Reasonable Simulation Research Articles

Simulation of SMAP and AMSR2 observations and estimation of multi-frequency vegetation optical depth using a discrete scattering model in the Tibetan grassland

Passive microwave observation at multiple frequencies has received increasing research interests due to its capability to provide comprehensive information of land surface properties. This paper contributes to the simulation of land surface emission and estimation of vegetation optical depth (VOD) at multiple frequencies using a discrete scattering model with a single set of model parameter values. Validity of the Tor Vergata (TVG) discrete scattering model in simultaneously reproducing the Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) and Advanced Microwave Scanning Radiometer 2 (AMSR2) C- (6.925 GHz) and X-band (10.7 GHz) observations over the Tibetan grassland ecosystem is evaluated. Frequency-specific and multi-frequency calibration strategies are implemented to find the suitable set of model parameter values and to isolate the impact of frequency on parameter values. On this basis, the calibrated TVG model is further used to estimate the VOD, and to investigate the impact of microwave frequency and observation angle on the emission simulations and VOD parameterization.The results show that both frequency-specific and multi-frequency calibration strategies achieve comparable and reasonable simulations of SMAP and AMSR2 observations, confirming the feasibility of using an identical physically-based model (i.e. the calibrated TVG model) to simulate multi-frequency land emission driven by a single set of model parameter values. As such, the dependence of emission components and VOD on frequency can be elaborated after isolating the impact of frequency on parameter values. The VOD values derived from the TVG simulations generally increase with increasing frequency and can be linearly correlated to the LAI variations, while current satellite-based retrievals have almost the same magnitude at the L-, C-, and X-band. The explanation for this can be that the retrieved VOD is different from the theoretical definition. Sensitivity test performed using the calibrated TVG model further shows that polarization-dependence of VOD becomes more apparent with the increasing observation angle and frequency. New parameterization has thus been developed to characterize the dependence of VOD on the frequency, observation angle, and polarization for grassland based on the results of sensitivity test. This study may provide new insights in improving model of land emission and retrievals of SM and VOD with physical interpretability based on multi-frequency satellite observations.

Statistical Analysis of Long-Term Aging Parameters of Laboratory- and Field-Aged Asphalt Binders Considering Rheological and Chemical Properties

Aging has long been recognized as an important factor that accelerates damage to flexible pavements. This study investigates the effects of long-term aging protocols on critical rheological and chemical parameters in the laboratory and field by focusing on the evaluation of aged binders under various aging conditions. To fabricate aged binders with various long-term aging levels, five laboratory aging protocols were considered, including a standard pressure aging vessel for binder and loose-mix aging protocols (70°C, 85°C, 95°C, and 135°C) with various aging durations. The rheological properties and chemical composition of laboratory-aged binders were extensively evaluated. For comparison, field-aged binders were extracted from 14 top layers and measured in a similar manner. A general linear model statistical analysis was conducted on the relationship between the rheological and chemical parameters to explore the potential differences between various aging protocols. Loose-mix aging in the range of 70–95° was found to be relatively closer to the in situ aging trend using statistical analyses. The results of this work provide a reasonable simulation of long-term-aged in-service asphalt pavement that can assist in material selection, mixture design, and maintenance operations.

Accelerated estimation of sea-spray-mediated heat flux using Gaussian quadrature: case studies with a coupled CFSv2.0-WW3 system

