Multipoint Model Research Articles

Aerodynamic space tether system as a system with distributed parameters

The motion of the aerodynamic tether system includes two stages - the deployment and the motion of the deployed system. The initial condition for the deployment model is that of a spacecraft with a rigidly connected second body in a circular orbit about the planet. The deployment process is based on the aerodynamic forces acting on bodies. After the separation of the rigid connection, further motions of the bodies are then controlled by the tether release mechanism which unreels the tether and controls the rate at which the tether is unreeled. The motion of the system is described by the multi-point model, splitting the tether into a number of parts and using the series of separations of material points from the spacecraft, thus forming a mathematical model of the system with distributed parameters. The aerodynamic forces acting on the tether are taken into consideration. This model makes it possible to calculate numerically elastic deformations and curvature of the tether and provides more accurate modelling if compared with the two-point model.

A method of determining flame radiation fraction induced by interaction burning of tri-symmetric propane fires in open space based on weighted multi-point source model

The interaction of multiple fires may lead to a higher flame height and more intense radiation flux than a single fire, which increases the possibility of flame spread and risks to the surroundings. Experiments were conducted using three burners with identical heat release rates (HRRs) and propane as the fuel at various spacings. The results show that flames change from non-merging to merging as the spacing decreases, which result in a complex evolution of flame height and merging point height. To facilitate the analysis, a novel merging criterion based on the dimensionless spacing S/zc was proposed. For non-merging flames (S/zc > 0.368), the flame height is almost identical to a single fire; for merging flames (S/zc ⩽ 0.368), based on the relationship between thermal buoyancy B and thrust P (the pressure difference between the inside and outside of the flame), a quantitative analysis of the flame height, merging point height, and air entrainment was formed, and the calculated merging flame heights show a good agreement with the measured experimental values. Moreover, the multi-point source model was further improved, and radiation fraction of propane was calculated. The data obtained in this study would play an important role in calculating the external radiation of propane fire.

Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction

Forecasting air pollution is considered as an essential key for early warning and control management of air pollution, especially in emergency situations, where big amounts of pollutants are quickly released in the air, causing considerable damages. Predicting pollution in such situations is particularly challenging due to the strong dynamic of the phenomenon and the various spatio-temporal factors affecting air pollution dispersion. In addition, providing uncertainty estimates of prediction makes the forecasting model more trustworthy, which helps decision-makers to take appropriate actions with more confidence regarding the pollution crisis. In this study, we propose a multi-point deep learning model based on convolutional long short term memory (ConvLSTM) for highly dynamic air quality forecasting. ConvLSTM architectures combines long short term memory (LSTM) and convolutional neural network (CNN), which allows to mine both temporal and spatial data features. In addition, uncertainty quantification methods were implemented on top of our model's architecture and their performances were further excavated. We conduct extensive experimental evaluations using a real and highly dynamic air pollution data set called Fusion Field Trial 2007 (FFT07). The results demonstrate the superiority of our proposed deep learning model in comparison to state-of-the-art methods including machine and deep learning techniques. Finally, we discuss the results of the uncertainty techniques and we derive insights.

Construction of Multi-point Quality Analysis Model of Tobacco Industry Co-production Based on Integrated “Platform + Module”

Under the multi-point production management mode, Researchers establish a unified cooperative production multi-point product quality analysis standard, set benchmarks, compare performance, create a process quality management atmosphere of ratio, learning, catching up and exceeding, so that multi-point quality management ability can be obtained together to improve, product quality has been continuously improved. At present, the quality analysis of multi-point processed products in cooperative production is only a simple average and standard deviation detection and comparison of key processes and individual indicators, or a single analysis of the appearance quality of cigarettes. The analysis is relatively isolated and no in-depth research is conducted. It is impossible to comprehensively and comprehensively compare the processing quality levels of various processing enterprises. In response to these problems, this paper compares and analyzes several quality controlling schemes, selects the “platform + module” scheme and designs and constructs it. The article discusses the specific implementation measures of the design plan. From the perspective of the implementation effect of the plan, it can provide guarantee for improving the production quality of cigarettes.

Design of sliding mode observer for the estimation of train car positions and in-train forces

Abstract For the control design of freight trains, the estimation of in-train forces is crucial to avoid safety issues caused by the possible failure of car couplers under excessive stress. However, the in-train forces and the relative positions of adjacent train cars are not directly measurable. To address this issue, this paper designs estimators of train car positions and in-train forces using only locomotive-based measurements. Using the multi-point train model, a cascade structure of robust sliding mode differentiators is developed. It provides finite-time estimation of train car positions and in-train forces. Simulation results illustrate the feasibility of the proposed approach.

