Thermal-EHD contacts lubricated with oil/refrigerant solutions: a new cavitation modeling approach based on refrigerant solubility

A first cavitation modeling with thermal effects for oil/refrigerant solutions lubricated ElastoHydroDynamic (EHD) point contacts is reported in this work. The solubility of the oil/refrigerant system is introduced into the Generalized Reynolds equation coupled with the elasticity equation and the energy conservation equation. The numerical results show a very good agreement with the published experimental results concerning film thickness prediction. Moreover, the present model describes the cavitation region on a physical basis. A discussion with other cavitation models from the literature is proposed. It puts into light the necessity of taking into account the solubility of the refrigerant into oil for such problems. Compared to pure oil, oil/refrigerant solutions can potentially reduce the amount of liquid oil for the next contact due to its higher cavitation intensity.

Coal Thickness Prediction Method Based on VMD and LSTM

The change in coal seam thickness has an important influence on coal mine safety and efficient mining. It is very important to predict coal thickness accurately. However, the accuracy of borehole interpolation and BP neural network is not high. Variational mode decomposition (VMD) has strong denoising ability, and the long short-term memory neural network (LSTM) is especially suitable for the prediction of complex sequences. This paper presents a method of coal thickness prediction using VMD and LSTM. Firstly, empirical mode decomposition (EMD) and VMD methods are used to denoise simple signals, and the denoising effect of the VMD method is verified. Then, the wedge-shaped coal thickness model is constructed, and the seismic forward modeling and analysis are carried out. The results show that the coal thickness prediction based on seismic attributes is feasible. On the basis of VMD denoising of the original 3D seismic data, VMD-LSTM is used to predict coal thickness and compared with the prediction results of the traditional BP neural network. The coal thickness prediction method proposed in this paper has high accuracy and basically coincides with the coal seam information exposed by existing boreholes. The minimum absolute error of the predicted coal thickness is only 0.08 m, and the maximum absolute error is 0.48 m. This indicates that VMD-LSTM has high accuracy in predicting coal thickness. The proposed coal thickness prediction method can be used as a new method for coal thickness prediction.

Professor Duncan Dowson, a source of inspiration

The current paper highlights the contribution of the Dowson and Higginson work to numerical line contact elastohydrodynamic lubrication film thickness prediction and the Hamrock and Dowson contribution to the film thickness prediction in elliptical contacts. This paper shows that, even by today’s standards, both the numerical pressure and film thickness results and the curve-fitted film thickness predictions are very accurate. As for the elliptical results, the authors show that the original predictions remain surprisingly accurate for moderately elliptical contact. For very long elliptical contacts, their prediction does not tend to a line contact asymptote. This paper then concludes that the predicted pressure spikes by Dowson, Higginson, and Hamrock are correct in shape and amplitude, at least near pure rolling conditions.

SEISMIC RESONANCE: WAVELET-FREE REFLECTIVITY RETRIEVAL VIA MODIFIED CEPSTRAL DECOMPOSITION

Spectral decomposition is a proven tool in seismic interpretation, aiding interpreters to highlight channels, map temporal bed thickness and other geological discontinuities. Once seismic data is spectrally decomposed, notch patterns in the amplitude spectra are indicative of the reservoir layer’s thickness and/or its interval velocity. Additional cepstral decomposition will allow direct extraction of bed time-thickness or arrival time under particular reflectivity series setup. We build on these observations to establish a more generalized workflow for reflectivity retrieval method without the need to understand the details of the wavelet, provided the starting seismic is stably phased via phase correction during processing. We demonstrate reflector time and its ‘apparent strength’ can be identified in a transformed seismic resonance domain resulted from a modified cepstrum analysis. In this domain, each reflector can be characterized from obvious linear hot spots. The timing and strength of those linear hot spots will reveal reflector times and scaled reflectivity coefficients. This new method is subsequently applied for thickness prediction of a target reservoir in a complex geological setting, with large thickness variations and weak impedance contrast with underlying lithology previously complicating identification of base-reservoir. In a deep-water field blind test, the sand thicknesses evaluated from this method are found to be close to true vertical thickness found in wells.

A hybrid data–model approach to map soil thickness in mountain hillslopes

Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.

Modeling of Rolling Force for Thick Plate of Multicomponent Alloys and Its Application on Thickness Prediction

During the rolling process of thick plate, the nonlinear specific plastic power that derived from the non-linear Mises yield criterion is difficult to be integrated, which has restricted the establishment of a rolling force model. To solve this problem, a new yield criterion is firstly established, and then used to derive a linear specific plastic power. Meanwhile, a kinematically admissible velocity field whose horizontal velocity component obeys the Logistic function is proposed to describe the metal flow of the deformed plate. On these bases, the rolling energy items including the internal deformation power of the deformed body, friction power on the contact surface, and shear power on the entry and exit sections are integrated successively, and the rolling force model is established. It is proved that the model can predict the rolling force well when compared with the actual data of multicomponent alloys. Besides, the formula for predicting the outlet thickness is ultimately given upon this derived model, and a good agreement is also found between the predicted values and the actual ones, since the absolute errors between them are within 0.50 mm.

How does DEM error impact the optimal grid resolution of soil evolution simulation?

<p>The soil thickness is the key controlling factor of local hydrology and geomorphologic characteristics. The accuracy, reliability and coverage of soil thickness map are required for reliable application. Though with quite distinct structures, models for simulating soil thickness take modern topographic data (normally digital elevation model, DEM) as one of the most important inputs. Understanding the effect of grid resolution on soil thickness prediction and selecting an appropriate resolution is crucial for the macro-scale modeling. In this study, we further explored the relationship between topographic resolution and simulation accuracy of soil thickness, and propose a new method to determine the optimal simulation resolution. A series of abstract hillslopes with different terrain noise and terrain complexity were construct and different resolutions of DEM were generated. We used a simple geomorphic based model to calculate topographic index (slope, aspect and curvature) and soil thickness. The results show that the truncation error and noise of DEM will propagate during the simulation process. Furtherly, the correlation curve between DEM resolution and the simulation error of soil thickness is a hook curve. The shape of the curve is mainly controlled by two factors, terrain noise and terrain complexity. By fitting the correlation curve of all hillslopes, the curve can be predicted by them, and the resolution corresponding to the error minimum be found out, which can be called the optimal simulation resolution of the soil thickness prediction model.</p>