Macroscopic Emission Research Articles

Elastodynamics based Modelling of Acoustic Emission for Earlier Bearing Damage Detection

It is crucial for many applications to detect bearing damage as early as possible to allow for scheduling of maintenance with lead times that minimize operational disruption. State of the practice is the detection of spalling but damage initiates prior to spalling as subsurface and surface cracks. Such damage is much harder to detect and to model. This study proposes a unique application of the nanofrictional Prandtl-Tomlinson model to predict macroscopic acoustic emission (AE) signals that occur at cracked interfaces under relative motion. The study integrates large deformation modelling of structures with elastodynamic simulations to investigate early AE signals generated under different bearing rotational speeds. Experimental studies are carried out to measure acoustic vibrations from metal-metal surface friction using fiber optic sensors and compared to those predicted by the model. Broad agreement of results highlights the validity of this framework.

Integrating the Bi-dimensional-GSOM traffic flow interfaced models with a macroscopic emission model for very large-scale traffic networks

In this study, a coupled macroscopic traffic models and pollutant emission model for a very large-scale traffic networks is proposed. In order to overcome the missing data for large networks a continuum traffic modeling is developed aiming at simulating traffic of very large and dense networks. The present paper aims to merge both macroscopic traffic models (i. e. the Bi-dimensional model, BIDIM) with the generic second order model (GSOM) using real data. These two models will be associated with an emission model. The method consists of proposing an interface node model to ensure the exchange between the BIDIM and the GSOM traffic model. Then a vehicle emission model COPERT was adopted to provide the emission factors based on average speeds, in order to calculate macroscopic emissions for large networks. The dynamic of the developed simulation model reproduces the propagation of the congestion between both models and the evolution of emission pollutant in the networks. Applied to real networks, Marne La Vallée site and the A4 highway in France, the output results are very promising.

Investigating the effect of dynamic traffic distribution on network-wide traffic emissions: An empirical study in Ningbo, China.

Urban road traffic is one of the primary sources of carbon emissions. Previous studies have demonstrated the close relationship between traffic flow characteristics and carbon emissions (CO2). However, the impact of dynamic traffic distribution on carbon emissions is rarely empirically studied on the network level. To fill this gap, this study proposes a dynamic network carbon emissions estimation method. The network-level traffic emissions are estimated by combining macroscopic emission models and recent advances in dynamic network traffic flow modeling, namely, Macroscopic Fundamental Diagram. The impact of traffic distribution and the penetration of battery electric vehicles on total network emissions are further investigated using the Monte Carlo method. The results indicate the substantial effect of network traffic distribution on carbon emissions. Using the urban expressway network in Ningbo as an example, in the scenario of 100% internal combustion engine vehicles, increasing the standard deviation of link-level traffic density from 0 to 15 veh/km-ln can result in an 8.9% network capacity drop and a 15.5% reduction in network carbon emissions. This effect can be moderated as the penetration rate of battery electric vehicles increases. Based on the empirical and simulating evidence, different expressway pollution management strategies can be implemented, such as petrol vehicle restrictions, ramp metering, congestion pricing, and perimeter control strategies.

A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning

Under the background of global warming and the energy crisis, the Chinese government has set the goal of carbon peaking and carbon neutralization. With the rapid development of machine learning, some advanced machine learning algorithms have also been applied to the control and prediction of carbon emissions due to their high efficiency and accuracy. In this paper, the current situation of machine learning applied to carbon emission prediction is studied in detail by means of paper retrieval. It was found that machine learning has become a hot topic in the field of carbon emission prediction models, and the main carbon emission prediction models are mainly based on back propagation neural networks, support vector machines, long short-term memory neural networks, random forests and extreme learning machines. By describing the characteristics of these five types of carbon emission prediction models and conducting a comparative analysis, we determined the applicable characteristics of each model, and based on this, future research ideas for carbon emission prediction models based on machine learning are proposed.

From aggregated traffic models to emissions quantification: connecting the missing dots

Traffic models based on the Macroscopic Fundamental Diagram have great potential for future applications of large-scale estimation and monitoring of exhaust emissions. These traffic models offer an aggregated representation of the traffic dynamics in the network, considering mean spatial speeds and average travel distances per zone in the city, which are then used to determine the exhaust emissions using a macroscopic emission model. This aggregated estimation of the travel emissions has an inherent bias when compared to a benchmark scenario consisting of a microscopic estimation of the emissions at the network level. This paper analyzes this discrepancy qualitatively and quantitatively. As our testbed, we consider the traffic data of the 1st of November collected by the project pNEUMA in downtown Athens, Greece. The benchmark scenario consists of the estimation of exhaust emissions using the Vehicle Specific Power model. For the aggregated estimation of travel emissions, we use the aggregated traffic dynamics based on the Macroscopic Fundamental Diagram dynamics and COPERT V emission model. Our results show that the aggregated methodology underestimates CO2 emissions by 30% and NOx emissions by 32%, in agreement with the existing literature. Despite the observed discrepancies between the estimated emissions, the aggregated traffic dynamics approach based on the Macroscopic Fundamental Diagram is computationally lighter than a microscopic one, and therefore more appropriate for large-scale applications.

