Adaptive swarm optimisation assisted surrogate model for pipeline leak detection and characterisation.

Pipelines are often subject to leakage due to ageing, corrosion and weld defects. It is difficult to avoid pipeline leakage as the sources of leaks are diverse. Various pipeline leakage detection methods, including fibre optic, pressure point analysis and numerical modelling, have been proposed during the last decades. One major issue of these methods is distinguishing the leak signal without giving false alarms. Considering that the data obtained by these traditional methods are digital in nature, the machine learning model has been adopted to improve the accuracy of pipeline leakage detection. However, most of these methods rely on a large training dataset for accurate training models. It is difficult to obtain experimental data for accurate model training. Some of the reasons include the huge cost of an experimental setup for data collection to cover all possible scenarios, poor accessibility to the remote pipeline, and labour-intensive experiments. Moreover, datasets constructed from data acquired in laboratory or field tests are usually imbalanced, as leakage data samples are generated from artificial leaks. Computational fluid dynamics (CFD) offers the benefits of providing detailed and accurate pipeline leakage modelling, which may be difficult to obtain experimentally or with the aid of analytical approach. However, CFD simulation is typically time-consuming and computationally expensive, limiting its pertinence in real-time applications. In order to alleviate the high computational cost of CFD modelling, this study proposed a novel data sampling optimisation algorithm, called Adaptive Particle Swarm Optimisation Assisted Surrogate Model (PSOASM), to systematically select simulation scenarios for simulation in an adaptive and optimised manner. The algorithm was designed to place a new sample in a poorly sampled region or regions in parameter space of parametrised leakage scenarios, which the uniform sampling methods may easily miss. This was achieved using two criteria: population density of the training dataset and model prediction fitness value. The model prediction fitness value was used to enhance the global exploration capability of the surrogate model, while the population density of training data samples is beneficial to the local accuracy of the surrogate model. The proposed PSOASM was compared with four conventional sequential sampling approaches and tested on six commonly used benchmark functions in the literature. Different machine learning algorithms are explored with the developed model. The effect of the initial sample size on surrogate model performance was evaluated. Next, pipeline leakage detection analysis - with much emphasis on a multiphase flow system - was investigated in order to find the flow field parameters that provide pertinent indicators in pipeline leakage detection and characterisation. Plausible leak scenarios which may occur in the field were performed for the gas-liquid pipeline using a three-dimensional RANS CFD model. The perturbation of the pertinent flow field indicators for different leak scenarios is reported, which is expected to help in improving the understanding of multiphase flow behaviour induced by leaks. The results of the simulations were validated against the latest experimental and numerical data reported in the literature. The proposed surrogate model was later applied to pipeline leak detection and characterisation. The CFD modelling results showed that fluid flow parameters are pertinent indicators in pipeline leak detection. It was observed that upstream pipeline pressure could serve as a critical indicator for detecting leakage, even if the leak size is small. In contrast, the downstream flow rate is a dominant leakage indicator if the flow rate monitoring is chosen for leak detection. The results also reveal that when two leaks of different sizes co-occur in a single pipe, detecting the small leak becomes difficult if its size is below 25% of the large leak size. However, in the event of a double leak with equal dimensions, the leak closer to the pipe upstream is easier to detect. The results from all the analyses demonstrate the PSOASM algorithm's superiority over the well-known sequential sampling schemes employed for evaluation. The test results show that the PSOASM algorithm can be applied for pipeline leak detection with limited training datasets and provides a general framework for improving computational efficiency using adaptive surrogate modelling in various real-life applications

Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

Deep learning surrogate models have shown promise in solving partial differential equations (PDEs). Among them, the Fourier neural operator (FNO) achieves good accuracy, and is significantly faster compared to numerical solvers, on a variety of PDEs, such as fluid flows. However, the FNO uses the Fast Fourier transform (FFT), which is limited to rectangular domains with uniform grids. In this work, we propose a new framework, viz., geo-FNO, to solve PDEs on arbitrary geometries. Geo-FNO learns to deform the input (physical) domain, which may be irregular, into a latent space with a uniform grid. The FNO model with the FFT is applied in the latent space. The resulting geo-FNO model has both the computation efficiency of FFT and the flexibility of handling arbitrary geometries. Our geo-FNO is also flexible in terms of its input formats, viz., point clouds, meshes, and design parameters are all valid inputs. We consider a variety of PDEs such as the Elasticity, Plasticity, Euler's, and Navier-Stokes equations, and both forward modeling and inverse design problems. Geo-FNO is $10^5$ times faster than the standard numerical solvers and twice more accurate compared to direct interpolation on existing ML-based PDE solvers such as the standard FNO

Surrogate-based design optimisation tool for dual-phase fluid driving jet pump apparatus

