Experimental and numerical study on the effects of the solenoid valve motion on the fuel pressure fluctuations for electronic unit pump systems of diesel engines

2017 ◽  
Vol 31 (11) ◽  
pp. 5545-5556 ◽  
Author(s):  
Fushui Liu ◽  
Ning Kang ◽  
Pei Wang ◽  
Yikai Li
Keyword(s):  
Diesel Engines ◽  
Numerical Study ◽  
Pressure Fluctuations ◽  
Solenoid Valve ◽  
Electronic Unit ◽  
Valve Motion ◽  
Electronic Unit Pump

Effects of structure parameters on the static electromagnetic characteristics of solenoid valve for an electronic unit pump

2016 ◽  
Vol 113 ◽  
pp. 119-130 ◽  
Author(s):  
Zuo-Yu Sun ◽  
Guo-Xiu Li ◽  
Lan Wang ◽  
Wei-Hong Wang ◽  
Qing-Xiu Gao ◽  
...  
Keyword(s):  
Solenoid Valve ◽  
Structure Parameters ◽  
Electronic Unit ◽  
Electronic Unit Pump ◽  
Electromagnetic Characteristics

scholarly journals Development of Electronic Unit Pump for Medium Speed Diesel Engines

2010 ◽  
Vol 45 (4) ◽  
pp. 567-571
Author(s):  
Kazuhiro Kitagawa ◽  
Ruban Denis ◽  
Takanori Egashira ◽  
Isao Takagawa
Keyword(s):  
Diesel Engines ◽  
Electronic Unit ◽  
Medium Speed ◽  
Electronic Unit Pump

Multi-objective Optimization of High-Speed Solenoid Valve for Biodiesel Electronic Unit Pump

2021 ◽  
Vol 01 ◽  
Author(s):  
Yuanqi Gu ◽  
Liyun Fan ◽  
Jianyu Zhang ◽  
Yun Bai
Keyword(s):  
Genetic Algorithm ◽  
Delay Time ◽  
High Speed ◽  
Solenoid Valve ◽  
Multi Objective Optimization ◽  
Electronic Unit ◽  
Response Delay ◽  
Multi Objective ◽  
Injection Quantity ◽  
Electronic Unit Pump

Background: A larger response delay of a high-speed solenoid valve will cause inaccurate fuel injection timing and imprecise cycle injection quantity, resulting in diesel engine emission and increased fuel consumption. Objective: Biodiesel as an ideal alternative fuel has exceptional advantages in energy conservation, emission reduction, and low-carbon environmental protection; however, matching with Electronic Unit Pump (EUP) and its impacts on solenoid valve operation need to be further studied. Method: In the present work, a numerical model of EUP fueled with biodiesel was established in an AMESim environment, which was validated by the experiment. Then, combined with the Design of Experiments (DOE) method, key parameters influencing the solenoid valve response delay were predicted: armature residual air gap, spring preload, poppet valve half-angle, valve needle diameter, and poppet valve diameter. Finally, taking the response delay time of solenoid valve as targets, multi-objective optimization model for high-speed solenoid valve was established using NSGA-II (non-dominated sorting genetic algorithm-II) genetic algorithm in modeFRONTIER platform. Results: The optimized results showed that the delay time of the solenoid valve closing is reduced by 6%, the opening delay time is reduced by 20.8%, the injection pressure peak is increased by 1.8MPa, which is beneficial to accurate injection quantity and the application of biodiesel in diesel engines.

scholarly journals Detecting Aortic Valve Anomaly From Induced Murmurs: Insights From Computational Hemodynamic Models

2021 ◽  
Vol 12 ◽  
Author(s):  
Shantanu Bailoor ◽  
Jung-Hee Seo ◽  
Stefano Schena ◽  
Rajat Mittal
Keyword(s):  
Aortic Valve ◽  
Pressure Fluctuations ◽  
Home Monitoring ◽  
Principal Component ◽  
Heart Sounds ◽  
Machine Learning Techniques ◽  
Conventional Angiography ◽  
Linear Discriminant ◽  
Valve Motion ◽  
Valve Function

Patients who receive transcatheter aortic valve replacement are at risk for leaflet thrombosis-related complications, and can benefit from continuous, longitudinal monitoring of the prosthesis. Conventional angiography modalities are expensive, hospital-centric and either invasive or employ potentially nephrotoxic contrast agents, which preclude their routine use. Heart sounds have been long recognized to contain valuable information about individual valve function, but the skill of auscultation is in decline due to its heavy reliance on the physician’s proficiency leading to poor diagnostic repeatability. This subjectivity in diagnosis can be alleviated using machine learning techniques for anomaly detection. We present a computational and data-driven proof-of-concept analysis of a novel, auscultation-based technique for monitoring aortic valve, which is practical, non-invasive, and non-toxic. However, the underlying mechanisms leading to physiological and pathological heart sounds are not well-understood, which hinders development of such a technique. We first address this by performing direct numerical simulations of the complex interactions between turbulent blood flow in a canonical ascending aorta model and dynamic valve motion in 29 cases with healthy and stenotic valves. Using the turbulent pressure fluctuations on the aorta lumen boundary, we model the propagation of heart sounds, as elastic waves, through the patient’s thorax. The heart sound may be recorded on the epidermal surface using a stethoscope/phonocardiograph. This approach allows us to correlate instantaneous hemodynamic phenomena and valve motion with the acoustic response. From this dataset we extract “acoustic signatures” of healthy and stenotic valves based on principal components of the recorded sound. These signatures are used to train a linear discriminant classifier by maximizing correlation between recorded heart sounds and valve status. We demonstrate that this classifier is capable of accurate prospective detection of anomalous valve function and that the principal component-based signatures capture prominent audible features of heart sounds, which have been historically used by physicians for diagnosis. Further development of such technology can enable inexpensive, safe and patient-centric at-home monitoring, and can extend beyond transcatheter valves to surgical as well as native valves.

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