Path Distribution Research Articles

Simulation analysis of radiometer effect and outgassing based on molecular particle source perturbation

The Laser Interferometer Space Antenna (LISA) mission is designed to detect space gravitational wave sources in the millihertz band. A critical factor in the success of this mission is the residual acceleration noise metric of the internal test mass (TM) within the ultra-precise inertial sensors. Existing studies indicate that the coupling effects of residual gas and temperature gradient fluctuations significantly influence this metric, primarily manifesting as the radiometer effect and the outgassing effect. However, current theoretical research methods are inadequate for accurately decoupling and predicting the contributions of these two effects. To this end, this paper conducts an in-depth decoupling analysis of the impacts of the radiometer effect and outgassing effect using a simulation method based on molecular particle source perturbation. By constructing a finite element simulation model that couples residual gas and temperature gradient fluctuations, we simulate molecular thermal motion based on fundamental theories such as Maxwell’s distribution function, the free path distribution law, and Knudsen’s adsorption layer hypothesis, in order to study the two manifestations of the radiometer effect and the outgassing. By analyzing and comparing the simulation results, theoretical results, and ground torsion results, we find that the simulation results align effectively with the measured ground torsion results. This alignment enhances our understanding of how the radiometer effect and outgassing impact the internal pressure changes in the sensitive probe, which cannot be sufficiently decoupled by theoretical calculations alone. This conclusion demonstrates that the simulation method can effectively analyze the coupling effects of temperature gradient fluctuation and residual gas in inertial sensors, providing an important theoretical basis and practical significance for developing physical models of inertial sensors aimed at predicting and optimizing overall performance.

Research on Optimizing Logistics and Distribution Paths Considering Carbon Emissions

With the intensification of global climate change, reducing carbon emissions has become an urgent task for various industries. The logistics industry has high carbon emissions, and traditional path planning ignores environmental impacts. The current hot topic is to study the optimization of logistics paths that consider carbon emissions, in order to achieve a win-win situation for both the economy and the environment. This article aims to explore relevant theories and methods, propose solutions to reduce logistics carbon emissions, and promote sustainable development. Taking the optimization of logistics distribution paths in a certain enterprise as the research object, starting from the global background of energy conservation and emission reduction, this paper proposes to take the total cost (including time window penalty, transportation, carbon emissions, and fixed costs) as the optimization objective, construct a carbon emission logistics distribution path optimization model with time window, solve it using the improved grey wolf optimization algorithm, and compare it with the original plan. The results showed that after optimization, the cost was significantly reduced, verifying the effectiveness of the model, reducing delivery costs, and improving the economic benefits of the enterprise. Not only does it solve the problems of the enterprise, but it also provides reference for related industries.

Optimization of Multi-Vehicle Cold Chain Logistics Distribution Paths Considering Traffic Congestion

Optimization of Multi-Vehicle Cold Chain Logistics Distribution Paths Considering Traffic Congestion

Optimisation of urban distribution paths for electric logistics vehicles based on shared pre-positioning warehouse mode

Optimisation of urban distribution paths for electric logistics vehicles based on shared pre-positioning warehouse mode

Optimization of cold chain logistics distribution path considering traffic condition and replenishment along the way.

Road traffic congestion on the cold chain logistics not only increase the cost and time, but also creates certain negative impact on the national carbon emissions. To fully utilize the traffic resources, this study has classified urban road traffic congestion and defined the various vehicle delivery speeds with dynamic congestion levels. Simultaneously, it has developed the cold chain products replenishment strategy by considering delivery route, multi-depot condition and even vehicle types, aiming to minimize the total cost and carbon emissions, and maximizing the cold chain products freshness. To achieve this, this study build up a multi-objective vehicle routing optimization model and designed a hybrid algorithm combining large-scale neighborhood search and NAGA-II. Through computational analysis, this algorithm effectively overcomes the weak local search capability of NAGA-II and efficiently solves multi-objective problems. Moreover, under the simulated random traffic congestion conditions, this model able to demonstrate relatively stable planning results and address complex road traffic situations. Finally, this study able to analyze the impacts of various replenishment strategies, by considering multiple depots and sensitivity coefficients of cold chain products from delivery objectives. The analysis results also provides valuable insights for actual cold chain logistics distribution industry.

