CAutoCSD-Evolutionary Search and Optimisation Enabled Computer Automated Control System Design

This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of “computer-aided control system design” (CACSD) to novel “computer-automated control system design” (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency domains. Such performance-prioritised unification is aimed at relieving practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-commitment to such schemes. With recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible, analytical and practical, and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, and meets multiple objectives in the design of an LTI controller for a non-minimum phase plant and offers a high-performance LTI controller network for a non-linear chemical process.

Acoustofluidic separation of cells andparticles

Acoustofluidics,the integration of acoustics and microfluidics,is a rapidly growing research field that is addressing challenges in biology,medicine,chemistry,engineering,and physics.In particular,acoustofluidic separation of biological targets from complex fluids has proven to be a powerful tool due to the label-free,biocompatible,and contact-free nature of the technology.By carefully designing and tuning the applied acoustic field,cells and other bioparticles can be isolated with high yield,purity,and biocompatibility.Recent advances in acoustofluidics,such as the development of automated,point-of-care devices for isolating sub-micron bioparticles,address many of the limitations of conventional separation tools.More importantly,advances in the research lab are quickly being adopted to solve clinical problems.In this review article,we discuss working principles of acoustofluidic separation,compare different approaches of acoustofluidic separation,and provide a synopsis of how it is being applied in both traditional applications,such as blood component separation,cell washing,and fluorescence activated cell sorting,as well as emerging applications,including circulating tumor cell and exosome isolation.

An acoustofluidic scanning nanoscope using enhanced image stacking and processing

Nanoscale optical resolution with a large field of view is a critical feature for many research and industry areas,such as semiconductor fabrication,biomedical imaging,and nanoscale material identification.Several scanning microscopes have been developed to resolve the inverse relationship between the resolution and field of view;however,those scanning microscopes still rely upon fluorescence labeling and complex optical systems.To overcome these limitations,we developed a dual-camera acoustofluidic nanoscope with a seamless image merging algorithm(alphablending process).This design allows us to precisely image both the sample and the microspheres simultaneously and accurately track the particle path and location.Therefore,the number of images required to capture the entire field of view(200×200μm)by using our acoustofluidic scanning nanoscope is reduced by 55-fold compared with previous designs.Moreover,the image quality is also greatly improved by applying an alpha-blending imaging technique,which is critical for accurately depicting and identifying nanoscale objects or processes.This dual-camera acoustofluidic nanoscope paves the way for enhanced nanoimaging with high resolution and a large field of view.

Size-Dependent Joule Heating of Gold Nanoparticles Using Capacitively Coupled Radiofrequency Fields

Capacitively coupled shortwave radiofrequency fields(13.56 MHz)resistively heat low concentrations(~1 ppm)of gold nanoparticles with a thermal power dissipation of~380 kW/g of gold.Smaller diameter gold nanoparticles(<50 nm)heat at nearly twice the rate of larger diameter gold nanoparticles(≥50 nm),which is attributed to the higher resistivity of smaller gold nanostructures.A Joule heating model has been developed to explain this phenomenon and provides critical insights into the rational design and engineering of nanoscale materials for noninvasive thermal therapy of cancer.

Predicting superhard materials via a machine learning informed evolutionary structure search

The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications.Herein,good agreement was found between experimental Vickers hardnesses,Hv,of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from:(i)first principles via AFLOW-AEL(AFLOW Automatic Elastic Library),and(ii)a machine learning(ML)model trained on materials within the AFLOW repository.Because H^(ML)_(v) values can be quickly estimated,they can be used in conjunction with an evolutionary search to predict stable,superhard materials.This methodology is implemented in the XTALOPT evolutionary algorithm.Each crystal is minimized to the nearest local minimum,and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter.Both the energy/enthalpy and H^(ML)_(v),Teter are employed to determine a structure’s fitness.This implementation is applied towards the carbon system,and 43 new superhard phases are found.A topological analysis reveals that phases estimated to be slightly harder than diamond contain a substantial fraction of diamond and/or lonsdaleite.

