Zero-dimensional Model Research Articles

Uncertainty quantification of microcirculatory characteristic parameters for recognition of cardiovascular diseases

BackgroundCardiovascular disease is one of the leading causes of death worldwide. However, according to studies, 90% of cardiovascular diseases can be prevented. Cardiovascular function parameters are an important basis for the diagnosis of cardiovascular diseases. The pulse wave also contains a wealth of physiological and pathological information, which can reflect the trend of cardiac function parameters at an early stage, so the measurement and analysis of the pulse wave signal becomes more and more important. The wearable pulse signal acquisition device has gradually become a new trend. In the mobile health scenario, convenient use is the prerequisite for long-term and rapid health monitoring. The data containing diverse pulse wave signals is the basis for obtaining more comprehensive and accurate human physiopathological information. Accurate data analysis and processing is the key to realizing the important goal of cardiovascular health monitoring. ObjectiveBased on the concept of mobile health care, wearable devices are developed to obtain physiological signals. The zero-dimensional model and the optimization algorithm are combined to complete the uncertainty quantification of the microcirculation parameters. Then, a feature set containing the cardiovasvular parameters can be constructed. The machine learning algorithm can be used to build a model that can accurately realize cardiovascular disease identification. MethodsThis paper adopts laboratory-developed equipment to acquire the wrist pulse wave and fingertip volume pulse wave. A total of 323 samples were collected from healthy populations, hypertensive patients and patients with coronary heart disease (CHD). The pulse blood flow model in fingertip microcirculation is established, and the uncertainty quantification of model parameters is completed based on slime mold algorithm (SMA). After comparing and analyzing the performance of four algorithms on pulse wave classification, the identification model of cardiovascular diseases is established based on the microcirculatory characteristic parameter set and random forest algorithm (RF). ResultsRF showed good classification performance among the four classification algorithms. The identification accuracy of the model built on the microcirculatory characteristic parameter set and RF algorithm all reached more than 88%. The highest recognition accuracy was 95.51% for coronary heart disease samples, 92.11% for healthy samples, and 88.55% for hypertensive samples. It can be seen that the model based on RF algorithm has a good ability to distinguish the characteristic parameters in different cardiovascular health states. ConclusionsThe wearable device designed in this paper can facilitate the daily health monitoring of cardiovascular disease. By using the combination of the physical model and machine learning model, the uncertainty quantification of microcirculation parameters and the identification of cardiovascular disease was finally completed. The recognition model based on machine learning provides a new idea and method for the research of cardiovascular health monitoring through pulse waves.

A hybrid analytical and numerical model for cross-over and performance decay in a unit cell vanadium redox flow battery

Developing an accurate and efficient model for cross-over is critical for improving the long-term performance of redox flow batteries (RFBs). This work presents a hybrid analytical and numerical model that combines a two-dimensional analytical solution to the active species, a one-dimensional analytical model for cross-over mechanisms, and a zero-dimensional numerical model for outlet concentrations of reactants. By comparing with experiment over 41 cycles (ca. 144 h), the model reported a mean voltage difference of 0.0089 V, mean time difference of 14 s per cycle (of 3.5 h), maximum relative difference for capacity and energy of 1.34% and 1.63%, respectively. The predicted mean concentrations for V2+, V3+, and VO2+ in membrane are 65% ∼ 77% of measured values from the literature. Upon validation, the model reproduced behaviors in electrolyte imbalance similar to those observed in experiments and numerical models, and revealed the control of cross-over, self-discharge, stoichiometry of side reactions, and Coulombic efficiency on electrolyte imbalance. The model also demonstrates excellent computational efficiency for simulating 41 cycles (around 37,000 points) within 3 ∼ 4 s. The demonstrated efficiency and accuracy for predicting cross-over and its impacts on voltage, capacity&energy decay, membrane concentrations, and electrolyte imbalance makes it a reliable tool for optimizing RFBs’ long-term performance.

Numerical Simulation of Hypervelocity Launch Process of Cased Telescoped Ammunition Armor-piercing Projectile

In order to study the interior ballistic characteristics of cased telescoped ammunition (CTA) armor-piercing projectile in the hypervelocity launch process, in this paper, based on the secondary ignition and propellant combustion technology, the zero-dimensional internal ballistic model of CTA is established. At the same time, in order to improve the lightweight of CTA and the energy utilization rate of the launch process, combustible cartridge materials are applied to the structure of cased telescoped ammunition. Numerical simulation of 105 mm CTA grenade shooting experiment has verified its correctness. On this basis, the internal ballistic process of 105 mm CTA armor-piercing projectile with semi-combustible cartridge is predicted and analyzed. When main charge propellant weight is 6 kg, the maximum pressure is 569.5 MPa and the muzzle velocity can reach 1702 m/s.

