Design and Dynamic Analysis of an Innovative Axial Shift Valvetrain System (ASVS) for Variable Stroke Engine

A variable valvetrain system is the key part of the variable stroke engine (VSE), which could achieve higher power performance and low-speed torque. An innovative axial shift valvetrain system (ASVS) was put forward to meet the air-charging requirements of a 2/4-stroke engine and complete a changeover within one working cycle. Two sets of intake and exhaust cam profiles for both intake and exhaust sides in the 2/4-stoke mode were designed for 2/4-stoke modes. Furthermore, a simulation model based on ADAMS was established to evaluate the dynamic valve motion and the contact force at different engine speeds. The dynamic simulation results show that the valve motion characteristics meet the challenges at the target engine speed of 3000 r/min. In two-stroke mode, the maximum intake valve lift could achieve 7.3 mm within 78°CaA, and the maximum exhaust valve lift could achieve 7.5 within 82°CaA on the exhaust side. In four-stroke mode, the maximum intake valve lift can achieve 8.8 mm within 140°CaA, and the maximum exhaust valve lift can achieve 8.4 mm within 140°CaA. The valve seating speeds are less than 0.3 m/s in both modes, and the fullness coefficients are more than 0.5 and 0.6 in the 2-stroke and 4-stroke mode, respectively. At the engine speed of 3000 r/min, the contact force on each component is acceptable, and the stress between cam and roller can meet the material requirement.

Mid-Diastolic Events (L Events): A Critical Review

Mid-diastolic events (L events) include three phenomena appreciable on echocardiography occurring during diastasis: mid-diastolic transmitral flow velocity (L wave), mid-diastolic mitral valve motion (L motion), and mid-diastolic mitral annular velocity (L’ wave). L wave is a known marker of advanced diastolic dysfunction in different pathological clinical settings such as left ventricle and atrial remodeling, overloaded states, and cardiomyopathies. Patients with L events have poor outcomes with a higher risk of developing heart failure symptoms and arrhythmic complications, including sudden cardiac death. The exact mechanism underlying the genesis of mid-diastolic events is not fully understood, just as the significance of these events in healthy young people or their presence at the tricuspid valve level. We also report an explicative case of a patient with L events studied using speckle tracking imaging of the left atrium and ventricle at the same reference heartbeat supporting the hypothesis of a post-early diastolic relaxation or a “two-step” ventricular relaxation for L wave genesis. Our paper seeks to extend knowledge about the pathophysiological mechanisms on mid-diastolic events and summarizes the current knowledge.

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

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.

An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

Parameterised patient-specific models of the heart enable quantitative analysis of cardiac function as well as estimation of regional stress and intrinsic tissue stiffness. However, the development of personalised models and subsequent simulations have often required lengthy manual setup, from image labelling through to generating the finite element model and assigning boundary conditions. Recently, rapid patient-specific finite element modelling has been made possible through the use of machine learning techniques. In this paper, utilising multiple neural networks for image labelling and detection of valve landmarks, together with streamlined data integration, a pipeline for generating patient-specific biventricular models is applied to clinically-acquired data from a diverse cohort of individuals, including hypertrophic and dilated cardiomyopathy patients and healthy volunteers. Valve motion from tracked landmarks as well as cavity volumes measured from labelled images are used to drive realistic motion and estimate passive tissue stiffness values. The neural networks are shown to accurately label cardiac regions and features for these diverse morphologies. Furthermore, differences in global intrinsic parameters, such as tissue anisotropy and normalised active tension, between groups illustrate respective underlying changes in tissue composition and/or structure as a result of pathology. This study shows the successful application of a generic pipeline for biventricular modelling, incorporating artificial intelligence solutions, within a diverse cohort.

X-Ray Characterization of Real Fuel Sprays for Gasoline Direct Injection

The effects of fuel blend properties on spray and injector performance has been investigated in a side-mount injector for Gasoline Direct Injection (GDI) using two certification fuel blends, Euro 5 and Euro 6. Several X-ray diagnostic techniques were conducted to characterize the injector and spray morphology. Detailed internal geometry of the GDI injector was resolved to 1.8 micrometers, through the use of hard X-ray tomography. The geometry characterization of this six-hole GDI, side mount injector, quantifies relevant hole and counterbore dimensions and reveals the intricate details within the flow passages, including surface roughness and micron-sized features. Internal valve motion was measured with a temporal resolution of 20 microseconds and a spatial resolution of 2.0 micrometers, for three injection pressures and several injector energizing strategies. The needle motion for both fuels exhibit similar lift profiles for common energizing commands. A combination of X-ray radiography and Ultra-Small-Angle X-ray Scattering (USAXS) was used to characterize the fuel mass distribution and the droplet sizing, respectively. Tomographic spray radiography revealed the near-nozzle distribution of fuel mass for each of the fuels, and the asymmetry produced by the angled nozzles. Under evaporative conditions, the two fuels show minor differences in peak fuel mass distribution during steady injection, though both exhibit fluctuations in injection during the early, transient phase. USAXS measurements of the path-specific surface area of the spray indicated lower peak values for the more evaporative conditions in the near nozzle region.

Improving the energy efficiency of a piston engine based on the use of a numerical method for forming the laws of motion of gas distribution valves

The increase in the power, economic and environmental performance of modern internal combustion engines is largely due to the improvement of the system that controls the gas exchange processes. Its characteristics determine the quality of filling and cleaning of the cylinders in various operating modes, the loss of power for gas exchange and, consequently, the indicator and effective indicators of the engine. The issues of mathematical modeling of gas exchange processes in combination with the study and improvement of the gas distribution mechanism are considered. The results of experimental and computational studies of gas exchange of tractor diesel are presented. Reserves for improving the gas exchange and the engine as a whole are identified based on the choice of optimal valve timing phases and valve motion laws. They provide a reduction in the modulus of the average pressure of the pump passages in the range of operating modes by 12 - 14 %, which contributed to a decrease in the specific effective fuel consumption by 1.4 ÷ 2.2 g/kWh. The above allows us to conclude that the use of a generalized step-by-step numerical method for synthesizing the law of motion of a pusher with an upper stand allows us to obtain the maximum efficiency characteristics of the gas distribution in the presence of a number of restrictions.