Abstract. Sea-spray-mediated heat flux plays an important role in air–sea heat transfer. Heat flux integrated over the droplet size spectrum can simulate well the total heat flux induced by sea spray droplets. Previously, a fast algorithm of spray flux assuming single-radius droplets (A15) was widely used, as the full-size spectrum integral is computationally expensive. Based on the Gaussian quadrature (GQ) method, a new fast algorithm (SPRAY-GQ) of sea-spray-mediated heat flux is derived. The performance of SPRAY-GQ is evaluated by comparing heat fluxes with those estimated from the widely used A15. The new algorithm shows a better agreement with the original spectrum integral. To further evaluate the numerical errors of A15 and SPRAY-GQ, the two algorithms are implemented into the coupled Climate Forecast System model version 2.0 (CFSv2.0) and WAVEWATCH III (WW3) system, and a series of 56 d simulations in summer and winter are conducted and compared. The comparisons with satellite measurements and reanalysis data show that the SPRAY-GQ algorithm could lead to more reasonable simulation than the A15 algorithm by modifying air–sea heat flux. For experiments based on SPRAY-GQ, the sea surface temperature at middle to high latitudes of both hemispheres, particularly in summer, is significantly improved compared with the experiments based on A15. The simulation of 10 m wind speed and significant wave height at middle to low latitudes of the Northern Hemisphere after the first 2 weeks is improved as well. These improvements are due to the reduced numerical errors. The computational time of SPRAY-GQ is about the same as that of A15. Therefore, the newly developed SPRAY-GQ algorithm has potential to be used for the calculation of spray-mediated heat flux in coupled models.

Artificial intelligence and edge computing for teaching quality evaluation based on 5G-enabled wireless communication technology

Cloud computing and artificial intelligence are now widely used for classroom teaching in higher learning institutes. The digital teaching supported to ICT technologies in colleges serves as a central point for the advancement of modern education; and has become as a mode of instruction and an approach to teaching. Digital teaching has emerged as a major driving force in the advancement of digital economy and digitization of education in colleges. In this paper, we investigate the movable information management system utilized in the digital teaching using edge computing and 5G wireless communication technology. Furthermore, we explain the idea of a mobile data scheme and presents a teaching platform based on the edge computing and 5G-enabled wireless communication technology. The main objective of this work is to develop a digital teaching framework for college students that, in fact, enables digital teaching, the collection, and incorporation of teaching information, the provision of modern education, and sharing of resources. Cutting-edge technology advancements in the educational platform have the potential to improve 5G communication. To implement the cutting-edge technology, all types of technological devices, smart devices, and gadgets from the Internet of Things (IoT) platform are used. We evaluated the proposed system through reasonable assumptions and numerical simulations. The experimental results reveal that the suggested system has significantly improved the teaching efficiency with which digital teaching management is managed in colleges. Moreover, the edge and 5G technology can significantly improve the system performance, in terms of response time, that can be as high as 11.45% when compared to non-cloud based approaches.

Research on biomechanical compatibility for a novel artificial anal sphincter with constant force.

Existing artificial anal sphincter studies have shown that biomechanical compatibility problem between artificial anal sphincter and rectum caused by long-term morphological changes of the tissue surrounding the implanted prosthesis can lead to device failure or tissue ischemic necrosis. In this article, a mechanical artificial anal sphincter with constant force clamping is designed based on the superelasticity of shape memory alloys, which improved the biomechanical compatibility of implantable artificial anal sphincter. Firstly, the anatomical structure and the biomechanical properties of the rectum are analyzed to obtain the size parameters and material parameters of the rectal model. Secondly, a novel artificial anal sphincter with constant force is designed to improve the biomechanical compatibility between the artificial sphincter and the rectum. Thirdly, the static analysis of artificial anal sphincter is carried out by finite element analysis. The simulation results show that the artificial anal sphincter can maintain a constant clamping force of 4N within a certain variation range of intestinal tissue thickness, which verifies the constant force characteristic of the artificial anal sphincter. The constant clamping force of the artificial anal sphincter to the rectum is 4N that is greater than the clamping force 3.99 N required to close the rectum, which verifies the effectiveness the artificial anal sphincter. The surface contact stress and the minimum principal stress of the rectum in the clamping state are less than the pressure threshold, which verifies the safety of the artificial anal sphincter. The novel artificial anal sphincter has better biomechanical compatibility and improves the mechanical match between artificial sphincter and intestinal tissue. This study may provide more reasonable and effective simulation data for in vivo experiments of artificial anal sphincter in future, which may provide theoretical and technical support for further research about clinical application of artificial anal sphincter.