Research on Optimization of Fresh Produce Multi-Temperature Joint Distribution Path Based on Improved Multi-Objective Dung Beetle Optimization Algorithm

Research on Optimization of Fresh Produce Multi-Temperature Joint Distribution Path Based on Improved Multi-Objective Dung Beetle Optimization Algorithm

PENENTUAN LINTASAN OPTIMAL DISTRIBUSI BARANG MENGGUNAKAN HYPERGRAPH - PARTITIONING DAN ALGORITMA GENETIKA

Efficient distribution of goods is critical in logistics management, which requires the selection of optimal distribution paths to achieve delivery targets with minimal total distance. This research combines Hypergraph-Partitioning and genetic algorithm to determine the optimal distribution path of goods to several customers. The Hypergraph-Partitioning divides the goods to be distributed equally to several vehicles, while the genetic algorithm is applied to determine the best distribution path in each partition. The results showed that the Hypergraph-Partitioning method successfully divided 62 customers into two partitions. The first partition serves 31 customers with a total demand of 865 loaves of bread, while the second partition also serves 31 customers with a total demand of 1,035 loaves of bread. The genetic algorithm was then used to find the shortest path for each partition, resulting in an efficient distribution solution.

Research on the Optimization of Cold Chain Logistics Distribution Path Considering Carbon Emissions

With the proposal of the "double-carbon" development goal, all walks of life are actively implementing the national green and low-carbon development requirements. As an important branch of the logistics industry, cold chain logistics is also a key area to achieve the goal of "double carbon". If the concept of low carbon is introduced into cold chain logistics and the distribution route is rationally planned, it can not only reduce logistics costs, but also reduce carbon emissions. Therefore, the minimization of the total distribution cost is taken as the optimization goal, Under the constraints of vehicle load and time window, the cold chain logistics distribution path optimization model is constructed with the goal of minimizing the total distribution cost. Then designed the genetic algorithm to solve, respectively from considering carbon emissions and carbon emissions two cases planning distribution route, and optimization before single temperature distribution scheme comparative analysis, the results show that the carbon emissions considering the cold chain distribution path optimization, and choose cold storage incubator type in distribution mode, not only can reduce the enterprise distribution cost, improve the distribution efficiency of distribution, but also can reduce the carbon emissions.

Multi-constraint distributed terminal distribution path planning for fresh agricultural products

Multi-constraint distributed terminal distribution path planning for fresh agricultural products

Mpemba-Like Sensory Withdrawal Effect

Biological sensors rely on the temporal dynamics of ligand concentration for signaling. The sensory performance is bounded by the distinguishability between the sensory state transition dynamics under different environmental protocols. This work presents a comprehensive theory to characterize arbitrary transient sensory dynamics of biological sensors. Here the sensory performance is quantified by the Kullback-Leibler (KL) divergence between the probability distributions of the sensor's stochastic paths. We introduce a novel benchmark to assess a sensor's transient sensory performance arbitrarily far from equilibrium. We identify a counterintuitive phenomenon in multistate sensors: while an initial exposure to high ligand concentration may hinder a sensor's sensitivity towards a future concentration up-shift, certain sensors may show a boost in sensitivity if the initial high concentration exposure is followed by a transient resetting at a low concentration environment. The boosted performance exceeds that of a sensor starting from an initially low concentration environment. This effect, reminiscent of a drug withdrawal effect, can be explained by the Markovian dynamics of the multistate sensor, similar to the Markovian Mpemba effect. Moreover, an exhaustive machine learning study of four-state sensors reveals a tight connection between the sensor's performance and the structure of the Markovian graph of its states. Published by the American Physical Society 2024

Hydrogen-rich gas generation from enhanced catalytic cracking of biomass tar and related model compounds on Ni-Fe/ASA@HZSM-5 catalyst: Pathways and mechanisms