Prediction of knock intensity and validation in an optical SI engine

To provide fundamental insights into the underlying mechanisms of knock occurrence in a spark-ignition (SI) engine, optical diagnostic measurements were performed at advanced spark timing under boosted conditions. Employing high-speed imaging, deflagration-to-detonation transition (DDT) processes were recorded. The states and locations of the unburned mixture more susceptible to autoignition were identified by statistically analyzing the recorded images of the spatial distribution of flame propagation and end-gas autoignition under distinct combustion modes. The relationships between the unburned mixture fraction (UMF) and two metrics associated with the knock intensity levels, namely the maximum amplitude of pressure oscillation (MAPO) and the peak in-cylinder pressure, Pmax, were examined. In contrast to common beliefs, we found that the knock intensity represented by MAPO is not strongly correlated with UMF because UMF alone is inherently insufficient to represent the thermo-chemical properties of the bulk mixture inhomogeneities and its chemical reactivity at the onset of end-gas autoignition occurrence. Instead, a much stronger correlation was found between UMF and a newly proposed normalized-peak-pressure metric, allowing an a prior prediction of Pmax, which is a critical parameter associated with the propensity of engine failure if Pmax exceeds the strength limit of the engine. To quantitatively predict MAPO under SI-engine conditions, a refined model was proposed by imposing the pressure and temperature traces into the zero-dimensional (0-D) reactor model, such that the thermochemical properties of the transient mixture state at the onset of end-gas autoignition are properly incorporated into the predictive criteria. The model was validated against a large experimental dataset consisting of 619 cycles under various operating conditions. The results show that the model can reliably predict the end-gas autoignition and the knock intensity levels dictated by MAPO regardless of the stochastic nature of the knock development process.

Combustion behavior of single iron particles, Part II: A theoretical analysis based on a zero-dimensional model

Following the ignition and solid-to-liquid phase transition of a fine (on the order of 10–100µm in diameter) iron particle, the self-sustained combustion of a liquid-phase droplet of iron and its oxides takes place. The objective of the current work is to develop an interpretive and explanatory model for the liquid-phase combustion of a single fine iron particle. A zero-dimensional physicochemical model is developed assuming fast internal processes, such that the combustion rate is limited by the rate of external oxygen (O2) transport. The model considers a particle covered by a shell of liquid-phase FeO enclosing a core of liquid-phase Fe. Stefan flow and diffusion are considered for the gas-transport of O2, while the gas-transport of gas-phase Fe and FeO are calculated via diffusion only. The outward gas-phase Fe and FeO consume inward-transported O2 to stoichiometrically form hematite (Fe2O3), and the remaining oxygen that reaches the particle surface is entirely consumed to form liquid-phase FeO. The time history of simulated particle temperature shows consistent overprediction of the peak particle temperature when compared to experimental temperature measurements, indicating that the assumption of fast internal kinetics may be incorrect. The model is also unable to capture the apparent slow cooling rate observed in experiments. A further analysis is performed through a heuristic model with a calibrated reaction-rate law, where the internal diffusion of reactive Fe and O ions may become rate-limiting. The calibration of the pre-exponential factor in the Arrhenius term to match the experimental peak temperature yielded good agreement of time to peak temperature, as well as the slow cooling rate. The heuristic model considering internal diffusion predicts a plateau in peak temperature with increasing oxygen concentration. Possible uncertainties of the models, as well as future work, are discussed.

Competitive formation of NO, NO2, and O3 in an air-flowing plasma reactor: A central role of the flow rate

Beyond ozone (O3) generation, diverse applications of dielectric barrier discharge (DBD) have been developed. When using ambient air, one of the longstanding challenges of DBD reactors has been the selective production of nitric oxide (NO), nitrogen dioxide (NO2), and O3. In this work, we report the competitive formation of NO, NO2, and O3 in an air-flowing surface DBD reactor. The temporal evolution of each chemical species was obtained by using in situ optical absorption spectroscopy. The possibility to select the plasma-chemistry mode (i.e., NO–, NO2–, or O3-dominant conditions) by adjusting the gas flow rate of the reactor was demonstrated with constant temperature and input power. As the flow rate increased from 260 to 1380 standard cubic centimeters per minute, the dominant chemical species changed from NO to NO2 [the achieved purity of NO2/(NO + NO2 + O3) was 99%]. With even higher flow rates, O3 appeared and dominated in the reactor [O3/(NO + NO2 + O3) was nearly 100%]. The experimental results were compared with zero-dimensional modeling, and the reactions involving NO, NO2, and O3 were analyzed in depth. Our findings will provide great guidance for future studies and for plasma applications of DBD reactors.

