Multi-zone Model Research Articles

Thermodynamic Modeling and Analysis of Boil-off Gas Generation and Self-Pressurization in Liquefied Carbon Dioxide Tanks

<i>The importance of the safe transport of liquefied carbon dioxide (LCO<sub>2</sub>) is increasing owing to environmental issues. When transporting a low-temperature liquid, boil-off gas generation and self-pressurization occur due to heat ingress, affecting the holding time of a low-temperature liquid tank. This study developed and compared three thermodynamic self-pressurization models to estimate the holding time of LCO<sub>2</sub>: Thermal homogeneous model (THM), Thermal two-zone model (TTZM), and Thermal multi-zone model (TMZM). Thermodynamic differential equations were solved for THM, and software was used for TTZM. For TMZM, the parameters were optimized using experimental data to determine the heat ratio parameter f and heat transfer parameters K<sub>1</sub> and K<sub>2</sub>. THM and TTZM estimated an unreasonably long holding time, approximately 42 days. The TMZM, however, showed a satisfactory holding time of 12–13 days. These results can help predict the self-pressurization in the storage tanks of LCO<sub>2</sub> and be applied to actual LCO<sub>2</sub> carrier cargo handling systems, with the modeling results indicating that the 12–13 days of LCO<sub>2</sub> self-pressurization based on the TMZM appears to be the most suitable.</i>

Evolution of lithium in the disc of the Galaxy and the role of novae

Lithium plays a crucial role in probing stellar physics, stellar and primordial nucleosynthesis, and the chemical evolution of the Galaxy. Stars are considered to be the main source of Li, yet the identity of its primary stellar producer has long been a matter of debate. In light of recent theoretical and observational results, we investigate in this study the role of two candidate sources of Li enrichment in the Milky Way, namely asymptotic giant branch (AGB) stars and, in particular, novae. We utilised a one-zone Galactic chemical evolution (GCE) model to assess the viability of AGB stars and novae as stellar sources of Li. We used recent theoretical Li yields for AGB stars, while for novae we adopted observationally inferred Li yields and recently derived delay time distributions (DTDs). Subsequently, we extended our analysis by using a multi-zone model with radial migration to investigate spatial variations in the evolution of Li across the Milky Way disc and compared the results with observational data for field stars and open clusters. Our analysis shows that AGB stars clearly fail to reproduce the meteoritic Li abundance. In contrast, novae appear as promising candidates within the adopted framework, allowing us to quantify the contribution of each Li source at the Sun's formation and today. Our multi-zone model reveals the role of the differences in the DTDs of Type Ia supernovae and novae in shaping the evolution of Li in the various galactic zones. Its results are in fair agreement with the observational data for most open clusters, but small discrepancies appear in the outer disc.

Structured Jet Model for Multiwavelength Observations of the Jetted Tidal Disruption Event AT 2022cmc

AT 2022cmc is a recently documented tidal disruption event that exhibits a luminous jet, accompanied by fast-declining X-ray and long-lasting radio and millimeter emission. Motivated by the distinct spectral and temporal signatures between the X-ray and radio observations, we propose a multizone model involving relativistic jets with different Lorentz factors. We systematically study the evolution of faster and slower jets in an external density profile, considering the continuous energy injection rate associated with time-dependent accretion rates before and after the mass fallback time. We investigate time-dependent multiwavelength emission from both the forward shock (FS) and reverse shock (RS) regions of the fast and slow jets, in a self-consistent manner. Our analysis demonstrates that the energy injection rate can significantly impact the jet evolution and subsequently influence the lightcurves. We find that the X-ray spectra and lightcurves could be described by electron synchrotron emission from the RS of the faster jet, in which the late-time X-ray upper limits, extending to 400 days after the disruption, could be interpreted as a jet break. Meanwhile, the radio observations can be interpreted as a result of synchrotron emission from the FS region of the slower jet. We also discuss prospects for testing the model with current and future observations.

