Effects of working conditions on the operating characteristics of an injection system applied on CNG fueled vehicles

A model-based study is conducted to examine the operating characteristics of an injection system applied on CNG fueled vehicles. This injection system is a combination of an electric pressure regulator, a rail tube, and a solenoid injector. The electric pressure regulator has a great potential to be widely used in injection systems of natural gas-fueled engines due to its flexible operation, which can help to improve the engine performance and reduce emission. This paper presents a simulation study using mathematical models to describe and analyze the operating characteristics of the gaseous fuel injection system, in which models of electric pressure regulator, solenoid fuel injector, and control model for electric pressure regulator are presented. The simulation results are compared with experimental data to validate the simulation models. Effects of working conditions, including coil resistance of the electric pressure regulator, inlet gas pressure, and set pressure in the rail tube, on the operating characteristics of the gaseous fuel injection system are investigated. Simulation results show that when the coil resistance of the electric pressure regulator is increased from 3.1 Ω to 4.1 Ω, the maximum fluctuation of the controlled gas pressure in the rail tube is reduced from 0.017 to 0.012 MPa, respectively. By decreasing the inlet gas pressure of the electric pressure regulator from 2.5 to 2.3 MPa, the controlled gas pressure in the rail tube is more stable with the maximum fluctuation significantly reduced from 0.012 to 0.002 MPa, respectively, which leads to stability in injection flow rate. The increase of set pressure in the rail tube from 0.5 to 0.7 MPa can help to improve the stability of the controlled gas pressure in the rail tube with the maximum fluctuation respectively reduced from 0.002 to 0.001 MPa.

Machine Learning for Monitoring of the Solenoid Valves Coil Resistance Based on Optical Fiber Squeezer

Solenoid valves represent indispensable elements in various engineering systems. Their failure can lead to unexpected problems. This failure may be caused by fluctuations in the coil resistance of the electromagnetic solenoid (EMS) which actuates these solenoid valves. Hence the need to monitor this parameter for a preventive maintenance of these actuators. The proposed method consists to use supervised machine learning to monitor coil resistance of the EMS valve. The EMS valve is coupled to an optical fiber squeezer which, acts as a force sensor. The solenoid armature applies a mechanical force to the optical fiber and changes the polarization state of the light that travels through the optical fiber and then this force infects the power of the light. A Simulink model is used to determine the open loop system step response. The identification of the system allows obtaining its transfer function, which depends on the parameters of the EMS and in particular on its coil resistance. By varying the coil resistance while fixing the other physical parameters of the EMS, we generate a database whose elements are the coefficients of the transfer function of the solenoid open loop and the electrical resistance of its coil. The generated database is used for training several supervised machine learning models whose predictors are the elements of the transfer function; the response is the coil resistance. The Gaussian process for regression allows to predict the variations of the coil resistance with the smallest relative error although it takes a relatively long time for the training compared to the other models used.

JUUL ‘new technology’ pods exhibit greater electrical power and nicotine output than previous devices

In 2019, JUUL Labs began marketing in the European Union ‘new technology’ pods that incorporated a new wick that it claimed provided ‘more satisfaction’. In this study, we compared design and materials of construction, electrical characteristics, liquid composition and nicotine and carbonyl emissions of new technology JUUL pods to their predecessors. Consistent with manufacturer’s claims, we found that the new pods incorporated a different wicking material. However, we also found that the new pod design resulted in 50% greater nicotine emissions per puff than its predecessor, despite exhibiting unchanged liquid composition, device geometry and heating coil resistance. We found that when connected to the new technology pods, the JUUL power unit delivered a more consistent voltage to the heating coil. This behaviour suggests that the new coil-wick system resulted in better surface contact between the liquid and the temperature-regulated heating coil. Total carbonyl emissions did not differ across pod generations. That nicotine yields can be greatly altered with a simple substitution of wick material underscores the fragility of regulatory approaches that centre on product design rather than product performance specifications.

Effects of Manufacturing Variation in Electronic Cigarette Coil Resistance and Initial Pod Mass on Coil Lifetime and Aerosol Generation

This work investigated the effects of manufacturing variations, including coil resistance and initial pod mass, on coil lifetime and aerosol generation of Vuse ALTO pods. Random samples of pods were used until failure (where e-liquid was consumed, and coil resistance increased to high value indicating a coil break). Initial coil resistance, initial pod mass, and e-liquid net mass ranged between 0.89 to 1.14 [Ω], 6.48 to 6.61 [g], and 1.88 to 2.00 [g] respectively. Coil lifetime was µ (mean) = 158, σ (standard deviation) = 21.5 puffs. Total mass of e-liquid consumed until coil failure was µ = 1.93, σ = 0.035 [g]. TPM yield per puff of all test pods for the first session (brand new pods) was µ = 0.0123, σ = 0.0003 [g]. Coil lifetime and TPM yield per puff were not correlated with either variation in initial coil resistance or variation in initial pod mass. The absence of e-liquid in the pod is an important factor in causing coil failure. Small bits of the degraded coil could be potentially introduced to the aerosol. This work suggests that further work is required to investigate the effect of e-liquid composition on coil lifetime and TPM yield per puff.