Inductive Determination of Rate-Reaction Equation Parameters for Dislocation Structure Formation Using Artificial Neural Network

The reaction-diffusion equation approach, which solves differential equations of the development of density distributions of mobile and immobile dislocations under mutual interactions, is a method widely used to model the dislocation structure formation. A challenge in the approach is the difficulty in the determination of appropriate parameters in the governing equations because deductive (bottom-up) determination for such a phenomenological model is problematic. To circumvent this problem, we propose an inductive approach utilizing the machine-learning method to search a parameter set that produces simulation results consistent with experiments. Using a thin film model, we performed numerical simulations based on the reaction-diffusion equations for various sets of input parameters to obtain dislocation patterns. The resulting patterns are represented by the following two parameters; the number of dislocation walls (p2), and the average width of the walls (p3). Then, we constructed an artificial neural network (ANN) model to map between the input parameters and the output dislocation patterns. The constructed ANN model was found to be able to predict dislocation patterns; i.e., average errors in p2 and p3 for test data having 10% deviation from the training data were within 7% of the average magnitude of p2 and p3. The proposed scheme enables us to find appropriate constitutive laws that lead to reasonable simulation results, once realistic observations of the phenomenon in question are provided. This approach provides a new scheme to bridge models for different length scales in the hierarchical multiscale simulation framework.

Accurate simulation on the forming and failure processes of fiber metal laminates: A review

Fiber metal laminates (FMLs) are a kind of composite material prepared by alternately arranging fiber layers and metal sheets at a certain temperature and pressure. It has been widely used in aerospace and automobile transportation for its excellent combined mechanical properties. For the forming and failure processes of FMLs, the interfacial behavior and damage evolution of components are hard to be observed experimentally. Therefore, it is of great importance to simulate them accurately. In this article, the development and application of FMLs were first introduced. Then the comparison of constitutive models in FMLs simulation was given, especially the dynamic constitutive model applied to the metal layer. After that, the important aspects of damage evolution, interface behavior, and model optimization in the simulation on the forming and failure process of FMLs were analyzed, and the emphasis is on the nonlinear progressive damage model of different materials, the construction of cohesive zone model and superior meshing methods. Furthermore, the experimental verifications of FMLs simulation were given. It is shown that the deformation behavior and damage characteristics of various kinds of FMLs during forming and failure processing can be accurately predicted by reasonable numerical simulation. Eventually, the future outlooks for numerical simulation of FMLs was proposed. Through this review, scholars and engineers who are interested in FMLs can systematically understand the numerical simulation work of FMLs, which is helpful in improving the quality of research in this field.

A numerical simulation method of residual magnetic field signals for evaluating deformation-induced stress concentration in ferromagnetic materials

The metal magnetic memory (MMM) technique is a promising non-destructive inspection method that is sensitive to early damage due to stress concentration in ferromagnetic components. However, quantitative analysis methods for evaluating the stress concentration induced by local plastic deformation have not yet been sufficiently studied due to the lack of a reasonable numerical simulation method. Based on the Jiles-Atherton (J-A) magneto-plastic model, which considers the combined effects of residual stress and strain, the change in the behaviour of residual magnetic field (RMF) signals in an X80 pipeline steel specimen with an indentation is calculated in this paper using finite element (FE) simulations, while systematic experimental research is also carried out. The results show that the model-predicted RMF signals are consistent with the experimental data, which demonstrates the validity of the numerical simulation method. Moreover, the comparison indicates that the RMF FE simulations can provide an effective and reliable way to determine the location and the degree of deformation-induced stress concentration. In addition, the effect of the residual stress and strain on the surface magnetic field and the model is discussed. The results of this study help to improve the accuracy of the MMM technique for evaluating the stress concentration caused by local plastic deformation.