Improving Police-Public Conflict Resolution to Improve Sustainability Decision Strategy

Negative police-public interactions in the United States and around the world raise legitimate concerns about human rights, public safety, and negative externalities that directly impact on UN Sustainable Development Goals 1 (No Poverty), 10 (Reduced Inequalities), and 16 (Peace, Justice, and Stronger Institutions). These interactions can be in the form of unnecessary or avoidable arrests, unpleasant vocal exchanges, physical violence, and even lethal force. Such events have happened in the United States and in other countries to a level that has caused concern both in government and in the public. These negative interactions can both exacerbate and stem from inequalities and act against both true justice and local peace, as well as easily weaken valuable institutions. In order to gain insight for the purpose of improving these aspects of social sustainability, such negative police-public interactions can be conceptualised by mechanisms of strategic interaction and subconscious games. That conceptual framework can provide underlying justification for a pathway to improvement. This study applies a multipoint gravitational model as a model of influence, and considers the presence of subconscious games in the style of Eric Berne and their impact on strategic interaction. The study then subdivides the police, government, and public into several logical categories regarding attitudes and opinions and associated decision strategy regarding negative police-public interaction, and models them according to Choice Waves within the framework of the Theory of Economic Parallel Rationality. That is then used to propose a mechanism for reducing and potentially eliminating negative police-public interactions and provide mathematical, statistical, and logical justification for improvement pathways. The current apparent misalignment of incentives identified in the study will continue to provide a significant challenge to overcome. The result of not doing so, however, is a continued cost to peace, true justice, and strength of institutions. This study’s ultimate purpose is to apply strategic decision analysis to help reduce violence and other forms of negative results during interactions between the police and the public.

Research on Multipoint Leak Location of Gas Pipeline Based on Variational Mode Decomposition and Relative Entropy

Pipeline leak detection has always been a relatively difficult technical problem; especially in urban pipeline leak detection, there are still many problems to be solved. A multipoint leak detection and location method for urban gas pipelines based on variational mode decomposition and relative entropy was proposed. Firstly, the experiment pipeline system was built, and the original signal was collected by acoustic emission technology; then, a variational model method was used to decompose the signal to obtain multiple intrinsic mode function (IMF) components with different characteristic scales. According to the characteristics of relative entropy, each component was analyzed, the appropriate IMF component was selected, and the selected component was reconstructed to obtain the observation signal. The multipoint leakage location model of the urban gas pipeline was established. The number of source signals was estimated by singular value decomposition, and the leakage signals were separated; finally, the accurate location of leakage point was achieved by cross-correlation positioning. The results showed that the average relative error of the pipeline leak location results is 2.97%, and the leak location accuracy is significantly improved, achieving the purpose of precise location.

MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain

To predict the multi-point vibration response in the frequency domain when the uncorrelated multi-source loads are unknown, a data-driven and multi-input multi-output least squares support vector regression (MIMO LS-SVR)-based method in the frequency domain is proposed. Firstly, the relationship between the measured multi-point vibration response and unmeasured multi-point vibration response is formulated using the transfer function in the frequency domain. Secondly, the data-driven multiple regression analysis problem of multi-point vibration response prediction in the frequency domain is described formally, and its mathematical model is established. With the measured multi-point vibration response as the input and the unmeasured multi-point vibration response as the output, the vibration response history data are assembled as a MIMO training dataset at each frequency. Thirdly, using the MIMO LS-SVR algorithm and MIMO history training dataset, the multi-point vibration response prediction model is built at each frequency point. By comparing the transmissibility matrix method, multiple linear regression model-based method, and MIMO neural network method, the application scope of the proposed method and its advantages are analyzed. The experimental results for acoustic and vibration experiment on a cylindrical shell verified that the MIMO LS-SVR-based method predicts the multi-point vibration response effectively when the loads are unknown, and has higher precision than the transfer function method, multiple linear regression method, MIMO neural network method, and transmissibility matrix method.

