RSS 1.0Engineering
Recent Submissions
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Feeling the heat: understanding stakeholders’ perceptions of residential-sector heating decarbonisation options in the UKThe reliance on natural gas for residential sector heating in the United Kingdom (UK) presently accounts for around a fifth of national greenhouse gas emissions. Phasing out this reliance is considered important as the UK's ‘Net-Zero’ ambitions. Alternatives to a reliance on natural gas, include the use of hydrogen boilers and electrification using heat pump technologies. The acceptance of these technologies among different societal actors (including publics) will play a significant role in which are backed, available, and used. The aim of this study was to consider the perceptions of key stakeholders in the UK residential heat decarbonisation sector about: (a) policies pertaining to this sector; (b) the factors likely to shape public acceptance of hydrogen and heat-pump technologies; and (c) the prospect of repurposing the gas pipeline network to support heat decarbonisation. Interviews were held with 12 stakeholders from the UK's residential heating sector. Interviews were supported using bespoke ‘flash cards’ to convey core details about each of the options under consideration. Interview principally considered the relative strengths and drawbacks of hydrogen versus ‘standard’ heat pump options, with discussions centring on four primary themes: Relative risks and benefits; Public choice and control over residential heating options; Engaging with the public throughout the energy transition; and Envisioning future energy transition scenarios for residential home heating. The findings confirm that the factors and actors feeding into the relative ‘acceptability’ of each option are manifold, and that the ‘acceptance’ of each option is tied to factors, such as: (a) the consistency of policy signals from government; (b) the relative affordability of the technology; and (c) both the physical infrastructure and social aspects of the local development context. The concept of repurposing the gas pipeline network was considered to be a good idea in principle, although enthusiasm was heavily caveated with reference to the practicalities of achieving this goal.
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Using convolution neural network methods for the ultrasound characterization of porosity across carbon fiber reinforced polymer layersThis study investigates the use of Convolutional Neural Network (CNN) with ultrasound imaging for the characterization of porosity across Carbon Fiber Reinforced Polymer (CFRP) layers using both simulated and experimental dataset. CFRPs are widely used in aerospace and other engineering fields due to their exceptional mechanical properties. However, porosity remains a critical defect that can significantly impair their performance. Traditional non-destructive testing (NDT) methods face some challenges in accurately detecting and characterizing porosity. The present work aims to overcome these challenges by developing a CNN-based approach to improve the detection and assessment of porosity across CFRP layers. The study relies on the development of a numerical model and the acquisition of real data from fabricated CFRP samples to successfully apply CNN techniques to evaluate porosity. The CNN model demonstrated fairly good accuracy and reliability, particularly with an increased number of dataset. The results suggest valuable opportunities for improving quality control in CFRP manufacturing processes. The study presents the potential of applying machine learning techniques for the non-destructive testing of CFRP, with a relative good amount of datasets. The present work contributes to the larger project of enhancing the reliability of CFRP structures and improving the composite materials' manufacturing processes.
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Numerical analysis of the thermal performance of packed bed thermal energy storage in adiabatic compressed air energy storage systemsThermal Energy Storage plays a significant role in Adiabatic Compressed Air Energy Storage. There is a limited understanding of how the operational conditions of Adiabatic Compressed Air Energy Storage affect the performance of packed-bed Thermal Energy Storage and vice versa. In this study, packed-bed rock Thermal Energy Storage units operating at different air pressures integrated into a large-scale, four-stage Adiabatic Compressed Air Energy Storage system were examined over a single charging and discharging cycle and over five consecutive cycles. A transient, two-dimensional axisymmetric numerical model of packed-bed rock Thermal Energy Storage was developed, and the charging, standby, and discharging phases were simulated. The results show that all packed-bed Thermal Energy Storage systems of the same size can effectively store and release thermal energy at various pressures. The thermal energy from the compressed air is absorbed and stored with no significant change in the outlet air temperature at around 298 K during the first two hours of charging. A slight rise in the outlet air temperature was observed at the end of the three-hour charging stage. Results also indicate that during five consecutive cycles in the Thermal Energy Storage unit at the lowest pressure, the amount of energy stored at the end of the three-hour charging stage increases slightly from 118.7 kJ/kg in the first cycle to 127.5 kJ/kg in the second cycle, 129.7 kJ/kg in the third cycle, 130.3 kJ/kg in the fourth cycle, and 130.5 kJ/kg in the fifth cycle.