A comparative study of four well established surrogate models used to predict the non-linear entrainment performance of a dual-phase fluid driving jet pump (JP) apparatus is performed. A JP design flow configuration comprising a dual-phase (air and water) flow driving a secondary gas-air flow, for which no one has ever provided a unique set of design solutions, is described. For the construction of the global approximations (GA), the response surface methodology (RSM), Kriging and the radial basis function artificial neural network (RBFANN), were primarily used. The stacked/ensemble models methodology was integrated in this study, to improve the predictive model results, thus providing accurate GA that facilitate the multi-variable non-linear response design optimisation. An error analysis of all four models along with a multiple model accuracy analysis of each case study were performed. The RSM, Kriging, RBFANN and stacked models formed part of the surrogate-based optimisation, having the entrainment ratio as the main objective function. Optimisation problems were solved by the interior-point algorithm and the genetic algorithm and incurred a hybrid formulation of both algorithms. A total of 60 optimisation problems were formulated and solved with all three approximation models. Results showed that the hybrid formulation having the level-2 ensemble Kriging model performed best, predicting the experimental performance results for all JP models within an error margin of less than 10 % in 90 % of the cases

Development of an Analysis and Design Optimization Framework for Marine Propellers

In this thesis, a framework for the analysis and design optimization of ship propellers is developed. This framework can be utilized as an efficient synthesis tool in order to determine the main geometric characteristics of the propeller but also to provide the designer with the capability to optimize the shape of the blade sections based on their specific criteria. A hybrid lifting-line method with lifting-surface corrections to account for the three-dimensional flow effects has been developed. The prediction of the correction factors is achieved using Artificial Neural Networks and Support Vector Regression. This approach results in increased approximation accuracy compared to existing methods and allows for extrapolation of the correction factor values. The effect of viscosity is implemented in the framework via the coupling of the lifting line method with the open-source RANSE solver OpenFOAM for the calculation of lift, drag and pressure distribution on the blade sections using a transition κ-ω SST turbulence model. Case studies of benchmark high-speed propulsors are utilized in order to validate the proposed framework for propeller operation in open-water conditions but also in a ship’s wake

Comparison of Deterministic and Bayesian Calibration of MFiX-PIC, Part 1: Settling Bed

Particle-in-Cell (PIC) approach for modeling dense granular flows has gained popularity in recent years due to its time to solution efficiency. The methodology is useful for modeling large-scale systems with a relatively lower computational cost. However, the method requires the definition of several empirical parameters whose effects are not well understood. A systematic approach to identify sensitivities and optimal settings of these parameters is required. Already, it is known that the choice of these parameters depends on a problem's flow regime. For instance, parameter values would be chosen differently for a settling bed or a fluidized bed. In this study, five different PIC model parameters were selected for calibration when applied to the case of particles settling in a dense medium. PIC implementation from the open-source software MFiX (MFiX-PIC) was used. This study extends the earlier work to assess the five model parameter settings using deterministic calibration by employing a statistical calibration methodology commonly referred as Bayesian calibration. Results from deterministic calibration are compared with Bayesian calibration, and up to 6.5 fold improvement in prediction accuracy is observed with the latter approach.Comment: 33 pages, 18 figures, git repository URL for some of the datasets: https://mfix.netl.doe.gov/gitlab/quality-assurance/PIC_calibration.gi

Optimisation of flow chemistry: tools and algorithms

The coupling of flow chemistry with automated laboratory equipment has become increasingly common and used to support the efficient manufacturing of chemicals. A variety of reactors and analytical techniques have been used in such configurations for investigating and optimising the processing conditions of different reactions. However, the integrated reactors used thus far have been constrained to single phase mixing, greatly limiting the scope of reactions for such studies. This thesis presents the development and integration of a millilitre-scale CSTR, the fReactor, that is able to process multiphase flows, thus broadening the range of reactions susceptible of being investigated in this way. Following a thorough review of the literature covering the uses of flow chemistry and lab-scale reactor technology, insights on the design of a temperature-controlled version of the fReactor with an accuracy of ±0.3 ºC capable of cutting waiting times 44% when compared to the previous reactor are given. A demonstration of its use is provided for which the product of a multiphasic reaction is analysed automatically under different reaction conditions according to a sampling plan. Metamodeling and cross-validation techniques are applied to these results, where single and multi-objective optimisations are carried out over the response surface models of different metrics to illustrate different trade-offs between them. The use of such techniques allowed reducing the error incurred by the common least squares polynomial fitting by over 12%. Additionally, a demonstration of the fReactor as a tool for synchrotron X-Ray Diffraction is also carried out by means of successfully assessing the change in polymorph caused by solvent switching, this being the first synchrotron experiment using this sort of device. The remainder of the thesis focuses on applying the same metamodeling and cross-validation techniques used previously, in the optimisation of the design of a miniaturised continuous oscillatory baffled reactor. However, rather than using these techniques with physical experimentation, they are used in conjunction with computational fluid dynamics. This reactor shows a better residence time distribution than its CSTR counterparts. Notably, the effect of the introduction of baffle offsetting in a plate design of the reactor is identified as a key parameter in giving a narrow residence time distribution and good mixing. Under this configuration it is possible to reduce the RTD variance by 45% and increase the mixing efficiency by 60% when compared to the best performing opposing baffles geometry