Product information diffusion model and reasoning process in consumer behavior

Information diffusion on social media has become a major approach in people's daily communication, and the value contained therein holds great interest for both academic and industrial communities. However, the process of information diffusion is affected by many factors, and the complexity of that process has not been fully explored. Most previous studies have concentrated on the strategies and driving forces in social media operations, as well as the identification of influential seed nodes, yet analyses of consumer behavior choice in the process of information diffusion are rare. Thus, This study proposes a multipoint cross-diffusion model based on MapReduce, which improves the single-point model and can better describe the product information diffusion process. On that basis, a Bayesian network model of product information diffusion was constructed to analyze the associations between factors and consumer behaviors. Moreover, the posterior probability of consumer behavior choice affected by a series of factors in the information forwarding process was considered and analyzed. This study's findings can be used to estimate the posterior probability that users will purchase, forward, or stay silent, thereby predicting the effect of product diffusion and obtaining the quantitative relationships between factors and consumer behavior.

Improvement of the weighted multi-point (WMP) radiation model for diffusive flames by the application of a set of stochastic optimisation algorithms

The weighted multi-point source model (WMP) has been proposed to model flame radiation from far to near distances, encompassing regions where the single point (SP) model cannot predict it adequately. To develop its formulation, the WMP has been studied as an inverse problem, being optimised to minimise the error between experimental and numerical data. Efficient optimisation methods are thus necessary, and a performance study applied to the WMP model is essential for its development. This study aims to evaluate which types of stochastic algorithms are the best for this problem and to improve the current radiation model comparing the results to those by previous studies. Five algorithms with different characteristics are chosen and tuned for the WMP problem with a design of experiments (DoE) methodology, and applied to each configuration of the model. The best performance was shown by the grey wolf optimiser (GWO), allying stability to fast convergence. The best solution improved previous results by 23.7%, and was also 82.72% better than the solution calculated with the SP model, and 76.24% better than the one calculated with the canonical WMP. A trend in radiation emission distribution is observed with results by previous studies, guiding better formulations in weight distribution.

A Simplified Two-Group Multipoint Kinetics Model

Abstract Detailed multiphysics modeling of nuclear power plants has become a necessity in the era of best-estimate analysis. For a number of transients with strong coupling between the neutronics in the reactor core and the fluid-dynamics in the primary circuit and overall heat transfer, it is required to carry out coupled system thermal hydraulics and core three-dimensional (3D) neutronics analysis. Point kinetics approach in the system thermal-hydraulics (TH) code RELAP5 limits its use for many reactivity-induced transients, which involve asymmetric core behavior. In a recent development, a simplified multipoint kinetics model has been coupled with system TH code RELAP5 to circumvent its inadequacy for the analysis of reactivity-induced transients involving asymmetric core behavior. The objective of this paper is to validate the simplified multipoint kinetics model against an asymmetric fast transient benchmark problem in a large power reactor. Time-step and nodalization sensitivity studies have been performed. It is demonstrated that the multipoint kinetics model results are in good agreement with the benchmark, advocating its applicability.

A Novel Method for Mitigating the GPS Multipath Effect Based on a Multi-Point Hemispherical Grid Model

The multipath effect is a crucial error source caused by the environment around the station and cannot be eliminated or mitigated by differential algorithms. Theoretically, the maximum value for the carrier phase is a quarter the wavelength, i.e., about 4.8 cm for the GPS L1 signal. Considering the increasing demands of high-precision applications, the multipath error has become a major factor affecting the accuracy and reliability of GPS millimeter-level data processing. This paper proposes a multi-point hemispherical grid model (MHGM) to mitigate the multipath effect. In this method, the hemisphere centered on each station is divided into a grid, and the multipath error at the station is estimated based on the parameterization of the grid points. The double-differenced (DD) observed-minus-calculated (OMC) values on some previous days are treated as the observation values to model the present multipath error. Contrary to the present methods which rely much on the platform of data collection and processing, MHGM can be potentially applied to GPS data processing with the existing hardware and software. Experiments in high-multipath and low-multipath environments are designed by mounting a baffle or not. The experimental results show that MHGM is effective in mitigating the multipath effect. When using data from the previous day, an average improvement of about 63.3% in the RMS of DD OMC can be made compared with that without correction, and this is basically consistent with the sidereal filtering (SF) method which is 63.0%. Furthermore, the effectiveness of the above two methods is better than that of the empirical site model (ESM). The kinematic positioning results are also basically consistent with the statistical results of the RMS values of DD OMC. Historical data from more than one day can more explicitly and effectively model the MHGM. Furthermore, compared with the SF method, the MHGM can be used not only to mitigate the multipath error, but also to orientate the sources of the multipath error around the station, and give guidance in the physical elimination of these sources.

