Showing papers by "Cranfield University published in 2018"

Carbon capture and storage (CCS): the way forward

Abstract: Carbon capture and storage (CCS) is broadly recognised as having the potential to play a key role in meeting climate change targets, delivering low carbon heat and power, decarbonising industry and, more recently, its ability to facilitate the net removal of CO2 from the atmosphere. However, despite this broad consensus and its technical maturity, CCS has not yet been deployed on a scale commensurate with the ambitions articulated a decade ago. Thus, in this paper we review the current state-of-the-art of CO2 capture, transport, utilisation and storage from a multi-scale perspective, moving from the global to molecular scales. In light of the COP21 commitments to limit warming to less than 2 °C, we extend the remit of this study to include the key negative emissions technologies (NETs) of bioenergy with CCS (BECCS), and direct air capture (DAC). Cognisant of the non-technical barriers to deploying CCS, we reflect on recent experience from the UK's CCS commercialisation programme and consider the commercial and political barriers to the large-scale deployment of CCS. In all areas, we focus on identifying and clearly articulating the key research challenges that could usefully be addressed in the coming decade.

Soil pH mediates the balance between stochastic and deterministic assembly of bacteria

Abstract: Little is known about the factors affecting the relative influences of stochastic and deterministic processes that govern the assembly of microbial communities in successional soils. Here, we conducted a meta-analysis of bacterial communities using six different successional soil datasets distributed across different regions. Different relationships between pH and successional age across these datasets allowed us to separate the influences of successional age (i.e., time) from soil pH. We found that extreme acidic or alkaline pH conditions lead to assembly of phylogenetically more clustered bacterial communities through deterministic processes, whereas pH conditions close to neutral lead to phylogenetically less clustered bacterial communities with more stochasticity. We suggest that the influence of pH, rather than successional age, is the main driving force in producing trends in phylogenetic assembly of bacteria, and that pH also influences the relative balance of stochastic and deterministic processes along successional soils. Given that pH had a much stronger association with community assembly than did successional age, we evaluated whether the inferred influence of pH was maintained when studying globally distributed samples collected without regard for successional age. This dataset confirmed the strong influence of pH, suggesting that the influence of soil pH on community assembly processes occurs globally. Extreme pH conditions likely exert more stringent limits on survival and fitness, imposing strong selective pressures through ecological and evolutionary time. Taken together, these findings suggest that the degree to which stochastic vs. deterministic processes shape soil bacterial community assembly is a consequence of soil pH rather than successional age.

A systematic review of augmented reality applications in maintenance

Abstract: Augmented Reality (AR) technologies for supporting maintenance operations have been an academic research topic for around 50 years now. In the last decade, major progresses have been made and the AR technology is getting closer to being implemented in industry. In this paper, the advantages and disadvantages of AR have been explored and quantified in terms of Key Performance Indicators (KPI) for industrial maintenance. Unfortunately, some technical issues still prevent AR from being suitable for industrial applications. This paper aims to show, through the results of a systematic literature review, the current state of the art of AR in maintenance and the most relevant technical limitations. The analysis included filtering from a large number of publications to 30 primary studies published between 1997 and 2017. The results indicate a high fragmentation among hardware, software and AR solutions which lead to a high complexity for selecting and developing AR systems. The results of the study show the areas where AR technology still lacks maturity. Future research directions are also proposed encompassing hardware, tracking and user-AR interaction in industrial maintenance is proposed.

Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey

Abstract: Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts’ predictions of the future development.

Skin-touch-actuated textile-based triboelectric nanogenerator with black phosphorus for durable biomechanical energy harvesting

Abstract: Textiles that are capable of harvesting biomechanical energy via triboelectric effects are of interest for self-powered wearable electronics. Fabrication of conformable and durable textiles with high triboelectric outputs remains challenging. Here we propose a washable skin-touch-actuated textile-based triboelectric nanogenerator for harvesting mechanical energy from both voluntary and involuntary body motions. Black phosphorus encapsulated with hydrophobic cellulose oleoyl ester nanoparticles serves as a synergetic electron-trapping coating, rendering a textile nanogenerator with long-term reliability and high triboelectricity regardless of various extreme deformations, severe washing, and extended environmental exposure. Considerably high output (~250–880 V, ~0.48–1.1 µA cm−2) can be attained upon touching by hand with a small force (~5 N) and low frequency (~4 Hz), which can power light-emitting diodes and a digital watch. This conformable all-textile-nanogenerator is incorporable onto cloths/skin to capture the low output of 60 V from subtle involuntary friction with skin, well suited for users’ motion or daily operations. Incorporation of triboelectric nanogenerators into textiles is attractive for self-powered wearable electronics. Here the authors employ black phosphorus with a hydrophobic coating in a durable, washable, and air permeable textile-based device that converts biomechanical motion into electricity.

