A two-dimensional monotonical logical grid algorithm

A star pattern identification algorithm, called modified grid algorithm, for attitude determination of spacecraft is addressed. The proposed algorithm is closely connected to a general pattern recognition technique. Each star is characterized by a well-defined pattern that can be determined by the surrounding stars. The so-called grid algorithm is one of the star identification algorithms based upon the pattern recognition approach. The proposed algorithm, is motivated by the conventional grid algorithm. To enhance the performance of the conventional grid algorithm, a modified method using polar grid, virtual grid, and multi-references is proposed. Simulation study is conducted for the demonstration of the new algorithm. The proposed modified grid approach turns out to make the grid algorithm more robust and reliable.

The finite-difference method (FDM) algorithm with staggered grid greatly improved the computational efficiency of solving differential equations in two dimensional simulation of GPR, but the strict Courant-Friedrich-Levy (CFL) condition must be satisfied. In three dimensional simulation of GPR, a maximum time-step size will lead to a serious numerical dispersion, while a minimum time-step size will increase the computation time with higher cost. The rotated staggered grid (RSG) algorithm achieves a balance between computational efficiency and numerical stability in three dimensional simulations, as it solves the linear correlation of field value along the diagonal nodes, even if a significantly larger time-step size is chosen, and it can achieve a numerical calculation accuracy as high as the conventional algorithm. The difference scheme of the three dimensional RSG was deduced together with the corresponding updated equations, and a numerical tunnel model with typical hidden disease was presented. The RSG and conventional algorithms were used in the three dimensional simulation. Meanwhile, the comparison of computational efficiency and numerical stability was analyzed, and the calculation result of this model has shown that the RSG algorithm has a quality of higher precision with numerical stability even at a large time-step sizes, which can further facilitate the interpretation of GPR data of typical hidden disease in tunnels.

With ongoing efforts to upgrade the traditional electric power system into a smart grid, emphasis is on the integration of state-of-the-art computer based online monitoring and control tools along with advanced communication technology. However, testing and validation of these devices and algorithms are required before implementation in a physical grid. New testing and validation method needs to be developed in a simulated environment in a lab. This paper discusses some of the applications of the "smart grid test bed" developed at the "Smart Grid Development and Research Investigation Lab (SGDRIL)" at Washington State University. The specific applications discussed in this paper include synchrophasor device testing, microgrid reconfiguration, voltage stability and vulnerability analysis.

Monolayer Adsorption of Ar and Kr on Graphite: Theoretical Isotherms and Spreading Pressures

Large-scale simulations of two-dimensional bidisperse granular fluids allow us to determine spatial correlations of slow particles via the four-point structure factor S(4)(q,t). Both cases, elastic (ϵ=1) and inelastic (ϵ<1) collisions, are studied. As the fluid approaches structural arrest, i.e., for packing fractions in the range 0.6≤ϕ≤0.805, scaling is shown to hold: S(4)(q,t)/χ(4)(t)=s(qξ(t)). Both the dynamic susceptibility χ(4)(τ(α)) and the dynamic correlation length ξ(τ(α)) evaluated at the α relaxation time τ(α) can be fitted to a power law divergence at a critical packing fraction. The measured ξ(τ(α)) widely exceeds the largest one previously observed for three-dimensional (3d) hard sphere fluids. The number of particles in a slow cluster and the correlation length are related by a robust power law, χ(4)(τ(α))≈ξ(d-p)(τ(α)), with an exponent d-p≈1.6. This scaling is remarkably independent of ϵ, even though the strength of the dynamical heterogeneity at constant volume fraction depends strongly on ϵ.

Monte Carlo values for the equation of state of the two-dimensional Lennard-Jones fluid are reported. The agreement with previous simulations is good, with the exception of the critical point region. However, the estimates of the critical temperature and density given here are lower than those previously reported. The first and second-order Barker-Henderson and first-order Weeks-Chandler-Andersen perturbation theories are applied to this system and are found to be satisfactory only at high densities. A semiempirical correction term to the second-order Barker-Henderson theory is constructed. The resulting parameterization of the Monte Carlo data is found to be accurate and useful at all densities and temperatures for which the Monte Carlo data exist.

