Emission Standards Research Articles

Multi-Step Ageing Prediction of NMC Lithium-Ion Batteries Based on Temperature Characteristics

The performance of lithium-ion batteries depends strongly on their ageing state; therefore, the monitoring and the prediction of the battery state of health (SoH) is necessary for an optimized and secured functioning of battery systems. This paper evaluates and compares three artificial neural network architectures for multi-step ageing prediction of lithium-ion cells: Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). These models use the features extracted from the cell’s temperature to predict the cell’s capacity. The features are extracted from experimental measurements of the cell’s surface temperature and selected based on Spearman correlation analysis. The prediction results were evaluated and compared considering three different percentages of the training dataset: 60%, 70%, and 80%. Training and testing data were generated experimentally based on accelerated ageing cycling tests. During these experiments, four Nickel Manganese Cobalt/Graphite (NMC) cells were cycled under a controlled temperature environment based on a dynamic current profile extracted from the Worldwide Harmonized Light Vehicles Test Cycles.

Assessment of Vehicle Carbon Emissions and Their Impact on Rainfall and Temperature in Ranchi, Jharkhand

Climate change is very serious problem to mankind as it is affecting climatic variables such as rainfall and temperature and which affects agriculture and livelihood of humans. The main reason of climate change and global warming is the increased greenhouse gas (GHG) emission. Greenhouse gases contain 72.4% carbon dioxide, making it the major component of GHG. Energy sector and transport sector are the two main emitter of CO2. The vehicular carbon emission of Ranchi is calculated for year 1997-2020 and is correlated against rainfall and temperature. The results shows that the vehicular carbon emission of Ranchi is increasing exponentially. The bi-variate correlation of CO2 signifies no significant correlation with rainfall however temperature is moderately correlated with CO2 emission. The partial correlation signifies the moderate influence of CO2 on rainfall and temperature of Ranchi.

Comparison of current and proposed travel modes for students considering travelling and sustainable indicators

In the twenty-first century, global warming has become one of the severe challenges. Around one-fourth of the total carbon footprint is contributed by transport vehicles. It is crucial to provide alternative solutions to unsustainable transportation systems. This study compares the present travel modes of students of the Capital University of Science and Technology, Islamabad, with the proposed sustainable travel mode. A campus survey (N = 565) was conducted to understand travel choices, travel times, origin and destinations, and student preferences, considering university buses and private vehicles. A comparison was made between the travel time analysis of university bus users and non-bus users. A sustainability analysis has been conducted to evaluate the effects of the present travel modes on students and the proposed university bus transport system. Five main factors were compared: fuel consumption and associated fuel costs, carbon and vehicular emissions, traffic noise pollution, traffic congestion, and accident ratio. The results show that 38% of students commute by private cars and 18% by motorbikes. Around 25% of the students avoid university buses due to long travel times, and 30% prefer because their parents ask them. On average, university buses take 56.8 min, and non-bus transports take 38.8 min for one-way travel. If all students travel by university busses, they could be 86% more sustainable in fuel costs and carbon emissions than non-bus travel modes. These results give useful policy implications for providing sustainable travel alternatives to educational institutions.

The effect of the ultra-low emission zone on PM2.5 concentration in Seoul, South Korea

As studies regarding the positive effect of Low Emission Zones (LEZs) and people's risk perception about air pollution have increased, more powerful and specific traffic regulation policies have been required. London is the first city in the world to implement an Ultra-low Emission Zone (ULEZ) in addition to the existing LEZ. Benchmarking London's ULEZ, a ULEZ policy was implemented in Seoul, South Korea on December 1, 2019. The goal of the policy is to improve air quality by prohibiting entry of vehicles registered nationwide into Seoul's ULEZ that do not meet a specific emission standard including diesel, gasoline, and LPG fuel-based vehicles. This study analyzed the effect of Seoul's ULEZ policy on the five major atmospheric pollutants (PM2.5, PM10, NO2, CO, SO2, O3) concentration in the zone, particularly focusing on PM2.5 concentration. The analysis employs Difference-in-Differences (DD) approach, comparing data from one year before and after the policy's implementation on December 1, 2019. The findings indicate that Seoul's ULEZ policy resulted in a 9.8% increase in PM2.5 concentrations. Conversely, the policy led to reductions in PM10, NO2, CO, and SO2 concentrations by 12.0%, 17.3%, 5.9%, and 10.8%, respectively, while the effect on O3 was statistically insignificant. These empirical results suggest that the ULEZ may need to incorporate more stringent emission standards, expand its coverage, or introduce additional measures to address the unintended increase in PM2.5 concentration.

