scholarly journals Aerosol tracer testing in Boeing 767 and 777 aircraft to simulate exposure potential of infectious aerosol such as SARS-CoV-2

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0246916
Author(s):  
Sean M. Kinahan ◽  
David B. Silcott ◽  
Blake E. Silcott ◽  
Ryan M. Silcott ◽  
Peter J. Silcott ◽  
...  
Keyword(s):  
Real Time ◽  
Optical Sensors ◽  
Aerosol Deposition ◽  
Contact Tracing ◽  
Breathing Zone ◽  
Tracer Testing ◽  
Biological Aerosols ◽  
Particle Penetration ◽  
Tracer Particles ◽  
Dynamics Models

The COVID-19 pandemic has reintroduced questions regarding the potential risk of SARS-CoV-2 exposure amongst passengers on an aircraft. Quantifying risk with computational fluid dynamics models or contact tracing methods alone is challenging, as experimental results for inflight biological aerosols is lacking. Using fluorescent aerosol tracers and real time optical sensors, coupled with DNA-tagged tracers for aerosol deposition, we executed ground and inflight testing on Boeing 767 and 777 airframes. Analysis here represents tracer particles released from a simulated infected passenger, in multiple rows and seats, to determine the exposure risk via penetration into breathing zones in that row and numerous rows ahead and behind the index case. We present here conclusions from 118 releases of fluorescent tracer particles, with 40+ Instantaneous Biological Analyzer and Collector sensors placed in passenger breathing zones for real-time measurement of simulated virus particle penetration. Results from both airframes showed a minimum reduction of 99.54% of 1 μm aerosols from the index source to the breathing zone of a typical passenger seated directly next to the source. An average 99.97 to 99.98% reduction was measured for the breathing zones tested in the 767 and 777, respectively. Contamination of surfaces from aerosol sources was minimal, and DNA-tagged 3 μm tracer aerosol collection techniques agreed with fluorescent methodologies.

scholarly journals Aerosol tracer testing in Boeing 767 and 777 aircraft to simulate exposure potential of infectious aerosol such as SARS-CoV-2

2021 ◽  
Author(s):  
Sean M Kinahan ◽  
David B Silcott ◽  
Blake E Silcott ◽  
Ryan M Silcott ◽  
Peter J Silcott ◽  
...  
Keyword(s):  
Real Time ◽  
Optical Sensors ◽  
Aerosol Deposition ◽  
Contact Tracing ◽  
Breathing Zone ◽  
Tracer Testing ◽  
Biological Aerosols ◽  
Particle Penetration ◽  
Tracer Particles ◽  
Dynamics Models

AbstractThe COVID-19 pandemic has reintroduced questions regarding the potential risk of SARS-CoV-2 exposure amongst passengers on an aircraft. Quantifying risk with computational fluid dynamics models or contact tracing methods alone is challenging, as experimental results for inflight biological aerosols is lacking. Using fluorescent aerosol tracers and real time optical sensors, coupled with DNA-tagged tracers for aerosol deposition, we executed ground and inflight testing on Boeing 767 and 777 airframes.Analysis here represents tracer particles released from a simulated infected passenger, in multiple rows and seats, to determine the exposure risk via penetration into breathing zones in that row and numerous rows ahead and behind the index case. We completed over 65 releases of 180,000,000 fluorescent particles from the source, with 40+ Instantaneous Biological Analyzer and Collector sensors placed in passenger breathing zones for real-time measurement of simulated virus particle penetration.Results from both airframes showed a minimum reduction of 99.54% of 1 µm aerosols from the index source to the breathing zone of a typical passenger seated directly next to the source. An average 99.97 to 99.98% reduction was measured for the breathing zones tested in the 767 and 777, respectively. Contamination of surfaces from aerosol sources was minimal, and DNA-tagged 3 µm tracer aerosol collection techniques agreed with fluorescent methodologies.

