CFD Simulation of Kaplan Turbine Rotating Union and the Development of a Real Time Diagnostic System

Author(s):  
Mateusz Kosek
PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250942
Author(s):  
Huseyin Tombuloglu ◽  
Hussein Sabit ◽  
Ebtesam Al-Suhaimi ◽  
Reem Al Jindan ◽  
Khaled R. Alkharsah

The outbreak of the new human coronavirus SARS-CoV-2 (also known as 2019-nCoV) continues to increase globally. The real-time reverse transcription polymerase chain reaction (rRT-PCR) is the most used technique in virus detection. However, possible false-negative and false-positive results produce misleading consequences, making it necessary to improve existing methods. Here, we developed a multiplex rRT-PCR diagnostic method, which targets two viral genes (RdRP and E) and one human gene (RP) simultaneously. The reaction was tested by using pseudoviral RNA and human target mRNA sequences as a template. Also, the protocol was validated by using 14 clinical SARS-CoV-2 positive samples. The results are in good agreement with the CDC authorized Cepheid`s Xpert® Xpress SARS-CoV-2 diagnostic system (100%). Unlike single gene targeting strategies, the current method provides the amplification of two viral regions in the same PCR reaction. Therefore, an accurate SARS-CoV-2 diagnostic assay was provided, which allows testing of 91 samples in 96-well plates in per run. Thanks to this strategy, fast, reliable, and easy-to-use rRT-PCR method is obtained to diagnose SARS-CoV-2.


2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Daniela De Venuto ◽  
Giovanni Mezzina

This paper details the design and the hardware implementation of a real-time diagnostic system based on FPGA for the muscle fibre conduction velocity estimation (MFCV). The MFCV is considered as a principal monitoring index for diabetic neuropathy (DPN), as well as in muscle fatigue assessment, to evaluate the muscle fibre status. The FPGA platform evaluates the MFCV during dynamic contractions (e.g., gait), by exploiting a multichannel sensing system composed of 4 wireless surface EMG electrodes, placed in pair on each leg. Raw data are digitized and made binary to create two bitstreams for each monitored limb. Then, a comparison between the two-bit streamed EMGs extracted from the same leg is carried out. The comparison, which allows extracting the MFCV, exploits a computationally light version of the cross-correlation method. The overall architecture implemented and validated on an Altera Cyclone V FPGA is HPS-free and exploits 22.5% ALMs, 10,874 ALUTs, 9.81% registers, 3.36% block memory, and <2.7% of the total wires available on the platform. The choice of FPGA as computing system lies in the possibility to determine resource utilization, related timing constraints for a future real-time ASIC implementation in wearable applications. From the actual muscle contraction during gait (cyclical starting point of the computing), the system spends about 316 ms to acquire useful data and 47.5 ms (on average) to process the signal and provide the output, dynamically dissipating 28.6 mW. The accuracy of the tool evaluation has been evaluated proving the repeatability of the measurements by in vivo test. In this context, 1250 contractions from each subject involved in a protocolled 10-meter walk have been acquired (n=10 subjects evaluated). On average, the same MFCV estimation has been extracted on 1184/1250 contractions (standard deviation of 11 contractions), reaching an accuracy of 94.7%. These estimations fully match the physiological value range reported in literature.


Author(s):  
John Agapiou

Machining process monitoring method is developed for detecting and diagnosis of the presence of chips at the toolholder-spindle interface. Although toolholders can be simply balanced before they are placed in the spindle, there can be some balancing problems remaining when one or more loose machining chips are attached at the toolholder-spindle interface(s) during a tool change. A method is developed by considering the natural and geometric unbalances of the toolholder-spindle system combined with an analysis of the toolholder tilt due to the presence of loose machining chips around the spindle. The method can be integrated on-line as a real-time expert diagnostic system for toolholder tilt due to the presence of loose machining chips at the spindle nose. The expert diagnostic system makes intelligent decisions on toolholder unbalance and concerns with chips at the interface that result in unwanted tilting and vibrations. The tool unbalance algorithm was able to monitor the toolholder tilting according to the results of this study.


2015 ◽  
Vol 68 (2) ◽  
pp. 113-118 ◽  
Author(s):  
Ikuyo Takayama ◽  
Hitoshi Takahashi ◽  
Mina Nakauchi ◽  
Shiho Nagata ◽  
Masato Tashiro ◽  
...  

