Alloy Design for a Fusion Power Plant

Fusion power is generated when hot deuterium and tritium nuclei react, producing alpha particles and 14 MeV neutrons. These neutrons escape the reaction plasma and are absorbed by the surrounding material structure of the plant, transferring the heat of the reaction to an external cooling circuit. In such high-energy neutron irradiation environments, extensive atomic displacement damage and transmutation production of helium affect the mechanical properties of materials. Among these effects are irradiation hardening, embrittlement, and macroscopic swelling due to the formation of voids within the material. To aid understanding of these effects, Bayesian neural networks were used to model irradiation hardening and embrittlement of a set of candidate alloys, reduced-activation ferritic-martensitic steels. The models have been compared to other methods, and it is demonstrated that a neural network approach to modelling the properties of irradiated steels provides a useful tool in the future engineering of fusion materials, and for the first time, predictions are made on irradiated property changes based on the full range of available experimental parameters rather than a simplified model. In addition, the models are used to calculate optimised compositions for potential fusion alloys. Recommendations on the most fruitful ways of designing future experiments have also been made. In addition, a classical nucleation theory approach was taken to modelling the incubation and nucleation of irradiation-induced voids in these steels, with a view to minimising this undesirable phenomenon in candidate materials. Using these models, recommendations are made with regards to the engineering of future reduced-activation steels for fusion applications, and further research opportunities presented by the work are reviewed

An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips

A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved the least mean squared error of 0.0223 with the misclassification rate of 7.435% for the 10 simulated trip prediction. The proposed method can identify abnormality of operational parameters at the confident level of ±6.3%

Numerical modeling and optimization of waterjet based surface decontamination

The mission of this study is to investigate the high-pressure waterjet based surface decontamination. Our specific objective is to develop a practical procedure for selection of process conditions at given constraints and available knowledge. This investigation is expected to improve information processing in the course of material decontamination and assist in the implementation of the waterjet decontamination technology into practice. The development of a realistic procedure for processing of a chaotic and non-accurate information constitutes the main accomplishment of this study. The research involved acquisition of representative information about removal of brittle, elastic and viscous deposits. As a result an extended database representing jet based decoating has been compiled and feasibility of the damage free decontamination of various surfaces including highly sensitive ones is demonstrated. Artificial Intelligence techniques (Fuzzy Logic, Artificial Neural Networks, Genetic Computing) have been applied for processing of the acquired information and a realistic procedure of such an application has been developed and demonstrated. This procedure enables us to integrate available information about surface in question and existing numerical models. The developed procedure allows a user to incorporate both qualitative (linguistic) and quantitative (crisp) information into a process model and to predict operational conditions for treatment of an unknown surface using a readily detectable single experimental parameter that characterizes a deposit/substrata combination. The suggested technique is shown to perform reliably in the case of incomplete and chaotic information, where the traditional regression based methods fail. Numerical simulations of the two-phase flow inside a waterjet nozzle are conducted. Numerical solutions of the partial differential equations of the two-phase turbulent jet flow are obtained using FLUENT package. The numerical prediction of jet velocity profiles and the interface between the two phases (water - air) inside a nozzle are in good agreement with experimental data available in the literature. Thus the current problem setup and the results of simulations can be applied to improvement in the nozzle design. A realistic procedure for the design of the jet based surfaces decontamination developed, as a result of this study, is applied for optimization of the removal of the paint, rust, tar and rubber from the steel surface

Multi-scale modelling and optimisation of sustainable chemical processes

This dissertation explores the process modelling and optimisation of chemical processes under sustainability criteria. Resting on process systems engineering techniques combined with life cycle assessment (LCA), we present implementation strategies to improve flowsheet performance and reduce environmental impacts from early design stages. We first address the relevance of sustainability assessments in the sector and present process and environmental modelling techniques available. Under the observation that chemical processes are subject to market, technical, and environmental fluctuations, we next present an approach to account for these uncertainties. Process optimisation is then tackled by combining surrogate modelling, objective-reduction, and multi-criteria decision analysis tools. The framework proved the enhancement of the assessments by reducing the use of computational resources and allowing the ranking of optimal alternatives based on the concept of efficiency. We finally introduce a scheme to assess sustainable performance at a multi-scale level, from catalysis development to planet implications. This approach aims to provide insights about the role of catalysis and establish priorities for process development, while also introducing absolute sustainability metrics via the concept of ‘Planetary boundaries’. Ultimately, this allows a clear view of the impact that a process incurs in the current and future status of the Earth. The capabilities of the methods developed are tested in relevant applications that address challenges in the sector to attain sustainable performance. We present how concepts like circular economy, waste valorisation, and renewable raw materials can certainly bring benefits to the industry compared to their fossil-based alternatives. However, we also show that the development of new processes and technologies is very likely to shift environmental impacts from one category to another, concluding that cross-sectorial cooperation will become essential to meet sustainability targets, such as those determined by the Sustainable Development Goals.Open Acces