This study focused on the design of complex tar model compounds (CTMC) and synthesized a highly efficient aluminum dross (ASA) combined with HZSM-5 molecular sieve co-loaded bimetallic Ni-Fe catalyst (Ni-Fe/ASA@HZSM-5), comprehensively evaluated the catalytic cracking of real tar and its model compound by adjusting the injection amount and injection state of tar, and catalyst types to obtain hydrogen-rich gas with high H2 and low CO2 content. The results showed under the conditions of the injection amount of 0.6 mL/min, pyrolysis temperature of 600 °C, reforming temperature of 800 °C in the liquid phase, Ni-Fe/ASA@HZSM-5 displays excellent catalytic cracking performance with CTMC conversion efficiency of 87.80 % (79.46 % of gas yield and 34.20 vol% of H2 yield). The possible cracking paths between different components of CTMC in the Ni-Fe catalytic system were summarized by the experimental data and product distribution, the catalytic mechanism of Ni–Fe based catalysts can be attributed to the formation of a Ni-Fe alloy and the synergistic effect of ASA and HZSM-5 co-carrier. Further, the catalytic cracking pathway of CTMC was enhanced in the gas phase, the higher CTMC conversion efficiency of 93.68 %, gas yield (a mass fraction of 82.51 %) and H2 (a volume fraction of 33.59 %) were obtained from catalytic cracking of CTMC, the liquid yield decreased by 12.39 %, significantly improved the cracking degree and gas production capacity of CTMC. The real tar showed the same catalytic pyrolysis path and product distribution. Under the same cracking conditions, the yields of gas, liquid and char were 84.85 %, 12.00 % and 3.15 % respectively, and obtained the hydrogen yield of 55.29 mL/g. The CTMC simplifies the cracking process and reduces polymerization, provides insights into the cracking mechanisms of heavy components in biomass tar. Furthermore, the synergy of Ni and Fe contributed to greater activity for CTMC and real tar cracking, ASA combined with HZSM-5 molecular sieve co-loaded bimetallic Ni-Fe catalysts have a promising application potential as highly efficient catalysts for tar removal and reutilization.

A mathematical model for predicting the electro-mechanical behavior of biomimetic crack sensors: Effect of crack depth and gap

Various biomimetic microstructures, such as porous, cracks, wrinkles, micro-pyramids, and micro-domes, are applied to improve the sensing performance of mechanical sensors. Among them, the crack-based strain sensors are widely investigated due to high sensitivity and fast response time. To clearly describe the relationship between crack morphology and sensor sensitivity, a mathematical model is developed for investigating the performance of a poly(3,4-ethylenedioxythiophene)-silicon oxide/polydimethylsiloxane (SiOx/PDMS) based crack strain sensor. First, the displacement field of a crack tip is calculated based on the theory of fracture mechanics, and the mathematical relationship between the crack depth, crack gap, and strain is obtained. The predicted crack depth of the SiOx thin film's thicknesses in 7.91 μm (SiOx/PDMS-7.91) is 2.82 μm, with the error of 3.75% compared to the experimental result. Correspondingly, the deviation of SiOx/PDMS-7.91 is 5.74% between the predicted crack gap and the experimental data. Second, above the aforementioned crack tip characteristics, the mathematical model based on crack edges contacts probability (CECP) is used to construct the relationship between the crack tip characteristics, applied stress, and device sensitivity. The maximum predicted sensitivity can reach 3562.68 compared with the experimental data of 3800.44, and the deviation is about 6.26%. Moreover, the CECP model has good universality with the other reported crack-based strain sensors. It can be concluded that crack morphology affects the distribution and quantity of conductive paths. When the strain sensor is subjected to external forces, brittle thin films generate a certain number of bumped-like elements for microcracks. The wider and deeper crack will increase the relative resistance change and the decrease of conductive paths, resulting in a rapid increase in the sensitivity of the strain sensor.

Vehicle-drone collaborative distribution path planning based on neural architecture search under the influence of carbon emissions

Intelligent operation and green distribution have become a mainstream task to enable fast development of urban distribution applications. However, how to improve the distribution efficiency with low operating costs, and mitigate environmental pollution with high service quality is still a significant challenge in the practical industry applications. To address the above challenge, in this paper, we take into account both the economic cost and environmental cost, and propose a joint distribution path planning model based on neural architecture search (NAS) for electric vehicles with double-decked drones. More specifically, in our design, the factors such as energy consumption and carbon emissions of vehicles and drones during different distribution stages are considered. Then, a mixed integer linear programming model is established under the constraints of customer time window, vehicle capacity and vehicle battery capacity. Based on this model, a hybrid genetic algorithm is proposed to solve the optimization problem, where the carbon emission cost is estimated by the convolution neural network model, which is optimized by the neural architecture search technique. We conduct extensive experiments to validate the effectiveness of the proposed method. The experimental results show that, compared with CPLEX, the proposed method excelled in both solution quality and speed, which verify the effectives of our hybrid algorithm in dealing with the 2E-VRPD problem, where delivery routes can be well optimized, the efficiency of vehicle-machine collaborative delivery can be improved, and delivery costs are reduced.