Study on selected parameters of engine with the active combustion chamber

The present study was focused on the combustion engine with a variable compression ratio (VCR), namely the four-stroke air-cooled engine with the active combustion chamber (ACC). An indicated pressure, torque, power, and specific fuel consumption of that engine were investigated experimentally as a goal of the present study. Experiments were conducted using two versions of an engine. Two parameters particularly influencing the ACC engine performance including the maximum compression ratio CRmax and the indicator γfm determining the correct operation of the ACC system, were described. It was found that the ACC engine allowed avoiding detonation combustion without changing the amount and composition of the combustible mixture, and even without delaying the ignition advance angle. In addition, the possible range of control of the combustion process allowed the ACC engine to operate with different types of hydrocarbon fuels, for example, in the form of petrol with various alcohol admixtures . The very intense flow of the combustible mixture inside the cylinder of the ACC engine allowed describing the combustion in the ACC engine with zero-dimensional mathematical models with the dual Vibe function providing the proper characterization of the heat release process. The use of very high maximum compression ratios allows the ACC engine to operate to a certain extent as a Homogeneous Charge Compression Ignition (HCCI) engine with high lambda coefficients.

Modelling The Hemodynamics of Coronary Ischemia

Acting upon clinical patient data, acquired in the pathway of percutaneous intervention, we deploy hierarchical, multi-stage, data-handling protocols and interacting low- and high-order mathematical models (chamber elastance, state-space system and CFD models), to establish and then validate a framework to quantify the burden of ischaemia. Our core tool is a compartmental, zero-dimensional model of the coupled circulation with four heart chambers, systemic and pulmonary circulations and an optimally adapted windkessel model of the coronary arteries that reflects the diastolic dominance of coronary flow. We guide the parallel development of protocols and models by appealing to foundational physiological principles of cardiac energetics and a parameterisation (stenotic Bernoulli resistance and micro-vascular resistance) of patients’ coronary flow. We validate our process first with results which substantiate our protocols and, second, we demonstrate good correspondence between model operation and patient data. We conclude that our core model is capable of representing (patho)physiological states and discuss how it can potentially be deployed, on clinical data, to provide a quantitative assessment of the impact, on the individual, of coronary artery disease.

Numerical Simulation of Liquid–Liquid Dispersion Characteristics in Screen-Type Static Mixers by Two Simplified Zero-Dimensional Models

In this work, two different zero-dimensional (0D) modeling approaches as well as PLIF measurements were adopted to analyze liquid–liquid dispersion characteristics in screen-type static mixers. On the framework of the population balance model (PBM), the 0D lumped model based on the volume-averaged turbulent dissipation rate (εAvg) was solved by the fixed pivot technique to predict the evolution of the droplet size distribution (DSD). The 0D homogeneous model accounts for the spatial inhomogeneity behind screens by coupling a probability density function of ε, which was obtained by two-phase flow CFD simulations. Different breakage kernels were applied and compared. Predicted results indicate that the 0D homogeneous model is more predictive to the evolution of the DSD with high prediction accuracy. Both simplified 0D models are computationally cheap compared with time-consuming CFD simulations. The effects of the screen geometry, oil volume fraction, and operating conditions on the dispersion performance were also investigated.

Energy and exergy analyses of hydrogen-fueled spark ignition engine with various air excess ratios and ignition timings

