Time-varying Roughness Research Articles

Research on the Method of Improving the Precision of Parts in Pearl River Delta Gear Enterprises: Analysis of Gear Transmission Error and Tooth Friction Coefficient Based on the Influence of Tooth Surface Roughness

Tooth surface roughness is an important index to evaluate the quality of gear processing, which affects the gear transmission error and tooth friction coefficient and changes the gear dynamics. Based on the fractal theory, the gear error correction model considering time-varying roughness is derived, and the new model of time-varying roughness and gear error is introduced into the Xu model to construct the friction coefficient model accounting for time-varying roughness and gear error, which has great reference value for improving the accuracy of gear error. The research results show that the root mean square value of gear error increases by 45.39% and 101.08% under comparison Ra = 0.8 μm, Ra = 1.6 μm, and R a = 3.2 μm, respectively, and the root mean square value of time-varying friction coefficient increases by 45.74% and 148.18%, respectively, which provides theoretical method support for precision management and quality management of gear processing in machinery enterprises.

Stochastic bias correction for RADARSAT-2 soil moisture retrieved over vegetated areas

SAR data provide the high-resolution images useful for monitoring environment, and natural resources. Nevertheless, it has been a great challenge to retrieve soil moisture over vegetated sites from SAR backscatter coefficients, as it is almost impossible to parameterize spatially heterogeneous and time-varying roughness, the effect of rainfall or canopy volume scattering with implicit equations. We suggest a Monte Carlo Method (MCM) as a strategy to mitigate non-linear errors in retrievals arising from rainfall, and vegetation growth. The Advanced Integral Equation Model (AIEM) is repeatedly run in a forward mode for establishing the Gaussian-distributed soil roughness and backscatter coefficients. The mean value of soil moisture ensembles inverted from those was taken as an optimal estimate. Local validations show that Root Mean Square Errors (RMSEs) were 0.05 ∼ 0.07 m3/m3 at the stations in Saskatchewan, Canada. Biases were 0.01 m3/m3. Spatial distribution illustrates that the retrieval biases were mitigated, resolving AIEM inversion errors.

Measuring and modeling event-based environmental flows: An assessment of HEC-RAS 2D rain-on-grid simulations

There is an immediate need to use available modeling tools to quantify environmental flows targets where changing climate and human activity has altered hydroecologically important streamflow regimes. A model performance assessment was undertaken using observed data collected from five nested gauging sites in a mixed land use watershed of the central US. An integrated modeling approach was used to couple The Soil and Water Assessment Tool (SWAT version 2012), and The Hydrologic Engineering Center's River Analysis System (HEC-RAS version 5.0.7). SWAT was used to generate effective rainfall needed to run HEC-RAS rain-on-grid two-dimensional hydrodynamic simulations. Model calibration results showed the potential usefulness of coupling SWAT and HEC-RAS using an integrated modeling approach. For example, PBIAS of 8.3%, NSE value of 0.84, and coefficient of determination (R2) value of 0.80 at a highly urbanized monitoring site used for model calibration. Split-site validation results showed PBIAS values that ranged from 10.4 to 33.8%, NSE values that ranged from 0.33 to 0.92, and R2 values that ranged from 0.86 to 0.97. Results showed that 2D rain-on-grid HEC-RAS simulations can produce realistic simulations of stage hydrograph response when: (1) areal effective precipitation is used for 2D HEC-RAS rain-on-grid forcing's, (2) HEC-RAS is calibrated to observed data during the event of interest, (3) there are not substantial sources of backwatering from outside the models geometric data, and (4) during saturated antecedent soil moisture conditions surface DEM's adequately describe overland flow paths. This model performance assessment is among the first, if not the first, to show calibration and validation results associated with 2D HEC-RAS rain-on-grid simulations at a watershed scale. Results highlight the need for time-varying roughness coefficients to account for soil moisture conditions, and point to the efficacy of using a SWAT/HEC-RAS integrated modeling approach to generate event-based environmental flows information.

Impact of rock fragment size on erosion process and micro-topography evolution of cone-shaped spoil heaps

Spoil heaps in sites disturbed by construction cause severe acceleration of erosion, threatening environment quality and personal safety. Rock fragments (RFs) are a common component of spoil heaps; therefore, this study was conducted to evaluate the effects of different size classes of RFs on cone-shaped spoil heap erosion. A 3D laser scanner was used to capture the surface micro-topography evolution. A multi-day rainfall simulation experiment at 1.5 mm min−1 rainfall intensity was conducted with 30 mass percent of RFs of different diameters (1–3.5, 3.5–7, 7–10, 10–15 cm). The results showed that RFs reduced runoff by 13.0% relative to the bare soil spoil heap, among which the RFs of the 7–10 cm size class performed best. Only the initial surface micro-topography factors increased with RF size. The stable micro-topography factors of 1–3.5 cm RF treatment were 59.2% larger than those for other RF treatments on average because of the slope slump. Spoil heaps containing small size (1–3.5) RFs more easily slumped with an area of 0.895 m2 and showed obvious changes in micro-topography. RFs in different size classes influenced soil loss of spoil heaps via runoff yield and effects on slope stability. Larger roughness increment and runoff rate were associated with greater soil loss rate (R2 = 0.828), but time-varying roughness had no effect on runoff. Overall, the results of this study provide a better understanding of the impact of RFs on spoil heap erosion.

Three-Dimensional Hydrodynamic Simulation of Tidal Flow through a Vegetated Marsh Area

AbstractSimulation of tidal flow in an estuarine marsh area is challenging because of resistance from spatially and temporally varied vegetation. This study simulated the tidal flow in the Davis Pond marsh area near New Orleans, Louisiana, using an open source program. To quantify the time-varying roughness coefficient, four empirical equations for calculating Manning’s roughness coefficient were incorporated into the program in addition to two options offered by the program to calculate the Chezy’s coefficient and one option to use the modified momentum equations and the k−ϵ turbulence model. Results showed that the time-varying roughness coefficient equations accounting for both the degree of submergence and the vegetation frontal area gave the closest matches with the observed data.

Dynamic Routing Model with Real-Time Roughness Updating for Flood Forecasting

A flood routing model with time-varying roughness updating was developed to simulate flows through natural channels based on the dynamic wave theory. Taking observed stages as the targets, a roughness updating technique was developed using the Gauss-Newton method to update the Manning n in each time step of the routing processes. The technique provides a reasonable roughness coefficient estimate and reliable initialization of stage profile for the forecast. The examinations including the initialization of stage profile, conservation of mass, iteration convergence, effectiveness evaluation, and convergence with different initial values were conducted to verify the predictive capability of the roughness updating technique. The forecasting results show that the stage recalculated by updating the Manning n in current time has a good agreement with the observed stage. The presented model can improve the forecast for a lead time up to 6h in the Tanshui River of northern Taiwan.