A continuum-microscopic method based on IRBFs and control volume scheme for viscoelastic fluid flows

A numerical computation of continuum-microscopic model for visco-elastic flows based on the Integrated Radial Basis Function (IRBF) Control Volume and the Stochastic Simulation Techniques (SST) is reported in this paper. The macroscopic flow equations are closed by a stochastic equation for the extra stress at the microscopic level. The former are discretised by a 1D-IRBF-CV method while the latter is integrated with Euler explicit or Predictor-Corrector schemes. Modelling is very efficient as it is based on Cartesian grid, while the integrated RBF approach enhances both the stability of the procedure and the accuracy of the solution. The proposed method is demonstrated with the solution of the start-up Couette flow of the Hookean and FENE dumbbell model fluids

Electronic noses based on metal oxide nanowires: A review

Metal oxides are ideal for the fabrication of gas sensors: they are sensitive to many gases while allowing the device to be simple, tiny, and inexpensive. Nonetheless, their lack of selectivity remains a limitation. In order to achieve good selectivity in applications with many possible interfering gases, the sensors are inserted into an electronic nose that combines the signals from nonselective sensors and analyzes them with multivariate statistical algorithms in order to obtain selectivity. This review analyzes the scientific articles published in the last decade regarding electronic noses based on metal oxide nanowires. After a general introduction, Section 2 discusses the issues related to poor intrinsic selectivity. Section 3 briefly reviews the main algorithms that have hitherto been used and the results they can provide. Section 4 classifies the recent literature into fundamental research, agrifood, health, security. In Section 5, the literature is analyzed regarding the metal oxides, the surface decoration nanoparticles, the features that differentiate the sensors in a given array, the application for which the device was developed, the algorithm used, and the type of information obtained. Section 6 concludes by discussing the present state and points out the requirements for their use in real-world applications

Multi-Agent Reinforcement Learning for Joint Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems

We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-infrastructure (V2I) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together. Due to the nature of fast channel variant in vehicular environment, traditional centralized optimization approach relying on global channel information might not be viable in C-V2X systems with large number of users. Utilizing a reinforcement learning (RL) approach, we propose a distributed resource allocation (RA) algorithm to overcome this challenge. Specifically, we model the RA problem as a multi-agent system. Based solely on the local channel information, each platoon leader, who acts as an agent, collectively interacts with each other and accordingly selects the optimal combination of sub-band and power level to transmit its signals. Toward this end, we utilize the double deep Q-learning algorithm to jointly train the agents under the objectives of simultaneously maximizing the V2I sum-rate and satisfying the packet delivery probability of each V2V link in a desired latency limitation. Simulation results show that our proposed RL-based algorithm achieves a close performance compared to that of the well-known exhaustive search algorithm.Comment: 6 pages, 4 figure

A time discretization scheme based on integrated radial basis functions for heat transfer and fluid flow problems

This paper reports a new numerical procedure, which is based on integrated radial basis functions (IRBFs) and Cartesian grids, for solving time-dependent differential problems that can be defined on non-rectangular domains. For space discretisations, compact five-point IRBF stencils [Journal of Computational Physics, vol. 235, pp. 302-321, 2013] are utilised. For time discretisations, a two-point IRBF scheme is proposed, where the time derivative is approximated in terms of not only nodal function values at the current and previous time levels but also nodal derivative values at the previous time level. This allows functions other than a linear one to also be captured well on a time step. The use of the RBF width as an additional parameter to enhance the approximation quality with respect to time is also explored. Various kinds of test problems of heat transfer and fluid flows are conducted to demonstrate attractiveness of the present compact approximations

A dissipative particle dynamics model for thixotropic materials exhibiting pseudo-yield stress behaviour