Evaluating the effectiveness of climate change policies and initiatives in the United States: A comprehensive review

Climate change presents profound challenges, demanding effective policies to mitigate its impacts. This paper reviews the effectiveness of climate policies in the United States, analyzing scholarly articles, government reports, and relevant literature. The review covers diverse policies such as greenhouse gas regulations, renewable energy initiatives, energy efficiency programs, and adaptation measures. It assesses their success in reducing carbon emissions, advancing a low-carbon economy, and enhancing resilience to climate impacts. Key findings highlight significant successes, such as the rise in renewable energy due to renewable portfolio standards and reduced emissions from vehicle fuel efficiency standards. State-level initiatives, like carbon pricing and renewable energy targets, also show promising outcomes. However, the review identifies substantial challenges, including political and economic obstacles, inconsistent policies across states, and the need for better federal-state-local collaboration. Additionally, disparities in policy benefits, with marginalized communities facing greater burdens and limited access to renewable resources, are significant concerns. To enhance the effectiveness of climate policies, the study recommends fostering coherent policies across all government levels, promoting equitable and inclusive climate solutions, raising public awareness, and supporting innovation in clean technologies. This comprehensive review provides valuable insights for policymakers and stakeholders to develop strategies that effectively address climate change, promote sustainability, and ensure a fair transition to a low-carbon future.

Improving Indoor Air Quality: Using Polymate-777A as a Formaldehyde Scavenger to Decrease Formaldehyde Emission in Plywood Panels

Indoor air quality is significantly impacted by volatile organic compounds, with formaldehyde being a prominent concern due to its emission from wood composite products. Formaldehyde emissions from wood composite products is a big concern for the environment and people's health. This study focused at how effective a product called Polymate-777A, the additive used as a formaldehyde scavenger is in reducing the formaldehyde emissions from plywood. This study investigates the efficacy of Polymate-777A, a formaldehyde scavenger, in reducing formaldehyde emissions from plywood panels. Through a series of controlled experiments, it was evaluated the impact of varying concentrations of Polymate-777A on formaldehyde release, utilizing standardized testing methods to quantify emissions. Results indicate that the incorporation of Polymate-777A significantly reduces formaldehyde levels, enhancing the safety and environmental sustainability of plywood products. The Results revels that when 1.5% or more Polymate-777A was used as scavenger for plywood manufacturing, the plywood met the emission standards set by Japan and Europe for formaldehyde emissions. This study suggests that using Polymate-777A could be a good way to reduce formaldehyde emissions from wood products, which would be better for the environment and people's health.

Breakup dynamics in a pressure-swirl injector for urea-water solution applications: A computational study

The co-optimization of in-cylinder combustion and after-treatment technology has become a major aspect in engine design and development, with the goal of meeting the increasingly restrictive emission regulations in the transportation industry. Selective Catalytic Reduction is a robust technology to control the emission of NOx, and the injection of urea in water solution is the exhaust tailpipe is a key aspect of its operation. The proposed work uses high-fidelity Computational Fluid Dynamics to characterize the atomization dynamics of the liquid jet in relevant cross-flow conditions. The study focuses on a commercial low-pressure (9 bar) pressure-swirl injector which is characterized in its internal geometry through high-resolution X-ray micro-computational tomography. The internal two-phase flow has been modeled according to the volume-of-fluid approach in a large eddy simulation framework and validated against near-nozzle X-ray radiography measurement. Moreover, characterizing the breakup dynamics for the swirling hollow cone formation, and assessing the influence of the cross-flow in the breakup dynamics was completed. The results have been reported proposing Re-Oh maps and probability density functions of the spray kinematics. Higher cross-flow momentum generates an increase in the jet intact length and a reduction of the liquid droplet diameters. The axial momentum of the jet is affected by the cross-flow already in the near-nozzle region, determining a relevant deviation of the spray velocities. This work aims to inform the initialization of Eulerian-Lagrangian spray models through the assignment of droplet kinematics and static one-way coupling between volume-of-fluid results and Lagrangian spray parcels, to be used for system-size domain simulations.