Reaching homogeneous field emission current from clusters of emitters with nonuniform heights

In field electron emission from pointed structures in a cluster or in an array, electrostatic depolarization due to neighboring emitters diminishes the local field enhancement factor (FEF). This effect can limit the maximum macroscopic emission current from devices. If emitters in a cluster are regularly spaced and uniformly tall, the local FEF will always present significantly larger values at the edges, making most of the cluster ineffective. In this work, we explore conducting emitters in a cluster that are not uniformly tall and numerically calculate the local electrostatic field and the macroscopic emitted current over classical emitter’s surfaces using the Murphy–Good emission model. Our results show the conditions to homogenize and, therefore, optimize the emission current extractable from the cluster.

Forecasting the Reduction in Urban Air Pollution by Expansion of Market Shares of Eco-Friendly Vehicles: A Focus on Seoul, Korea.

Due to global climate change, various countries have agreed upon the use of conventions. In this study, the eco-friendly vehicular policy on carbon neutrality implemented in Seoul, Korea, was examined. To this end, various policy-based scenarios were set, and the changes in automotive exhaust gas emissions were evaluated and compared. The evaluation method combined macroscopic and microscopic emission models as its analysis framework. Micro-traffic data available in Korea were used for analyses, and the results for all autonomous districts were derived to cover the entire area of Seoul. The findings confirmed that the most effective measure is the initial replacement of old, mid-size, or large diesel passenger cars with eco-friendly vehicles (Middle-sized: Scenario 2-1 5.52%, Scenario 2-2 6.86%, Scenario 3-1 80.93%, and Scenario 3-2 83.98%). The replacement of old vehicles exhibited the highest effect in all tested scenarios, while the initial replacement of diesel vehicles was more effective than the replacement of gasoline and liquified petroleum gas vehicles (Diesel: Scenario2-1 6.64%, Scenario 2-2 8.21%, Scenario3-1 86.23%, and Scenario 3-2 90.51%). Among the autonomous districts of Seoul, the Gangnam-gu area exhibited the largest emission-reduced effect among all the tested scenarios (Gangnam-gu: Scenario 2-1 5.80%, Scenario 2-2 6.74%, Scenario 3-1 80.44%, and Scenario 3-2 82.62%). Overall, it was demonstrated that the findings of this study may have significant policy implications in terms of urban emission changes pertaining to transportation.

Confinement-Tunable Transition Dipole Moment Orientation in Perovskite Nanoplatelet Solids and Binary Blends.

Tuning the transition dipole moment (TDM) orientation in low-dimensional semiconductors is of fundamental and practical interest, as it enables high-efficiency nanophotonics and light-emitting diodes. However, despite recent progress in nanomaterials physics and chemistry, material systems that allow continuous tuning of the TDM orientation remain rare. Here, combining k-space photoluminescence spectroscopy and multiscale modeling, we demonstrate that the TDM orientation in lead halide perovskite (LHP) nanoplatelet (NPL) solids is largely confinement-tunable through the NPL geometry that regulates the anisotropy of Bloch states, dielectric confinement, and exciton fine structure. We further quantified the role of uniaxial ordering during NPL assembly in modifying the macroscopic emission directionality of thin films, which is especially important in actual optoelectronic devices. Our theoretical framework successfully corroborates the previous prediction of exciton bright level order reversal with experimental evidence of a counterintuitive reduction of in-plane dipole ratio in ultrathin (one- and two-monolayer-thick) NPLs, even at room temperature. More interestingly, the NPLs retain their TDM orientation in binary blends irrespective of interparticle energy transfer, owing to the phase segregation and NPL-NPL decoupling, enabling the design of films whose fluorescence exhibits an intrinsic angle-dependent color gradient.