Several automated pollen monitors have recently become available, most using conventional and/or machine-learning algorithms to detect pollen and classify their taxa. An international intercomparison campaign was organised by the EUMETNET AutoPollen Programme and the ADOPT COST Action in Munich (March&amp;#8211;July 2021) (Maya-Manzano et al. 2023). The study showed that some automatic systems, especially those that have built-in correction factors to compensate for losses of sampling, detection and classification, agreed with the averaged pollen concentration of four manual Hirst-type measurements. The manual pollen counting method, relying on human operators, is still used today due to a lack of traceable metrological standards for pollen monitoring. Additionally, existing reference particle counters have been validated up to particle sizes around 20 &amp;#181;m only (Vasilatou et al. 2022). In this study, we evaluated the shake-the-box particle tracking/detection algorithm (Novara et al. 2023) to serve as a reference measurement of particle number concentration. A volume is illuminated with an expanded light sheet produced by a double-pulse laser, and four calibrated double-frame cameras record images of the particles inside this volume. The algorithm determines the position and velocity of each particle, allowing the measurement of particle number concentration directly in the air without sampling inlets that could influence the measurement.Particle tracking was validated against the Inkjet Aerosol Generator at the Japanese National Metrology Institute NMIJ and the reference optical particle counter at METAS using an 11-D spectrometer (Grimm GmbH, Germany) as a transfer standard and size-certified polystyrene particles. Expanded uncertainties (coverage factor k=2) were 24 %, 10 %, and 7 % for particle sizes 15 &amp;#956;m, 20 &amp;#956;m, and 26 &amp;#956;m, respectively. The good agreement among the three methods shows that the shake-the-box particle tracking method can be applied as a reference for particle number concentration.We used a laboratory-based method for characterising the performance of bioaerosol monitors as a whole unit (hardware plus identification algorithms) using the particle tracking method and, in a separate experiment, freshly sampled pollen. Experiments were carried out with the SwisensPoleno Jupiter, which combines light scattering, inline digital holography and ultraviolet laser-induced fluorescence with machine learning to classify different particles. For a pollen grain to be measured, it must be sampled, detected and correctly classified. &amp;#160;Each stage is subject to particle losses, leading to a measurement efficiency below 100 %. Particle tracking measurements of pollen (Pinus, Zea Mays) resulted in an average counting efficiency of about 43 %, neglecting particle losses in the Sigma-2 sampling head and issues with the classification of particles. The unit-to-unit variability of the Poleno instruments was 30 % based on measurement with three units. Independent experiments with Alnus glutinosa, Betula pendula and Corylus avellana showed that the major source of losses, however, originates from the pollen classification algorithm, which is trimmed to best correlate with Hirst measurements (currently the defacto reference for pollen monitoring). This highlights the need for an independent, standardised method for evaluating classifier losses.

We are interested in statistical solutions of McKean-Vlasov-Fokker-Planck equations. An example of motivation is the Navier-Stokes equation for the vorticity of a two-dimensional incompressible fluid flow. We propose an original and efficient numerical method to compute moments of such solutions. It is a stochastic particle method with random weights. These weights are defined through nonparametric estimators of a regression function and convey the uncertainty on the initial condition of the considered equation. We prove an existence and uniqueness result for a class of nonlinear stochastic differential equations (SDEs), and we study the relationship between these nonlinear SDEs and statistical solutions of the corresponding McKean-Vlasov equations. This result forms the foundation of our stochastic particle method where we estimate the convergence rate in terms of the numerical parameters: the number of simulated particles and the time discretization step.

Finite-size scaling for near-critical continuum fluids at constant pressure

Communication in the two-way listen-in vertex-disjoint paths mode

Nonequilibrium molecular dynamics simulation of shear viscosity by a uniform momentum source-and-sink scheme

This paper presents numerical procedures for a coupled scheme that combines immersed boundary and discrete element methods to simulate two-dimensional fluid and particle motion. We simulated the dead-end, constant-flux microfiltration of dispersed particles and investigated the effect of hydrodynamic interactions between the fluid and particles on the transmembrane pressure. The permeation of a single particle through a pore was first simulated to obtain the fundamental characteristics of transmembrane pressure behavior. The effects of the particle dispersion volume fraction and the pore size on the transmembrane pressure were then investigated. The results show that the transmembrane pressure increases and fluctuates even in the absence of membrane fouling when a particle penetrates the pore. Transmembrane pressure behavior is also significantly affected by the number of particles within the pore and the location of particles around the pore.

