Search results for: heat optimization

Authors: Huan Ting Liao

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In a typical manufacturing environment, computer numerical control (CNC) machining is essential for automating production through precise computer-controlled tool operations, significantly enhancing efficiency and ensuring consistent product quality. However, traditional CNC production lines often rely on manual loading and unloading, limiting operational efficiency and scalability. Although automated loading systems have been developed, they frequently lack sufficient intelligence and configuration efficiency, requiring extensive setup adjustments for different products and impacting overall productivity. This research addresses the job shop scheduling problem (JSSP) in CNC machining environments, aiming to minimize total completion time (makespan) and maximize CNC machine utilization. We propose a novel approach using reinforcement learning (RL), specifically the Q-learning algorithm, to optimize scheduling decisions. The study simulates the JSSP, incorporating robotic arm operations, machine processing times, and work order demand allocation to determine optimal processing sequences. The Q-learning algorithm enhances machine utilization by dynamically balancing workloads across CNC machines, adapting to varying job demands and machine states. This approach offers robust solutions for complex manufacturing environments by automating decision-making processes for job assignments. Additionally, we evaluate various layout configurations to identify the most efficient setup. By integrating RL-based scheduling optimization with layout analysis, this research aims to provide a comprehensive solution for improving manufacturing efficiency and productivity in CNC-based job shops. The proposed method's adaptability and automation potential promise significant advancements in tackling dynamic manufacturing challenges.

Keywords: job shop scheduling problem, reinforcement learning, operations sequence, layout optimization, q-learning

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Authors: Luzie Krings

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The electrification of the transport sector is increasingly recognized as a vital strategy for decarbonization. However, integrating electric vehicles (EVs) into the energy grid poses challenges due to decentralized power units and the intermittent nature of renewable energy sources. Vehicle-to-grid (V2G) technology offers a compelling solution by enabling EVs to function as mobile storage units, providing system services, reducing grid congestion, and offering economic incentives. This potential is particularly significant in freight transport, which accounts for 38% of transport-related emissions. The aggregated use of energy storage in this sector can facilitate grid stability and renewable energy integration. Despite this, existing optimization methods for energy markets frequently overlook operational constraints, such as fixed schedules and state-of-charge requirements, while redispatch markets remain underutilized. This study introduces a risk-averse optimization model for marketing EV flexibilities across multiple energy markets in Germany. Using a linear optimization framework, the model incorporates technical, regulatory, and user constraints. EVs are modeled as energy storage units, and the integration of renewable energy sources, such as photovoltaic (PV) and wind energy, is evaluated. To benchmark performance, unidirectional charging with dynamic tariffs is used as the reference scenario. The research examines four distinct logistics depot fleets, each with varying capacities and schedules, to simulate commercial EV operations. The methodology employs a multi-market optimization model that integrates Day-Ahead, Intraday, and Redispatch energy markets, each with specific trading conditions and temporal offsets. The tool, developed using the Python-based library energy pilot by Fraunhofer IEE, also explores scenarios where proprietary renewable energy sources are incorporated to maximize benefits. By accounting for charging schedules, market requirements, and technical constraints, the study aims to enhance grid stability and improve economic outcomes and integration of renewable energies. The findings highlight the economic, environmental, and grid-related advantages of optimizing EV flexibility. Compared to the reference scenario of unidirectional charging, bidirectional strategies delivered an approximate economic benefit of 20%. Furthermore, the integration of proprietary renewable energy sources increased by 15%, demonstrating the potential for environmental gains. The study revealed that the duration of a single charging cycle has a greater impact on economic benefits than the total daily charging time spread across multiple cycles. This underscores the marketing potential of vehicles with extended idle times rather than frequent charging cycles. In conclusion, optimizing energy trading through flexible EV portfolios and efficient charging infrastructure offers substantial cost savings, particularly by increasing the number of charging stations and extending charging cycle durations. By leveraging multiple marketing options, high investment costs can be offset through enhanced revenues. Further gains could be achieved by simultaneously optimizing all trading options, though this approach introduces risks from price volatility and unreliable redispatch capacities. As electrified trucks are modeled as energy storage units, the study's findings are applicable to other forms of energy storage, offering a scalable and transferable framework for future energy systems.