Corrosion Science and Technology

Abstract: Overview of Corrosion and Protection Strategies Corrosion in Aqueous Media Thermal Oxidation Environmentally Sensitive Cracking Strategies for Corrosion Control Some Symbol Conventions and Equations Structures Participating in Corrosion Processes Origins and Characteristics of Structure The Structure of Water and Aqueous Solutions The Structures of Metal Oxides The Structures of Metals Thermodynamics and Kinetics of Corrosion Processes Thermodynamics of Aqueous Corrosion Kinetics of Aqueous Corrosion Thermodynamics and Kinetics of Dry Oxidation Mixed-Metal Systems Galvanic Stimulation Galvanic Protection The Intervention of Stress Environment-Sensitive Cracking Enhanced Corrosion in Flowing or Turbulent Aqueous Media Stress-Corrosion Cracking (SCC) Corrosion Fatigue Enhanced Corrosion in Flowing or Turbulent Aqueous Media Precautions against Stress-Induced Failures Protective Coatings Surface Preparation Electrodeposition Hot-Dip Coatings Conversion Coatings Paint Coatings for Metals Corrosion of Iron and Steels Iron and Steel Microstructures Rusting Oxidation of Iron and Steels Stainless Steels Phase Equilibria Commercial Stainless Steels Resistance to Aqueous Corrosion Resistance to Dry Oxidation Applications Applications of Cast Stainless Steels Corrosion Resistance of Aluminum and Aluminum Alloys Physical Metallurgy of Some Standard Alloys Corrosion Resistance Corrosion Resistance of Copper and Copper Alloys Chemical Properties and Corrosion Behavior of Pure Copper Constitutions and Corrosion Behavior of Copper Alloys Corrosion Resistance of Nickel and Its Alloys Chemical Properties and Corrosion Behavior of Pure Nickel Constitutions and Corrosion Behavior of Nickel Alloys Corrosion Resistance of Titanium and Its Alloys Chemical Characteristics and Corrosion Behavior of Pure Titanium Constitutions and Corrosion Behavior of Titanium Alloys Applications Cathodic Protection Principles Buried Pipelines and Distribution Systems Cathodic Protection in Open Waters Side Reactions and Overprotection Measuring Instruments Corrosion and Corrosion Control in Aviation Airframes Gas Turbine Engines Corrosion Control in Automobile Manufacture Overview Corrosion Protection for Automobile Bodies Corrosion Protection for Engines Bright Trim Corrosion Control in Food Processing and Distribution General Considerations The Application of Tinplate for Food and Beverage Cans Dairy Industries Brewing Control of Corrosion in Building Construction Introduction Structures Cladding Metal Roofs, Siding, and Flashing Plumbing and Central Heating Installations Corrosion of Metals in Timber Application of Stainless Steels in Leisure Pool Buildings Corrosion Control in Marine Environments Natures of Marine Environments Ships Offshore Platforms Corrosion Control in Steam-Raising Plant for Power Generation Fossil Fuel-Fired Boilers Recirculating Pressurized Water Reactor (PWR) Steam Generators Principles of Corrosion Testing Accelerated Tests Exposure Tests Pilot Tests Stress-Enhanced Corrosion Tests Tests for Resistance to Thermal Oxidation Index

A Review on Nanofluids: Fabrication, Stability, and Thermophysical Properties

Abstract: Nanofluids have been receiving great attention in recent years due to their potential usage, not only as an enhanced thermophysical heat transfer fluid but also because of their great importance in applications such as drug delivery and oil recovery. Nevertheless, there are some challenges that need to be solved before nanofluids can become commercially acceptable. The main challenges of nanofluids are their stability and operational performance. Nanofluids stability is significantly important in order to maintain their thermophysical properties after fabrication for a long period of time. Therefore, enhancing nanofluids stability and understanding nanofluid behaviour are part of the chain needed to commercialise such type of advanced fluids. In this context, the aim of this article is to summarise the current progress on the study of nanofluids, such as the fabrication procedures, stability evaluation mechanism, stability enhancement procedures, nanofluids thermophysical properties, and current commercialisation challenges. Finally, the article identifies some possible opportunities for future research that can bridge the gap between in-lab research and commercialisation of nanofluids.