Many studies have been conducted to determine the efficiency and emission of hydrogen internal combustion engines. However, only a few of these investigations have performed exergy analysis (especially when important parameters, such as excess air ratio, vary) based on the second law of thermodynamics. Accordingly, in this study, experiments were conducted on a commercial automotive spark ignition engine. The engine is modified to run on hydrogen as fuel at 1500 rpm with various spark timings and excess air ratios under a wide-open throttle. In addition, zero-dimensional two-zone modeling was conducted by matching and examining the experimental results considering both energy (first law of thermodynamics) and exergy (second law of thermodynamics) analyses.The results indicate that with the same excess air ratio, the trend exergy analysis results, such as increasing exhaust exergy loss and decreasing heat transfer exergy loss by retarding ignition timing, are similar to the energy analysis results. Additionally, irreversibility increased as the spark timing retarded owing to the reduction in combustion temperature caused by prolonged combustion duration. In considering various excess air ratios, the gap between the energy and exergy analysis results was evaluated: the heat transfer exergy loss increased from 27% to 31%, the exhaust exergy loss decreased from 20% to 15%, and irreversibility was maintained at approximately 10%. Irreversibility was maintained because of the offset between the irreversibility increment (due to the decrease in combustion temperature) and decrement (due to the decrease in fuel amount). Moreover, the relationship between the thermo-mechanical exergy rise rate and behavior of each exergy component was examined.

Modelling of fast fueling of pressurized hydrogen tanks for maritime applications

This paper studies fast fueling of gaseous hydrogen into large hydrogen (H2) tanks suitable for maritime applications. Three modeling methods have been developed and evaluated: (1) Two-dimensional computational fluid dynamic (CFD) modeling, (2) One-dimensional wall discretized modeling, and (3) Zero-dimensional modeling. A detailed 2D CFD simulation of a small H2-tank was performed and validated with data from literature and then used to simulate a large H2-tank. Results from the 2D-model show non-uniform temperature distribution inside the large tank, but not in the small H2-tank. The 1D-model can predict the mean temperature in small H2-tanks, but not the inhomogeneous temperature field in large H2-tanks. The 0D-model is suitable as a screening tool to obtain rough estimates. Results from the modeling of the large H2-tank show that the heat transfer to the wall during fast filling is inhibited by heat conduction in the wall which leads to an unacceptably high mean hydrogen temperature.

Diesel Engine Fault Prediction Using Artificial Intelligence Regression Methods

Predictive maintenance has been employed to reduce maintenance costs and production losses and to prevent any failure before it occurs. The framework proposed in this work performs diesel engine prognosis by evaluating the absolute value of the failure severity using random forest (RF) and multilayer perceptron (MLP) neural networks. A database was implemented with 3500 failure scenarios to overcome the problem of inducing destructive failures in diesel engines. Diesel engine failure signals were developed with the zero-dimensional thermodynamic model inside a cylinder coupled with the crankshaft torsional vibration model. Artificial neural networks and random forest regression models were employed for classifying and quantifying failures. The methodology was applied alongside an engine simulator to assess effectiveness and accuracy. The best-fitting performance was obtained with the random forest regressor with an RMSE value of 0.10 ± 0.03%.

Development of a numerical method for the rapid prediction of ignition performance of biomass particles

Ignition is a critical step of the combustion process of biomass, due to its substantial impact on flame characteristics, process efficiency, and pollutant formation. This paper aims to develop a robust zero-dimensional ignition model as an effective tool to quickly estimate the ignition behaviour of biomass fuels. The numerical approach integrates an established devolatilization model with a well-tested mechanism for gas-phase reaction kinetics. The ignition delay time is determined from the sum of the devolatilization time and ignition time of the evolved gas species, as specified by the maximum in the OH radical concentration. The central premise of the modelling routine is that kinetics govern the ignition delay during biomass initial heating and thermochemical conversion which makes the elemental fuel composition the most important fuel property and heating rate the most important reactor condition. The predictions of the model are tested and verified against published experimentally measured ignition data for seven pulverized biomass fuels and are found to be in good agreement. The model for the first time can reasonably distinguish ignition behaviours of different biomass fuels, enabling its use in wider industrial applications.

HCOO− aq degradation in droplets by OHaq in an atmospheric pressure glow discharge

Plasmas in contact with liquids can degrade organic molecules in a solution, as reactive oxygen and nitrogen species produced in the plasma solvate into the liquid. Immersing small droplets (tens of microns in diameter) in the plasma can more rapidly activate the liquid compared to treating a large volume of liquid with a smaller surface-to-volume ratio. The interactions between a radio frequency glow discharge sustained in He/H2O and a water droplet containing formate (HCOO− aq) immersed in and flowing through the plasma were modeled using a zero-dimensional global plasma chemistry model to investigate these activation processes. HCOO − aq interacts with OHaq, which is produced from the solvation of OH from the gas phase. The resulting HCOO − aq concentrations were benchmarked with previously reported experimental measurements. The diameter of the droplet, initial HCOO − aq concentration, and gas flow rate affect only the HCOO − aq concentration and OHaq density, leaving the OH density in the gas phase unaffected. Power deposition and gas mixture (e.g. percentage of H2O) change both the gas and liquid phase chemistry. A general trend was observed: during the first portion of droplet exposure to the plasma, OHaq primarily consumes HCOO − aq. However, O2 − aq, a byproduct of HCOO − aq consumption, consumes OHaq once O2 − aq reaches a critically large density. Using HCOO − aq as a surrogate for OHaq-sensitive contaminants, combinations of residence time, droplet diameter, water vapor density, and power will determine the optimum remediation strategy.