Method to observe Jupiter’s radio emissions at high resolution using multiple LOFAR stations: a first case study of the Io-decametric emission using the Irish IE613, French FR606, and German DE604 stations

ABSTRACT The Low Frequency Array (LOFAR) is an international radio telescope array, consisting of 38 stations in the Netherlands and 14 international stations spread over Europe. Here, we present an observation method to study the Jovian decametric radio emissions from several LOFAR stations (here Birr Castle in Ireland, Nançay in France, and Postdam in Germany), at high temporal and spectral resolution. This method is based on prediction tools, such as radio emission simulations and probability maps, and data processing. We report an observation of Io-induced decametric emission from 2021 June, and a first case study of the substructures that compose the macroscopic emissions (called millisecond bursts). The study of these bursts makes it possible to determine the electron populations at the origin of these emissions. We then present several possible future avenues for study based on these observations. The methodology and study perspectives described in this paper can be applied to new observations of Jovian radio emissions induced by Io, but also by Ganymede or Europa, or Jovian auroral radio emissions.

Field emission characteristics of ZnO nanowires grown by catalyst-assisted MOCVD on free-standing inorganic nanomembranes

ZnO field emitters on freely suspended inorganic nanomembranes (NMs) were synthesized by catalyst-assisted metal organic chemical vapor deposition (MOCVD) using the precursors zinc acetylacetonate hydrate and oxygen. The morphology and the possibly involved growth mechanisms of the randomly distributed ZnO nanostructures were investigated by scanning electron microscopy and by energy-dispersive x-ray spectroscopy. The findings indicate a growth process that involves the vapor–liquid–solid as well as the vapor–solid–solid mechanism. The field emission (FE) properties of such ZnO nanowire (NW) samples showed to be highly reproducible and independent of the investigated Si-based substrate types. Herein, a new type of flexible substrate for the MOCVD process has been introduced that allows for the direct growth of ZnO NWs on free-standing membranes for potential FE-based sensor applications. A turn-on field of 4.1 V µm−1 was detected for a macroscopic emission current density of 10 µA cm−2 and the stability test revealed fluctuations of only 9% around the mean emission current over a duration of 3 h proving a reliable and stable operation of such devices. Moreover, approaches were identified to further enhance the FE characteristics of the ZnO NWs by variation of the synthesis parameters and by enlargement of the NM area.

Emission exposure optimum for a single-destination dynamic traffic network

• We develop a dynamic model to describe human exposure to vehicular emissions. • A free-flow optimal solution exists that minimizes the total emission exposure. • The health impact of vehicular emissions can be reduced by managing traffic flow. • There is a tradeoff between total time cost and total emission exposure. In this paper, an integrated dynamic framework is applied to model human exposure to vehicular emissions. The framework contains three components: a time-dependent macroscopic emission model, a dynamic traffic flow model, and a line source Gaussian plume model to capture the spatial dispersion of emissions. A human exposure-based dynamic system-optimal problem is then formulated to minimize system-wide emission exposure. An illustrative network and the Sioux Falls network are used to test the proposed framework and to examine how traffic dynamics, population characteristics, and meteorological conditions affect emission exposure at various network locations. The results provide evidence that regional emission exposure can be reduced by dynamically managing network traffic. At least under the specific network setup, the paper provides a counterexample to the common conception that traffic congestion and emissions (exposure) can be minimized simultaneously, which implies that traffic system operators must properly leverage the tradeoff between congestion and emissions.

Coupling traffic originated urban air pollution estimation with an atmospheric chemistry model

Due to increasing issues of air pollution in urban areas continuous research is being conducted to upgrade models, which can predict the distribution of pollutants and thus enable timely interventions to mitigate their negative effects. To support these efforts, traffic data from an integrated transport model was used to drive the COPERT traffic emission model and the WRF-Chem atmospheric chemistry model. With reliable macroscopic traffic data from the Budapest region, traffic state estimations were calculated for every fifteen minutes of the day using dynamic assignment with predefined and time-varying static demand matrices. Then the COPERT vehicular emission model of average speeds was applied to provide the emission factors, so that the macroscopic emissions for the traffic network could be calculated. As a next step the WRF-Chem online coupled weather and atmospheric chemistry model was adapted to estimate atmospheric dispersion of pollutants (CO, NOx, O3). The coupled models are presented in a 2-day case study with qualitative comparison of obtained results with measurements. As a result, it can be stated that combining macroscopic road traffic modeling with atmospheric models can enhance the estimation efficiency of urban air pollution.