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Biomimetic moth‑eye structures fabricated by double‑exposure lithography using coplanar three‑beam laser interferenceThis study presents a coplanar three-beam laser interference lithography (LIL) method for fabricating biomimetic moth-eyestructures. The research delves into the mechanism of cross-scale two-periodic structure formation and devises a doubleexposure lithography approach based on coplanar three-beam interference to regulate the parameters of these structures. A comparison with microlens arrays of the same period reveals that the biomimetic moth-eye structure shows enhanced transmittance and a wider field of view, attributable to its internal nanoscale arrays. The contrast of diffracted light distribution between the two structures further validates that the unique structural features of the biomimetic moth-eye structure lead to a more uniform light distribution. This work offers a facile method for fabricating biomimetic moth-eye structures, holding potential applications in diverse optical domains, including high-efficiency optical sensors, anti-reflective coatings, and advanced imaging systems.
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Experimental study of a PH-CAES system: proof of conceptThis article presents the experimental results of a novel energy storage system that combines CAES (Compressed Air Energy Storage) with PHES (Pumped Hydro Energy Storage) technologies. As a reference, we called this system PH-CAES. In this alternative solution two storage tanks, the first with compressed air and the second with water, are separated by a valve. When electric power is required, the valve is opened and water flows to a Pelton turbine, which is coupled to an electric generator. Water from the Pelton turbine is discharged into a third tank. To store energy and recover the initial state, water is pumped back. We built a prototype to assess the PH-CAES performance, with focus on the power generation system. Experimental conversion efficiency was 45%, whilst the rational efficiency remained close to 30%. We also presented a discussion based on the second law of thermodynamics to show that there is a compromise between tanks exergies that maximizes the system performance. We also provide an operating map of this PH-CAES system to assist authors on new studies about this novel technology.
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Adiabatic compressed air energy storage systemsAdiabatic Compressed Air Energy Storage (ACAES) is a thermo-mechanical storage concept that utilizes separate mechanical and thermal exergy storages to transfer energy through time. In this document, a short technology evolution report is followed by a complete thermodynamic modelling, in which frequently used components are addressed under energy and exergy viewpoints. Then, the recent commercial CAES projects and future ACAES challenges have been investigated, resulting in several identified outstanding challenges still to overcome in order to establish ACAES position as a major contender as a large-scale energy storage system.
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Sustainability of micro electrochemical machining: discussionMicro electrochemical machining is one of the promising non-conventional machining methods which has created new horizon in Micro and Nano product technologies including MEMS, defense, medical and automobile industries. An existing challenge in manufacturing has been known as the lack of identified methodology and measurement science to evaluate the sustainability of the process performance. This challenge would be more critical when it comes to Micro and Nano manufacturing process. This paper presents a review on challenges encountered in micro electrochemical machining considering it as a sustainable manufacturing process.
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Modelling of electric field distribution in a non-thermal plasma reactor using COMSOL multiphysicsThe importance of the electric field and charged particles dynamics in various applications including plasma reactors has been recognized more than ever. Furthermore, the role of the multiphysics modelling and simulation in the process of investigation, design and product prototyping is also becoming more popular due to increased speed of computers and advanced software techniques. In this work, electric field distribution in a non-thermal plasma (NTP) reactor has been studied using COMSOL multiphysics for the application of NOx reduction in the emission control. NTP was created in dielectric barrier discharge (DBD) cylindrical reactor with high voltage- ground electrodes. This study investigates the electric field distribution in non-thermal plasma under different reactor configurations. We investigated electric field distribution with and without applying space charge density; reactor design with multi ground electrodes, reactor design with a 1.2 m ground electrode and variable HV electrode dimensions. This study has provided an overall insight on the electric field distribution in non-thermal plasma and can be used as a guide for the electric field behaviour within a nitrogen gas non-thermal plasma.
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Electro-osmosis dewatering as an energy efficient technique for drying food materialsIn recent times, there has been a rise in interest on the applications of electro-technology based food processing methods. With drying in food industries being an energy intensive process with huge environmental impacts, the objective of this study was to design and develop a purpose-built laboratory system, for experimentally characterising an energy efficient electro-osmosis dewatering system. Electro-osmosis is a unique dewatering technique in which, moisture in food materials are removed by the application of low electric field (5-30 V). Different food materials namely, yogurt, orange pulp and egg whites were tested using electro-osmosis at 15 V and 30 V over 15 min and 30 min, respectively. The energy consumption (kWh), carbon footprint (kgCO2e) and cost indices (£/kg dried food) were also evaluated and compared with thermal drying.