Keywords: electric vehicles, energy markets, energy storage, energy grid

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Authors: Mrinal Jana, Geetanjali Panda

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In this paper a methodology is developed to solve a nonlinear fractional programming problem in which the coefficients of the objective function and constraints are interval parameters. This model is transformed into a general optimization problem and relation between the original problem and the transformed problem is established. Finally the proposed methodology is illustrated through a numerical example.

Keywords: fractional programming, interval valued function, interval inequalities, partial order relation

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Authors: Ahmad Kayvani Fard, Yehia Manawi

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Qatar’s primary source of fresh water is through seawater desalination. Amongst the major processes that are commercially available on the market, the most common large scale techniques are Multi-Stage Flash distillation (MSF), Multi Effect distillation (MED), and Reverse Osmosis (RO). Although commonly used, these three processes are highly expensive down to high energy input requirements and high operating costs allied with maintenance and stress induced on the systems in harsh alkaline media. Beside that cost, environmental footprint of these desalination techniques are significant; from damaging marine eco-system, to huge land use, to discharge of tons of GHG and huge carbon footprint. Other less energy consuming techniques based on membrane separation are being sought to reduce both the carbon footprint and operating costs is membrane distillation (MD). Emerged in 1960s, MD is an alternative technology for water desalination attracting more attention since 1980s. MD process involves the evaporation of a hot feed, typically below boiling point of brine at standard conditions, by creating a water vapor pressure difference across the porous, hydrophobic membrane. Main advantages of MD compared to other commercially available technologies (MSF and MED) and specially RO are reduction of membrane and module stress due to absence of trans-membrane pressure, less impact of contaminant fouling on distillate due to transfer of only water vapor, utilization of low grade or waste heat from oil and gas industries to heat up the feed up to required temperature difference across the membrane, superior water quality, and relatively lower capital and operating cost. To achieve the objective of this study, state of the art flat-sheet cross-flow DCMD bench scale unit was designed, commissioned, and tested. The objective of this study is to analyze the characteristics and morphology of the membrane suitable for DCMD through SEM imaging and contact angle measurement and to study the water quality of distillate produced by DCMD bench scale unit. Comparison with available literature data is undertaken where appropriate and laboratory data is used to compare a DCMD distillate quality with that of other desalination techniques and standards. Membrane SEM analysis showed that the PTFE membrane used for the study has contact angle of 127º with highly porous surface supported with less porous and bigger pore size PP membrane. Study on the effect of feed solution (salinity) and temperature on water quality of distillate produced from ICP and IC analysis showed that with any salinity and different feed temperature (up to 70ºC) the electric conductivity of distillate is less than 5 μS/cm with 99.99% salt rejection and proved to be feasible and effective process capable of consistently producing high quality distillate from very high feed salinity solution (i.e. 100000 mg/L TDS) even with substantial quality difference compared to other desalination methods such as RO and MSF.

Keywords: membrane distillation, waste heat, seawater desalination, membrane, freshwater, direct contact membrane distillation

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Authors: Mohammad Hossein Mohammadi Sanjani, Ashknaz Oraee, Oriol Gomis Bellmunt, Vinicius Albernaz Lacerda Freitas

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The use of distributed and renewable sources in power systems has grown significantly, recently. One the most popular sources are wind farms which have grown massively. However, ¬wind farms are connected to the grid, this can cause problems such as reduced voltage stability, frequency fluctuations and reduced dynamic stability. Variable speed generators (asynchronous) are used due to the uncontrollability of wind speed specially Doubley Fed Induction Generators (DFIG). The most important disadvantage of DFIGs is its sensitivity to voltage drop. In the case of faults, a large volume of reactive power is induced therefore, use of FACTS devices such as SVC and STATCOM are suitable for improving system output performance. They increase the capacity of lines and also passes network fault conditions. In this paper, in addition to modeling the reactive power control system in a DFIG with converter, FACTS devices have been used in a DFIG wind turbine to improve the stability of the power system containing two synchronous sources. In the following paper, recent optimal control systems have been designed to minimize fluctuations caused by system disturbances, for FACTS devices employed. For this purpose, a suitable method for the selection of nine parameters for MPSH-phase-post-phase compensators of reactive power compensators is proposed. The design algorithm is formulated ¬¬as an optimization problem searching for optimal parameters in the controller. Simulation results show that the proposed controller Improves the stability of the network and the fluctuations are at desired speed.