Effects of Manufacturing Variation in Electronic Cigarette Coil Resistance and E-liquid Characteristics on Coil Lifetime and Aerosol Generation

This work investigated the effects of manufacturing variations including coil resistance, initial pod mass, and e-liquid color on coil lifetime and aerosol generation of Vuse ALTO pods. Random samples of pods were used until failure (where e-liquid was consumed, and coil resistance increased to high value indicating a coil break). Initial coil resistance, initial pod mass, and e-liquid net mass ranged between 0.89 to 1.14 [], 6.48 to 6.61 [g], and 1.88 to 2.00 [g] respectively. Coil lifetime with light color e-liquid was (mean) = 149, (standard deviation) = 10.7 puffs while pods with dark color e-liquid was = 185, = 22.7 puffs with a difference of ~36 puffs (p <0.001). Total mass of e-liquid consumed until coil failure was = 1.93, = 0.035 [g]. TPM yield per puff of all test pods for the first session (brand new pods) was = 0.0123, = 0.0003 [g]. During usage, TPM yield per puff of pods with light color e-liquid was relatively steady while it was continuously decreasing for pods with dark e-liquid. Coil lifetime and TPM yield per puff were not correlated with either variation in initial coil resistance or variation in initial pod mass. The absence of e-liquid in the pod is an important factor in causing coil failure. Small bits of the degraded coil could be potentially introduced to the aerosol. There is a potential correlation of e-liquid color with both coil lifetime and TPM yield per puff. Change of e-liquid color might have been a result of oxidation which changed some nicotine into nicotyrine.

Radio Frequency Coils for Hyperpolarized 13C Magnetic Resonance Experiments with a 3T MR Clinical Scanner: Experience from a Cardiovascular Lab

Hyperpolarized 13C magnetic resonance (MR) is a promising technique for the noninvasive assessment of the regional cardiac metabolism since it permits heart physiology studies in pig and mouse models. The main objective of the present study is to resume the work carried out at our electromagnetic laboratory in the field of radio frequency (RF) coil design, building, and testing. In this paper, first, we review the principles of RF coils, coil performance parameters, and estimation methods by using simulations, workbench, and MR imaging experiments. Then, we describe the simulation, design, and testing of different 13C coil configurations and acquisition settings for hyperpolarized studies on pig and mouse heart with a clinical 3T MRI scanner. The coil simulation is performed by developing a signal-to-noise ratio (SNR) model in terms of coil resistance, sample-induced resistance, and magnetic field pattern. Coil resistance was calculated from Ohm’s law and sample-induced resistances were estimated with a finite-difference time-domain (FDTD) algorithm. In contrast, the magnetic field per unit current was calculated by magnetostatic theory and a FDTD algorithm. The information could be of interest to graduate students and researchers working on the design and development of an MR coil to be used in 13C studies.

Method for Quantifying Variation in the Resistance of Electronic Cigarette Coils

In electronic nicotine delivery systems (ENDS), coil resistance is an important factor in the generation of heat energy used to change e-liquid into vapor. An accurate and unbiased method for testing coil resistance is vital for understanding its effect on emissions and reporting results that are comparable across different types and brands of ENDS and measured in different laboratories. This study proposes a robust, accurate and unbiased method for measuring coil resistance. An apparatus is used which mimics the geometric configuration and assembly of ENDS reservoirs, coils and power control units. The method is demonstrated on two commonly used ENDS devices—the ALTO by Vuse and JUUL. Analysis shows that the proposed method is stable and reliable. The two-wire configuration introduced a positive measurement bias of 0.086 (Ω), which is a significant error for sub-ohm coil designs. The four-wire configuration is far less prone to bias error and is recommended for universal adoption. We observed a significant difference in the coil resistance of 0.593 (Ω) (p < 0.001) between the two products tested. The mean resistance and standard deviation of the reservoir/coil assemblies was shown to be 1.031 (0.067) (Ω) for ALTO and 1.624 (0.033) (Ω) for JUUL. The variation in coil resistance between products and within products can have significant impacts on aerosol emissions.

Method for Quantifying Variation in Resistance of Electronic Cigarette Coils

In Electronic Nicotine Delivery System (ENDS), coil resistance is an important factor in the generation of heat energy used to change e-liquid into vapor. An accurate and unbiased method for testing coil resistance is vital for understanding its effect on emissions and reporting results that are comparable across different types and brands of ENDS and measured in different laboratories. This study proposes a robust, accurate and unbiased method for measuring coil resistance. An apparatus is used which mimics the geometric configuration and assembly of ENDS pods and power control units. The method is demonstrated on two commonly used ENDS devices, the ALTO by Vuse and JUUL. Analysis shows that the proposed method is stable and reliable. The two-wire configuration introduced a positive measurement bias of 0.086 [ohm], which is a significant error for sub-ohm coil designs. The four-wire configuration is far less prone to bias error. We observed a significant difference in coil resistance of 0.593 [ohm] (p&lt;0.001) between the two products tested. The mean resistance and standard deviation of the pod coil assemblies was shown to be 1.031 (0.067) [ohm] for ALTO and 1.624 (0.033) [ohm] for JUUL. The variation in coil resistance between products and within products can have significant impacts on aerosol emissions.