End-point prediction of basic oxygen furnace (BOF) steelmaking based on improved twin support vector regression

In this paper, a novel prediction method for low carbon steel is proposed based on an improved twin support vector regression algorithm. 300 qualified samples are collected by the sublance measurements from the real plant. The simulation results show that the prediction models can achieve a hit rate of 96 % for carbon content within the error bound of 0,005 % and 94 % for temperature within the error bound of 15 °C. The double hit rate reaches to 90 %. It indicates that the proposed method can provide a significant reference for real BOF applications, and also it can be extended to the prediction of other metallurgical industries

Reduction of Nonlinear Intersubcarrier Intermixing in Coherent Optical OFDM by a Fast Newton-Based Support Vector Machine Nonlinear Equalizer

A fast Newton-based support vector machine (N-SVM) nonlinear equalizer (NLE) is experimentally demonstrated, for the first time, in 40 Gb/s 16-quadrature amplitude modulated coherent optical orthogonal frequency division multiplexing at 2000 km of transmission. It is shown that N-SVM-NLE extends the optimum launched optical power by 2 dB compared to the benchmark Volterra-based NLE. The performance improvement by N-SVM is due to its ability of tackling both deterministic fiber-induced nonlinear effects and the interaction between nonlinearities and stochastic noises (e.g., polarization-mode dispersion). An N-SVM is more tolerant to intersubcarrier nonlinear crosstalk effects than Volterra-based NLE, especially when applied across all subcarriers simultaneously. In contrast to the conventional SVM, the proposed algorithm is of reduced classifier complexity offering lower computational load and execution time. For a low C-parameter of 4 (a penalty parameter related to complexity), an execution time of 1.6 s is required for N-SVM to effectively mitigate nonlinearities. Compared to conventional SVM, the computational load of N-SVM is ∼6 times lower

Improvement of source and wind field input of atmospheric dispersion model by assimilation of concentration measurements: Method and applications in idealized settings

AbstractThe problem of correcting the pollutant source emission rate and the wind velocity field inputs in a puff atmospheric dispersion model by data assimilation of concentration measurements has been considered. Variational approach to data assimilation has been used, in which the specified cost function is minimized with respect to source strength and/or wind field. The analyzed wind field satisfied the constraints derived from the conditions of mass conservation and linearized flow equations for perturbations from the first guess wind field. ‘Identical twin’ numerical experiments have been performed for the validation of the method. The first guess estimation errors of source emission rate and wind field were set to a factor of up to 10 and up to 6m/s respectively. The calculations results showed that in most studied cases an improvement of vector wind difference (VWD) error by about 0.7–1m/s could be achieved. The resulting normalized mean square error (NMSE) of concentration field was also reduced significantly

Smart Substation Network Fault Classification Based on a Hybrid Optimization Algorithm

Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods

Inverse problems in thermoacoustics

Thermoacoustics is a branch of fluid mechanics, and is as such governed by the conservation laws of mass, momentum, energy and species. While computational fluid dynamics (CFD) has entered the design process of many applications in fluid mechanics, its success in thermoacoustics is limited by the multi-scale, multi-physics nature of the subject. In his influential monograph from 2006, Prof. Fred Culick writes about the role of CFD in thermoacoustic modeling: The main reason that CFD has otherwise been relatively helpless in this subject is that problems of combustion instabilities involve physical and chemical matters that are still not well understood. Moreover, they exist in practical circumstances which are not readily approximated by models suitable to formulation within CFD. Hence, the methods discussed and developed in this book will likely be useful for a long time to come, in both research and practice. [. . . ] It seems to me that eventually the most effective ways of formulating predictions and theoretical interpretations of combustion instabilities in practice will rest on combining methods of the sort discussed in this book with computational fluid dynamics, the whole confirmed by experimental results. Despite advances in CFD and large-eddy simulation (LES) in particular, unsteady simulations for more than a few selected operating points are computationally infeasible. The ‘methods discussed in this book’ refer to reduced-order models of thermoacoustic oscillations. Whether intentional or not, the last sentence anticipates the advent of data-driven methods, and encapsulates the philosophy behind this work. This work brings together two workhorses of the design process: physics-informed reduced-order models and data from higher-fidelity sources such as simulations and experiments. The three building blocks to all our statistical inference frameworks are: (i) a hierarchical view of reduced-order models consisting of states, parameters and governing equations; (ii) probabilistic formulations with random variables and stochastic processes; and (iii) efficient algorithms from statistical learning theory and machine learning. While leveraging advances in statistical and machine learning, we demonstrate the feasibility of Bayes’ rule as a first principle in physics-informed statistical inference. In particular, we discuss two types of inverse problems in thermoacoustics: (i) implicit reduced-order models representative of nonlinear eigenproblems from linear stability analysis; and (ii) time-dependent reduced-order models used to investigate nonlinear dynamics. The outcomes of statistical inference are improved predictions of the state, estimates of the parameters with uncertainty quantification and an assessment of the reduced-order model itself. This work highlights the role that data can play in the future of combustion modeling for thermoacoustics. It is increasingly impractical to store data, particularly as experiments become automated and numerical simulations become more detailed. Rather than store the data itself, the techniques in this work optimally assimilate the data into the parameters of a physics-informed reduced-order model. With data-driven reduced-order models, rapid prototyping of combustion systems can feed into rapid calibration of their reduced-order models and then into gradient-based design optimization. While it has been shown, e.g. in the context of ignition and extinction, that large-eddy simulations become quantitatively predictive when augmented with data, the reduced-order modeling of flame dynamics in turbulent flows remains challenging. For these challenging situations, this work opens up new possibilities for the development of reduced-order models that adaptively change any time that data from experiments or simulations becomes available.Schlumberger Cambridge International Scholarshi

Parameterschätzung für marine Ökosystemmodelle in 3-D

The aim of this work is to provide a computational-science-based foundation for the parameter identification of marine ecosystem models. For this purpose a general programming interface is introduced to enable a flexible coupling of marine ecosystems to fluid dynamics on source code level. This interface fits into the biogeochemical model structure as well as into an optimization context. Moreover, a parallel simulation and solver software is implemented that combines the introduced interface with an efficient, transport-matrix-based simulation. The software is founded on a free and portable programming library. It is written from scratch, basically validated and exemplary used for a derivative-based optimization experiment. Part of the software additionally provides a basis for the numerical experiments carried out subsequently. They address an approach used for the computation of sensitivities with respect to model parameters and an alternative optimization approach that does not require model evaluations, respectively. In addition, results are included in this work that has been achieved in collaboration with other authors. The first joint work is about porting the software to graphic processing units, the second is about its usage for surrogate-based optimization.Das Ziel der vorliegenden Arbeit ist es, eine informationstechnische Grundlage für die Parameteridentifikation bei marinen Ökosystemmodellen zu schaffen. Dazu wird eine Programmierschnittstelle vorgestellt, die es allgemein ermöglicht, marine Ökosysteme auf Quelltextebene an Strömungsmodelle zu koppeln. Diese Schnittstelle fügt sich sowohl in die biogeochemische Modellstruktur als auch in den Optimierungskontext ein. Des weiteren wird eine parallele Simulations- und Lösungssoftware implementiert, die eine effiziente, transportmatrixbasierte Strömungssimulation und die vorgestellte Schnittstelle miteinander kombiniert. Die Software wird auf der Grundlage einer freien und portablen Bibliothek neu erstellt, grundsätzlich validiert und exemplarisch für ein ableitungsbasiertes Optimierungsverfahren eingesetzt. Ein Teil der Software bildet zusätzlich eine Basis für die im weiteren Verlauf der Arbeit durchgeführten numerischen Experimente. Dabei handelt es sich um einen Ansatz zur Berechnung von Sensitivitäten bezüglich der Modellparameter, beziehungsweise um einen alternativen Optimierungsansatz, der ohne Modellauswertung auskommt. Zusätzlich werden Resultate in die Arbeit aufgenommen, die in Zusammenarbeit mit anderen Autoren erzielt wurden. Es handelt sich dabei um die Portierung der Software auf Grafikkarten und um deren Einsatz im Bereich der surrogat-basierten Optimierung