Improving the efficiency of machine learning in simulating sedimentary heavy metal contamination by coupling preposing feature selection methods

Sediment cores were collected from Taihu Lake in China. The chronology was determined by radionuclide. Heavy metals and magnetic properties of each core slice were assessed, respectively. The concentrations of most heavy metals in sediments surged at 20 cm from the surface, accompanying the increase in the concentrations of single-domain magnetic particles. This may be resulted from the influence of anthropic activities on the lake's environment after the 1970s. Two feature selection methods, random forest (RF) and maximal information coefficient (MIC), were combined with support vector machine (SVM) model to simulate heavy metals, with the inclusion of selected magnetic and physicochemical parameters. Compared with the modeling results obtained with the full set of parameters, a reasonable simulation performance was obtained with RF and MIC. RF performed better than MIC by increasing the R2 of simulation models for Cd, Cr, Cu, Pb, and Sb. For heavy metals with high ecological risks (As, Cd, Cr, Hg, Pb, Sb), the correlation coefficients for observed and predicted data ranged from 0.73 to 0.97 with only 14–27% of the parameters selected by RF as input variables. The RF-RBF-SVM enabled heavy metal predictions based on the magnetic properties of the lake sediments.

Modelling an air handling unit, building and occupant variation regarding energy, moisture and frost protection based on measurements of an air handling unit and occupants’ moisture supply

Objectives of this paper are to propose a reasonable simulation model that can handle condensation and frost formation in heat exchangers dependent on moisture concentration and outdoor climate conditions, and to analyze risk of frost formation and discuss different frost protection strategies. Using psychometrics, American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) heat exchanger calculation, condensation and frost calculation methods, simulations models for heat recovery ventilation and energy recovery ventilation are developed. It is concluded that the risk of frost in heat exchangers is higher in northern Sweden than in southern Sweden and Denmark due to low outdoor air temperatures during the winter, however heat recovery systems in Copenhagen and Gothenburg still has a risk of frost formation even though these cities has relatively warm weather conditions. Out of several simulation models that are created during the study, the detailed model that takes condensation effect into consideration, is proven to be worthy to be used in further investigations, due to results that more accurately represent real word conditions. Risk of frost formation becomes significantly lower in every city when heat recovery ventilation is changed into energy recovery ventilation. Even though heat recovery ventilation is simulated only with the simplified model, it is safe to assume that it will still be effective in reality. Although bypass strategies were effective at eliminating the risk of frost in heat recovery units, they are not as efficient from an energy perspective. The more air is by-passed through the cross-flow plate heat exchanger, the fewer opportunities there are to recover energy from exhaust air, which leads to lower supply air temperatures and higher energy need for heating a living space.

Development of an integrated machine-learning and data assimilation framework for NOx emission inversion

As major air pollutants, nitrogen oxides (NOx, mainly comprising NO and NO2) not only have adverse effects on human health but also contribute to the formation of secondary pollutants, such as ozone and particulate nitrate. To acquire reasonable NOx simulation results for further analysis, a reasonable emission inventory is needed for three-dimensional chemical transport models (3D-CTMs). In this study, a comprehensive emission adjustment framework for NOx emission, which integrates the simulation results of the 3D-CTM, surface NO2 measurements, the three-dimensional variational data assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NOx emissions over China for the summers of 2015 and 2020. Compared with the simulation using prior NOx emissions, the root-mean-square error, normalized mean error, and normalized mean bias decreased by approximately 40 %, 40 %, and 60 % in NO2 simulation using posterior NOx emissions corrected by the framework proposed in this work. Compared with the emissions for 2015, the NOx emission generally decreased by an average of 5 % in the simulation domain for 2020, especially in Henan and Anhui provinces, where the percentage reductions reached 24 % and 19 %, respectively. The proposed framework is sufficiently flexible to correct emissions in other periods and regions. The framework can provide reliable and up-to-date emission information and can thus contribute to both scientific research and policy development relating to NOx pollution.