Keywords: renewable energy sources, optimization wind power plant, stability, reactive power compensator, double-feed induction generator, optimal control, genetic algorithm

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Authors: Luis Amador Jimenez, Md. Shohel Amin

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Pavement performance models are based on projections of observed traffic loads, which makes uncertain to study funding strategies in the long run if history does not repeat. Neural networks can be used to estimate deterioration rates but the learning rate and momentum have not been properly investigated, in addition, economic evolvement could change traffic flows. This study addresses both issues through a case study for roads of Montreal that simulates traffic for a period of 50 years and deals with the measurement error of the pavement deterioration model. Travel demand models are applied to simulate annual average daily traffic (AADT) every 5 years. Accumulated equivalent single axle loads (ESALs) are calculated from the predicted AADT and locally observed truck distributions combined with truck factors. A back propagation Neural Network (BPN) method with a Generalized Delta Rule (GDR) learning algorithm is applied to estimate pavement deterioration models capable of overcoming measurement errors. Linear programming of lifecycle optimization is applied to identify M&R strategies that ensure good pavement condition while minimizing the budget. It was found that CAD 150 million is the minimum annual budget to good condition for arterial and local roads in Montreal. Montreal drivers prefer the use of public transportation for work and education purposes. Vehicle traffic is expected to double within 50 years, ESALS are expected to double the number of ESALs every 15 years. Roads in the island of Montreal need to undergo a stabilization period for about 25 years, a steady state seems to be reached after.

Keywords: pavement management system, traffic simulation, backpropagation neural network, performance modeling, measurement errors, linear programming, lifecycle optimization

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Authors: Ravi Patel, Krishna K. Krishnan

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In global supply chains, appropriate and sustainable suppliers play a vital role in supply chain development and feasibility. In a larger organization with huge number of suppliers, it is necessary to divide suppliers based on their past history of quality and delivery of each product category. Since performance of any organization widely depends on their suppliers, well evaluated selection criteria and decision-making models lead to improved supplier assessment and development. In this paper, SCOR® performance evaluation approach and ISO standards are used to determine selection criteria for better utilization of supplier assessment by using hybrid model of Analytic Hierchchy Problem (AHP) and Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (FTOPSIS). AHP is used to determine the global weightage of criteria which helps TOPSIS to get supplier score by using triangular fuzzy set theory. Both qualitative and quantitative criteria are taken into consideration for the proposed model. In addition, a multi-product and multi-time period model is selected for order allocation. The optimization model integrates multi-objective integer linear programming (MOILP) for order allocation and a hybrid approach for supplier selection. The proposed MOILP model optimizes order allocation based on manufacturing process and product tolerances as per manufacturer’s requirement for quality product. The integrated model and solution approach are tested to find optimized solutions for different scenario. The detailed analysis shows the superiority of proposed model over other solutions which considered individual decision making models.

Keywords: AHP, fuzzy set theory, multi-criteria decision making, multi-objective integer linear programming, TOPSIS

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Authors: Qiuying Li, Fan Chen

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It is a shared opinion that sustainable development requires continuously updated, meaning that apparent changes in the way we usually produce our buildings are strongly needed. In China’s construction field, the associated environmental, health problems are quite prominent.Especially low sustainable performance (as opposed to Green creation) flooding the real estate boom and high-speed urban and rural urbanization. Currently, we urgently need to improve the existing design basis,objectives,scope and procedures,optimization design portfolio.More new evaluation system designed to facilitate the building to enhance the overall level.

Keywords: design for sustainability, design and materials, ecomaterials, sustainable architecture and urban design

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Authors: Zina Ghiloufi, Tahar Khir

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A transient three-dimensional computational fluid dynamics (CFD) model is developed to determine the velocity and temperature distribution in different positions cold room during pre-cooling of dates. The turbulence model used is the k-ω Shear Stress Transport (SST) with the standard wall function, the air. The numerical results obtained show that cooling rate is not uniform inside the room; the product at the medium of room has a slower cooling rate. This cooling heterogeneity has a large effect on the energy consumption during cold storage.