AIR QUALITY IN YEREVAN IN THE CONTEXT OF CLIMATE CHANGE

Over the past five years, the atmospheric air quality in Yerevan has continued to deteriorate, primarily attributed to a significant increase in airborne dust, particularly during the hot summer months that coincide with the peak tourist season. This phenomenon reflects a complex interplay among atmospheric emissions, air quality, emission source altitudes, changes in chemical composition, solar radiation, weather patterns, and topographic conditions. Sources of dust pollution in Yerevan include industrial activities, vehicular emissions, road dust, and construction activities. Data from various administrative districts indicate that, over the past five years, concentrations of dust, nitrogen dioxide, and sulfur dioxide frequently exceed established standards. Additionally, there are unregulated static sources of dust pollution within the city. This study conducts a comparative analysis of the primary pollutants in the context of climate change and urbanization in Yerevan over the last five years. It also assesses green areas and evaluates the effectiveness of greening initiatives, while considering compliance with national legislation in the Republic of Armenia.

Efficient microplastics adsorption in aqueous environments via bidirectional ordered graphene oxide/nanocellulose aerogels

Microplastics not only accumulate various harmful substances but also are ingested by marine organisms and humans, causing immeasurable impacts. Therefore, the removal of microplastics has become a crucial proposition for addressing the issue of microplastic pollution. This study investigated a bidirectional ordered graphene oxide (GO)/nanocellulose aerogels (D-DPGG) to remove microplastics from water bodies. The concentration of microplastics before and after adsorption was measured using a fluorescence spectrophotometer. D-DPGG aerogel exhibited excellent adsorption performance for microplastics (241.56 mg/g) and maintained high adsorption efficiency (>80 %) over 20 cycles of adsorption testing. Additionally, the link between GO and dual-directional treatment significantly improved the aerogel microstructure. It had an apparent layered structure in the transverse direction and improves the mechanical properties. D-DPGG aerogel not only served as an effective solution in adsorption but also held promise as a novel material in directional structural design.

MOF-derived three-dimensional porous dodecahedral structured bimetallic Mn/Co-C-N composite for high-performance durable oxygen reduction reaction electrocatalysts

Investigating high-efficiency oxygen reduction reaction (ORR) catalysts is one of the most effective methods for addressing the sluggish kinetics at the fuel cell cathode. Bimetallic three-dimensional porous materials have garnered significant attention due to their diverse structures, large specific surface area and synergistic catalytic effects. Herein, we synthesized a bimetallic three-dimensional porous dodecahedral structure, Mn/Co-C-N, derived from MOF using a straightforward approach. Experimental reults confirm that the strategic incorporation of Mn enhances the electrocatalytic activity for ORR. Meanwhile, the synergistic effects of Mn and Co, as well as the advantages of the dodecahedral structure for expediting electron transfer, all contribute to the exceptional ORR performance. Arc testing in an alkaline electrolyte reveals that the initial potential (Eonset) and the half-wave potential (E1/2) are 0.89 V and 0.80 V, closely approximating those of commercial Pt/C (20 wt%). Following 10 000 stability test cycles, the half-wave potential exhibits a mere 8 mV change, confirming its remarkable stability.

Detection and analysis of ship emissions using single-particle mass spectrometry: A land-based field study in the port of rostock, Germany

The regulation of ship emissions has become more restrictive due to their significant impact on global air quality, particularly in coastal regions. According to the International Maritime Organization (IMO) regulations, current restrictions mainly limit the sulfur content of the fuel mass to 0.5 % and 0.1 % respectively. In compliance with these regulations, exhaust SO2 cleaning systems (scrubbers) and new low-sulfur fuels are increasingly used. For comprehensive monitoring of ship emissions, advanced measurement techniques are demanded. Our study reports on the results of a land-based field campaign conducted in the port of Rostock, Germany. The chosen location strategically positions the measurement setup to capture all incoming and outgoing ships passing within a distance of up to 2 km. Potential ship exhaust plumes are indicated by rapid changes in particle number and size distribution monitored by an optical particle sizer (OPS) and a scanning mobility particle sizer (SMPS). Additionally, single-particle mass spectrometry (SPMS) was used to qualitatively characterize ambient single-particles (0.2–2.5 μm) by their chemical signatures. In a one-week time span, the exhaust plumes of 73 ships were identified. The high sensitivity of SPMS to transition metals and polycyclic aromatic hydrocarbons (PAH) in individual particles make it possible to distinguish between different marine fuels.

