PERFORMANCE OF SITE VELOCITY PREDICTION IN SUNDALAND

Global Positioning System (GPS) technique has been extensively implemented in determination of crustal deformation globally. With the ability of providing solution up to milimeter (mm) level, this technique has proven to provide a precise estimate of site velocity that represents the actual motion of tectonic plate over a period. Therefore, this study aims to evaluate the site velocity estimation from GPS-derived daily position of station, respective to the global plate motion model and predicted site velocity via Least-Squares Collocation (LSC) method within the tectonically active region of Sundaland. The findings have indicated that stations with precise velocity estimates were consistent with global plate model and predicted velocity, with velocity residuals of 5 mm – 10 mm. However, stations that were severely impacted by continuous earthquake events such as in Sumatra were believed to be induced by the impact with consistently large velocity residuals up to 37 mm. Following the outcomes, this study has provided an insight on the post-seismic decay period plate motion which are induced by continuous tectonic activities respective to modelled plate motion.

Selection of Existing Sail Designs for Multi-Fidelity Surrogate Models

Velocity Prediction Programs (VPPs) are commonly used to help predict and compare the performance of different sail designs. A VPP requires an aerodynamic input force matrix which can be computationally expensive to calculate, limiting its application in industrial sail design projects. The use of multi-fidelity kriging surrogate models has previously been presented by the authors to reduce this cost, with high-fidelity data for a new sail being modelled and the low-fidelity data provided by data from existing, but different, sail designs. The difference in fidelity is not due to the simulation method used to obtain the data, but instead how similar the sail’s geometry is to the new sail design. An important consideration for the construction of these models is the choice of low-fidelity data points, which provide information about the trend of the model curve between the high-fidelity data. A method is required to select the best existing sail design to use for the low-fidelity data when constructing a multi-fidelity model. The suitability of an existing sail design as a low fidelity model could be evaluated based on the similarity of its geometric parameters with the new sail. It is shown here that for upwind jib sails, the similarity of the broadseam between the two sails best indicates the ability of a design to be used as low-fidelity data for a lift coefficient surrogate model. The lift coefficient surrogate model error predicted by the regression is shown to be close to 1% of the lift coefficient surrogate error for most points. Larger discrepancies are observed for a drag coefficient surrogate error regression.

Medical Decision Making for Cardiac MRI with CFD “Detection of Severe Stenosis Using a 5D Model of the Descending Aorta”

The aim of this study is to develop a reliable 5D (x, y, z, time, flow dimension) model for medical decision making. Sophisticated techniques for the assessment of serious stenosis were developed using time-dependent instantaneous pressure gradients through the aorta (flow rate, Reynolds number, velocity, etc.). A 74 cardiac MRI scan and 3057 scans were performed on a 10-year-old patient with congenital valve and valvular aortic stenosis on sensitive MRI and coarctation (operated and then dilated) in the sense of shone syndrome. The occlusion rate was estimated to be 80.5%. The stenosis area was approximately 15 mm long and 10 mm high. The fluid solver (NS) exhibited a significant shear stress of −3.735 × 10−5 Pa within the first 10 iterations. There was a significant drop in the flux mass of −0.0050 (kg/s), as well as high blood turbulence in vortex field lines and low geometry Reynolds cells. The fifth dimension was used for negative velocity prediction (−81.4 cm/s). The discoveries of the 5D aortic simulation are convincing based on the evaluation of its physical and biomedical features.

ANALYSIS OF YACHT PERFORMANCE UNDER THE IOR RULE: THE RELATIONSHIP BETWEEN GEOMETRY AND SPEED POTENTIAL

The International Offshore rule (IOR) provided a handicapping system for racing yacht between 1972 and 1994. During this period great advances in both the materials used in construction and designs specifically to the rule, were made. The popular press discussed, at great length, how loopholes in the rules were exploited to gain a favourable rating. This led to the perception that the exploitation of geometric measurements was leading to boats with poor performance characteristics. This paper aims to address this, firstly through an analysis of the geometric parameters and their evolution through the early part of the IOR era. The paper concludes by undertaking a velocity prediction analysis of a series of boats, a technique that was in its infancy at the time these yachts were designed. The analyses show that the geometric parameters did evolve with time, but not necessarily in line with the understanding behind good performance. Penalties in the rule dictated the direction of design. However, the performance analysis did show that judging yachts based on rated characteristics could lead to misinterpretation, but in general, the performance data aligned reasonably well with assumed performance in specific sailing conditions. The velocity prediction analysis also concluded that the performance of yachts between 1972 and 1981 did increase regardless of the geometric form being dictated by the rules.

Individualised Ball Speed Prediction in Baseball Pitching Based on IMU Data

Ball velocity is considered an important performance measure in baseball pitching. Proper pitching mechanics play an important role in both maximising ball velocity and injury-free participation of baseball pitchers. However, an individual pitcher’s characteristics display individuality and may contribute to velocity imparted to the ball. The aim of this study is to predict ball velocity in baseball pitching, such that prediction is tailored to the individual pitcher, and to investigate the added value of the individuality to predictive performance. Twenty-five youth baseball pitchers, members of a national youth baseball team and six baseball academies in The Netherlands, performed ten baseball pitches with maximal effort. The angular velocity of pelvis and trunk were measured with IMU sensors placed on pelvis and sternum, while the ball velocity was measured with a radar gun. We develop three Bayesian regression models with different predictors which were subsequently evaluated based on predictive performance. We found that pitcher’s height adds value to ball velocity prediction based on body segment rotation. The developed method provides a feasible and affordable method for ball velocity prediction in baseball pitching.

Research on prediction method of S-wave velocity based on deep learning

Aiming at the s-wave velocity prediction problem, based on the analysis of the advantages and disadvantages of the empirical formula method and the rock physics modeling method, combined with the s-wave velocity prediction principle, the deep learning method is introduced, and a deep learning-based logging s-wave velocity prediction method is proposed. This method uses a deep neural network algorithm to establish a nonlinear mapping relationship between reservoir parameters (acoustic time difference, density, neutron porosity, shale content, porosity) and s-wave velocity, and then applies it to the s-wave velocity prediction at the well point. Starting from the relationship between p-wave and s-wave velocity, the study explained the feasibility of applying deep learning technology to s-wave prediction and the principle of sample selection, and finally established a reliable s-wave prediction model. The model was applied to s-wave velocity prediction in different research areas, and the results show that the s-wave velocity prediction technology based on deep learning can effectively improve the accuracy and efficiency of s-wave velocity prediction, and has the characteristics of a wide range of applications. It can provide reliable s-wave data for pre-stack AVO analysis and pre-stack inversion, so it has high practical application value and certain promotion significance.