Closed-loop control of product properties in metal forming

Metal forming processes operate in conditions of uncertainty due to parameter variation and imperfect understanding. This uncertainty leads to a degradation of product properties from customer specifications, which can be reduced by the use of closed-loop control. A framework of analysis is presented for understanding closed-loop control in metal forming, allowing an assessment of current and future developments in actuators, sensors and models. This leads to a survey of current and emerging applications across a broad spectrum of metal forming processes, and a discussion of likely developments.Engineering and Physical Sciences Research Council (Grant ID: EP/K018108/1)This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.cirp.2016.06.00

Optimizing IC engine efficiency: A comprehensive review on biodiesel, nanofluid, and the role of artificial intelligence and machine learning

Transportation and power generation have historically relied upon Internal Combustion Engines (ICEs). However, because of environmental impact and inefficiency, considerable research has been devoted to improving their performance. Alternative fuels are necessary because of environmental concerns and the depletion of non-renewable fuel stocks. Biodiesel has the potential to reduce emissions and improve sustainability when compared to diesel fuel. Several researchers have examined using nanofluids to increase biodiesel performance in internal combustion engines. Due to their thermal and physical properties, nanoparticles in a host fluid improve engine combustion and efficiency. This comprehensive review examines three key areas for improving ICE efficiency: biodiesel as an alternative fuel, application of nanofluids, and artificial intelligence (AI)/machine learning (ML) integration. The integration of AI/ML in nanoparticle-infused biodiesel offers exciting possibilities for optimizing production processes, enhancing fuel properties, and improving engine performance. This article first discusses, the benefits of biodiesel concerning the environment and various difficulties associated with its usage. The review then explores the effects and characteristics of nanofluids in IC engines, aiming to know their impact on engine emissions and performance. After that, this review discusses the utilization of AI/ML techniques in enhancing the biodiesel-nanofluid combustion process. This article sheds light on the ongoing efforts to make ICE technology more environmentally friendly and energy-efficient by examining current research and emerging patterns in these fields. Finally, the review presents the challenges and future perspectives of the field, paving the way for future research and improvement

Technology 2002: the Third National Technology Transfer Conference and Exposition, Volume 1

The proceedings from the conference are presented. The topics covered include the following: computer technology, advanced manufacturing, materials science, biotechnology, and electronics

An Integrated Approach to Determine Phenomenological Equations in Metallic Systems

It is highly desirable to be able to make predictions of properties in metallic materials based upon the composition of the material and the microstructure. Unfortunately, the complexity of real, multi-component, multi-phase engineering alloys makes the provision of constituent-based (i.e., composition or microstructure) phenomenological equations extremely difficult. Due to these difficulties, qualitative predictions are frequently used to study the influence of microstructure or composition on the properties. Neural networks were used as a tool to get a quantitative model from a database. However, the developed model is not a phenomenological model. In this study, a new method based upon the integration of three separate modeling approaches, specifically artificial neural networks, genetic algorithms, and monte carlo was proposed. These three methods, when coupled in the manner described in this study, allows for the extraction of phenomenological equations with a concurrent analysis of uncertainty. This approach has been applied to a multi-component, multi-phase microstructure exhibiting phases with varying spatial and morphological distributions. Specifically, this approach has been applied to derive a phenomenological equation for the prediction of yield strength in a+b processed Ti-6-4. The equation is consistent with not only the current dataset but also, where available, the limited information regarding certain parameters such as intrinsic yield strength of pure hexagonal close-packed alpha titanium