A universally applicable 0D combustion model with a novel analysis of the critical factors affecting combustion in spark-ignition engines

Notwithstanding the continuous technological development of spark-ignition engines, the physics of in-cylinder phenomena has not been explicitly clarified to date. Furthermore, the advent of diverse engine technologies and in-cylinder compositions has increased the difficulty of understanding these phenomena using existing analysis methods. Under those circumstances, the modeling based on the fundamental combustion behaviors is becoming critical for analyzing combustion. Built upon the zero-dimensional flame surface density model, therefore, the models proposed herein attempt to reproduce a vast majority of combustion behaviors revealed to date, such as differential diffusion, thermo-diffusive instability, cutoff scales, distributed reaction, and flame quenching. As a result, we successfully predicted the combustion of numerous operating points of four different SI engines, including the extremely lean operating points of gasoline/air mixture up to [Formula: see text]. Furthermore, the extensive thermodynamic ranges are explored for the validation of the models with the aid of continuously variable valve timing modules; the pressure and the calculated temperature of the unburned mixture at ignition timing are varied from 4.5 to 28.6 bar and 559–794 K, respectively. In this study, we suggest an expression for a fully-developed turbulent flame speed well-fitted with the explored ranges of chemical and thermodynamic states by inspecting the influencing factors for flame wrinkling. Additionally, we analyze combustion using the developed models and identify several distinctive features of both stoichiometric and lean-burn points. Lastly, this study reveals the importance of flame thickness (or inner cutoff), which has a comparable or more severe effect on early flame propagation compared to planar laminar flame speed.

Investigation of Summertime Ozone Formation and Sources of Volatile Organic Compounds in the Suburb Area of Hefei: A Case Study of 2020

Ground surface ozone (O3) is an emerging concern in China due to its complex formation process. In August 2020, field measurements of O3, NOx, and volatile organic compounds (VOCs) were carried out in Hefei’s western suburbs. The pollution features of VOCs and O3 formation were thoroughly analyzed. The total VOC concentration was 42.26 ppb, with the dominant contributor being oxygenated VOCs (OVOCs). Seven emission sources were recognized using the positive matrix factorization (PMF) model, including aged air masses, combustion sources, fuel evaporation, industrial emissions, vehicular emission, solvent utilization, and biogenic emission. Ozone generation mainly occurred under an NOx-limited regime based on the zero-dimensional box model analysis. According to the scenario analysis, the 13% cut in O3 might be achieved by the 10% and 30% reduction in NOx and VOCs, respectively. The O3 budget analysis demonstrates its high ozone production rate during the pollution period. The influence of regional transport cannot be ignored for high O3 pollution. This paper provides scientific evidence for O3 production and the strategies of reducing O3 by controlling its precursors.

Experimental and numerical modeling of plasma start-up assisted by electron drift injection on J-TEXT

Start-up is one of the critical phases for tokamak discharge. The electron drift injection (EDI) system has been developed on J-TEXT for start-up studies. A breakdown experiment with EDI-assisted start-up has been conducted, which verified the effect of pre-ionization by EDI to achieve start-up at a lower ohmic field voltage. A zero-dimensional (0D) model has been developed to explain the effect of EDI quantitatively. The comparison between the experiment and simulation verified the credibility of this model. Based on this model, a comparison between pure ohmic heating start-up and EDI-assisted start-up was presented, showing that EDI improved ionization, causing a lower delay to the peak of hydrogen ionization and radiation losses and a smoother rise in the electron and ion energy. This result quantitatively verified the pre-ionization effect of EDI on start-up . The effects of injecting different currents and electron energy were investigated. A better pre-ionization effect was realized by increasing the injected current, which can be a reference for the upgrading of the EDI system.