Galactic Chemical Evolution Models Favor an Extended Type Ia Supernova Delay-time Distribution

Type Ia supernovae (SNe Ia) produce most of the Fe-peak elements in the Universe and therefore are a crucial ingredient in galactic chemical evolution models. SNe Ia do not explode immediately after star formation, and the delay-time distribution (DTD) has not been definitively determined by supernova surveys or theoretical models. Because the DTD also affects the relationship among age, [Fe/H], and [α/Fe] in chemical evolution models, comparison with observations of stars in the Milky Way is an important consistency check for any proposed DTD. We implement several popular forms of the DTD in combination with multiple star formation histories for the Milky Way in multizone chemical evolution models that include radial stellar migration. We compare our predicted interstellar medium abundance tracks, stellar abundance distributions, and stellar age distributions to the final data release of the Apache Point Observatory Galactic Evolution Experiment. We find that the DTD has the largest effect on the [α/Fe] distribution: a DTD with more prompt SNe Ia produces a stellar abundance distribution that is skewed toward a lower [α/Fe] ratio. While the DTD alone cannot explain the observed bimodality in the [α/Fe] distribution, in combination with an appropriate star formation history it affects the goodness of fit between the predicted and observed high-α sequence. Our model results favor an extended DTD with fewer prompt SNe Ia than the fiducial t −1 power law.

Experimental and simulation study of flow field characteristics of a disturbing rotary centrifugal air classifier

In the present study, a new disturbing rotary centrifugal classifier was designed and a method was proposed to characterize its pressure signals. The models for evaluating the classification effects were established based on multi-zones. It was found that the flow field, processing parameters, and material properties significantly affected on the particle force and classification performance. The pressure with high amplitude and energy analyzed using Hilbert-Huang Transform (HHT) concentrated within 200–400 Hz, and the screening cage facilitated energy transfer to lower frequency bands. Correlation analysis demonstrated the correlation coefficients (A5 and A6) between adjacent measurement points increased with frequency, further enhanced by the screening cage. The flow field of simulation was characterized by a low-pressure, high-velocity gradient at the center and a high-pressure, low-velocity gradient at the sidewalls and the gas velocity and flow for experiment achieved the maximum at z = 0.50 m, while the magnitude of pressure change was significant at the inlet end and discharge end, where the velocity gradient and pressure drop had the same trend and small errors for experiments and simulation. The flow field affected the competing relationship between centrifugal and drag forces on the particles with different kinematic behavior during the classification process. The errors between the predicted values of the theoretical, simulated, and experimental cut particle size models and the true values were all within 10 %, based on which the multi-zone classification efficiency model applicable to the disturbing centrifugal classifier was established, and it was found that the models all had high prediction accuracy and suitability. The article delves into the correlation between pressure signals, flow field characteristics and particle behavior, aiming to provide an important reference for the internal flow field and classification performance of air classifiers.

GRB 211211A: The Case for an Engine-powered over r-process-powered Blue Kilonova

The recent gamma-ray burst (GRB) GRB 211211A provides the earliest (∼5 hr) data of a kilonova (KN) event, displaying bright (∼1042 erg s−1) and blue early emission. Previously, this KN was explained using simplistic multicomponent fitting methods. Here, in order to understand the physical origin of the KN emission in GRB 211211A, we employ an analytic multizone model for r-process-powered KNe. We find that r-process-powered KN models alone cannot explain the fast temporal evolution and the spectral energy distribution (SED) of the observed emission. Specifically, (i) r-process models require high ejecta mass to match early luminosity, which overpredicts late-time emission, while (ii) red KN models that reproduce late emission underpredict early luminosity. We propose an alternative scenario involving early contributions from the GRB central engine via a late low-power jet, consistent with plateau emission in short GRBs and GeV emission detected by Fermi-LAT at ∼104 s after GRB 211211A. Such late central engine activity, with an energy budget of ∼a few percent of that of the prompt jet, combined with a single red KN ejecta component, can naturally explain the light curve and SED of the observed emission, with the late-jet–ejecta interaction reproducing the early blue emission and r-process heating reproducing the late red emission. This supports claims that late low-power engine activity after prompt emission may be common. We encourage early follow-up observations of future nearby GRBs and compact binary merger events to reveal more about the central engine of GRBs and r-process events.