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Comprehensive review on the use of machine learning techniques applied to the ultrasound data for the characterisation of porosity across carbon fibre reinforced polymer layersCarbon fibre reinforced polymers (CFRP) are increasingly being used in different industries, including the Automotive and aerospace sectors. One important reason for this is because they have interesting structural and mechanical properties compared to metallic materials. Their high strength-to-weight ratio makes them a preferred choice for high-stress applications. However, CFRPs are often subjected to various defects during their manufacturing that can significantly alter their structural integrity and durability. Amongst these defects, the occurrence of void formation (known as porosity) is the most common. Many methods have been developed for the characterisation of porosity including the ones based on the use of ultrasound data. The present work aims at providing a comprehensive review of the application of machine learning (ML) techniques to the mapping and characterisation of porosity across CFRP composites. The types of ML used, and their potentials for improving the accuracy of porosity detection are presented and discussed. It is particularly noted that ML techniques can extract unique features from CFRP complex ultrasound data with a relatively good level of accuracy. This result suggests that these techniques, particularly the convolutional neural network (CNN), would overcome the limitations of traditional signal processing techniques.
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Advanced MMC-based hydrostatic bearings for enhanced linear motion in ultraprecision and micromachining applicationsThis study investigates the impact of material selection on the performance of linear slideways in ultraprecision machines used for freeform surface machining. The primary objective is to address challenges related to load-bearing capacity and limited bandwidth in slow tool servo (STS) techniques. Multi-body dynamic (MBD) simulations are conducted to evaluate the performance of two materials, alloy steel and metal matrix composite (MMC), within the linear slideway system. Key performance parameters, including acceleration, velocity, and displacement, are analyzed to compare the two materials. The findings reveal that MMC outperforms alloy steel in acceleration, velocity, and displacement, demonstrating faster response times and greater linear displacement, which enhances the capabilities of STS-based ultraprecision machining. This study highlights the potential of utilizing lightweight materials, such as MMC, to optimize the performance and efficiency of linear slideways in precision engineering applications.
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On the application of machine learning techniques to map porosity across carbon fibre reinforced polymer layersCarbon Fibre Reinforced Polymer (CFRP) composites are extensively used in the Automotive industries due to their excellent structural and mechanical properties. However, the occurrence of porosity within these materials can significantly affect their performance and durability. Porosity, defined as void inclusion, often occurs during the manufacturing process for these materials. Even for small amounts of porosity, this defect can alter the composite’s mechanical properties by reducing its inter-laminar shear strength. It is therefore important to characterise and accurately map this defect, characterising the porosity distribution within CFRP layers. In this work, a Finite Element method that accounts for circular cross-section pores subjected to an ultrasound excitation is developed. This simulated data is then used to apply a Machine Learning (ML) technique such as Convolutional Neural Networks (CNN) to characterise the porosity within the CFRP sample. This technique leverages the capabilities of ML algorithms to analyse and interpret ultrasound data for porosity detection. By training the ML model on a dataset of ultrasound images and corresponding porosity measurements, the model can learn patterns and features indicative of porosity. Results obtained for the simulation data are presented and discussed. The application of CNN in processing ultrasound data has shown exceptional potentials in identifying and quantifying porosity. Results obtained after applying this technique to real ultrasound data measured with an immersion tank are also presented. CNN technique shows interesting capabilities for extracting defects such as porosity from complex ultrasound data. This work contributes to a vast project that aims at underpinning the design of more efficient composite structures.
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Detection of porosity across CFRP layers using machine learning techniques applied to theoretical and experimental ultrasound dataThere is an increasing use of Fiber-Reinforced Polymer composites as a replacement of metallic components in the transport applications such as aircraft and automobile. These structures are known to depict interesting and superior mechanical properties. However, these structures are often subjected to defects that alter their efficiency. Porosity such as voids inclusion, is among the most common of these defects. Since, porosity reduces the mechanical performance of such composite structures, it is important to detect and characterise its level and location across the layers. This study deals with the detection of porosity across CFRP layers using Machine Learning (ML) techniques Applied to theoretical and experimental ultrasound data. Different samples of CFRP composites with various levels of porosity are fabricated and tested for this study. The experimental data is acquired using an ultrasound immersion tank. The theoretical study for this work is built around both analytical and numerical approaches accounting for realistic conditions of the composites testing. Both simulated and measured data are used to apply a ML technique, mainly the Convolutional Neural Networks (CNN), to detect and characterise the porosity within the CFRP layers. C-scan and B-scan results are analysed and presented to demonstrate the potentials of the CNN technique to characterise such defects. It is observed that CNN technique has some interesting potentials for extracting defects such as porosity from complex ultrasound data.