On the politics of constitutional law: an environmental case study of the role of politics and economics in constitutional appeals

AbstractThe U.S. government has responded to concern about climate change through regulation of carbon-dioxide emissions by the Environmental Protection Agency under the Clean Air Act. Regulation of the electric power industry has been central to the fight to reduce carbon-dioxide emissions, but these regulations have been challenged by constitutional appeals that have resulted in episodic advances and reversals. Grounds for constitutional appeal of environmental laws are many, but at their core is the question of whether a given law is consistent with the constitution. If constitutionality is central to the positions the attorneys general take on appeals of environmental regulations, political and economic factors should not affect their positions. Empirical evidence presented in this case study shows, however, that these factors are correlated with the positions the attorneys general of the U.S. states and District of Columbia took towards constitutional appeals of regulations of carbon-dioxide emissions from the electric power industry. At a broader level, these findings shed useful light on the question of how constitutional appeals are brought.

Accelerated surface brightening in China: The decisive role of reduced anthropogenic aerosol emissions

A profound comprehension of the key determinants behind the fluctuations in surface solar radiation (SSR) over decadal timescales is essential for advancing the fields of energy meteorology, climatology, and solar resources. While there is a general agreement among prior research on the significant impact of aerosols on SSR, a granular understanding of the specific influence that varying aerosol types exert on SSR remains elusive. This investigation delves into the respective impacts of aerosols and cloud radiative effects across China, utilizing data from the Clouds and the Earth's Radiant Energy System (CERES). It further dissects the distinct impacts of different aerosol types on the decadal-scale variations in SSR, employing spectral aerosol optical depths derived from the MODIS aerosol products. The findings reveal a predominantly positive trend in SSR, ranging from 1.5 to 2.5 W/m2/yr between the years 2011 and 2022 for the majority of China's regions. However, certain areas within Northeast and Western China exhibit a decline in SSR exceeding −1 W/m2/yr. Notably, in Eastern China, a downward trend in aerosol radiative effect is observed, with a pronounced reduction of approximately −2 W/m2/yr noted specifically during the summer in Northern China. The study infers that the reduction in urban industrial emissions and biomass burning aerosols is linked to the acceleration of the brightening phenomenon in China. Our results underscore that stringent regulation of anthropogenic emissions could yield dual benefits: environmental amelioration and an upsurge in solar resource. These insights merit serious contemplation in the formulation of future policy and decision-making frameworks.

Prediction of cancer therapy related cardiac dysfunction by using a machine learning approach with cardiac magnetic resonance images

Abstract Background There is inconsistent data on the value of baseline and early cardiac function assessment on identifying patients at risk of cancer therapy related cardiac dysfunction (CTRCD). Whether machine learning (ML) approaches applied to cardiac MRI (CMR) images may help risk stratify patients is unclear. Purpose In women with HER2+ breast cancer receiving sequential anthracyclines and trastuzumab we sought to (i) assess if a ML approach applied to pre-cancer therapy and early post anthracycline CMR short axis (SAX) cines can help identify patients at risk for future CTRCD and (ii) whether this approach is superior to using clinical and manual quantitative CMR data. Methods Women with HER2+ breast cancer from the EMBRACE-MRI study underwent 5 CMR studies (pre-cancer therapy, post anthracyclines, 3, 6 and 12 months during trastuzumab). CTRCD was defined using the CREC criteria by end of treatment. Three models were created: 1) clinical data (demographics, cardiovascular risk factors and medications); 2) clinical and manual quantitative CMR data (right and left ventricular volumes and LVEF); and 3) ML model based on SAX cine CMR images only. We focused on the pre-cancer therapy CMR and 1st visit post-anthracycline CMR. All images underwent standardised formatting. A 3D convolutional neural networks model architecture was used. The model consisted of multiple convolutional layers followed by max pooling and global average pooling layers. The output of the convolutional layers were then fed into a dense layer of 128 nodes incorporating an L2 activity regularizer and a subsequent dropout layer. To train and tune the model, the process was iterated 50 times. In each iteration, the patient participant dataset was shuffled, and a random 80% of patients were selected for training and tuning. The tuned model was then applied to the remaining 20% of patients in each iteration, and this entire cycle of shuffling, training, and testing was repeated 50 times with replacement. Area Under the Curve (AUC), accuracy, sensitivity, and specificity were calculated on the test set and averaged across folds to determine each model’s performance. Results 135 women were included (mean age 51.1±9.2 years) mean doxorubicin equivalent was dose 204.9±12.5mg/m2. Cardiovascular risk factors were prevalent: 33 (24%) had a smoking history; 21 (15%) had hypertension. 37 (27%) developed CTRCD. The ML model using baseline CMR cine data only had a higher AUC (0.84 95% CI [0.83-0.86]) for the detection of CTRCD compared to clinical data (0.62 95% CI [0.59-0.65]) and clinical and CMR data (0.62 95% CI [0.60-0.65]). This further improved with the addition of post-anthracycline CMR data (Table 1). Conclusion A ML model examining early cardiac MRI data with short axis cine CMR images performed better than those using clinical or manual quantitative CMR data for the prediction of CTRCD. Further studies including external validation are needed.Table 1 Mean and 95% CIs