Spatial scale affects the relative role of stochasticity versus determinism in soil bacterial communities in wheat fields across the North China Plain.

Abstract: The relative importance of stochasticity versus determinism in soil bacterial communities is unclear, as are the possible influences that alter the balance between these. Here, we investigated the influence of spatial scale on the relative role of stochasticity and determinism in agricultural monocultures consisting only of wheat, thereby minimizing the influence of differences in plant species cover and in cultivation/disturbance regime, extending across a wide range of soils and climates of the North China Plain (NCP). We sampled 243 sites across 1092 km and sequenced the 16S rRNA bacterial gene using MiSeq. We hypothesized that determinism would play a relatively stronger role at the broadest scales, due to the strong influence of climate and soil differences in selecting many distinct OTUs of bacteria adapted to the different environments. In order to test the more general applicability of the hypothesis, we also compared with a natural ecosystem on the Tibetan Plateau. Our results revealed that the relative importance of stochasticity vs. determinism did vary with spatial scale, in the direction predicted. On the North China Plain, stochasticity played a dominant role from 150 to 900 km (separation between pairs of sites) and determinism dominated at more than 900 km (broad scale). On the Tibetan Plateau, determinism played a dominant role from 130 to 1200 km and stochasticity dominated at less than 130 km. Among the identifiable deterministic factors, soil pH showed the strongest influence on soil bacterial community structure and diversity across the North China Plain. Together, 23.9% of variation in soil microbial community composition could be explained, with environmental factors accounting for 19.7% and spatial parameters 4.1%. Our findings revealed that (1) stochastic processes are relatively more important on the North China Plain, while deterministic processes are more important on the Tibetan Plateau; (2) soil pH was the major factor in shaping soil bacterial community structure of the North China Plain; and (3) most variation in soil microbial community composition could not be explained with existing environmental and spatial factors. Further studies are needed to dissect the influence of stochastic factors (e.g., mutations or extinctions) on soil microbial community distribution, which might make it easier to predictably manipulate the microbial community to produce better yield and soil sustainability outcomes.

Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System

Abstract: As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg–Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.

In search of a circular supply chain archetype – a content-analysis based literature review

Abstract: This paper addresses questions of how extant research discourses concerning the sustainability of supply chains contribute to understanding about circularity in supply chain configurations that support restorative and regenerative processes, as espoused by the Circular Economy ideal. In response to these questions, we develop a content-based literature analysis to progress theoretical body of knowledge and conceptualise the notion of a circular supply chain. We derive an archetypal form from four antecedent sustainable supply chain narratives - ‘reverse logistics’, ‘green supply chains’, ‘sustainable supply chain management’ and ‘closed-loop supply chains’. This paper offers five propositions about what the circular supply chain archetype represents in terms of its scope, focus and impact. Novel insights lead to a definition of circular supply chain and a more coherent foundation for future inquiry and practice.

Emergent Properties of Microbial Activity in Heterogeneous Soil Microenvironments: Different Research Approaches Are Slowly Converging, Yet Major Challenges Remain.

Abstract: Over the last 60 years, soil microbiologists have accumulated a wealth of experimental data showing that the usual bulk, macroscopic parameters used to characterize soils (e.g., granulometry, pH, soil organic matter and biomass contents) provide insufficient information to describe quantitatively the activity of soil microorganisms and some of its outcomes, like the emission of greenhouse gases. Clearly, new, more appropriate macroscopic parameters are needed, which reflect better the spatial heterogeneity of soils at the microscale (i.e., the pore scale). For a long time, spectroscopic and microscopic tools were lacking to quantify processes at that scale, but major technological advances over the last 15 years have made suitable equipment available to researchers. In this context, the objective of the present article is to review progress achieved to date in the significant research program that has ensued. This program can be rationalized as a sequence of steps, namely the quantification and modeling of the physical-, (bio)chemical-, and microbiological properties of soils, the integration of these different perspectives into a unified theory, its upscaling to the macroscopic scale, and, eventually, the development of new approaches to measure macroscopic soil characteristics. At this stage, significant progress has been achieved on the physical front, and to a lesser extent on the (bio)chemical one as well, both in terms of experiments and modeling. In terms of microbial aspects, whereas a lot of work has been devoted to the modeling of bacterial and fungal activity in soils at the pore scale, the appropriateness of model assumptions cannot be readily assessed because relevant experimental data are extremely scarce. For the overall research to move forward, it will be crucial to make sure that research on the microbial components of soil systems does not keep lagging behind the work on the physical and (bio)chemical characteristics. Concerning the subsequent steps in the program, very little integration of the various disciplinary perspectives has occurred so far, and, as a result, researchers have not yet been able to tackle the scaling up to the macroscopic level. Many challenges, some of them daunting, remain on the path ahead.

Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

Abstract: Acoustic emission (AE) technique can be successfully utilized for condition monitoring of various machining and industrial processes. To keep machines function at optimal levels, fault prognosis model to predict the remaining useful life (RUL) of machine components is required. This model is used to analyze the output signals of a machine whilst in operation and accordingly helps to set an early alarm tool that reduces the untimely replacement of components and the wasteful machine downtime. Recent improvements indicate the drive on the way towards incorporation of prognosis and diagnosis machine learning techniques in future machine health management systems. With this in mind, this work employs three supervised machine learning techniques; support vector machine regression, multilayer artificial neural network model and gaussian process regression, to correlate AE features with corresponding natural wear of slow speed bearings throughout series of laboratory experiments. Analysis of signal parameters such as signal intensity estimator and root mean square was undertaken to discriminate individual types of early damage. It was concluded that neural networks model with back propagation learning algorithm has an advantage over the other models in estimating the RUL for slow speed bearings if the proper network structure is chosen and sufficient data is provided.

Sustainability of bioplastics: Opportunities and challenges

Abstract: Recycling is groundwork of the worldwide efforts to diminish the amount of plastics in waste. Mostly around 7.8–8.2 million tons of poorly-used plastics enter the oceans every year. Non-biodegradable plastics settlements in landfills are uncertain, which hinders the production of land resources. Non-biodegradable plastic solid wastes, carbon dioxide, greenhouse gases, various air pollutants, cancerous polycyclic aromatic hydrocarbons and dioxins, released to the environment cause severe damage and harmfulness to the inhabitants. Due to the bio-degradability and renewability of biopolymers, petroleum-based plastics can be replaced with bio-based polymers in order to minimize the environmental risks. In this review article, bio-degradability of polymers has been discussed. The mechanisms of bio-recycling have been particularly emphasized in the present article.

Revealing internal flow behaviour in arc welding and additive manufacturing of metals

Abstract: Internal flow behaviour during melt-pool-based metal manufacturing remains unclear and hinders progression to process optimisation. In this contribution, we present direct time-resolved imaging of melt pool flow dynamics from a high-energy synchrotron radiation experiment. We track internal flow streams during arc welding of steel and measure instantaneous flow velocities ranging from 0.1 m s−1 to 0.5 m s−1. When the temperature-dependent surface tension coefficient is negative, bulk turbulence is the main flow mechanism and the critical velocity for surface turbulence is below the limits identified in previous theoretical studies. When the alloy exhibits a positive temperature-dependent surface tension coefficient, surface turbulence occurs and derisory oxides can be entrapped within the subsequent solid as result of higher flow velocities. The widely used arc welding and the emerging arc additive manufacturing routes can be optimised by controlling internal melt flow through adjusting surface active elements. Understanding what happens to the liquid in melt pools during welding and metal-based additive manufacturing remains a challenge. Here, the authors directly image internal melt pool dynamics using synchrotron radiation to show surface tension affects flow speed, orientation and surface turbulence.

Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection

Abstract: Early detection of incipient faults in industrial processes is increasingly becoming important, as these faults can slowly develop into serious abnormal events, an emergency situation, or even failure of critical equipment. Multivariate statistical process monitoring methods are currently established for abrupt fault detection. Among these, the canonical variate analysis (CVA) was proven to be effective for dynamic process monitoring. However, the traditional CVA indices may not be sensitive enough for incipient faults. In this work, an extension of CVA, called the canonical variate dissimilarity analysis (CVDA), is proposed for process incipient fault detection in nonlinear dynamic processes under varying operating conditions. To handle the non-Gaussian distributed data, the kernel density estimation was used for computing detection limits. A CVA dissimilarity based index has been demonstrated to outperform traditional CVA indices and other dissimilarity-based indices, namely the dissimilarity analysis, recursive dynamic transformed component statistical analysis, and generalized canonical correlation analysis, in terms of sensitivity when tested on slowly developing multiplicative and additive faults in a continuous stirred-tank reactor under closed-loop control and varying operating conditions.