Search

Motion characteristics are dimensionless peak values of velocity, acceleration, jerk, and acceleration multiplied by velocity of a motion program. In general, these peak values of synthesized motion programs should be as low as possible. Some trigonometric motion programs are widely used because they have a good compromise of all motion characteristics. A property in common for trigonometric motion programs is that their acceleration functions can be expressed as a qualitative shape of a sinusoidal function. The interval of the sinusoidal function is divided into several zones having different linear slopes. The acceleration function can be easily shaped by specifying presented phase angle function to synthesize desired motion programs. To improve the kinematic quantities of trigonometric motion programs, this paper addresses an alternative phase angle function to obtain synthesized motion programs with simultaneous reduction in all the motion characteristics. The synthesis process and results are illustrated by examples.

In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km2, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash–Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins.

Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.

Noah (version 2.7.1), the community land-surface model (LSM) of NCEP-NCAR, which is widely used to describe the land-surface processes either in stand-alone or in coupled land--atmospheric model systems, is recognized because snow--water equivalent (SWE) can be underestimated. Noah's SWE bias can be attributed to its simple snow sub-model, which does not e effectively describe the physical processes during snow accumulation and melt period. To improve SWE simulation in the Noah LSM, the Utah Energy Balance (UEB) snow model is implemented in Noah to test alternate snow- surface temperature and snow-melt outflow schemes. Snow surface temperature was estimated using force--restore method and snow melt event is regulated by accounting for the internal energy of the snowpack. The modified Noah SWE is compared with the SWE observed at California's NRCS SNOTEL stations for seven water years: 2002-2008, while the model snow-surface temperature is verified with observed surface-temperature data at an observation site in Utah. The experiments show that modification in Noah's snow process substantially reduced SWE estimation bias while keeping the simplicity of the Noah LSM. The results suggest that the model did not benefit from the alternate temperature representation but primary improvement can be attributed to the substituted snow melt process. [ABSTRACT FROM AUTHOR]

The cathode erosion rate, arc root velocity and output power of a well-type cathode (WTC), non-transferred plasma torch operating in air are studied experimentally in this paper. An external solenoid to generate a magnetically driven arc and a circular swirler to produce a vortex flow structure are equipped in the studied torch system, which is designed to reduce the erosion rate at the cathode. A least square technique is applied to correlate the system parameters, i.e. current, axial magnetic field and mass flow rate, with the cathode erosion rate, arc root velocity and system power output. In the studied WTC torch system, the cathode erosion has a major thermal erosion component and a minor component due to the ion-bombardment effect. The cathode erosion increases with the increase of current due to the enhancement in both Joule heating and ion bombardment. The axial magnetic field can significantly reduce the cathode erosion by reducing the thermal loading of cathode materials at the arc root and improving the heat transfer to gas near the cathode. But, the rise in the mass flow rate leads to the deterioration of erosion, since the ion-bombardment effect prevails over the convective cooling at the cathode. The most dominant system parameter to influence the arc root velocity is the axial magnetic field, which is mainly contributed to the magnetic force driving the arc. The growth in current has a negative impact on increasing the arc root velocity, because the friction force acting at the spot due to a severe molten condition becomes the dominant component counteracting the magnetic force. The mass flow rate also suppresses the arc root velocity, as a result of which the arc root moves in the direction against that of the swirled working gas. All system parameters such as current, magnetic field and gas flow rate increase with the increase in the torch output power. The experimental evidences suggest that the axial magnetic field is the most important parameter to operate the straight-polarity WTC plasma torch at high output power with a limited cathode erosion rate. This emphasizes the importance of an external magnetic field on a WTC torch system for reducing the erosion at the cathode. [ABSTRACT FROM AUTHOR]