Influence of hydrodynamic and functional nonlinearities of blood flow in the cerebral vasculature on cerebral perfusion and autoregulation pressure reserve

Ensuring the transport of oxygenated blood to the brain is one of the priorities of the human body. In the literature, there are many models of cerebral circulation with different levels of complexity used to assess pathological conditions, support clinical decisions, and learn about the relationships governing cerebral circulation. This paper presents a zero-dimensional cerebral circulation model that considers hydrodynamic nonlinearities and autoregulation mechanisms. The model has been verified using a computational fluid dynamics (CFD) model of the Circle of Willis (CoW) and its supplying and outgoing branches. Despite the considerable simplicity, the presented model captured the dominant features of cerebral circulation and provides good agreement with the CFD model. The errors in relation to the CFD model did not exceed 2.6% and 9.9% for the symmetrical and highly asymmetrical CoW configurations, respectively. The practical application of the model was demonstrated for predicting the autoregulation pressure reserve for different diameters of natural anastomoses: Posterior and Anterior Communicating Arteries. The advantages and limitations of the model were discussed.

Biogenic volatile organic compounds dominated the near-surface ozone generation in Sichuan Basin, China, during fall and wintertime

The complex air pollution driven by both Ozone (O3) and fine particulate matter (PM2.5) significantly influences the air quality in the Sichuan Basin (SCB). Understanding the O3 formation during autumn and winter is necessary to understand the atmospheric oxidative capacity. Therefore, continuous in-site field observations were carried out during the late summer, early autumn and winter of 2020 in a rural area of Chongqing. The total volatile organic compounds (VOCs) concentration reported by a Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS) were 13.66 ± 9.75 ppb, 5.50 ± 2.64 ppb, and 9.41 ± 5.11 ppb in late summer, early autumn and winter, respectively. The anthropogenic VOCs (AVOCs) and biogenic VOCs (BVOCs) were 8.48 ± 7.92 ppb and 5.18 ± 2.99 ppb in late summer, 3.31 ± 1.89 ppb and 2.19 ± 0.93 ppb in autumn, and 6.22 ± 3.99 ppb and 3.20 ± 1.27 ppb in winter. A zero-dimensional atmospheric box model was employed to investigate the sensitivity of O3-precursors by relative incremental reactivity (RIR). The RIR values of AVOCs, BVOCs, carbon monoxide (CO), and nitrogen oxides (NOx) were 0.31, 0.71, 0.09, and -0.36 for late summer, 0.24, 0.59, 0.22, and -0.38 for early autumn, and 0.30, 0.64, 0.33 and -0.70 for winter, and the results showed that the O3 formation of sampling area was in the VOC-limited region, and O3 was most sensitive to BVOCs (with highest RIR values, > 0.6). This study can be helpful in understanding O3 formation and interpreting the secondary formation of aerosols in the winter.

Real-Time Simulation of CNG Engine and After-Treatment System Cold Start Part 1: Transient Engine-Out Emission Prediction Using a Stochastic Reactor Model

<div class="section abstract"><div class="htmlview paragraph">During cold start of natural gas engines, increased methane and formaldehyde emissions can be released due to flame quenching on cold cylinder walls, misfiring and the catalyst not being fully active at low temperatures. Euro 6 legislation does not regulate methane and formaldehyde emissions. New limits for these two pollutants have been proposed by CLOVE consortium for Euro 7 scenarios. These proposals indicate tougher requirements for aftertreatment systems of natural gas engines.</div><div class="htmlview paragraph">In the present study, a zero-dimensional model for real-time engine-out emission prediction for transient engine cold start is presented. The model incorporates the stochastic reactor model for spark ignition engines and tabulated chemistry. The tabulated chemistry approach allows to account for the physical and chemical properties of natural gas fuels in detail by using a-priori generated laminar flame speed and combustion chemistry look-up tables. The turbulence-chemistry interaction within the combustion chamber is predicted using a K-k turbulence model. The optimum turbulence model parameters are trained by matching the experimental cylinder pressure and engine-out emissions of nine steady-state operating points.</div><div class="htmlview paragraph">Subsequently, the trained engine model is applied for predicting engine-out emissions of a WLTP passenger car engine cold start. The predicted engine-out emissions comprise nitrogen oxide, carbon monoxide, carbon dioxide, unburnt methane, formaldehyde, and hydrogen. The simulation results are validated by comparing to transient engine measurements at different ambient temperatures (-7°C, 0°C, 8°C and 20°C). Additionally, the sensitivity of engine-out emissions towards air-fuel-ratio (λ=1.0 and λ=1.3) and natural gas quality (H-Gas and L-Gas) is investigated.</div></div>