Dynamic Response of Rock-like Materials Based on SHPB Pulse Waveform Characteristics

Rock-like brittle materials under dynamic load will show more complex dynamic mechanical properties than those under static load. The relationship between pulse waveform characteristics and strain rate effect and inertia effect is rarely discussed in the split-Hopkinson pressure bar (SHPB) numerical simulation research. In response to this problem, this paper discusses the effects of different pulse types and pulse waveforms on the incident waveform and dynamic response characteristics of specimens based on particle flow code (PFC). The research identifies a critical interval of rock dynamic strength, where the dynamic strength of the specimen is independent of the strain rate but increases with the amplitude of the incident stress wave. When the critical interval is exceeded, the dynamic strength is determined by the strain rate and strain rate gradient. The strain rate of the specimen is only related to the slope of the incident stress wave and is independent of its amplitude. It is also determined that the inertia effect cannot be eliminated in the SHPB. The slope of the velocity pulse waveform determines the strain rate of the specimen, the slope of the force pulse waveform determines the strain rate gradient of the specimen, and the upper bottom time determines the strain rate of the specimen. It provides a reference for SHPB numerical simulation. A dynamic strength prediction model of rock-like materials is then proposed, which considers the effects of strain rate and strain rate gradient.

A unified description of gravity- and kinematics-induced segregation forces in dense granular flows

Particle segregation is common in natural and industrial processes involving flowing granular materials. Complex, and seemingly contradictory, segregation phenomena have been observed for different boundary conditions and forcing. Using discrete element method simulations, we show that segregation of a single particle intruder can be described in a unified manner across different flow configurations. A scaling relation for the net segregation force is obtained by measuring forces on an intruder particle in controlled-velocity flows where gravity and flow kinematics are varied independently. The scaling law consists of two additive terms: a buoyancy-like gravity-induced pressure gradient term and a shear rate gradient term, both of which depend on the particle size ratio. The shear rate gradient term reflects a kinematics-driven mechanism whereby larger (smaller) intruders are pushed toward higher (lower) shear rate regions. The scaling is validated, without refitting, in wall-driven flows, inclined wall-driven flows, vertical silo flows, and free-surface flows down inclines. Comparing the segregation force with the intruder weight results in predictions of the segregation direction that match experimental and computational results for various flow configurations.

To the Development of the Phenomenology of Fast Shear Flows of Grain Materials

Phenomenological approaches to describing the rheological behavior of granular materials under conditions of rapid and quasi-plastic shear deformations are considered. A unified approach to the phenomenological-logical description of the physical parameter, called the temperature of the granular medium, and the mechanisms of shear stress generation is proposed. A description is given of the mechanism for generating shear stresses under the action of a flow of pulses directed along the shear rate gradient and caused by transverse quasi-diffusion of particles. This mechanism is taken into account in the rheological model in addition to the traditional mechanism of generating kinetic shear stresses under the action of tangential shock pulses.

High resolution measurements with miniature neutron scintillators in the SUR-100 zero power reactor

Three 1-mm3 miniature fiber-coupled scintillators have been used to perform cm-wise resolution measurements of the thermal neutron flux within experimental channels of the SUR-100 facility, a zero power thermal reactor operated by the Institute of Nuclear Technology and Energy Systems at the University of Stuttgart. The detection system is developed at the École Polytechnique Fédérale de Lausanne in collaboration with the Paul Scherrer Institut. Thermal neutrons count rates were measured along the experimental channels I and II, which cross the reactor at the center and tangentially to the core, respectively. The reactor was modelled with the Monte Carlo neutron transport code Serpent-2.1.31. The comparison of experimental and computed reaction rate distributions showed a good agreement within the core region, with discrepancies within 2σ. An unexpected discrepancy, probably caused by a geometric inconsistency in the computational model of the reactor, was observed in the reflector region of the experimental channel I, where a 20% difference (i.e. 8σ) was found between experimental and simulated results. Significant discrepancies, respectively worth 10σ and 15σ, were noticed at distance, in the lead shielding region, for both experimental channels I and II. In addition, reaction rate gradients across the 2.6 cm and 5.4 cm diameters of both channels were measured. A horizontal reaction rate gradient of (9.09 ± 0.20) % was measured within 2.4 cm across the diameter of the experimental channel II, with a difference from computed results of 2%. The absence of a vertical reaction rate gradient inside the experimental channel I was confirmed by measurements.

Exploring shear-induced segregation in controlled-velocity granular flows

Particle segregation in geophysical and industrial granular flows is typically driven by gravity and shear. While gravity-induced segregation is relatively well understood, shear-induced segregation is not. In particular, what controls segregation in the absence of gravity and the interplay between shearand gravity-driven segregation remain unclear. Here, we explore the shear-induced segregation force on an intruder particle in controlled-velocity granular flows where the shear profile is systematically varied. The shear-induced segregation force is found to be proportional to the shear rate gradient, which effectively pushes the large intruder from lower to higher shear rate regions. A scaling law is developed for the segregation force that is accurate over a wide range of overburden pressures and shear rates, and hence inertial numbers.

Late Quaternary slip rate of the Aksay segment and its rapidly decreasing gradient along the Altyn Tagh fault

Constraining the fault slip rate on a fault can reveal the strain accumulation and partitioning pattern. The Aksay segment, the eastern segment of the Altyn Tagh fault, as the starting area where the slip rate of the Altyn Tagh fault decreases, is a strain partitioning zone. The spatial and temporal distribution of its fault slip rate is of great significance to clarify the strain-partitioning pattern of the eastern Altyn Tagh fault. In this study, we determined the slip rates at four sites along the Aksay segment. The results demonstrated that the slip rate decreases dramatically, with an overwhelmingly high slip gradient of ∼9.8 mm/yr/100 km (a 9.8 mm/yr reduction of slip rate occurs over a distance of 100 km) within a distance of ∼50 km. The slip rate gradient along strike at the Aksay segment is four times that of the Subei segment to the eastward termination of the Altyn Tagh fault. Our results indicate that the slip rate gradient along the Altyn Tagh fault is not uniform and decreases eastward with variable slip rate gradients on different segments, resulting in the uplift of the mountains oblique to the Altyn Tagh fault.

Research on Multi-Channel Semantic Fusion Classification Model

In this work, we propose a multi-channel semantic fusion convolutional neural network (SFCNN) to solve the problem of emotional ambiguity caused by the change of contextual order in sentiment classification task. Firstly, the emotional tendency weights are evaluated on the text word vector through the improved emotional tendency attention mechanism. Secondly, the multi-channel semantic fusion layer is leveraged to combine deep semantic fusion of sentences with contextual order to generate deep semantic vectors, which are learned by CNN to extract high-level semantic features. Finally, the improved adaptive learning rate gradient descent algorithm is employed to optimize the model parameters, and completes the sentiment classification task. Three datasets are used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the SFCNN model has the high steady-state precision and generalization performance.