Time-accurate CFD conjugate analysis of transient measurements of the heat-transfer coefficient in a channel with pin fins

Heat-transfer coefficients(HTC)on surfaces exposed to convection environments are often measured by transient techniques such as thermochromic liquid crystal(TLC)or infrared thermography.In these techniques,the surface temperature is measured as a function of time,and that measurement is used with the exact solution for unsteady,zero-dimensional(0-D)or one-dimensional(1-D)heat conduction into a solid to calculate the local HTC.When using the 0-D or 1-D exact solutions,the transient techniques assume the HTC and the free-stream or bulk temperature characterizing the convection environment to be constants in addition to assuming the conduction into the solid to be 0-D or 1-D.In this study,computational fluid dynamics(CFD)conjugate analyses were performed to examine the errors that might be invoked by these assumptions for a problem,where the free-stream/bulk temperature and the heat-transfer coefficient vary appreciably along the surface and where conduction into the solid may not be 0-D or 1-D.The problem selected to assess these errors is flow and heat transfer in a channel lined with a staggered array of pin fins.This conjugate study uses three-dimensional(3-D)unsteady Reynolds-averaged Navier-Stokes(RANS)closed by the shear-stress transport(SST)turbulence model for the gas phase(wall functions not used)and the Fourier law for the solid phase.The errors in the transient techniques are assessed by comparing the HTC predicted by the time-accurate conjugate CFD with those predicted by the 0-D and 1-D exact solutions,where the surface temperatures needed by the exact solutions are taken from the time-accurate conjugate CFD solution.Results obtained show that the use of the 1-D exact solution for the semi-infinite wall to give reasonably accurate“transient”HTC(less than 5%〇relative error).Transient techniques that use the 0-D exact solution for the pin fins were found to produce large errors(up to 160%relative error)because the HTC varies appreciably about each pin fin.This study also showed that HTC measured by transient techniques could differ considerably from the HTC obtained under steady-state conditions with isothermal walls.

A discrete element exploration of V-shaped breakout failure mechanisms in underground opening

V-shaped breakouts,which may appear in underground opening during excavation,are the results of two different failure mechanisms:tensile spalling and shear fracturing.This study uses discrete elements in exploring the conditions that would lead to different breakout mechanisms under plane strain conditions.The test tunnel of the Mine-by Experiment in Lac du Bonnet granite batholith is adopted as the base problem.In order to carry out the study,some fundamental issues need to be addressed.First,an exponential softening bond that enables the incorporation of fracture energy is adopted.In order to obtain a reasonable ratio between the uniaxial compressive strength,rc,and the uniaxial tensile strength,rt,discrete disc particles are tied together to form an irregular shape clump as the basic discrete element.This effort is supported by a successful reproducing of test results from Lac du Bonnet granite in DEM modeling.The issue of sensitivity of discrete particle size on results is examined.The reduction of strength with increase in specimen size is also modeled.After the calibration work is completed,the Mine-by tunnel behavior is studied.Finally,this study shows that a reduction in rc/rt ratio,under the same setup,would cause the failure mechanism to transit from tensile spalling to shear fracturing in V-shaped breakouts.

A data-driven indirect method for nonlinear optimal control

Nonlinear optimal control problems are challenging to solve due to the prevalence of local minima that prevent convergence and/or optimality.This paper describes nearest-neighbors optimal control(NNOC),a data-driven framework for nonlinear optimal control using indirect methods.It determines initial guesses for new problems with the help of precomputed solutions to similar problems,retrieved using k-nearest neighbors.A sensitivity analysis technique is introduced to linearly approximate the variation of solutions between new and precomputed problems based on their variation of parameters.Experiments show that NNOC can obtain the global optimal solution orders of magnitude faster than standard random restart methods,and sensitivity analysis can further reduce the solving time almost by half.Examples are shown on optimal control problems in vehicle control and agile satellite reorientation demonstrating that global optima can be determined with more than 99%reliability within time at the order of 10-100 milliseconds.