MINAS TIRITH: a new tool for simulating radiation-induced DNA damage at the cell population level

Objective. The mechanisms of radiation-induced DNA damage can be understood via the fundamental acquisition of knowledge through a combination of experiments and modeling. Currently, most biological experiments are performed by irradiating an entire cell population, whereas modeling of radiation-induced effects is usually performed via Monte Carlo simulations with track structure codes coupled to realistic DNA geometries of a single-cell nucleus. However, the difference in scale between the two methods hinders a direct comparison because the dose distribution in the cell population is not necessarily uniform owing to the stochastic nature of the energy deposition. Thus, this study proposed the MINAS TIRITH tool to model the distribution of radiation-induced DNA damage in a cell population. Approach. The proposed method is based on precomputed databases of microdosimetric parameters and DNA damage distributions generated using the Geant4-DNA Monte Carlo Toolkit. First, a specific energy was assigned to each cell of an irradiated population for a particular absorbed dose following microdosimetric formalism. Then, each cell was assigned a realistic number of DNA damage events according to the specific energy respecting the stochastic character of its occurrence. Main results. This study validated the MINAS TIRITH tool by comparing its results with those obtained using the Geant4-DNA track structure code and a Geant4-DNA based simulation chain for DNA damage calculation. The different elements of comparison indicated consistency between MINAS TIRITH and the Monte Carlo simulation in case of the dose distribution in the population and the calculation of the amount of DNA damage. Significance. MINAS TIRITH is a new approach for the calculation of radiation-induced DNA damage at the cell population level that facilitates reasonable simulation times compared to those obtained with track structure codes. Moreover, this tool enables a more direct comparison between modeling and biological experimentation.

Who is on the right track? Behavior-based prediction of diagnostic success in a collaborative diagnostic reasoning simulation

BackgroundMaking accurate diagnoses in teams requires complex collaborative diagnostic reasoning skills, which require extensive training. In this study, we investigated broad content-independent behavioral indicators of diagnostic accuracy and checked whether and how quickly diagnostic accuracy could be predicted from these behavioral indicators when they were displayed in a collaborative diagnostic reasoning simulation.MethodsA total of 73 medical students and 25 physicians were asked to diagnose patient cases in a medical training simulation with the help of an agent-based radiologist. Log files were automatically coded for collaborative diagnostic activities (CDAs; i.e., evidence generation, sharing and eliciting of evidence and hypotheses, drawing conclusions). These codes were transformed into bigrams that contained information about the time spent on and transitions between CDAs. Support vector machines with linear kernels, random forests, and gradient boosting machines were trained to classify whether a diagnostician could provide the correct diagnosis on the basis of the CDAs.ResultsAll algorithms performed well in predicting diagnostic accuracy in the training and testing phases. Yet, the random forest was selected as the final model because of its better performance (kappa = .40) in the testing phase. The model predicted diagnostic success with higher precision than it predicted diagnostic failure (sensitivity = .90; specificity = .46). A reliable prediction of diagnostic success was possible after about two thirds of the median time spent on the diagnostic task. Most important for the prediction of diagnostic accuracy was the time spent on certain individual activities, such as evidence generation (typical for accurate diagnoses), and collaborative activities, such as sharing and eliciting evidence (typical for inaccurate diagnoses).ConclusionsThis study advances the understanding of differences in the collaborative diagnostic reasoning processes of successful and unsuccessful diagnosticians. Taking time to generate evidence at the beginning of the diagnostic task can help build an initial adequate representation of the diagnostic case that prestructures subsequent collaborative activities and is crucial for making accurate diagnoses. This information could be used to provide adaptive process-based feedback on whether learners are on the right diagnostic track. Moreover, early instructional support in a diagnostic training task might help diagnosticians improve such individual diagnostic activities and prepare for effective collaboration. In addition, the ability to identify successful diagnosticians even before task completion might help adjust task difficulty to learners in real time.

A Comparison of Deep Reinforcement Learning Models for Isolated Traffic Signal Control

Traditional control methods may not be adaptive enough for ever-changing traffic dynamics. Hence, extensive deep reinforcement learning (DRL) methods have been utilized to solve the traffic signal timing control problem because DRL can adaptively learn optimal policy through analyzing experience samples generated by interaction with the environment. However, there is an urgent need for researchers to answer which DRL algorithm should be adopted in practice and how to select model settings. Therefore, we introduce a reasonable simulation platform to test and compare different DRL methods. Specifically, we evaluate seven prevailing DRL algorithms under our defined model settings from two aspects: training and execution performance. Testing results indicate that the soft actor–critic (SAC) outperforms other DRL algorithms and the maximum pressure method in most cases. To our best knowledge, this is also the first study to apply SAC and value distribution methods for traffic signal control. To answer how to select model settings, we compare the execution performance of the DRL algorithms with different state, action, and reward settings. Experimental results reveal the superiority of our model-setting choices. All these findings have enlightening effects on other traffic decision management problems, such as ramp and multi-intersection control.