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Development of ultrasound inversion methods for characterising features in 3D woven composite materialsThere is an increasing interest in the use of 3D woven composites in applications that require improved strength-to-weight ratios. In addition, the use of these structures helps to reduce CO2 emissions. Woven composites offer many benefits including many possible architectures with high ratios of strain to failure. Woven composites are structures made by interlacing some continuous fibres (known as wefts) in one direction and other continuous fibres (known as warps) in a perpendicular direction. In the case of 3D woven composites, the third direction is reinforced by other continuous fibres known as binder. This study deals with the development of ultrasound inversion methods to characterize features in 3D woven composite materials. The study focuses on orthogonal weave-type only. Both theoretical (simulated) and measured data are analysed and used to calculate features such as the warps, wefts, and binder locations. The analytical-signal response, including the definition of three instantaneous parameters, is analysed and their capabilities to calculate the warp, weft and binder locations are demonstrated. These instantaneous parameters are the instantaneous amplitude, phase and frequency. The simulated data is obtained from a 3D time domain Finite Element model whereas the measured data is acquired from scanning a built specimen using an ultrasound immersion tank. The inversion techniques developed in this study can be extended to other 3D woven weave-types.
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Stoneage site detected by high resolution seismic methodIn 2014, we first noticed on high resolution seismic profiles acquired by a Teledyne high-resolution Chirp III subbottom profiler in the well-documented Stone Age settlement of Atlit-Yam, located off Israel’s Carmel coast irregular disturbances in the water column and we named it “haystacks”. We speculated if these disturbances could be related to the flint debitage (blades) documented at the flint workshop in the survey area. The ChirpIII instrument sweeps the frequency interval 2 kHz – 20 kHz and operate in two bands 2-8 and 8-20 KHz. Acoustic experiments in laboratory had previously shown that flint blades could exhibit resonance when exposed to certain frequencies (3–23 kHz, with the main area being 7–12 kHz). Acoustic modeling confirmed this and modelling showed that even flint debitage buried below 2 m of sand could resonance. In Demark practical ChirpIII (that sweep the frequency interval 2 kHz – 20 kHz) used on flint debitage and natural cracked flint placed at the seafloor showed that flint debitage produced “haystacks” on seismic profiles whereas the natural cracked flint did not. Test of buried debitage showed that it created resonance and produced “haystacks”. In the dredged part of the Svanemøllen Harbour, Copenhagen we by coincidence located “haystacks” while testing instrumentation setting. In the following three years, we recorded data on three days to outline the area where “haystacks” are present and to confirm that the “haystacks” were a permanent phenomenon. The interpretation of the seismic data reveal that the haystacks are related sub bottom areas characterized by shallow basins and rivers in a near coastal setting and that the “haystacks” are located at the rim of the basins or in the basins. In order to test if there was a correspondence between the “haystacks” and possible debitage 11 shallow vibrocores, with a max length of 1 m, were drilled below locations of “haystacks”. Based on the cores we found up to 36 cm of silt below the dredged seafloor before we reached a sandy cover of up to 80 cm representing part of the basin configuration. The sandy interval is underlain by till clay. Two cores centrally placed in the surveyed area confirmed the presence of man knapped flint at a depth of 80-90 cm below the sea floor. The Svanemøllen Harbour site is a hitherto unknown buried Stone Age settlement and this is the first time that such a site has been acoustically detected (Teledyne Chirp III) and verified by drilling. Acoustic modelling of the retrieved pieces of man knapped flint is carried out to confirm that the debitage can be brought to resonance. Due to the relative sea level rise a significant part of the submerged Stone Age sites must worldwide be expected to be buried in the seafloor sediments. This paper underlines the importance of the development of cost-effective methods for detecting such buried cultural deposits.