Monte Carlo (MC) methods are recognized as the gold-standard for dose calculation, however they have not replaced analytical methods up to now due to their lengthy calculation times. GPU-based applications allow MC dose calculations to be performed on time scales comparable to conventional analytical algorithms. This study focuses on validating our GPU-based MC code for proton dose calculation (gPMC) using an experimentally validated multi-purpose MC code (TOPAS) and compare their performance for clinical patient cases. Clinical cases from five treatment sites were selected covering the full range from very homogeneous patient geometries (liver) to patients with high geometrical complexity (air cavities and density heterogeneities in head-and-neck and lung patients) and from short beam range (breast) to large beam range (prostate). Both gPMC and TOPAS were used to calculate 3D dose distributions for all patients. Comparisons were performed based on target coverage indices (mean dose, V95, D98, D50, D02) and gamma index distributions. Dosimetric indices differed less than 2% between TOPAS and gPMC dose distributions for most cases. Gamma index analysis with 1%/1 mm criterion resulted in a passing rate of more than 94% of all patient voxels receiving more than 10% of the mean target dose, for all patients except for prostate cases. Although clinically insignificant, gPMC resulted in systematic underestimation of target dose for prostate cases by 1–2% compared to TOPAS. Correspondingly the gamma index analysis with 1%/1 mm criterion failed for most beams for this site, while for 2%/1 mm criterion passing rates of more than 94.6% of all patient voxels were observed. For the same initial number of simulated particles, calculation time for a single beam for a typical head and neck patient plan decreased from 4 CPU hours per million particles (2.8–2.9 GHz Intel X5600) for TOPAS to 2.4 s per million particles (NVIDIA TESLA C2075) for gPMC. Excellent agreement was demonstrated between our fast GPU-based MC code (gPMC) and a previously extensively validated multi-purpose MC code (TOPAS) for a comprehensive set of clinical patient cases. This shows that MC dose calculations in proton therapy can be performed on time scales comparable to analytical algorithms with accuracy comparable to state-of-the-art CPU-based MC codes.

Quantum-inspired evolutionary algorithm (QEA) has been designed by integrating some quantum mechanical principles in the framework of evolutionary algorithms. They have been successfully employed as a computational technique in solving difficult optimization problems. It is well known that QEAs provide better balance between exploration and exploitation as compared to the conventional evolutionary algorithms. The population in QEA is evolved by variation operators, which move the Q-bit towards an attractor. A modification for improving the performance of QEA was proposed by changing the selection of attractors, namely, versatile QEA. The improvement attained by versatile QEA over QEA indicates the impact of population structure on the performance of QEA and motivates further investigation into employing fine-grained model. The QEA with fine-grained population model (FQEA) is similar to QEA with the exception that every individual is located in a unique position on a two-dimensional toroidal grid and has four neighbors amongst which it selects its attractor. Further, FQEA does not use migrations, which is employed by QEAs. This paper empirically investigates the effect of the three different population structures on the performance of QEA by solving well-known discrete benchmark optimization problems.

We present a rigorous derivation for off-lattice implementations of the so-called "random-walk" algorithm recently introduced by Wang and Landau [Phys. Rev. Lett. 86, 2050 (2001)]. Originally developed for discrete systems, the algorithm samples configurations according to their inverse density of states using Monte Carlo moves; the estimate for the density of states is refined at each simulation step and is ultimately used to calculate thermodynamic properties. We present an implementation for atomic systems based on a rigorous separation of kinetic and configurational contributions to the density of states. By constructing a "uniform" ensemble for configurational degrees of freedom-in which all potential energies, volumes, and numbers of particles are equally probable-we establish a framework for the correct implementation of simulation acceptance criteria and calculation of thermodynamic averages in the continuum case. To demonstrate the generality of our approach, we perform sample calculations for the Lennard-Jones fluid using two implementation variants and in both cases find good agreement with established literature values for the vapor-liquid coexistence locus.