Application of Smoothed Particle Hydrodynamics (SPH) in Nearshore Mixing: A Comparison to Laboratory Data

A weakly compressible smoothed particle hydrodynamics (WCSPH) method is used to simulate the nearshore flow hydrodynamics. The wave induced dispersion and diffusion are determined for monochromatic waves with significant wave height of 0.12 m and the wave period of 1.2 sec (Sop=5%) based on WCSPH wave dynamics. The hydrodynamics of WCSPH model are compared to the laboratory results obtained from series of LDA measurements. The overall mixing coefficients across the nearshore are determined from WCSPH hydrodynamics. The mixing coefficients obtained are compared with the values determined from a series of fluorometric studies performed in a large-scale facility in DHI, Denmark. The results show that the wave profiles are in good agreement with the experimental data. The WCSPH model is proven to be well capable of estimating the dispersion across the nearshore

SOLUTE DISPERSION IN THE NEARSHORE DUE TO OBLIQUE WAVES

An experimental study has been conducted in a large scale basin at Danish Hydraulic Institute (DHI). Simultaneous measurements of hydrodynamics using Laser Doppler Anemometry (LDA) and fluorescent tracer studies were undertaken within the surfzone under a regular wave condition with waves approaching the shore at 20. Through a series of hydrodynamic and tracer measurements and their comparison with the existing theoretical values, this study quantifies the physical processes and their integrated effects on a solute tracer in the nearshore zone subject to combined waves and the induced longshore currents. A theoretical dispersion model has been developed, adopting both experimental and theoretical velocimetry approaches. The results of theoretical model have been compared to the tracer data. Using the results from this study together with all known previous studies of dispersion measurements within the surfzone, good agreement exists

Sliding mode observer design for decentralized multi-phase flow estimation

Robust flow measurement in multi-phase systems has extensive applications in understanding, design, and operation of complex environmental, energy and industrial processes. The nonlinearity and spatiotemporal variability of the interactions between different flow phases makes the multi-phase flow measurement a challenging task. Two Sliding Mode Observer (SMO) schemes are proposed in this study for the state estimation of a decentralized multi-phase flow measurement system. The developed observers are shown to be theoretically valid and numerically applicable for a real-life case study data. The multi-phase flow system considered in this paper can be described as two interconnected sub-systems including fluid and gas sub-systems, and two scenarios are considered in the design of the observers. The first scenario considers the interconnections as bounded disturbance (SMOD), while the second scenario considers the interconnections as an uncertainty (SMOU). Hence, the Sliding Mode Observers are adopted to mitigate the effects of disturbance in the system and uncertainties in the parameters. Numerical simulations are conducted using MATLAB and dynamic HYSYS simultaneously, using the data obtained from field-based multi-phase flow measurements. The results demonstrate the appropriateness and robustness of the proposed Sliding Mode Observer (SMO) for estimation of the multi-phase fluid specifications including the density, velocity, and the volume phases fraction in each subsystem. The analysis of the results highlights that the proposed model is computationally efficient with fast transient response, accurate tracking capability of the real process data, and very low steady-state error. This study shows that choosing an appropriate Lyapunov-Krasovsky function results in the asymptotic stability of the decentralized system and improves the performance of the proposed observers. Uncertainty analysis is conducted on the velocity estimation results obtained from the Sliding Observers. Overall, SMOU method shown better performance with RMSE of 0.24%, while RMSE of 0.46% was achieved for the SMOD. Comparison of the numerical results with the field-based flow measurement, as a benchmark, shows that although uncertainty in SMOU is approximately half of the uncertainty in SMOD, state estimation for both schemes was achieved in a finite time with high order of precision. It was shown that both observers developed in this study are well capable of estimating the multi-phase flow variables and states

Large eddy simulation of turbidity currents in a narrow channel with different obstacle configurations

© 2020, The Author(s). Turbidity currents are frequently observed in natural and man-made environments, with the potential of adversely impacting the performance and functionality of hydraulic structures through sedimentation and reduction in storage capacity and an increased erosion. Construction of obstacles upstream of hydraulic structures is a common method of tackling adverse effects of turbidity currents. This paper numerically investigates the impacts of obstacle’s height and geometrical shape on the settling of sediments and hydrodynamics of turbidity currents in a narrow channel. A robust numerical model based on LES method was developed and successfully validated against physical modelling measurements. This study modelled the effects of discretization of particles size distribution on sediment deposition and propagation in the channel. Two obstacles geometry including rectangle and triangle were studied with varying heights of 0.06, 0.10 and 0.15 m. The results show that increasing the obstacle height will reduce the magnitude of dense current velocity and sediment transport in narrow channels. It was also observed that the rectangular obstacles have more pronounced effects on obstructing the flow of turbidity current, leading to an increase in the sediment deposition and mitigating the impacts of turbidity currents