Broadband Modeling and Simulation Strategy for Conducted Emissions of Power Electronic Systems Up to 400 MHz

Energy efficiency is becoming one of the most important topics in electronics. Among others, wide band-gap semiconductors can raise efficiency and lead to shrinking volumes in power conversion systems. As different markets have regulations that require different designs, it is necessary to cope with a large variety of similar designs. By using effective modeling and simulation strategies, the efforts of building these variants can be diminished, and re-designs can be avoided. In this paper, we present a universally valid way to come to reasonable simulation results for conducted emissions of a power electronic system in the frequency range from 150 kHz up to 400 MHz. After giving an overview of the state-of-the-art, the authors show how to implement and set up a simulation environment for a gallium-nitride (GaN) power converter. It shows how to differentiate between important and not that important components for Electromagnetic Compatibility (EMC), how to model these components, the printed circuit board, the load, and the setup, including the Line Impedance Stabilization Networks (LISNs), etc. Multiport S-parameter strategies as well as vector fitting methods are employed. Computational costs are kept low, and all simulations are verified with measurements; thus, this model is valid up to 400 MHz.

A bridge on Lomnitz type creep laws via generalized fractional calculus

This paper is dedicated to achieving the unification of the scattered Lomnitz type creep laws. Thereupon, a novel generalized fractional calculus, that is, Katugampola-like fractional calculus is well-defined and employed as an effective tool, along with some fundamental qualities as by-products. By virtue of the hereditary theory of viscoelasticity and Volterra integral equation method, a compatible stress-strain relation with memory effects and time-varying viscosity established results in a unified Lomnitz creep law. Then, a comparative analysis on the creep function and relaxation function inherited from the unified Lomnitz creep law is exhibited via reasoning and numerical simulation. Besides, physical interpretation of such unified Lomnitz creep law is also provided with the aid of the modified spring-dashpot model of Maxwell type. Results carried out in this paper might provide a full characterization of the creep of igneous rocks to meet some special theoretical and experimental requirements.

Modeling a severe wintertime Asian dust event observed in the East Asia region: Sensitivity of the WRF-Chem dust emission schemes

This study investigates the capability of the Weather Research and Forecasting-Chemistry (WRF-Chem) model in modeling a severe wintertime Asian dust event, which originated from the East Asia dust source region during 20–22 February 2015. The WRF-Chem simulations were conducted changing with the five embedded dust emission schemes of the UC01, UC04, UC11, GO01, and GA19 schemes. The model reasonably simulated meteorological fields, capturing the characteristic meteorological features over the dust source regions during the severe wintertime Asian dust event. A good meteorological comparison led to the reasonable simulation of the occurrence and transport of the Asian dust plumes, comparing well with the surface dust occurrence reports and the satellite-detected dust signals of aerosol optical depth (AOD) and extinction coefficient. However, considerable (approximately fivefold) differences existed in the dust emission amounts simulated by the different dust emission schemes. From the model-measurement comparison of surface PM10 concentration, it was found that the UC01 and UC04 schemes could simulate the severe wintertime Asian dust plumes in the dust source regions, capturing the dust occurrence timing and the concentration level reasonably. However, the performance of the schemes significantly depended on near-surface wind speed and soil moisture amount. On the contrary, the UC11, GO01, and GA19 schemes failed to simulate the dust plumes, revealing intrinsic limitations for severe wintertime dust plume simulation. Furthermore, this study suggests the realistic representation of the Asian dust source region and the inclusion of frozen soil parameterization in the WRF-Chem model to predict severe wintertime Asian dust events realistically.

