assess whether the changes in these data can predict clinical events. Qualitative analysis of interviews conducted with participants, carers and clinical staff will assess acceptability and utility of the system.Ethics and dissemination Ethical approval for this study has been granted by the London-Camberwell St Giles Research Ethics Committee (REC) (REC number: 17/LO/2066). Results will be submitted for publication in peer-reviewed journals, presented at conferences and inform the design of a larger trial assessing recovery after TBI