A novel dynamic predictive control model for variable air volume air conditioning systems using deep learning

Model-based predictive control has proven effective for managing indoor temperature and humidity in variable air volume (VAV) air conditioning systems. However, delays in temperature and humidity adjustments in response to changes in internal and external environmental conditions can impede high-performance operation. This study introduces a dynamic predictive control model for the multi-zone indoor temperature and humidity of VAV systems, designed to predict and control these parameters in real-time. Utilizing the RC network two-node wall structure method and backward finite difference scheme, a multi-zone building model is developed. Coupled with a deep fuzzy cognitive map (DFCM) for temperature and humidity prediction, and controlled by a PID controller, the model dynamically regulates the opening degrees of terminal air dampers and chilled water valves. Implemented on a networked control platform in a large commercial building, the model consistently maintained indoor temperature and humidity within ±0.5 °C and ±0.1 g/(kg dry air) of the target values under varying conditions, demonstrating substantial improvements in stability and operational efficiency. The research enhances the predictability and control of HVAC systems through advanced algorithmic integration, improving real-time indoor climate management in complex building environments. This method offers a practical improvement in HVAC control technologies, which could be beneficial for applications in commercial building management systems, providing a useful tool for supporting energy efficiency and sustainability in modern infrastructures.

An artificial neural network based approach to air supply control in large indoor spaces considering occupancy dynamics

Occupancy dynamics can significantly influence indoor thermal environments, especially in large indoor spaces. It is difficult for conventional feedback control systems to respond promptly to occupancy dynamics because of the substantial thermal inertia of large spaces, which leads to unfavorable thermal conditions in environments regulated by such systems. To address this challenge, this study proposes an air supply control approach based on artificial neural networks (ANNs). In the proposed approach, a large space is divided into multiple zones and an ANN model is used to characterize the relationship between occupancy dynamics and the supply air flow rates of each zone, thereby expediting the response of the air-conditioning system to occupancy dynamics. First, a multi-zone thermal environment model was developed to accurately emulate the thermal behavior of each zone. Next, employing the developed model of the environment, the optimal air flow rates required for each zone to maintain the desired thermal environment were estimated for various boundary conditions, which were used as pretraining data for four candidate ANNs. Finally, the best-performing ANN candidate, Long Short-Term Memory (LSTM), was adopted in a case study building via a comparison against several conventional air supply control methods. The results from the case studies demonstrate that the proposed approach can effectively expedite the system response to occupancy dynamics, thereby minimizing the occurrence of overcooling and overheating, and lowering the occupancy-weighted thermal discomfort level by 73.1 %. The proposed approach holds promise for real-time applications based on digital twin architecture.

A hybrid data assimilation method for reconstructing airflow path parameters of a multi-zone model

The indoor airborne pollutant has a great impact on indoor air quality. During the calculation process of pollutant distribution, some difficult-to-obtain parameters are critical in ensuring the calculation accuracy. In this study, we proposed a hybrid data assimilation method combined with Ensemble Kalman Filter (EnKF) and Gibbs sampling to reconstruct airflow path parameters (flow coefficient and flow index) in a multi-zone building model. Specially, we built a scaled multi-zone building model and used CO2 as the tracer gas to simulate the pollutant distribution in the building. The monitored sensor concentration data were used as prior information to reconstruct airflow path parameters of the scaled building model. And the performances of different data assimilation methods were compared. The results show that the EnKF method can reduce the calculation time by 46 % compared to the Gibbs sampling method, but the predicted accuracy decreases by 38 %. However, when we used the hybrid algorithm to reconstruct the airflow path parameters, the calculation time was reduced by 67 % compared to the EnKF method, and the predicted reconstruction accuracy is 7 % higher than the Gibbs sampling method.

