Optimization of Burgers creep damage model of frozen silty clay based on fuzzy random particle swarm algorithm

The creep characteristics of frozen rock and soil are crucial for construction safety in cases of underground freezing. Uniaxial compression tests and uniaxial creep tests were performed at temperatures of − 10, − 15, − 20, and − 25 °C for silty clay used in Nantong metro freezing construction to investigate the effect law of the stress–strain curves and creep curves. However, owing to the complex effects of factors such as temperature and ground pressure, the mechanical properties of underground frozen silty clay are uncertain. The Burgers creep damage model was established by using an elastic damage element to simulate the accelerated creep stage. The traditional particle swarm optimization algorithm was improved using the inertia weight and the fuzzy random coefficient. The creep parameters of the Burgers damage model were optimized using the improved fuzzy random particle swarm algorithm at different temperatures and pressure levels. Engineering examples indicated that the optimized creep model can more effectively characterize the creep stages of frozen silty clay in Nantong metro freezing construction. The improved fuzzy random particle swarm algorithm has wider engineering applicability and faster convergence than the traditional algorithm.

Trailing-edge noise reduction of a wing by a surface modification

There is an increased emphasis on reducing airframe noise in the last decades. Airframe noise is sound generated by the interaction of a turbulent flow with the aircraft geometry, and significantly contributes to the overall noise production during the landing phase. One examples of airframe noise is the noise generated at a wing's trailing edge, i.e., trailing-edge noise. In this contribution, we numerically explore the local application of riblets for the purpose of trailing-edge noise reduction. Two configurations are studied: i) a clean NACA0012 wing section as a reference, and ii) the same configuration with riblets installed at the wing's aft part. The numerical investigation follows a hybrid computational aeroacoustics approach, where the time-average flow is studied by means of RANS. Noise sources are generated by means of a stochastic approach called Fast Random Particle Mesh method. The results show a deceleration of the flow behind the riblets. Furthermore, the turbulent kinetic energy indicates increased unsteadiness behind the riblets which is shifted away from the wall due to the presence of the riblets. Lastly, the sound sources are investigated by means of the 3D Lamb-vector, which indicates a slight reduction in magnitude near the trailing edge.

Branching Process Model applied to the 1/f Noise; Formalization

In order to discuss the 1⁄f problem, the statistics of branching process of particle in a multiplicative medium are developed by taking account of random particle immigration. The probability distribution function of the number of particles founded in the medium at any fixed time and/or of particles detected in a time interval are obtained in closed form. These results are applied to the case that exactly two particles are produced by branching.

A Faster Fourier Transform? Computing Small-Scale Power Spectra and Bispectra for Cosmological Simulations in 𝒪(N2) Time

We present $\mathcal {O}(N^2)$ estimators for the small-scale power spectrum and bispectrum in cosmological simulations. In combination with traditional methods, these allow spectra to be efficiently computed across a vast range of scales, requiring orders of magnitude less computation time than Fast Fourier Transform based approaches alone. These methods are applicable to any tracer; simulation particles, halos or galaxies, and take advantage of the simple geometry of the box and periodicity to remove almost all dependence on large random particle catalogs. By working in configuration-space, both power spectra and bispectra can be computed via a weighted sum of particle pairs up to some radius, which can be reduced at larger k, leading to algorithms with decreasing complexity on small scales. These do not suffer from aliasing or shot-noise, allowing spectra to be computed to arbitrarily large wavenumbers. The estimators are rigorously derived and tested against simulations, and their covariances discussed. The accompanying code, HIPSTER, has been publicly released, incorporating these algorithms. Such estimators will be of great use in the analysis of large sets of high-resolution simulations.

Optimal Metric Evaluation-Based Multicue Inverse Sparse Appearance Model for Object Tracking

In order to obtain the discriminative compact appearance model for tracking objects effectively, this paper proposes a new structural tracking strategy that includes multicue inverse sparse appearance model and optimal metric evaluation between online robust templates and a limited number of particle samples in the looping process. Multicue inverse sparse appearance model globally improves the efficient selection of informative particle samples that can avoid the cumbersome coding and decoding cost for the trivial random particle samples. Only the most potential crucial cases are involved in each tracking loop. This refrains from unreasonable, rough numerical reduction of particle samples and also keeps the unbiasedness and dynamic stochasticness of the sampling process. Meanwhile, low-rank self-representatives for positive and negative samples facilitate the formulation of a suitable code book that arranges the useful sparse coefficients for feature bags and facilitates optimal metric evaluation for online training. It also alleviates the accuracy degradation of tracking occluded objects and improves the robustness of the tracker. Both of them preserve the discriminative compactness of target which speeds up particle filtering localization to separate the target object from distractors. Moreover, the proposed method exploits online appearance representations to learn the sharing compact information that avoids massive calculation burdens for massive visual data.

Стохастическая ячеистая модель очистки моторного масла от механических примесей объёмным фильтрованием

Приведено описание ячеистой модели фильтрования, которая создавалась на сочетании вероятностно-статистических методов с точным описанием на основе физических законов поведения дисперсной фазы при сложном взаимодействии её с дисперсионной средой. Выделены основные силы, действующие на частицу в потоке при фильтровании. Показано, что наибольшее воздействие на отсев оказывают силы Лондона-Ван-дер-Ваальса и электрокинетические, обусловленные полярными молекулами продуктов старения масла и моюще-диспергирующими присадками, которыми оно легируется. Выяснено, что в наименьшей степени на отсев влияют силы тяжести и Архимеда. Объединение детерминированных и случайных воздействий на дисперсную фазу осуществлено на базе уравнения Колмогорова Фоккера Планка. Показаны методы его формирования так, чтобы рассматриваемому случайному переносу частиц в поровом пространстве, идентифицированному совокупностью обобщенных координат и скоростей, соответствовало уравнение для многомерной плотности вероятности, отождествляемой с концентрацией дисперсной фазы. Приведены расчётные формулы для компонентов скорости частиц, по которым можно рассчитать граничную траекторию, определить безразмерную координату и фракционный коэффициент отсева. Определены краевые условия стохастической задачи фильтрования. Записаны интегралы для расчета фракционного отсева через паток вероятности. Проанализированы возможности детерминированной и стохастической ячеистых моделей фильтрования, показана их адекватность.A description is given of a cellular filtering model that was created using a combination of probabilistic and statistical methods with an accurate description based on the physical laws of the behavior of the dispersed phase during its complex interaction with the dispersion medium. The main forces acting on a particle in a stream during filtration are identified. It has been shown that the London-Van der Waals forces and electro kinetic forces caused by polar molecules of oil aging products and detergent-dispersant additives with which it is doped have the greatest impact on screening. It has been found that gravity and Archimedes force have the leas effect on screening. The combination of deterministic and random effects on the dispersed phase was carried out based on the Kolmogorov Fokker Planck equation. The methods of its formation are shown so that the equation for the multidimensional probability density, identified with the concentration of the dispersed phase, corresponds to the random particle transport in the pore space, identified by the set of generalized coordinates and velocities. Calculation formulas are given for the particle velocity components by which one can calculate the boundary trajectory and determine the dimensionless coordinate and fractional dropout coefficient. The boundary conditions of the stochastic filtering problem are determined. The integrals for calculating the fractional dropout through the molasses of probability are recorded. The possibilities of deterministic and stochastic cellular filtration models are analyzed, and their adequacy is shown.