scholarly journals Numerical Fluid Dynamics Simulation for Drones’ Chemical Detection

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 69
Author(s):  
Fabio Marturano ◽  
Luca Martellucci ◽  
Andrea Chierici ◽  
Andrea Malizia ◽  
Daniele Di Giovanni ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Early Detection ◽  
Hazardous Materials ◽  
Dynamics Simulation ◽  
Chemical Agent ◽  
Mandatory Requirement ◽  
Selective Detectors ◽  
Human Operators ◽  
Application Fields ◽  
Specific Technology

The risk associated with chemical, biological, radiological, nuclear, and explosive (CBRNe) threats in the last two decades has grown as a result of easier access to hazardous materials and agents, potentially increasing the chance for dangerous events. Consequently, early detection of a threat following a CBRNe event is a mandatory requirement for the safety and security of human operators involved in the management of the emergency. Drones are nowadays one of the most advanced and versatile tools available, and they have proven to be successfully used in many different application fields. The use of drones equipped with inexpensive and selective detectors could be both a solution to improve the early detection of threats and, at the same time, a solution for human operators to prevent dangerous situations. To maximize the drone’s capability of detecting dangerous volatile substances, fluid dynamics numerical simulations may be used to understand the optimal configuration of the detectors positioned on the drone. This study serves as a first step to investigate how the fluid dynamics of the drone propeller flow and the different sensors position on-board could affect the conditioning and acquisition of data. The first consequence of this approach may lead to optimizing the position of the detectors on the drone based not only on the specific technology of the sensor, but also on the type of chemical agent dispersed in the environment, eventually allowing to define a technological solution to enhance the detection process and ensure the safety and security of first responders.

scholarly journals Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David R. Rutkowski ◽  
Alejandro Roldán-Alzate ◽  
Kevin M. Johnson
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Velocity Fields ◽  
Dynamics Simulation ◽  
Patient Specific ◽  
4D Flow Mri ◽  
4D Flow ◽  
Flow Phase ◽  
Flow Mri ◽  
Trained Network

AbstractBlood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.

scholarly journals Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3616
Author(s):  
Jan Ubbo van Baardewijk ◽  
Sarthak Agarwal ◽  
Alex S. Cornelissen ◽  
Marloes J. A. Joosen ◽  
Jiska Kentrop ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Physiological Signals ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Chemical Agent ◽  
Counter Measures ◽  
Detection And Identification ◽  
Life Saving ◽  
Future Work

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.

Sign in / Sign up

Export Citation Format

Share Document