Precipitation–Flotation Process for Molybdenum and Uranium Separation from Wastewater

The mining of molybdenum and uranium ores inevitably results in the generation of large volumes of wastewater containing low concentrations of metals, which poses significant threats to the environment. This study presents a novel precipitation–flotation process for the simultaneous separation of molybdenum and uranium from wastewater. A systematic investigation was conducted on the impacts of the type of precipitant, flotation reagent type, and flotation parameters on the experimental results. Ferric salt served better as a precipitant than aluminum salt and humic acid did, and sodium dodecyl sulfate (SDS) was more suitable than sodium dodecyl benzene sulfonate for acting as a surfactant and foaming agent. Under specific conditions, including a pH of 6.6, an Fe3+ dosage of 0.6 mmol·L−1, an SDS dosage of 40 mg·L−1, an air flow rate of 25 mL·min−1, and a flotation time of 10 min, the removal efficiencies of molybdenum and uranium reached 96.6% and 93.6%, respectively. After flotation, the molybdenum concentration, uranium concentration, chemical oxygen demand, and turbidity of the treated water all meet the emission standards. Furthermore, the metal removal mechanisms, including the particle size distribution, functional group structure, surface element composition, microstructure, and element distribution, were elucidated on the basis of characterization of the precipitation–flotation products.

Characterizing Aircraft Exhaust Emissions and Impact Factors at Tianjin Binhai International Airport via Open-Path Fourier-Transform Infrared Spectrometer

The growth of the civil aviation industry has raised concerns about the impact of airport emissions on human health and the environment. The aim of this study was to quantify the emissions of sulfur dioxide (SO2), nitrogen oxides (NOX), and carbon monoxide (CO) from in-service aircraft via open-path Fourier-transform infrared (OP-FTIR) spectroscopy at Tianjin Binhai International Airport. The results suggest that the CO and NOX emission indices (EIs) for five common aircraft/engine combinations exhibited substantial discrepancies from those reported in the International Civil Aviation Organization (ICAO) databank. Notably, during the idling, approach, and take-off phases, the CO EIs exceeded the ICAO’s standard values by (11.04 ± 10.34)%, (56.37 ± 18.54)%, and roughly 2–5 times, respectively. By contrast, the NOX EIs were below the standard values by (39.15 ± 5.80)%, (13.57 ± 3.67)%, and (21.22 ± 4.03)% in the same phases, respectively. The CO and NOX EIs increased by 31–41% and decreased by 23–24%, respectively, as the ambient temperature decreased from −3 °C to −13 °C. This was attributed to lower temperatures reducing fuel evaporation, leading to inefficient combustion and increased CO emissions and lowering the combustion temperature and pressure, resulting in reduced NOX emissions. The CO EIs had a positive correlation with humidity (adjusted R2: 0.715–0.837), while the NOX EIs were negatively correlated with humidity (adjusted R2: 0.758–0.859). This study’s findings indicate that humidity is a crucial factor impacting aircraft exhaust emissions. Overall, this research will contribute to the development of scientifically informed emission standards and enhanced environmental management practices in the aviation sector.

Large Room-Temperature Electrocaloric Effect in Lead-Free Relaxor Ferroelectric Ceramics with Wide Operation Temperature Range.