Many materials (e.g., gels, colloids, concentrated cohesive sediments, etc.) exhibit a stable solid form at rest, and liquify once subjected to an applied stress exceeding a critical value – a yield-stress behaviour. This can be qualitatively explained by the forming and destruction of the fluid microstructure [1], and it may be modelled as a thixotropic and yield stress material. In this paper, we propose a mesoscopic model which is able to mimic a thixotropic and yield stress behaviour using a particle-based technique known as dissipative particle dynamics (DPD). The DPD technique satisfies conservation of mass and momentum and it has been applied successfully for a number of problems involving complex-structure fluids, such as polymer solutions, suspensions of rigid particles, droplets, biological fluids, etc. In this work, an indirect linkage dissipative particle model (ILDP) is proposed based on qualitative microstructural physics, which results in a non-Newtonian fluid with observed yield stress and thixotropic properties. The model comprises of two types, or species, of DPD particles – with only repulsive conservative force between the same species, and with repulsive force at short range and attractive force at long range between different species. Numerical results show that the proposed DPD fluid can represent some observed complex behaviours, such as yield stress and thixotropic effects

What controls the stable isotope composition of precipitation in the Mekong Delta? A model-based statistical approach

This study analyzes the influence of local and regional climatic factors on the stable isotopic composition of rainfall in the Vietnamese Mekong Delta (VMD) as part of the Asian monsoon region. It is based on 1.5 years of weekly rainfall samples. In the first step, the isotopic composition of the samples is analyzed by local meteoric water lines (LMWLs) and single-factor linear correlations. Additionally, the contribution of several regional and local factors is quantified by multiple linear regression (MLR) of all possible factor combinations and by relative importance analysis. This approach is novel for the interpretation of isotopic records and enables an objective quantification of the explained variance in isotopic records for individual factors. In this study, the local factors are extracted from local climate records, while the regional factors are derived from atmospheric backward trajectories of water particles. The regional factors, i.e., precipitation, temperature, relative humidity and the length of backward trajectories, are combined with equivalent local climatic parameters to explain the response variables d18O, d2H, and d-excess of precipitation at the station of measurement. The results indicate that (i) MLR can better explain the isotopic variation in precipitation (R2 D0.8) compared to single-factor linear regression (R2 D0.3); (ii) the isotopic variation in precipitation is controlled dominantly by regional moisture regimes (ca 70 %) compared to local climatic conditions (ca 30 %); (iii) the most important climatic parameter during the rainy season is the precipitation amount along the trajectories of air mass movement; (iv) the influence of local precipitation amount and temperature is not significant during the rainy season, unlike the regional precipitation amount effect; (v) secondary fractionation processes (e.g., sub-cloud evaporation) can be identified through the d-excess and take place mainly in the dry season, either locally for d18O and d2H, or along the air mass trajectories for d-excess. The analysis shows that regional and local factors vary in importance over the seasons and that the source regions and transport pathways, and particularly the climatic conditions along the pathways, have a large influence on the isotopic composition of rainfall. Although the general results have been reported qualitatively in previous studies (proving the validity of the approach), the proposed method provides quantitative estimates of the controlling factors, both for the whole data set and for distinct seasons. Therefore, it is argued that the approach constitutes an advancement in the statistical analysis of isotopic records in rainfall that can supplement or precede more complex studies utilizing atmospheric models. Due to its relative simplicity, the method can be easily transferred to other regions, or extended with other factors. The results illustrate that the interpretation of the isotopic composition of precipitation as a recorder of local climatic conditions, as for example performed for paleorecords of water isotopes, may not be adequate in the southern part of the Indochinese Peninsula, and likely neither in other regions affected by monsoon processes. However, the presented approach could open a pathway towards better and seasonally differentiated reconstructio

Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting

This article presents a research approach to enhancing the quality of short-term power output forecasting models for photovoltaic plants using a Long Short-Term Memory (LSTM) recurrent neural network. Typically, time-related indicators are used as inputs for forecasting models of PV generators. However, this study proposes replacing the time-related inputs with clear sky solar irradiance at the specific location of the power plant. This feature represents the maximum potential solar radiation that can be received at that particular location on Earth. The Ineichen/Perez model is then employed to calculate the solar irradiance. To evaluate the effectiveness of this approach, the forecasting model incorporating this new input was trained and the results were compared with those obtained from previously published models. The results show a reduction in the Mean Absolute Percentage Error (MAPE) from 3.491% to 2.766%, indicating a 24% improvement. Additionally, the Root Mean Square Error (RMSE) decreased by approximately 0.991 MW, resulting in a 45% improvement. These results demonstrate that this approach is an effective solution for enhancing the accuracy of solar power output forecasting while reducing the number of input variables