Transition Path Dynamics of Non-Markovian Systems across a Rough Potential Barrier.

Transition paths refer to rare events in physics, chemistry, and biology where the molecules cross barriers separating stable molecular conformations. The conventional analysis of the transition path times employs a diffusive and memoryless transition over a smooth potential barrier. However, it is widely acknowledged that the free energy profile between two minima in biomolecular processes is inherently not smooth. In this article, we discuss a theoretical model with a parabolic rough potential barrier and obtain analytical results of the transition path distribution and mean transition path times by incorporating absorbing boundary conditions across the boundaries under the driving of Gaussian white noise. Further, the influence of anomalous dynamics in rough potential driven by a power-law memory kernel is analyzed by deriving a time-dependent scaled diffusion coefficient that coarse-grains the effects of roughness, and the system's dynamics is reduced to a scaled diffusion on a smooth potential. Our theoretical results are tested and validated against numerical simulations. The findings of our study show the influence of the boundary conditions, barrier height, barrier roughness, and memory effect on the transition path time distributions in a rough potential, and the validity of the scaling diffusion coefficient has been discussed.

Experimental investigation of the electromagnetic acceleration mechanisms in an applied-field magnetoplasmadynamic thruster

Abstract The high specific impulse and efficiency of the applied-field magnetoplasmadynamic thruster (AF-MPDT) make it one of the most promising electric thrusters in deep space exploration. However, the crucial electromagnetic acceleration mechanisms are still not clearly investigated, which limits performance improvement. The electromagnetic acceleration mechanism is closely related to the current path and magnetic field distribution in the plume. Experiments are conducted using a water-cooled Hall probe in the steady-state AF-MPDT plume. The radial, azimuthal and axial magnetic fields are measured, then the current density and Lorentz force density are calculated. The results show that the outflow current accounts for 63% to 82% of the thruster discharge current depending on the strength of the applied magnetic field. Moreover, the outflow current can extend the range of electromagnetic force action, which in turn increases the effect of electromagnetic acceleration. The radial Lorentz force is numerically dominant, and the combined effect of the radial Lorentz force and axial Lorentz force is to compress the plasma toward the axis. In electromagnetic acceleration, Self-field contributions are less than 5%, while E× B acceleration constitutes −12.2%–21.2%, and diamagnetic acceleration dominates at approximately 76.7%–90.5%. Finally, a method for evaluating the rotational velocity was presented based on the MHD equations. The centrifugal force was then calculated by combining this with the plasma density. At the thruster outlet, the centrifugal force is significant and cannot be ignored in comparison to the radial Lorentz force.

Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions

Logistics, as a significant field for achieving energy-saving and carbon reduction goals, is recognized as a crucial direction for realizing the global “double carbon” objective, while vehicle path optimization is an effective method for promoting energy efficiency and reducing carbon emissions. In this paper, an improved genetic algorithm is proposed for optimizing logistics and distribution paths concerning the carbon emissions of fuel vehicles throughout the logistics and distribution process, and a low-carbon logistics and distribution path model is constructed based on time windows, vehicle loading, and carbon emissions. The INNC method is adopted to initialize the population, and an enhanced genetic algorithm (GA-LNS) is designed to solve the model in conjunction with a large-scale neighborhood vector search algorithm. The results indicate that the initialization of the population using the INNC method produces a higher-quality initial solution. Compared to traditional genetic algorithms and particle swarm optimization, the GA-LNS algorithm exhibits superior robustness, effectively addressing the limitations of traditional genetic algorithms that rely on initial solutions and are prone to local optima. By comparing the computational results of the low-carbon logistics distribution path model constructed in this study with those of traditional optimization objective models, it is demonstrated that this model effectively balances the trade-offs between objectives and benefits, achieving the lowest total logistics distribution cost while promoting sustainable low-carbon logistics. The research findings provide a theoretical foundation for optimizing logistics vehicle paths and formulating energy-saving and carbon reduction implementation plans in China.