Novel biofuel blends for diesel engines: Optimizing engine performance and emissions with C. cohnii microalgae biodiesel and algae-derived renewable diesel blends

Fossil diesel is a significant global fuel; however, environmental concerns and resource scarcity necessitate alternative fuels. Third-generation microalgae biodiesel (MB), despite its carbon neutrality, leads to higher oxides of nitrogen (NOx) emissions and poor engine performance. Renewable diesel (RD), also known as hydrotreated vegetable oil (HVO), could reduce these issues. A quasi-dimensional multi-zone combustion model is employed in this study to look into how different fuel blends of diesel, C. cohnii MB, and algae-derived RD affect the performance, combustion, and emissions of a compression ignition (CI) engine. The study uses a 2000 rpm engine arrangement with different engine loads, and validates the computational analysis with an experimental test engine arrangement. The study aims to explore the potential of sustainable biofuels in CI engines. D70MB30 (70 vol% diesel, and 30 vol% MB) fuel blend leads to a higher ignition delay period (IDP) while lowering peak cylinder pressure (PCP) and peak heat release rate (PHRR) compared to D100; however, when RD is added to diesel-MB fuel blends, it reduces IDP and increases PCP and PHRR. Brake specific fuel consumption (BSFC) for the D70MB30 blend rises by 5.28–8.9 %, while brake thermal efficiency (BTE) reduces by 0.98–4.27 %. However, RD in MB blends reduces BSFC by 1.57–3.41 % and marginally enhances BTE, resulting in greater fuel economy and efficiency. D70MB30 fuel blend results in higher specific carbon dioxide (CO2) emissions and oxides of nitrogen (NOx) emissions while lowering particulate matter (PM) and smoke emissions compared to neat diesel due to its high intrinsic oxygen content. In contrast, RD with the MB blend reduces specific CO2 and NOx emissions; however, it increases PM and smoke emissions due to the NOx-PM trade-off. The optimum results occur with a full load and D70MB15RD15 (70 vol% diesel, 15 vol% MB, and 15 vol% RD) fuel blend. Compared to the D70MB30 blend, D70MB15RD15 reduces BSFC, specific CO2, and NOx by 2.15 %, ∼1%, and 8.37 %, respectively, while increasing PM emissions at 100 % load.

Inverse identification of a pollutant source in an apartment with multiple rooms by joint multi-zone model and CFD

In case a hazardous agent is accidentally released inside a building, both the pollutant source location and the temporal release rates must be determined in order to guide emergency actions. Inverse modeling based on either a multi-zone model or computational fluid dynamics (CFD) may help to find the pollutant source. This investigation proposed the inverse identification of both the release location and dynamic release rate profile of a pollutant source in an apartment with multiple rooms. In the first stage of the solution, the room containing the pollutant source was identified by the inverse multi-zone model; then, in the second stage, the occupant who released the pollutant was determined by the inverse CFD model. Both models solved for the possible pollutant release rate profiles with a regularized, inverted matrix at all candidate zones/positions based on information from a sensor. Next, the occurrence probability at each candidate zone/position was interpreted by the Bayesian probability model with input from another sensor. The results revealed that the two types of models could be used in conjunction to efficiently identify both the room in which the pollutant was released and the exact occupant who released it, as well as the dynamic release rate profile.

Assessment of longitudinal dispersion and flow resistance near floodplains through riparian woodlands

Riparian woodlands prevent bank erosions, recycle minerals, sustain biodiversity, act as flow resistance on floodplains, and filter pollutants. The emergent trees characterize woodlands with different spacing arrangements that dictate flow resistance and longitudinal dispersion of the pollutants in compound channel flow. The single- and multistage compound channels exist in urban and natural watercourses with riparian and transplanted trees on different stages of the floodplain. This study numerically validates the planting of vegetation in lines on single- and multistage floodplains using a wall-modeled large-eddy simulation model. Post-validation, the focus of the study was to assess the hydrodynamic behavior and mixing around the floodplain and main channel section of different tested configurations. The approximation of flow structures for the various configurations of tree plantations shows stronger vortices with significant characteristic length scales for floodplains closer to the main channel. The intensity of the secondary current is higher for denser planted trees at junctions of floodplains. For higher flow events, drag force contributions for staged floodplains with trees on both stages are 45–41%, and trees on the top stage contribute 27-22% to the total frictional force budget. The subsequent investigation shows that the in-line trees geometrical configuration and spacing arrangement on the floodplain dictates flow resistance and longitudinal dispersion of the pollutants and contamination in channel flow. The results show that the overall reduction in discharge for floodplains with tree planting is 19.8–36.2% for single-stage and 10.4–23.6% for multistage compound channels. The longitudinal dispersion coefficients for each multi-zone model predict a 61% and 41% dispersion reduction, respectively, in single- and multistage floodplains with planted trees. Floodplains with denser tree spacing have a maximum zonal discharge reduction of 45% for a single-stage and 27.2% and 28.0% for multistage channels. These findings strongly suggest that the planting parameters of spacing-to-diameter ratio and floodplain geometry play a pivotal role in floodplain management from the perspective of contaminant dispersion and flood risk reduction during high-flow events.