In order to obtain large room-temperature electrocaloric effect (ECE) and wide operation temperature range simultaneously in lead-free ceramics, we proposed designing a relaxor ferroelectric with a Tm (the temperature at which the maximum dielectric permittivity is achieved) near-room temperature and glass addition. Based on this strategy, we designed and fabricated lead-free 0.76NaNbO3-0.24BaTiO3 (NN-24BT) ceramics with 1wt.% BaO-B2O3-SiO2 glass addition, which showed distinct relaxor ferroelectric characteristics with strongly diffused phase transition and a Tm near-room temperature. Based on a direct measurement method, a large ΔT (adiabatic temperature change) of 1.3 K was obtained at room temperature under a high field of 11.0 kV mm-1. Additionally, large ECE can be maintained (>0.6 K@6.1 kV mm-1) over a broad temperature range from 23 °C to 69 °C. Moreover, the ECE displayed excellent cyclic stability with a variation in ΔT below ±7% within 100 test cycles. The comprehensive ECE performance is significantly better than other lead-free ceramics. Our work provides a general and effective approach to designing lead-free, high-performance ECE ceramics, and the approach possesses the potential to be utilized to improve the ECE performance of other lead-free ferroelectric ceramic systems.

Advanced hybrid neural network techniques for minimizing gas turbine emissions

Purpose Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS). Design/methodology/approach The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies. Findings The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R2 of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R2 of 0.887 and a slight overestimation bias of +0.01. Research limitations/implications While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research. Practical implications Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs. Social implications Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment. Originality/value This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.

Optimal control of Hydrocarbon Reducer (HC) injection based on Trust-Region-Reflective Algorithm (TRRA) and physical models for Diesel Oxidation Catalyst (DOC)

The aim of this paper is to control the DOC outlet gas temperature between 600 ± 15 °C by optimizing the hydrocarbon (HC) injection into the Diesel Oxidation Catalyst (DOC) for active regeneration of the downstream Diesel Particulate Filter (DPF). First, based on the physical model of the DOC thermal dynamics, the energy conservation equation for the gas phase temperature is simplified by using variable substitution, and the energy conservation equation for the solid phase temperature is simplified by considering only the heat generated by the HC injection. By solving the simplified model using the Trapezoidal Rule-Backward Differentiation Formula 2 (TR-BDF2) method and optimizing the model parameters in combination with the Gauss-Newton method, the computational efficiency and accuracy were significantly improved. Next, the DOC downstream temperature observer is designed by combining the solution characteristics of the DOC model with the unscented Kalman filter (UKF) algorithm to ensure that the catalyst operates within the optimal temperature range. Then, this paper verifies the accuracy of the model and observer through bench tests covering four steady-state operation conditions and World Harmonized Transient Cycle (WHTC) test cycle, and the results show that the model and observer exhibit high accuracy in both steady-state and transient operation conditions. Finally, the DOC temperature was successfully maintained between 600 ± 15 °C by optimizing the control of the HC injection rate, thus achieving the expected temperature control target. These research results provide theoretical and practical support for diesel engine emission control and DPF active regeneration.

An optimization DEM modelling method for soil-fibre undrained cyclic loading tests model based on the influence range of fibre

The computational cost of discrete element modelling is high owing to the limitations of particle size and contact in fibre modelling. This paper proposes an optimised discrete element method (DEM) for a hybrid model of soil and fibres based on the fibre influence range. First, a relative velocity state function is established based on the relative motion state between the fibres and soil particles under undrained cyclic loading. Subsequently, the influence range of the fibres is determined using the relative velocity function based on the first few cycles of the undrained cyclic loading numerical tests. Cluster and clump models of the fibre are then generated based on the influence range of the fibre. Finally, a symmetrical shape of the optimised model is developed by extracting the distribution length of the edge curve of the influence range along the vertical direction of the axis. In this study, the proposed optimised DEM was validated through a series of undrained cyclic loading numerical tests on fibre-reinforced soil. The results of the optimised model were highly consistent with those of the traditional model, and the computational time was significantly reduced. The cyclic loading timing for determining the range of influence of the fibre was analysed. The optimised model based on the influence range of the 15th cycles not only restored almost the same results but also saved the calculation cost by nearly eight times. The optimised model established based on the influence range after the 15th cycles had a slight influence on the results. In addition, the applicability of the optimised model is discussed. This paper provides new insights into the establishment of a hybrid model of soil and fibres.