Fire Hazard of Lithium-ion Battery Energy Storage Systems: 1. Module to Rack-scale Fire Tests

Lithium-ion batteries (LIB) are being increasingly deployed in energy storage systems (ESS) due to a high energy density. However, the inherent flammability of current LIBs presents a new challenge to fire protection system design. While bench-scale testing has focused on the hazard of a single battery, or small collection of batteries, the more complex burning behavior of a commercially design module (collection of batteries) or rack (collection of modules) is still largely unknown. In this study, a series of small- to large-scale free burn fire tests were conducted on ESS comprised of either iron phosphate (LFP) or lithium nickel oxide/lithium manganese oxide (LNO/LMO) batteries. Small-scale tests showed that a thermal runaway event could lead to a self-propagating fire for both the LFP and LNO/LMO batteries with a significantly greater heat release rate (HRR) generated from the LNO/LMO battery. Intermediate- and large-scale tests showed that the burning of a single module was sufficient to involve all other modules within the same ESS rack for both types of battery chemistries. The different burning behavior of the two battery chemistries was further demonstrated with the LNO/LMO battery generating a maximum HRR nearly three times that of the LFP battery. To better understand the hazard associated with these fires, a multi-point source model was used to analyze the radiative heat exposure to the environment. The end result is an experimentally validated data set that can be used to estimate the heat exposure to objects surrounding an ESS fire, such as ESS racks across an aisle space in a large deployment, or other nearby combustibles. These data also provide the basis for evaluating the effectiveness of sprinkler protection at reducing the fire hazard and protecting the surroundings.

Robust nonlinear model predictive control for automatic train operation based on constraint tightening strategy

AbstractThis paper studies the problem of automatic train operation (ATO) robust nonlinear model predictive control under considering multiple objectives and constraints. After establishing a nonlinear multipoint model with uncertain bounded disturbance, a robust nonlinear model predictive control algorithm to meet the punctuality of train operation and energy consumption for ATO is proposed based on constraint tightening strategy. Moreover, theoretical analysis of the feasibility and stability for the speed loop system are presented. Then, with the objective of reference electromagnetic torque tracking and low switching frequency, a model predictive direct torque control algorithm with one‐step delay compensation is proposed. Feasibility of the proposed algorithm is ensured by using deadlock prediction method, and convergence analysis of the torque loop is given simultaneously. Lastly, the effectiveness of these two algorithms are verified by numerical simulation.

Multi-point Mooring PID Control Based on Kalman Filtering-Genetic Algorithm-Slow Disturbance Observer Compensation

An adaptive PID control technique based on the Kalman Filtering (KF) Genetic Algorithm (GA) and Slow Disturbance Observer Compensation (SDOC) is proposed for multi-point mooring system in this paper. The multi-point mooring model, which is simplified to two-point mooring, is established, its self-tuning PID mooring controller based on KF-GA-SDOC is designed. The multi-point mooring model is updated on-line with the KF and SDOC to filter and observe time-varying signal; the PID mooring control gains are adjustable parameters by selection, crossover and mutation operator of GA; the mooring control system based on KF-GA-SDOC PID is optimized. The simulation results show that the designed KF-GA-SDOC PID mooring controller can maintain good control performance under different conditions.

Adaptivity-Enhanced Path Tracking System for Autonomous Vehicles at High Speeds

The path tracking is a basic function of the autonomous vehicle. Generally, it is difficult to realize the path tracking with high accuracy at high speeds. To solve this problem, an adaptivity-enhanced path tracking system (AEPTS) is investigated in this paper. The adaptive performance of this AEPTS is realized via three aspects. The first is to achieve preview distance adaptivity by a dynamic multi-point preview model (DMPPM), which provides the desired course deviation according to the vehicle speed and the reference path. The second is to improve the adaptivity of an adaptive fuzzy steering controller (AFSC) with an adaptive steering gain model which is built via the path curvature and vehicle speed. The third is that an optimal parameter surface is devised to enhance the adaptivity of the AEPTS at high speeds. Such a surface can provide the optimal coefficients for the AEPTS, and it is implemented using the particle swarm optimization (PSO) and a curve fitting method. At last, simulation experiments are given to verify the adaptivity of the proposed method.

METHOD OF DETERMINING AN EQUIVALENT SLOPE IN THE IMITATION MODEL OF THE CUT OF CARS ROLLING FROM HUMP

The purpose of this work is to develop a method for determining the rational equivalent slope of cut of cars rolling on a hump yard. The methodology. Using the thread release model, convert the multipoint release model to a two-point model. Originality. A two-point mathematical model of cut of cars was proposed, which has the same accuracy as the well-known multipoint model with a significantly smaller amount of calculations. Practical value. The proposed technique allows minimizing the time for calculating the movement of cut of cars in a simulation model, which is important for control systems, where simulation models are used in real time.