Large eddy simulation of turbidity currents in a narrow channel with different obstacle configurations

Turbidity currents are frequently observed in natural and man-made environments, with the potential of adversely impacting the performance and functionality of hydraulic structures through sedimentation and reduction in storage capacity and an increased erosion. Construction of obstacles upstream of hydraulic structures is a common method of tackling adverse effects of turbidity currents. This paper numerically investigates the impacts of obstacle’s height and geometrical shape on the settling of sediments and hydrodynamics of turbidity currents in a narrow channel. A robust numerical model based on LES method was developed and successfully validated against physical modelling measurements. This study modelled the effects of discretization of particles size distribution on sediment deposition and propagation in the channel. Two obstacles geometry including rectangle and triangle were studied with varying heights of 0.06, 0.10 and 0.15 m. The results show that increasing the obstacle height will reduce the magnitude of dense current velocity and sediment transport in narrow channels. It was also observed that the rectangular obstacles have more pronounced effects on obstructing the flow of turbidity current, leading to an increase in the sediment deposition and mitigating the impacts of turbidity currents

Dynamics of sediment transport and erosion-deposition patterns in the locality of a detached low-crested breakwater on a cohesive coast

Understanding the dynamics of sediment transport and erosion-deposition patterns in the locality of a coastal structure is vital to evaluating the performance of coastal structures and predicting the changes in coastal dynamics caused by a specific structure. The nearshore hydro-morphodynamic responses to coastal structures vary widely, as these responses are complex functions with numerous parameters, including structural design, sediment and wave dynamics, angle of approach, slope of the coast and the materials making up the beach and structures. This study investigated the sediment transport and erosion-deposition patterns in the locality of a detached low-crested breakwater protecting the cohesive shore of Carey Island, Malaysia. The data used for this study were collected from field measurements and secondary sources from 2014 to 2015. Sea-bed elevations were monitored every two months starting from December 2014 to October 2015, in order to quantify the sea-bed changes and investigate the erosion-deposition patterns of the cohesive sediment due to the existence of the breakwater. In addition, numerical modelling was also performed to understand the impacts of the breakwater on the nearshore hydrodynamics and investigate the dynamics of fine sediment transport around the breakwater structure. A coupled two-dimensional hydrodynamics-sediment transport model based on Reynolds averaged Navier-Stokes (RANS) equations and cell-centered finite volume method with flexible meshing approach was adopted for this study. Analysis of the results showed that the detached breakwater reduced both current speed and wave height behind the structure by an average of 0.12 m/s and 0.1 m, respectively. Also, the breakwater made it possible for trapped suspended sediment to settle in a sheltered area by approximately 8 cm in height near to the first main segment of the breakwater, from 1 year after its construction. The numerical results were in line with the field measurements, where sediment accumulations were concentrated in the landward area behind the breakwater. In particular, sediment accumulations were concentrated along the main segments of the breakwater structure during the Northeast (NE) season, while concentration near the first main segment of the breakwater were recorded during the Southwest (SW) season. The assessment illustrated that the depositional patterns were influenced strongly by the variations in seasonal hydrodynamic conditions, sediment type, sediment supply and the structural design. Detached breakwaters are rarely considered for cohesive shores; hence, this study provides new, significant benefits for engineers, scientists and coastal management authorities with regard to seasonal dynamic changes affected by a detached breakwater and its performance on a cohesive coast

Modelling solute transport in water disinfection systems : effects of temperature gradient on the hydraulic and disinfection efficiency of serpentine chlorine contact tanks