A Comparative Study on Seismic Behaviour of Plan Irregular Buildings

Abstract: Asymmetrical plan irregularities occur when the horizontal layout of a building is non uniform or unsymmetrical in one or more axes. These irregularities may arise from variations in building shape, size, or configuration, leading to nonuniform distribution of mass, stiffness, or load paths throughout the structure. Considering equivalent static analysis (ESA) and response spectrum analysis (RSA) as per IS 1893-Part 1 (2016) considering seismic zones III, structure with square shaped plan performs better than the other shaped models (viz. T, L, H and Plus shapes) with least values of storey displacement and storey drift ratio, and also with higher value of storey stiffness. Thus, square plan shaped structure is seismically more resistant than T, L, H and Plus shaped structures.

The Wealth Creation in Indian and American Businesses

This paper discusses the comparative analysis of wealth production and distribution by looking at the business contexts and earning functions where there are two sides consisting of earning and spending between India and the USA. This research looks at the business inquiries, wealth-centric cultural attitudes, and economic systems in both countries. The USA has skewed more toward innovation and consumer-driven economies, whereas India is more technology, manufacturing, and services-based - all of which are still driven primarily by the traditional values that are focused on long-term financial security. This study synthesizes the extant body of literature to probe how different earning strategies such as the difference between lowering costs and innovation, the spending patterns including corporate social responsibility initiatives and much more may impact the economic outcomes and societal well-being in each country. This paper will show the comparative analysis between India and the USA which will provide information that will help explain the various paths of wealth distribution and accumulation for the two countries.

Применение технологии цифрового анализа керна для изучения фильтрационно-емкостных свойств и структуры высокопроницаемых пород подземных хранилищ газа

The paper presents the results of pore space studies of highly porous reservoir rocks of underground gas storage (UGS) facilities using the digital analysis of computed microtomography images. The methodology of complex nondestructive analysis of structural and filtration-capacitance properties has been developed. Structural heterogeneities and rock fracturing were evaluated. 3D models of specimen inner space were created on the basis of multi-scale images. The values of open and closed porosity, geodesic tortuosity were calculated, the characteristics of percolation paths in the studied rocks were analyzed for different directions of intrusion. Conclusions were made about the homogeneity of percolation path distribution over the rock volume. The spatial distribution of porosity in the rocks was studied, and porometry analysis of the rocks was carried out. Numerical modeling of filtration processes on the obtained structures in the framework of Stokes approximation for three selected directions in the rock by means of GeoDict software was carried out. It is shown that there is no pronounced dependence of changes in filtration properties in the selected directions on the quantitative characteristics of the pore space. The conclusion is made about the degree of anisotropy of filtration-capacitance properties of rocks. The good correspondence of the characteristics measured in the course of digital analysis with in-situ data and experimentally obtained laboratory values is shown. The described technique allows to simplify data acquisition on the characteristics of fine-grained reservoir rocks, and is designed to extend the approaches to nondestructive analysis of core material. The obtained reservoir properties data is necessary for the operational models development of UGS, clarifying integral reservoir properties and filling hydrodynamic models of hydrocarbon storage and production facilities.

Prediction Method for the Distribution of the Effective Dominant Path of Sand Bodies Transporting Oil and Gas and Its Application

To study the oil and gas distribution law in the slope area of a hydrocarbon-bearing basin, one must first define the dominant hydrocarbon transport paths of sand bodies as the superimposed area of the contiguously distributed area of the sand bodies and the paleotectonic ridges at the top interface of the formation. Then, the oil and gas supply area of the oil source faults must be defined, as the overlapped area of the favorable transport paths of oil source faults and the width of their associated fracture zones. Stacking the two areas, we can delineate the distribution of the effective dominant paths of sand bodies transporting oil and gas. The results from our case study of the Qinan area show that the effective dominant paths of sand bodies transporting oil and gas in the lower sub-member within the first member of the Shahejie Formation (Es1L) in the Qinan area are distributed throughout the region, with better development in the west than in the east. And, the effective dominant paths in the eastern part extend from north to south, while those in the western part extend from northeast to southwest. These are favorable for the convergence of oil and gas generated from source rocks in the third member of the Shahejie Formation (Es3) in the main depression of Qikou Sag in the Es1L in the Qinan area. The results are consistent with the fact that oil and gas discovered in the Es1L in the Qinan area are mainly distributed in the central and western regions, with a small amount in the southeast. Therefore, the suggested method is feasible for predicting the effective dominant paths of sand bodies transporting oil and gas.