129 research outputs found
PYCSP3: Modeling Combinatorial Constrained Problems in Python
In this document, we introduce PYCSP, a Python library that allows us to
write models of combinatorial constrained problems in a simple and declarative
way. Currently, with PyCSP, you can write models of constraint satisfaction
and optimization problems. More specifically, you can build CSP (Constraint
Satisfaction Problem) and COP (Constraint Optimization Problem) models.
Importantly, there is a complete separation between modeling and solving
phases: you write a model, you compile it (while providing some data) in order
to generate an XCSP3 instance (file), and you solve that problem instance by
means of a constraint solver. In this document, you will find all that you need
to know about PYCSP, with more than 40 illustrative models
Fault tolerant architectures for integrated aircraft electronics systems, task 2
The architectural basis for an advanced fault tolerant on-board computer to succeed the current generation of fault tolerant computers is examined. The network error tolerant system architecture is studied with particular attention to intercluster configurations and communication protocols, and to refined reliability estimates. The diagnosis of faults, so that appropriate choices for reconfiguration can be made is discussed. The analysis relates particularly to the recognition of transient faults in a system with tasks at many levels of priority. The demand driven data-flow architecture, which appears to have possible application in fault tolerant systems is described and work investigating the feasibility of automatic generation of aircraft flight control programs from abstract specifications is reported
The intersection spectrum of Skolem sequences and its applications to lambda fold cyclic triple systems, together with the Supplement
A Skolem sequence of order n is a sequence S_n=(s_{1},s_{2},...,s_{2n}) of 2n
integers containing each of the integers 1,2,...,n exactly twice, such that two
occurrences of the integer j in {1,2,...,n} are separated by exactly j-1
integers. We prove that the necessary conditions are sufficient for existence
of two Skolem sequences of order n with 0,1,2,...,n-3 and n pairs in same
positions. Further, we apply this result to the fine structure of cyclic two,
three and four-fold triple systems, and also to the fine structure of
lambda-fold directed triple systems and lambda-fold Mendelsohn triple systems.
For a better understanding of the paper we added more details into a
"Supplement".Comment: The Supplement for the paper "The intersection spectrum of Skolem
sequences and its applications to lambda fold cyclic triple systems" is
available here. It comes right after the paper itsel
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Combining artificial intelligence and robotic system in chemical product/process design
Product design for formulations is an active and challenging area of research. The new challenges of a fast-paced market, products of increasing complexity, and practical translation of sustainability paradigms require re-examination the existing theoretical frameworks to include the advantages from business and research digitalization. This thesis is based on the hypotheses that (i) new products with desired properties can be discovered by using a robotic platform combined with an intelligent optimization algorithm, and (ii) we can the connect data-driven optimisation with physico-chemical knowledge generation, which will result in a suitable model for translation of product discovery to production, thus impacting on the process development steps towards industrial applications. This thesis focuses on two complex physicochemical systems as case studies, namely the oil-in-water shampoo system and sunscreen products.
Firstly, I report the coupling of a machine-learning classification algorithm with the Thompson-Sampling Efficient Multi-Optimization (TSEMO) for the simultaneous optimization of continuous and discrete outputs. The methodology was successfully applied to the design of a formulated liquid product of commercial interest for which no physical models are available. Experiments were carried out in a semi-automated fashion using robotic platforms triggered by the machine-learning algorithms. The proposed closed-loop optimization framework allowed to find suitable recipes meeting the customer-defined criteria within 15 working days, outperforming human intuition in the target performance of the formulations. The framework was then extended to co-optimization of both formulation and process conditions and ingredients selection.
Secondly, I report the methods for the identification of new physical knowledge in a complex system where a prior knowledge is insufficient. The application of feature engineering methods in sun cream protection prediction was discussed. It was found that the concentration of UVA and UVB filters are key features, together with product viscosity, which match with the experts’ domain knowledge in sun cream product design. It was also found that through the combination of feature engineering and machine learning, high-fidelity model could be constructed. Furthermore, a modified mixed-integer nonlinear programming (MINLP) formulation for symbolic regression method was proposed for identification of physical models from noisy experimental data. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variables.The methodology was proven to be successful in identifying the correct physical models describing the relationship between shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of chemical reactions.
The work of this thesis shows that machine learning methods, together with automated experimental system, can speed-up the R&D process of formulated product design as well as gain new physical knowledge of the complex systems
LIPIcs, Volume 258, SoCG 2023, Complete Volume
LIPIcs, Volume 258, SoCG 2023, Complete Volum
LIPIcs, Volume 248, ISAAC 2022, Complete Volume
LIPIcs, Volume 248, ISAAC 2022, Complete Volum
Mesoscale fluid simulation with the Lattice Boltzmann method
PhDThis thesis describes investigations of several complex fluid effects., including
hydrodynamic spinodal decomposition, viscous instability. and self-assembly of a
cubic surfactant phase, by simulating them with a lattice Boltzmann computational
model.
The introduction describes what is meant by the term "complex fluid", and why
such fluids are both important and difficult to understand. A key feature of complex
fluids is that their behaviour spans length and time scales. The lattice Boltzmann
method is presented as a modelling technique which sits at a "mesoscale" level
intermediate between coarse-grained and fine-grained detail, and which is therefore
ideal for modelling certain classes of complex fluids.
The following chapters describe simulations which have been performed using
this technique, in two and three dimensions. Chapter 2 presents an investigation
into the separation of a mixture of two fluids. This process is found to involve several
physical mechanisms at different stages. The simulated behaviour is found to be in
good agreement with existing theory, and a curious effect, due to multiple competing
mechanisms, is observed, in agreement with experiments and other simulations.
Chapter 3 describes an improvement to lattice Boltzmann models of Hele-Shaw
flow, along with simulations which quantitatively demonstrate improvements in both
accuracy and numerical stability. The Saffman-Taylor hydrodynamic instability is
demonstrated using this model.
Chapter 4 contains the details and results of the TeraGyroid experiment, which
involved extremely large-scale simulations to investigate the dynamical behaviour
of a self-assembling structure. The first finite- size-effect- free dynamical simulations
of such a system are presented. It is found that several different mechanisms are
responsible for the assembly; the existence of chiral domains is demonstrated, along
with an examination of domain growth during self-assembly.
Appendix A describes some aspects of the implementation of the lattice Boltzmann
codes used in this thesis; appendix B describes some of the Grid computing
techniques which were necessary for the simulations of chapter 4.
Chapter 5 summarises the work, and makes suggestions for further research and
improvement.Huntsman Corporation Queen Mary University Schlumberger Cambridge Researc
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