A surrogate approach for estimating vehicle-related emissions under heterogenous traffic conditions

ABSTRACT Vehicle emission analysis currently faces a trade-off between easy-to-use, low-accuracy macroscopic models, and computationally intensive, high-accuracy microscopic models. In this study, we develop a surrogate model that leverages microscopic traffic and emission simulations to predict link-level emission rates. The input variables are obtained by aggregating 1 Hz simulated vehicle trajectories into hourly traffic condition factors (e.g., link average/variation of speed, truck fleet percentage, road grade, etc.). The emission ground truth data are generated using the Motor Vehicle Emission Simulator (MOVES) opmode-based analysis module. We explore different parameter and machine learning model structures to establish the statistical relationship of the input variables and the link-level emission rates. We demonstrate the ability of our model to accurately estimate vehicle-related emissions by using the Columbia, South Carolina road network as an example. This model can serve as a high-level planning tool to assess the impacts of emissions from transportation projects. Implications: Vehicle emission analysis is facing trade-offs between easy-to-use macroscopic emission models with low accuracy and computationally intensive microscopic models with high accuracy. Existing studies attempted to cope with the trade-off by pre-selecting representative emission rates but are still subject to the risk of not considering differentiated traffic patterns by using single emission rate. To fill in the knowledge gap in the literature, we develop a surrogate approach that fully integrates driving trajectories of heterogenous traffic patterns into a link-level emissions estimation model considering road characteristics. The model can achieve high accuracy and utilize publicly available traffic data in vehicle emission prediction. We apply the proposed model in a middle size city road network and demonstrate its capability to capture and quantify the impacts of traffic patterns on link-level vehicle-related emissions. Additionally, the proposed model can serve as a sketch planning tool for researchers and transportation air quality practitioners to quickly assess bounds of emissions benefits due to traffic operational and transportation strategies.

A coupled macroscopic traffic and pollutant emission modelling system for Barcelona

We present a coupled macroscopic traffic and emission modelling system tailored to the Barcelona metropolitan area that allows estimating hourly road transport emissions at road link level. We use the developed system to perform an emission sensitivity analysis of typically high uncertainty emission features and assess their impact. We also explore the uncertainties of our system compared to a microscopic approach in a representative area of Barcelona. The developed macroscopic system shows a high sensitivity to spatially-resolved vehicle fleet composition inputs, meteorological effects on diesel engines (+19% in NOx) and non-exhaust sources (80% of total PM emissions). The comparison with the microscopic system shows that discrepancies grow as a function of the congestion level, up to +65% in NOx. The resulting coupled system will be used in further steps of the research to evaluate the impact of traffic management strategies upon urban emissions and air quality levels in Barcelona.

Comparison of macroscopic and microscopic emission characteristics of large area field emitters based on carbon nanotubes and graphene

Nanostructured multitip surfaces have sufficient potential to obtain the high emission currents necessary to develop stable and noninertial sources of free electrons with increased levels of permissible currents. The key to understanding the processes of formation and stability of macroscopic emission currents from these large area field emitters (LAFEs) is assessing the local characteristics of individual emission sites. Herein, a method for determining the local emission characteristics of nanoscale emission sites is developed via processing the glow pattern data and a system for rapidly recording the current–voltage characteristics of LAFEs.

Comparison of Support Vector and Non-Linear Regression Models for Estimating Large-Scale Vehicular Emissions, Incorporating Network-Wide Fundamental Diagram for Heterogeneous Vehicles

Estimation of vehicular emissions at network level is a prominent issue in transportation planning and management of urban areas. For large networks, macroscopic emission models are preferred because of their simplicity. However, these models do not consider traffic flow dynamics that significantly affect emissions production. This study proposes a network-level emission modeling framework based on the network-wide fundamental diagram (NFD), via integrating the NFD properties with an existing microscopic emission model. The NFD and microscopic emission models are estimated using microscopic and mesoscopic traffic simulation tools at different scales for various traffic compositions. The major contribution is to consider heterogeneous vehicle types with different emission generation rates in a network-level model. This framework is applied to the large-scale network of Chicago as well as its central business district. Non-linear and support vector regression models are developed using simulated trajectory data of 13 simulated scenarios. The results show a satisfactory calibration and successful validation with acceptable deviations from the underlying microscopic emissions model regardless of the simulation tool that is used to calibrate the network-level emissions model. The microscopic traffic simulation is appropriate for smaller networks, while mesoscopic traffic simulation is a proper means to calibrate models for larger networks. The proposed model is also used to demonstrate the relationship between macroscopic emissions and flow characteristics in the form of a network emissions diagram. The results of this study provide a tool for planners to analyze vehicular emissions in real time and find optimal policies to control the level of emissions in large cities.