The full activation mechanism of the adenosine A1 receptor revealed by GaMD and Su-GaMD simulations

The full activation process of G protein-coupled receptor (GPCR) plays an important role in cellular signal transduction. However, it remains challenging to simulate the whole process in which the GPCR is recognized and activated by a ligand and then couples to the G protein on a reasonable simulation timescale. Here, we developed a molecular dynamics (MD) approach named supervised (Su) Gaussian accelerated MD (GaMD) by incorporating a tabu-like supervision algorithm into a standard GaMD simulation. By using this Su-GaMD method, from the active and inactive structure of adenosine A1 receptor (A1R), we successfully revealed the full activation mechanism of A1R, including adenosine (Ado)-A1R recognition, preactivation of A1R, and A1R-G protein recognition, in hundreds of nanoseconds of simulations. The binding of Ado to the extracellular side of A1R initiates conformational changes and the preactivation of A1R. In turn, the binding of Gi2 to the intracellular side of A1R causes a decrease in the volume of the extracellular orthosteric site and stabilizes the binding of Ado to A1R. Su-GaMD could be a useful tool to reconstruct or even predict ligand-protein and protein-protein recognition pathways on a short timescale. The intermediate states revealed in this study could provide more detailed complementary structural characterizations to facilitate the drug design of A1R in the future.

Development of a carbon accumulation model for estimating the concentration of 14C in Japanese radish

ABSTRACT Atmospheric 14C released from nuclear fuel reprocessing plants can accumulate in crop plants. Japanese radish (daikon) is a major crop plant in the area surrounding the Japan’s first commercial nuclear fuel reprocessing plant in Rokkasho. We previously developed a dynamic two-compartment model for 14C accumulation in the leaves and roots of Japanese radish. This model was constructed using measured data obtained under controlled experimental conditions in a growth chamber; however, it was not capable of simulating plant carbon accumulation in actual fields resulting from photosynthesis and respiration under fluctuating environments. In order to apply our previous model to actual fields, the present study aims to modify the model by incorporating the environmental responses of photosynthesis and respiration. The modified model was validated with observed temporal changes in the carbon inventories of the leaves and roots of Japanese radish in the field. The model estimates were found to be in relatively good agreement with the observed data. In addition, our modified model provides reasonable simulation results for 14C accumulation in Japanese radish exposed to 14CO2 at different growth stages. The results of this study indicate that our modified model could contribute to the estimation of 14C accumulation in Japanese radish under field conditions.

Experimental and kinetic modeling study on auto-ignition properties of ammonia/ethanol blends at intermediate temperatures and high pressures

The auto-ignition properties of ammonia (NH3)/ethanol (C2H5OH) blends close to engine operating conditions were investigated for the first time. Specifically, the ignition delay times (IDT) of ammonia/ethanol blends were measured in a rapid compression machine (RCM) at elevated pressures of 20 and 40 bar, five C2H5OH mole fractions from 0% to 100%, three equivalence ratios (ϕ) of 0.5, 1.0 and 2.0, and intermediate temperatures between 820 and 1120 K. The measurements reveal that ethanol can drastically promote the reactivity of ammonia, e.g., the auto-ignition temperature with merely 1% C2H5OH in fuel decreases accordingly around 110 K at 40 bar as compared to that of neat ammonia. Moreover, the promotion efficiency of ethanol is higher than hydrogen and methane with a factor of 5 and 10 under the same condition. Different dependences of IDT on the equivalence ratio were observed with different ethanol fractions in the blends, i.e., the IDTs of the 5%, 10% and 100% C2H5OH in fuel decrease with an increase of ϕ, but an opposite trend was observed in the mixture with 1% C2H5OH. A new chemical kinetic mechanism for NH3/C2H5OH mixtures was developed and it is highlighted that the addition of cross-reactions between the two fuels is necessary to obtain reasonable simulations. Basically, the newly developed mechanism can reproduce the measurements of IDT very well, whereas it overestimates the reactivity of the stoichiometric and fuel-rich mixture with 1% C2H5OH in fuel. The sensitivity, reaction pathway, as well as rate of production analysis indicated that the ethanol addition to ammonia fuel blends provides key interaction pathways and enriches the O/H radical pool which further promotes the auto-ignition process.