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Mobile calibration for bus-based urban sensingIn bus-based sensing, public transport serves as a mobile urban sensing platform. While offering much higher geographical coverage, the low-cost sensors mounted on vehicles can be less accurate and demand more frequent calibration, which may be challenging for large vehicle fleets. As calibration is performed by relating mobile sensor readings to those of fixed reference stations, the placement of reference stations is very important. In this work, we propose an algorithm for computing the optimal locations for reference stations to maximize the sensing coverage. Contrary to prior work, coverage is defined in terms of geographical area, extending a certain distance away from the route trajectory, which represents the actual sensing capacity of the vehicles. The proposed algorithm computes it using geographical set operations, such as spatial join and subtraction to compute the unique contribution of each bus route. We evaluate the approach using real bus trajectories from Manhattan, USA, and compare it with a random baseline and prior work. The results indicate that given the bus routes, a complete sensing coverage can be achieved using a single reference station with a maximum 2-hop calibration path.
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Exploring the effects of compression ratio and initial flame kernel radius on 1 combustion characteristics and fuel economy of a dual-fuel spark ignition engine 2 under oxy-fuel combustion modeIn order to mitigate greenhouse effect and promote carbon neutrality, Oxy-Fuel Combustion (OFC) technology implemented in the Internal Combustion Engine (ICE) has been an effective and promising approach to reduce or even eliminate CO2 emissions from the transportation sector. This research contributes novel insights into the effects of compression ratio (𝛿𝐶𝑅) and initial flame kernel radius (𝑅𝐹𝐾) on combustion characteristics and fuel economy of a Dual-Fuel Spark Ignition (DFSI) engine under OFC mode by a numerical method. The research results show that by increasing 𝛿𝐶𝑅 from 8.6 to 13.6, an apparent reduction can be seen in equivalent Brake Specific Fuel Consumption (BSFCE). The corresponding ignition delay (𝜃𝐹) has a reduction of 10 degrees, while combustion duration (𝜃𝐶) are relatively stable. Moreover, the maximum cylinder pressure (𝑃𝑚𝑎𝑥) has a rise of 8 bar and 20 bar at low load and mid-high load, respectively. By increasing 𝑅𝐹𝐾 from 0.2 mm to 1.2 mm, 𝑃𝑚𝑎𝑥 and 𝜑𝑃𝑚𝑎𝑥 each presents a monotonic trend of growth and advancement, respectively. The reduction of 𝜃𝐹 at low load and mid-high load is each 28.5 degrees and 34.9 degrees. In the meantime, both BSFCE and in-cylinder temperature show a low level of sensitivity. The research findings could provide valuable insights for enhancing the combustion performance and economy of DFSI engines under OFC mode to mitigate the greenhouse effect.
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A submerged and buried Mesolithic site off the Svanemøllen Harbour, Copenhagen, Denmark: acoustic detection (HALD) and verification through coringTeledyne Chirp III high resolution seismic profiles have through three years been recorded at an approximately waterdepth of 6.0-9.0 m with clear concentration of acoustic ‘haystack’ features in the dredged Svanemøllen Harbor, Copenhagen. The recordings show haystacks related to preserved shallow basins and rivers in the paleo coastal setting. Eleven short vibrocores were retrieved below pronounced haystacks and a sandy interval, underlain by clayey till and overlain by harbor silt, represent the basin configuration. Two cores contained 4 pieces of knapped flint in the sandy interval (statistically density of around 230 pieces per square meter), while the remaining cores did not reach the desired depth. Finite Element (FE) modeling reveals that small pieces of knapped flint resonance and that the acoustic impedance of the flint is high. Svanemøllen Harbour site is a hitherto unknown buried Stone Age settlement and this is the first time that such a site has been acoustically detected (Teledyne Chirp III), verified by drilling and modelled by FE. Innomar and Geopulse data acquired at the site did not register haystacks. Due to the relative sea level rise a significant part of the submerged Stone Age sites must worldwide be expected to be buried in the seafloor sediments.
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Observer-based control for time-delayed quasi-one-sided Lipschitz nonlinear systems under input saturationThis paper addresses the observer-based controller design problem for nonlinear time-delayed systems under input saturation. The nonlinearities are supposed to satisfy the quasi-onesided Lipschitz condition, which is less conservative than the one-sided Lipschitz condition. Based on the nonlinear matrix inequalities, control law for nonlinear systems subject to input saturation, time delays, and unavailable states, some sufficient conditions have been developed for an augmented system containing the system state vector and the error vector to ensure the convergence of all states to zero. The paper used a decoupling approach to reduce the complexity of the corresponding observer and controller gain computations. Finally, the effectiveness of the developed results is validated using suitable examples.