Multizone Modeling for Hybrid Thermal Energy Storage

This study presents a one-dimensional mathematical model developed to simulate multi-zone thermal storage systems using phase change materials (PCMs). The model enables precise analysis of temperature distribution in the layered storage based on several PCM configurations and properties. It is distinguished by its adaptability to various tank geometries and the number of PCM capsules, enabling its application under diverse operating conditions. By simplifying the implementation of heat transfer processes that depend on the shape of the capsule and the thermal properties of the PCM, the computation time can be reduced to a level that makes simulations over longer periods feasible. Experimental validation confirmed the accuracy of the model, with deviations below 6%, underscoring its practical applicability. The study demonstrates that individual layering in the storage tank can be achieved by filling it with PCMs of different melting points without compromising the maximum storage capacity. It is shown that including a PCM layer can maintain the outlet temperature 20% longer while storing 14% more energy. The results point out the model’s potential to improve the performance of thermal storage systems through targeted PCM layer configurations. The model serves as the basis for the planning and optimization of these systems.

Revisiting High-energy Polarization from Leptonic and Hadronic Blazar Scenarios

X-ray and MeV polarization can be powerful diagnostics for leptonic and hadronic blazar models. Previous predictions are mostly based on a one-zone framework. However, recent IXPE observations of Mrk 421 and 501 strongly favor a multizone framework. Thus, the leptonic and hadronic polarization predictions need to be revisited. Here we identify two generic radiation transfer effects, namely, double depolarization and energy stratification, that can have an impact on the leptonic and hadronic polarization. We show how they are generalized from previously known multizone effects of the primary electron synchrotron radiation. Under our generic multizone model, the leptonic polarization degree is expected to be much lower than the one-zone prediction, unlikely detectable in most cases. The hadronic polarization degree can reach a value as high as the primary electron synchrotron polarization during simultaneous multiwavelength flares, consistent with the one-zone prediction. Therefore, IXPE and future X-ray and MeV polarimeters such as eXTP, COSI, and AMEGO-X, have good chances to detect hadronic polarization during flares. However, the hadronic polarization cannot be well constrained during the quiescent state. Nonetheless, if some blazar jets possess relatively stable large-scale magnetic structures, as suggested by radio observations, a nontrivial polarization degree may show up for the hadronic model after a very long exposure time (≳1 yr).

Annual performance of solar chimney for a multi-story building based on Modelica multizone model

Integration of solar chimney is an effective passive strategy for facilitating fresh air flow and offering significant potential for building energy conservation. This research evaluates the optimal design and control strategy for a six-story office building that includes a solar chimney, focusing on stack effect ventilation and energy performance throughout the year. The ventilation rates in different configurations of solar chimney are compared to meet fresh air requirements. The results indicate that the separated solar chimney ensures uniform airflow across each floor, with ventilation standard-reaching rates ranging from 79.5 % to 88.6 %. Furthermore, maximums of air flow rate occur at each floor, with critical cavity width values of 0.5 m, 0.4 m, 0.4 m, 0.3 m, 0.3 m, and 0.2 m on the 1st to 6th floor, respectively. Under the condition of equal cavity inlet area, air flow rates rise with the increasing length/width ratios. Finally, when the outdoor temperatures fall beyond the design range, controlling excessive ventilation generated by solar chimney leads to a 19.6 % reduction in total energy consumption over the year, with 71.5 % of the savings during winter.