Multirate Integration for Real-Time Simulation of Wheel Loader Hydraulics

1999 ◽  
Author(s):  
Laurenţiu I. Buzdugan ◽  
Ole Balling ◽  
Peter Chien-Te Lee ◽  
Claus Balling ◽  
Jeffrey S. Freeman ◽  
...  
Keyword(s):  
Real Time ◽  
System Modeling ◽  
Pressure Fluctuations ◽  
Integration Scheme ◽  
Step Size ◽  
Integration Algorithm ◽  
Wheel Loader ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Dynamics Models

Abstract This paper details a real-time simulation of an articulating wheel loader, which is comprised of a multibody system modeling the chassis and the bucket assembly and a set of subsystems. The hydraulic subsystem is modeled by a set of ODE’s which represent the oil pressure fluctuations in the system. An Adams-Bashforth-Moulton integration algorithm has been implemented using the Nordsieck form to develop a constant step-size multirate integration scheme, modeling the interaction between the hydraulic subsystem and multibody dynamics models. An example illustrating the simulation of a wheel loader bucket operation is presented at the end of the paper.

IIOT and Real Time Data Analytics - Maximizing the Impact on Safety and Productivity

2021 ◽  
Author(s):  
Ryan Daher ◽  
Nesma Aldash
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Data Analytics ◽  
Contact Tracing ◽  
Time Data ◽  
Seamless Integration ◽  
Location Services ◽  
Industrial Internet ◽  
Potential Applications ◽  
The Impact

Abstract With the global push towards Industry 4.0, a number of leading companies and organizations have invested heavily in Industrial Internet of Things (IIOT's) and acquired a massive amount of data. But data without proper analysis that converts it into actionable insights is just more information. With the advancement of Data analytics, machine learning, artificial intelligence, numerous methods can be used to better extract value out of the amassed data from various IIOTs and leverage the analysis to better make decisions impacting efficiency, productivity, optimization and safety. This paper focuses on two case studies- one from upstream and one from downstream using RTLS (Real Time Location Services). Two types of challenges were present: the first one being the identification of the location of all personnel on site in case of emergency and ensuring that all have mustered in a timely fashion hence reducing the time to muster and lessening the risks of Leaving someone behind. The second challenge being the identification of personnel and various contractors, the time they entered in productive or nonproductive areas and time it took to complete various tasks within their crafts while on the job hence accounting for efficiency, productivity and cost reduction. In both case studies, advanced analytics were used, and data collection issues were encountered highlighting the need for further and seamless integration between data, analytics and intelligence is needed. Achievements from both cases were visible increase in productivity and efficiency along with the heightened safety awareness hence lowering the overall risk and liability of the operation. Novel/Additive Information: The results presented from both studies have highlighted other potential applications of the IIOT and its related analytics. Pertinent to COVID-19, new application of such approach was tested in contact tracing identifying workers who could have tested positive and tracing back to personnel that have been in close proximity and contact therefore reducing the spread of COVID. Other application of the IIOT and its related analytics has also been tested in crane, forklift and heavy machinery proximity alert reducing the risk of accidents.

Dynamic Formation Evaluation to Reduce Uncertainty and Confirm Completed Intervals in Brown Fields

2021 ◽  
Author(s):  
Muhamad Aizat Kamaruddin ◽  
Ayham Ashqar ◽  
Muhammad Haniff Suhaimi ◽  
Fairus Azwardy Salleh
Keyword(s):  
Real Time ◽  
Optical Sensors ◽  
Sensor Technology ◽  
Innovative Technology ◽  
Fluid Type ◽  
Formation Evaluation ◽  
Reservoir Fluid ◽  
Logging While Drilling ◽  
Dynamic Formation