Spatiotemporal variability of leaf photosynthesis and its linkage with microclimates across an environment-controlled greenhouse

Environmental controls in a greenhouse improve microclimates, thereby enhancing photosynthesis, but they create spatiotemporal non-uniformity of photosynthesis, with implications for unstable crop production. However, there has been no research focussing on the spatiotemporal variability of photosynthesis arising from greenhouse environmental controls. Here we visualise spatiotemporal distributions of leaf photosynthetic rate ( A ) and assess its linkages with microclimates [air temperature ( T a ), water vapour concentration ( W a ), CO 2 concentration ( C a ), and leaf-boundary-layer conductance ( g a )] across a strawberry greenhouse during daytime under roof ventilation and CO 2 enrichment, using physical, physiological, and biochemical models for A and mobile observations of the microclimates. The distributions of A were variable during the daytime and were non-uniform across the greenhouse under the influence of the microclimate distributions arising from the environmental controls. In particular, with the roof ventilation, spatial variations of T a and g a were most associated with non-uniformity in A through the physical process of the energy budget determining the leaf temperature and thus affecting leaf physiological properties (photosynthetic capacities and stomatal conductance). With CO 2 enrichment, in addition to the roof ventilation, spatial variations of C a further increased non-uniformity in A through large variations of Rubisco-limited and RuBP-limited rates in the biochemical process of leaf photosynthesis. Spatial non-uniformity of A arising from the environmental controls ranged from 15% to 69% during the daytime. These findings indicate the importance of considering the spatiotemporal variability of photosynthesis with respect to its physical, physiological, and biochemical processes, in addition to that due to microclimates, for optimising greenhouse environmental controls. • Spatiotemporal variability of leaf photosynthetic rate ( A ) in a daytime greenhouse. • Multipoint model simulation of A was assisted by mobile observation of microclimates. • Greenhouse environmental controls varied microclimates in space and time. • Microclimate distributions enhanced spatiotemporal non-uniformity in A . • Spatial non-uniformity in A varied from 15% to 69% during the daytime.

Meso–Macro Coupled Analysis of Pressure-Dependent Friction of Rubber

To deepen the understanding of the frictional sliding phenomena of rough surfaces, advances are required in numerical analysis methods at various spatial scales. In this study, to examine the microscopic behavior of a rough asperity contact corresponding to a bulk contact on the macroscopic scale, a loop-type meso–macro coupled analysis scheme is proposed. A mesoscale numerical model and a macroscopic friction model are required for the proposed multi-scale analysis. A friction model was adopted based on the multipoint contact model for the mesoscale model, and the pressure- and state-dependent elastoplastic analogy friction model was used for the macroscale model. In the proposed meso¬¬–macro coupled analysis, the parameter set for the elastoplastic analogy friction model was first identified via a numerical friction test using the mesoscale multipoint contact model assuming various conditions. Then, a macroscale finite element analysis incorporating the elastoplastic analogy friction model was performed for the macroscopic analysis of contact between a rough rubber hemisphere and a smooth plate. Here, the information from the mesoscale rough surfaces were reflected in the macroscale finite element analysis. Finally, a mesoscale localization analysis was performed in which the macroscopic histories of several typical locations were obtained by finite element analysis and used as boundary conditions for the mesoscale model. It is suggested that the microscopic sliding process of rough surfaces represented by the finite element analysis can be examined using the proposed method.

Leakage Signal Analysis of Urban Gas Pipeline Based on Improved Variational Mode Decomposition

Aiming at problems of multipoint leakage source detection and low positioning accuracy in urban gas pipelines, a multipoint leak location method base on improved variational mode decomposition (VMD) was proposed. By improving the VMD decomposition of the original leakage signal, the parameters of the VMD were optimized to reduce the influence of noise and weak correlation signals on the leak location. Then the multi-point leakage location model of pipeline was established, and the sensitive modal component Intrinsic mode function (IMF) with the most leakage information was selected by multiscale entropy. According to the characteristics of the blind source separation method, the relevant time delays of the simultaneous leakage of multiple points on the pipeline and the frequency of the signal are extracted. Finally, The location of the leak source is determined according to the principle of cross-correlation. The experimental results show that compared with the direct cross-correlation method and the VMD-based method, the proposed multipoint leak diagnosis method has less error, the minimum relative error is 1.61%, and the positioning accuracy is higher.