Optimal fissile distribution in multiplying systems: Illustrative examples with Monte Carlo simulation and Pontryagin’s maximum principle

In multiplying systems, such as nuclear reactors and criticality experiments, it is desirable to place the fissile material in the optimal or ‘best’ way to reduce the critical mass of the system as well as to achieve uniform fuel burnup. This paper considers two methods, namely Pontryagin’s maximum principle (PMP) and Monte Carlo (MC) perturbation for estimating a minimum critical mass configuration. These methods are applied to an elementary multizone model of a pressurized water reactor (PWR) and a criticality experiment to estimate the minimum critical mass. It is found that while two-group diffusion theory with PMP predicts a minimum critical mass, more detailed MC simulations with MCNP5 show a consistent reduction in critical mass when fissile fuel is placed in inner zones. Such a distribution reduces the fissile material requirement but is undesirable due to the higher power peaking. MC simulations show that for a three-zone model of the KORI 1 PWR, a uniform fissile distribution gives criticality for 1.09 atomic percent (at.%) enrichment, whereas non-uniform fissile distribution (0.6, 1.6, 0.6 at.%) reduces the critical mass by 14%. The changes found from MC simulations were subsequently predicted from first- and second-order derivative sampling. It was found that substantial computational savings can be achieved for large-scale optimization problems. In the case of a criticality experiment, MC derivative sampling was also used to estimate optimal fissile distribution for minimizing the critical mass.

A semi-analytical pressure and rate transient analysis model for inner boundary and propped fractures exhibiting dynamic behavior under long-term production conditions

The loss of hydrocarbon production caused by the dynamic behavior of the inner boundary and propped fractures under long-term production conditions has been widely reported in recent studies. However, the quantitative relationships for the variations of the inner boundary and propped fractures have not been determined and incorporated in the semi-analytical models for the pressure and rate transient analysis. This work focuses on describing the variations of the inner boundary and propped fractures and capturing the typical characteristics from the pressure transient curves.A generalized semi-analytical model was developed to characterize the dynamic behavior of the inner boundary and propped fractures under long-term production conditions. The pressure-dependent length shrinkage coefficients, which quantify the length changes of the inner zone and propped fractures, are modified and incorporated into this multi-zone semi-analytical model. With simultaneous numerical iterations and numerical inversions in Laplace and real-time space, the transient solutions to pressure and rate behavior are determined in just a few seconds. The dynamic behavior of the inner boundary and propped fractures on transient pressure curves is divided into five periods: fracture bilinear flow (FR1), dynamic PFs flow (FR2), inner-area linear flow (FR3), dynamic inner boundary flow (FR4), and outer-area dominated linear flow (FR5). The early hump during FR2 period and a positive upward shift during FR4 period are captured on the log-log pressure transient curves, reflecting the dynamic behavior of the inner boundary and propped fractures during the long-term production period.The transient pressure behavior will exhibit greater positive upward trend and the flow rate will be lower with the shrinkage of the inner boundary. The pressure derivative curve will be upward earlier as the inner boundary shrinks more rapidly. The lower permeability caused by the closure of un-propped fractures in the inner zone results in greater upward in pressure derivative curves. If the permeability loss for the dynamic behavior of the inner boundary caused by the closure of un-propped fractures is neglected, the flow rate will be overestimated in the later production period.

Integrated 1D Simulation of Aftertreatment System and Chemistry-Based Multizone RCCI Combustion for Optimal Performance with Methane Oxidation Catalyst

This paper presents a comprehensive investigation into the design of a methane oxidation catalyst aftertreatment system specifically tailored for the Wärtsilä W31DF natural gas engine which has been converted to a reactivity-controlled compression ignition NG/Diesel engine. A GT-Power model was coupled with a predictive physical base chemical kinetic multizone model (MZM) as a combustion object. In this MZM simulation, a set of 54 species and 269 reactions as chemical kinetic mechanism were used for modelling combustion and emissions. Aftertreatment simulations were conducted using a 1D air-path model in the same GT-Power model, integrated with a chemical kinetic model featuring 15 catalytic reactions, based on activation energy and species concentrations from combustion outputs. The latter offered detailed exhaust composition and exhaust thermodynamic data under specific operating conditions, effectively capturing the intricate interactions between the investigated aftertreatment system, combustion, and exhaust composition. Special emphasis was placed on the formation of intermediate hydrocarbons such as C2H4 and C2H6, despite their concentrations being lower than that of CH4. The analysis of catalytic conversion focused on key species, including H2O, CO2, CO, CH4, C2H4, and C2H6, examining their interactions. After consideration of thermal management and pressure drop, a practical choice of a 400 mm long catalyst with a density of 10 cells per cm2 was selected. Investigations of this catalyst’s specification revealed complete CO conversion and a minimum of 89% hydrocarbon conversion efficiency. Integrating the exhaust aftertreatment system into the air path resulted in a reduction in engine-indicated efficiency by up to 2.65% but did not affect in-cylinder combustion.