Abstract Uncertainties in fluid typing and contacts within Sarawak Offshore brown field required a real time decision. To enhance reservoir fluid characterisation and confirm reservoir connectivity prior to well final total depth (TD). Fluid typing while drilling was selected to assure the completion strategy and ascertain the fluvial reservoir petrophysical interpretation. Benefiting from low invasion, Logging While Drilling (LWD) sampling fitted with state of ART advanced spectroscopy sensors were deployed. Pressures and samples were collected. The well was drilled using synthetic base mud. Conventional logging while drilling tool string in addition to sampling tool that is equipped with advanced sensor technology were deployed. While drilling real time formation evaluation allowed selecting the zones of interest, while fluid typing was confirmed using continually monitored fluids pump out via multiple advanced sensors, contamination, and reservoir fluid properties were assessed while pumping. Pressure and sampling were performed in drilling mode to minimise reservoir damage, and optimise rig time, additionally sampling while drilling was performed under circulation conditions. Pressures were collected first followed by sampling. High success in collecting pressure points with a reliable fluid gradient that indicated a virgin reservoir allowed the selection of best completion strategy without jeopardising reserves, and reduced rig time. Total of seven samples from 3 different reservoirs, four oil, and three formation water. High quality samples were collected. The dynamic formation evaluation supported by while drilling sampling confirmed the reservoir fluid type and successfully discovered 39ft of oil net pay. Reservoir was completed as an oil producer. The Optical spectroscopy measurements allowed in situ fluid typing for the quick decision making. The use of advanced optical sensors allowed the sample collection and gave initial assessment on reservoir fluids properties, as a result cost saving due to eliminating the need for additional Drill Stem Test (DST) run to confirm the fluid type. Sample and formation pressures has confirmed reservoir lateral continuity in the vicinity of the field. The reservoir developed as thick and blocky sandstone. Collected sample confirmed the low contamination levels. Continuous circulation mitigated sticking and potential well-control risks. This is the first time in surrounding area, advanced optical sensors are used to aid LWD sampling and to finalize the fluid identification. The innovative technology allowed the collection of low contamination. The real-time in-situ fluid analysis measurement allowed critical decisions to be made real time, consequently reducing rig downtime. Reliable analysis of fluid type identification removed the need for additional run/service like DST etc.

Personnel Real Time Tracking in Hazardous Areas Using Wearable Technologies and Machine Learning

2021 ◽  
Author(s):  
Tareq Aziz AL-Qutami ◽  
Fatin Awina Awis
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Communication Systems ◽  
Average Distance ◽  
Location Estimation ◽  
Machine Learning Algorithms ◽  
Contact Tracing ◽  
Localization Algorithm ◽  
Positioning Systems ◽  
Wearable Technologies

Abstract Real-time location information is essential in the hazardous process and construction areas for safety and emergency management, security, search and rescue, and even productivity tracking. It's also crucial during pandemics such as the COVID-19 pandemic for contact tracing to isolate those who came to the proximity of infected individuals. While global positioning systems (GPS), can address the demand for location awareness in outdoor environments, another accurate location estimation technology for indoor environments where GPS doesn't perform well is required. This paper presents the development and deployment of an end-to-end cost-effective real-time personnel location system suitable for both indoor and outdoor hazardous and safe areas. It leverages on facility wireless communication systems, wearable technologies such as smart helmets and wearable tags, and machine learning. Personnel carries the client device which collects location-related information and sends it to the localization algorithm in the cloud. When the personnel moves, the tracking dashboard shows client location in real-time. The proposed localization algorithm relies on wireless signal fingerprinting and machine learning algorithms to estimate the location. The machine learning algorithm is a mix of clustering and classification that was designed to scale well with bigger target areas and is suitable for cloud deployment. The system was tested in both office and industrial process environments using consumer-grade handphones and intrinsically safe wearable devices. It achieved an average distance error of less than 2 meters in 3D space.

Real-time social distance alerting and contact tracing using image processing

2021 ◽  
pp. 297-315
Author(s):  
Balaji Muthazhagan ◽  
Aparnasri Panchapakesan ◽  
Suriya Sundaramoorthy
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Social Distance ◽  
Contact Tracing
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