Environmentally friendly traffic control strategy - A case study in Xi’an city

Given the increasing painful repercussions of air pollution caused by massive vehicular emissions and the unsustainable development of transport sectors, the integration of traffic models with emission estimation models has been investigated in depth to evaluate the performance of the pre-determined environment-friendly transportation policies. Nevertheless, there is a lack of the optimization approach to design the traffic control strategy combining the coupled models. Therefore, an optimization framework is developed to optimize the traffic control strategies to mitigate the vehicular emissions in the considered network. The proposed framework integrates a macroscopic analytical traffic model with a macroscopic emissions estimation model to account for the vehicle emissions, and then genetic algorithm (GA) is employed to minimize vehicle emissions. A congested urban area in Xi’an city that consists of two signalized intersections is selected for a case study. The performance of the signal plans derived by the proposed framework is compared with the currently in use signal plans via the microscopic simulator AIMSUN. It indicates that the proposed traffic control scheme reduces expected vehicle trip travel time and vehicle emissions of four sorts of pollutants for the considered area. This optimization framework helps traffic operators to design environment-friendly traffic signal control strategy.

Experimental confirmation of the nearly power-law relation between macroscopic current and characteristic current density in carbon nanotube-based large-area field emitters

Carbon nanotube (CNT) films are potential candidates for viable applications in large-area field electron emission based X-ray sources. An important issue in large-area field electron emitters (LAFEs) is the connection between the macroscopic emission current iM and the characteristic current density JC, linking experimental observations to field emission theories. The quantity JC is often associated with the site in the LAFE with the maximum field enhancement factor (FEF). This convention is used in practice because it mimics the one used for single-tip field emitters. Within the mainstream Fowler-Nordheim (FN) theory, a linear relation between iM and JC is expected. However, recent theoretical works within the framework of the standard FN theory have predicted a nearly power-law dependence between these two quantities. This work experimentally confirms those theoretical predictions for CNT-based LAFEs. Experimental studies, supported by glow pattern analysis, were conducted over a wide range of emitted currents and, importantly, confirm the general validity of the two-component FEF for CNT-based LAFEs, which can be viewed as a simple approach to this nearly power-law dependence.

The feasibility of magnetic reconnection powered blazar flares from synchrotron self-Compton emission

Order of magnitude variability has been observed in the blazar sub-class of Active Galactic Nuclei on minute timescales. These high-energy flares are often difficult to explain with shock acceleration models due to the small size of the inferred emitting region, with recent particle-in-cell (PIC) simulations showing that magnetic reconnection is a promising alternative mechanism. Here, we present a macroscopic emission model physically motivated by PIC simulations, where the energy for particle acceleration originates from the reconnecting magnetic field. We track the radial growth and relative velocity of a reconnecting plasmoid, modelling particle acceleration and radiative losses from synchrotron and synchrotron self-Compton (SSC) emission. To test the viability of magnetic reconnection as the mechanism behind rapid blazar flares we simultaneously fit our model to the observed light-curve and SED from the 2016 TeV flare of BL Lacertae. We find generally that, without considering external photons, reconnecting plasmoids are unable to produce Compton-dominant TeV flares and so cannot reproduce the observations due to overproduction of synchrotron emission. Additionally, problematically large plasmoids, comparable in size to the entire jet radius, are required to emit sufficient SSC gamma-rays to be observable. However, our plasmoid model can reproduce the rapid TeV lightcurve of the flare, demonstrating that reconnection is able to produce rapid, powerful TeV flares on observed timescales. We conclude that while reconnection can produce SSC flares on the correct timescales, the primary source of TeV emission cannot be SSC and the size of plasmoids required may be implausibly large.

Numerical analysis of the notional area in cold field electron emission from arrays

The notional area of field emission is an important parameter to correlate characteristic current density to the emission current, linking field emission theories to experimental observations. Recently, it has been reported that the notional area of emission contributes to the high brightness of large diameter emitters. Thus, it is necessary to understand how the notional area of emission depends on physical and geometrical parameters. In this work, we carried out numerical simulations to evaluate the notional area, An, considering cold field electron emission from a hemisphere on a cylindrical post (HCP) emitter in an array. An HCP is suitable to model classically carbon nanotubes or carbon nanofibres-like emitters. We provide the dependence of An on a wide range of physical and geometrical parameters, namely: the separation between the HCP emitters, the aspect ratio, radius, local work function and the macroscopic emission current. We explain the behavior of An as a function of these parameters and show in which cases An can be considered nearly constant. Our numerical results are within the framework of the standard Fowler–Nordheim (FN) theory and can simplify the modeling of the field emission phenomenon, because it directly relates simulation predictions to the currents observable experimentally. Also, this work provides information for experimentalists that can be useful to check the validity of the Schottky–Nordheim (SN) barrier upon the elementary FN theory.