The intermediate neutron capture process

Context. The observed surface abundance distributions of carbon-enhanced metal-poor (CEMP) r/s stars suggest that these stars could have been polluted by an intermediate neutron capture process (the so-called i-process) occurring at intermediate neutron densities between the r- and s-processes. Triggered by the ingestion of protons inside a convective He-burning zone, the i-process could be hosted in several sites, a promising one being the early AGB phase of low-mass, low-metallicity stars. The i-process remains affected however by many uncertainties, including those of nuclear origin, since it involves hundreds of nuclei for which reaction rates have not yet been determined experimentally. Aims. We investigate both the systematic and statistical uncertainties associated with theoretical nuclear reaction rates of relevance during the i-process and explore their impact on the i-process elemental production, and subsequently on the surface enrichment, of a low-mass, low-metallicity star during the early AGB phase. Methods. We used the TALYS reaction code to estimate both the model and parameter uncertainties affecting the photon strength function and the nuclear level densities, and hence the radiative neutron capture rates. The impact of correlated systematic uncertainties was estimated by considering different nuclear models, as was detailed in Paper II. In contrast, the uncorrelated uncertainties associated with local variation in model parameters were estimated using a variant of the backward-forward Monte Carlo method to constrain the parameter changes to experimentally known cross sections before propagating them consistently to the neutron capture rates. The STAREVOL code (Siess 2006, A&A, 448, 717) was used to determine the impact of nuclear uncertainties on the i-process nucleosynthesis in a 1 M⊙ [Fe/H] = –2.5 model star during the proton ingestion event in the early AGB phase. A large nuclear network of 1160 species coherently coupled to the transport processes was solved to follow the i-process nucleosynthesis. Results. We find that the uncorrelated parameter uncertainties lead the surface abundance uncertainties of elements with Z ≥ 40 to range between 0.5 and 1.0 dex, with odd-Z elements displaying higher uncertainties. The correlated model uncertainties are of the same order of magnitude, and both model and parameter uncertainties have an important impact on potential observable tracers such as Eu and La. We find around 125 important (n, γ) reactions impacting the surface abundances, including 28 reactions that have a medium to high impact on the surface abundance of elements that are taken as observable tracers of i-process nucleosynthesis in CEMP stars. Conclusions. Both the correlated model and uncorrelated parameter uncertainties need to be estimated coherently before being propagated to astrophysical observables through multi-zone stellar evolution models. Many reactions are found to affect the i-process predictions and will require improved nuclear models guided by experimental constraints. Priority should be given to the reactions influencing the observable tracers.

Kinetic modeling of ion formation during diesel combustion with different fuel injection timing

Ions are formed during the combustion process in internal combustion engines. The measurement of ions inside the combustion chamber produces reliable information about the combustion process. The present study focuses on the formation of ions inside the combustion chamber of diesel engines with different injection timing. For this purpose, a multi-zone thermodynamic model is utilized to simulate the closed cycle of the engine. To understand the kinetic behavior of the ions, the model is connected to an ionic chemical kinetics mechanism with 336 reactions and 81 species. Six important ionic reactions comprising 5 ions are used in the ionic mechanism. Dvode differential equation solver is also employed to calculate the energy and kinetics equations. The developed model has an acceptable accuracy in predicting the performance and pollutants of diesel engines. Based on the results, the ion formation is delayed by delaying the fuel injection timing. The maximum amount of in-cylinder ions depends on injection timing. In-cylinder ion current can predict the start of combustion accurately.