2012 ◽  
Vol 12 (5) ◽  
pp. 2661-2679 ◽  
Author(s):  
M. S. Bourqui ◽  
A. Yamamoto ◽  
D. Tarasick ◽  
M. D. Moran ◽  
L.-P. Beaudoin ◽  
...  

Abstract. A new global real-time Lagrangian diagnostic system for stratosphere-troposphere exchange (STE) developed for Environment Canada (EC) has been delivering daily archived data since July 2010. The STE calculations are performed following the Lagrangian approach proposed in Bourqui (2006) using medium-range, high-resolution operational global weather forecasts. Following every weather forecast, trajectories are started from a dense three-dimensional grid covering the globe, and are calculated forward in time for six days of the forecast. All trajectories crossing either the dynamical tropopause (±2 PVU) or the 380 K isentrope and having a residence time greater than 12 h are archived, and also used to calculate several diagnostics. This system provides daily global STE forecasts that can be used to guide field campaigns, among other applications. The archived data set offers unique high-resolution information on transport across the tropopause for both extra-tropical hemispheres and the tropics. This will be useful for improving our understanding of STE globally, and as a reference for the evaluation of lower-resolution models. This new data set is evaluated here against measurements taken during a balloon sonde campaign with daily launches from three stations in eastern Canada (Montreal, Egbert, and Walsingham) for the period 12 July to 4 August 2010. The campaign found an unexpectedly high number of observed stratospheric intrusions: 79% (38%) of the profiles appear to show the presence of stratospheric air below than 500 hPa (700 hPa). An objective identification algorithm developed for this study is used to identify layers in the balloon-sonde profiles affected by stratospheric air and to evaluate the Lagrangian STE forecasts. We find that the predictive skill for the overall intrusion depth is very good for intrusions penetrating down to 300 and 500 hPa, while it becomes negligible for intrusions penetrating below 700 hPa. Nevertheless, the statistical representation of these deep intrusions is reasonable, with an average bias of 24%. Evaluation of the skill at representing the detailed structures of the stratospheric intrusions shows good predictive skill down to 500 hPa, reduced predictive skill between 500 and 700 hPa, and none below. A significant low statistical bias of about 30% is found in the layer between 500 to 700 hPa. However, analysis of missed events at one site, Montreal, shows that 70% of them coincide with candidate clusters of trajectories that pass through Montreal, but that are too dispersed to be detected in the close neighbourhood of the station. Within the limits of this study, this allows us to expect a negligible bias throughout the troposphere in the spatially averaged STE frequency derived from this data set, for example in climatological maps of STE mass fluxes. This first evaluation is limited to eastern Canada in one summer month with a high frequency of stratospheric intrusions, and further work is needed to evaluate this STE data set in other months and locations.


1992 ◽  
Vol 17 (24) ◽  
pp. 1797 ◽  
Author(s):  
C. J. Gaeta ◽  
P. V. Mitchell ◽  
D. M. Pepper
Keyword(s):  

2001 ◽  
Vol 84 (6) ◽  
pp. 1-9 ◽  
Author(s):  
Masanori Nishio ◽  
Qinghui Liu ◽  
Tomoyuki Miyazaki ◽  
Noriyuki Kawaguchi ◽  
Tetuo Sasao ◽  
...  

1998 ◽  
Vol 34 (6) ◽  
pp. 1342-1350 ◽  
Author(s):  
D.F. Garcia ◽  
J.M. Lopez ◽  
F.J. Suarez ◽  
J. Garcia ◽  
F. Obeso ◽  
...  

1993 ◽  
Vol 115 (3) ◽  
pp. 268-277 ◽  
Author(s):  
K. Ramamurthi ◽  
C. L. Hough

Machining economics may be improved by automating the replacement of cutting tools. In-process diagnosis of the cutting tool using multiple sensors is essential for such automation. In this study, an intelligent real-time diagnostic system is developed and applied towards that objective. A generalized Machining Influence Diagram (MID) is formulated for modeling different modes of failure in conventional metal cutting processes. A faster algorithm for this model is developed to solve the diagnostic problem in real-time applications. A formal methodology is outlined to tune the knowledge base during training with a reduction in training time. Finally, the system is implemented on a drilling machine and evaluated on-line. The on-line response is well within the desired response time of actual production lines. The instance and the accuracy of diagnosis are quite promising. In cases where drill wear is not diagnosed in a timely manner, the system predicts wear induced failure and vice versa. By diagnosing at least one of the two failure modes, the system is able to prevent any abrupt failure of the drill during machining.


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