Chlo­rine resid­ual plays a key role in de­ter­min­ing the qual­ity of treated wa­ter and waste­water. One of the most crit­i­cal fac­tors af­fect­ing chlo­rine de­cay rates is flow and am­bi­ent tem­per­a­ture. De­tailed knowl­edge of tem­per­a­ture im­pacts on the ef­fi­ciency and per­for­mance of chlo­rine con­tact tanks will en­able op­ti­mum de­sign and op­er­a­tion of wa­ter and waste­water treat­ment in­fra­struc­tures. This pa­per de­vel­ops a ro­bust and com­pu­ta­tion­ally ef­fi­cient three-di­men­sional nu­mer­i­cal sim­u­la­tion model us­ing Reynolds-av­er­aged Navier-Stokes equa­tions (RANS) with tur­bu­lence clo­sure model. A non-re­ac­tive tracer trans­port model is de­vel­oped by im­ple­ment­ing three-di­men­sional ad­vec­tion-dif­fu­sion equa­tion. The Chlo­rine de­cay processes are sim­u­lated us­ing Reynolds-av­er­aged species trans­port model. Tem­per­a­ture ef­fects on den­sity and vis­cos­ity is sim­u­lated through Millero and, Pois­son and Vo­gel equa­tions, re­spec­tively. Eight sce­nar­ios with vari­a­tion in in­flow and am­bi­ent tem­per­a­ture are sim­u­lated in this study. The res­i­dence time dis­tri­b­u­tion (RTD) and hy­draulic ef­fi­ciency in­dexes are de­ter­mined for the sim­u­la­tion sce­nar­ios. It is shown that small fluc­tu­a­tion in in­flow and am­bi­ent tem­per­a­ture cause a sig­nif­i­cant change in chlo­rine con­cen­tra­tion and per­for­mance of dis­in­fec­tion tank. The analy­sis of nu­mer­i­cal sim­u­la­tions in­di­cated that in­crease in am­bi­ent and in­flow tem­per­a­ture can in­crease chlo­rine de­cay by up to 75 %, lead­ing to un­de­sir­able dis­in­fec­tion con­se­quences and dis­rup­tion of wa­ter treat­ment processes. The nu­mer­i­cal model de­vel­oped within this study was suc­cess­fully val­i­dated against ex­per­i­men­tal mea­sure­ments and it is shown to be ro­bust and ef­fi­cient tool to de­ter­mine op­ti­mum in­flow and am­bi­ent tem­per­a­ture con­fig­u­ra­tions for high-ef­fi­ciency wa­ter treat­ment processes and to pre­vent mi­croor­gan­ism resid­ual and by-prod­ucts dis­in­fec­tion for­ma­tion. The com­pu­ta­tional frame­work pre­sented in this study can in­form op­ti­mum de­sign of wa­ter and waste­water treat­ment processes

Forecasting global climate drivers using Gaussian processes and convolutional autoencoders

Machine learning (ML) methods have become an important tool for modelling and forecasting complex high-dimensional spatiotemporal datasets such as those found in environmental and climate modelling applications. ML approaches can offer a fast, low-cost alternative to short-term forecasting than expensive numerical simulation while addressing a significant outstanding limitation of numerical modelling by being able to robustly and dynamically quantify predictive uncertainty. Low-cost and near-instantaneous forecasting of high-level climate variables has clear applications in early warning systems, nowcasting, and parameterising small-scale locally relevant simulations. This paper presents a novel approach for multi-task spatiotemporal regression by combining data-driven autoencoders with Gaussian Processes (GP) to produce a probabilistic tensor-based regression model. The proposed method is demonstrated for forecasting one-step-ahead temperature and pressure on a global scale simultaneously. By conducting probabilistic regression in the learned latent space, samples can be propagated back to the original feature space to produce uncertainty estimates at a vastly reduced computational cost. The composite GP-autoencoder model was able to simultaneously forecast global temperature and pressure values with average errors of 3.82 °C and 638 hPa, respectively. Further, on average the true values were within the proposed posterior distribution 95.6% of the time illustrating that the model produces a well-calibrated predictive posterior distribution

Physics-informed neural networks for statistical emulation of hydrodynamical numerical models

The application of numerical models for flood and inundation modelling has become widespread in the past decades as a result of significant improvements in computational capabilities. Computational approaches to flood forecasting have significant benefits compared to empirical approaches which estimate statistical patterns of hydrological variables from observed data. However, there is still a significant computational cost associated with numerical flood modelling at high spatio-temporal resolutions. This limitation of numerical modelling has led to the development of statistical emulator models, machine learning (ML) models designed to learn the underlying generating process of the numerical model. The data-driven approach to ML involves relying entirely upon a set of training data to inform decisions about model selection and parameterisations. Deep learning models have leveraged data-driven learning methods with improvements in hardware and an increasing abundance of data to obtain breakthroughs in various fields such as computer vision, natural language processing and autonomous driving. In many scientific and engineering problems however, the cost of obtaining data is high and so there is a need for ML models that are able to generalise in the ‘small-data’ regime common to many complex problems. In this study, to overcome extrapolation and over-fitting issues of data-driven emulators, a Physics-Informed Neural Network model is adopted for the emulation of all two-dimensional hydrodynamic models which model fluid according the shallow water equations. This study introduces a novel approach to encoding the conservation of mass into a deep learning model, with additional terms included in the optimisation criterion, acting to regularise the model, avoid over-fitting and produce more physically consistent predictions by the emulator