The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.

Machine Learning for Fluid Mechanics

An efficient ghost-cell immersed boundary method (GCIBM) for simulating turbulent flows in complex geometries is presented. A boundary condition is enforced through a ghost cell method. The reconstruction procedure allows systematic development of numerical schemes for treating the immersed boundary while preserving the overall second-order accuracy of the base solver. Both Dirichlet and Neumann boundary conditions can be treated. The current ghost cell treatment is both suitable for staggered and non-staggered Cartesian grids. The accuracy of the current method is validated using flow past a circular cylinder and large eddy simulation of turbulent flow over a wavy surface. Numerical results are compared with experimental data and boundary-fitted grid results. The method is further extended to an existing ocean model (MITGCM) to simulate geophysical flow over a three-dimensional bump. The method is easily implemented as evidenced by our use of several existing codes.

A Ghost-Cell Immersed Boundary Method for Flow in Complex Geometry

In this chapter we give an overview of the field of iterative learning control (ILC). We begin with a detailed description of the ILC technique, followed by two illustrative examples that give a flavor of the nature of ILC algorithms and their performance. This is followed by a topical classification of some of the literature of ILC and a discussion of the connection between ILC and other common control paradigms, including conventional feedback control, optimal control, adaptive control, and intelligent control. Next, we give a summary of the major algorithms, results, and applications of ILC given in the literature. This discussion also considers some emerging research topics in ILC. As an example of some of the new directions in ILC theory, we present some of our recent results that show how ILC can be used to force a desired periodic motion in an initially non-repetitive process: a gas-metal arc welding system. The chapter concludes with summary comments on the past, present, and future of ILC.

Iterative Learning Control: An Expository Overview

Introduction. The development augmented reality devices allow physicians to incorporate data visualization into diagnostic and treatment procedures to improve work efficiency, safety, and cost and to enhance surgical training. However, the awareness of possibilities of augmented reality is generally low. This review evaluates whether augmented reality can presently improve the results of surgical procedures. Methods. We performed a review of available literature dating from 2010 to November 2016 by searching PubMed and Scopus using the terms “augmented reality” and “surgery.” Results. The initial search yielded 808 studies. After removing duplicates and including only journal articles, a total of 417 studies were identified. By reading of abstracts, 91 relevant studies were chosen to be included. 11 references were gathered by cross-referencing. A total of 102 studies were included in this review. Conclusions. The present literature suggest an increasing interest of surgeons regarding employing augmented reality into surgery leading to improved safety and efficacy of surgical procedures. Many studies showed that the performance of newly devised augmented reality systems is comparable to traditional techniques. However, several problems need to be addressed before augmented reality is implemented into the routine practice.

https://downloads.hindawi.com/journals/jhe/2017/4574172.pdf

Recent Development of Augmented Reality in Surgery: A Review

Computer-assisted oral and maxillofacial surgery (OMS) has been rapidly evolving since the last decade. State-of-the-art surgical navigation in OMS still suffers from bulky tracking sensors, troublesome image registration procedures, patient movement, loss of depth perception in visual guidance, and low navigation accuracy. We present an augmented reality navigation system with automatic marker-free image registration using 3-D image overlay and stereo tracking for dental surgery. A customized stereo camera is designed to track both the patient and instrument. Image registration is performed by patient tracking and real-time 3-D contour matching, without requiring any fiducial and reference markers. Real-time autostereoscopic 3-D imaging is implemented with the help of a consumer-level graphics processing unit. The resulting 3-D image of the patient's anatomy is overlaid on the surgical site by a half-silvered mirror using image registration and IP-camera registration to guide the surgeon by exposing hidden critical structures. The 3-D image of the surgical instrument is also overlaid over the real one for an augmented display. The 3-D images present both stereo and motion parallax from which depth perception can be obtained. Experiments were performed to evaluate various aspects of the system; the overall image overlay error of the proposed system was 0.71 mm.

Augmented Reality Navigation With Automatic Marker-Free Image Registration Using 3-D Image Overlay for Dental Surgery

Forward kinematics solution of Stewart platform using neural networks

The possibility of correlating the hardness to the tensile properties of a material has been investigated using Assab 760 steel, mild steel and API Std 5LX grade X60 pipeline steel that have been heat-treated for different times at various tempering temperatures and 6063-T1 aluminium that has been solution heat-treated. It is found that the strain hardening coefficient and the strength coefficient of all materials tested were linearly related to the hardness, irrespective of the type of hardness measurement used. Using these relationships, equations were defined to estimate the yield and ultimate tensile stresses of the materials. Good agreement between experimental results and estimated values was obtained for all materials studied. The feasibility of using the present findings in non-destructive field testing is discussed.

On the prediction of tensile properties from hardness tests

Some new understanding of a single-lens binocular stereovision system using a biprism (2F filter) is presented. One image captured by this system is divided into two halves and assumed to be one stereo image pair captured by two virtual cameras generated by the biprism. Hence, this system can also be called a virtual stereovision system. Two different approaches to understanding this system are introduced. One approach is based on a camera calibration technique and another is based on a geometrical analysis of ray sketching. Both approaches enable this system to perform depth recovery in a close range like a typical stereovision system. As the approach based on geometrical analysis requires no complex calibration, great implementation and calibration effort can be saved, in contrast with a normal stereovision system. This approach provides a way to build a binocular stereovision system with a simpler implementation but sufficient accuracy. A complete analysis of this system and the related calibration, depth recovery, and experimentation techniques are presented. Experimentation results are presented to prove the effectiveness of both approaches used to understand this system.

Virtual stereovision system: new understanding on single-lens stereovision using a biprism

Overlapping radiofrequency ablation (RFA) in large tumor treatment requires multiple insertions of electrodes, which often compromise its efficacy and predictability. Surgical robot is a promising candidate for the execution of multiple RFA in large tumor treatment in terms of accuracy and speed. In this paper, we share our experience of design and implementation of a novel robotic system specialized for overlapping RFA. It consists of two components: a robotic manipulator and an automatic ablation planning module. The manipulator architecture is designed to facilitate the kinematic requirement for the multiple overlapping ablation technique within the constraints of minimally invasive surgery. An efficient “Voxel Growing” algorithm is adopted to automatically produce the ablation points according to the tumor's profile. The feasibility of the proposed robotic system is demonstrated by extensive simulation- and experiment-based evaluation conducted on ex vivo porcine liver.

Kah Bin Lim

Papers

Forward kinematics solution of Stewart platform using neural networks

On the prediction of tensile properties from hardness tests

Virtual stereovision system: new understanding on single-lens stereovision using a biprism

A Robotic System for Overlapping Radiofrequency Ablation in Large Tumor Treatment

The correlation of acoustic emission with rate of corrosion