Keywords: CFD, cold room, cooling rate, dDates, numerical simulation, k-ω (SST)

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Authors: Tomas Vincze, Michal Micjan, Milan Pavuk, Martin Weis

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In recent years, alternative materials are gaining attention to replace polycrystalline and amorphous silicon, which are a standard for low requirement devices, where silicon is unnecessarily and high cost. For that reason, metal oxides are envisioned as the new materials for these low-requirement applications such as sensors, solar cells, energy storage devices, or field-effect transistors. Their most common way of layer growth is sputtering; however, this is a high-cost fabrication method, and a more industry-suitable alternative is the sol-gel method. In this group of materials, many oxides exhibit a semiconductor-like behavior with sufficiently high mobility to be applied as transistors. The sol-gel method is a cost-effective deposition technique for semiconductor-based devices. Copper oxides, as p-type semiconductors with free charge mobility up to 1 cm2/Vs., are suitable replacements for poly-Si or a-Si:H devices. However, to reach the potential of silicon devices, a fine-tuning of material properties is needed. Here we focus on the optimization of the electrical parameters of copper oxide-based field-effect transistors by modification of precursor solvent (usually 2-methoxy ethanol). However, to achieve solubility and high-quality films, a better solvent is required. Since almost no solvents have both high dielectric constant and high boiling point, an alternative approach was proposed with blend solvents. By mixing isopropyl alcohol (IPA) and 2-methoxy ethanol (2ME) the precursor reached better solubility. The quality of the layers fabricated using mixed solutions was evaluated in accordance with the surface morphology and electrical properties. The IPA:2ME solution mixture reached optimum results for the weight ratio of 1:3. The cupric oxide layers for optimal mixture had the highest crystallinity and highest effective charge mobility.

Keywords: copper oxide, field-effect transistor, semiconductor, sol-gel method

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Authors: M. J. Park, S. H. Lee, C. H. Lee, O. M. Kwon

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This paper investigates stability problem for linear systems of neutral type with time-varying delay. By constructing various Lyapunov-Krasovskii functional, and utilizing some mathematical techniques, the sufficient stability conditions for the systems are established in terms of linear matrix inequalities (LMIs), which can be easily solved by various effective optimization algorithms. Finally, some illustrative examples are given to show the effectiveness of the proposed criterion.

Keywords: neutral systems, time-delay, stability, Lyapnov method, LMI

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Authors: Peter Krupa, Svetozár Malinarič

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Transient plane source method has been used to measure the thermal diffusivity and thermal conductivity of a compact isostatic electro-ceramics at room temperature. The samples were fired at temperatures from 100 up to 1320 degrees Celsius in steps of 50. Bulk density and specific heat capacity were also measured with their corresponding standard uncertainties. The results were compared with further thermal analysis (dilatometry and thermogravimetry). Structural processes during firing were discussed.

Keywords: TPS method, thermal conductivity, thermal diffusivity, thermal analysis, electro-ceramics, firing

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Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

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Authors: H. Demirtaş, İ. H. Kara, H. Ahlatcı

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Welding failures of steel pipes in power plants usually occur in weld zones. This is similar for the economizer, water walls and superheaters in the power plants where SA 210 Gr. C steel pipes are used. Although these steel pipes have very good welding properties, the welding parameters are also important for the welding life. Welding processes of this pipes are carried out by TIG and SMA techniques. In this study SA 210 Gr. C steel pipes were welded by TIG method and investigated how PWHT affected the welding properties. The results show that this steel does not require post weld heat treatment.

Keywords: SA 210 Gr. C steel pipes, TIG welding, HAZ region, Widmanstatten ferrite

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Authors: Mohammad Yousefikia, Sara Moridpour, Ehsan Mazloumi

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Perceiving track geometry deterioration decisively influences the optimization of track maintenance operations. The effective management of this deterioration and increasingly utilized system with limited financial resources is a significant challenge. This paper provides a review of degradation models relevant for railroad tracks. Furthermore, due to the lack of long term information on the condition development of tram infrastructures, presents the methodology which will be used to derive degradation models from the data of Melbourne tram network.

Keywords: deterioration modeling, asset management, railway, tram

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Authors: Vishal Mahale, Jayashree Bijwe, Sujeet K. Sinha

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Range of thermal conductivity (TC) of Friction Materials (FMs) is a critical issue since lower TC leads to accumulation of frictional heat on the working surface, which results in excessive fade while higher TC leads to excessive heat flow towards back-plate resulting in boiling of brake-fluid leading to ‘spongy brakes’. This phenomenon prohibits braking action, which is most undesirable. Therefore, TC of the FMs across the brake pads should not be high while along the brake pad, it should be high. To enhance TC, metals in the forms of powder and fibers are used in the FMs. Apart from TC improvement, metals provide strength and structural integrity to the composites. Due to higher TC Copper (Cu) powder/fiber is a most preferred metallic ingredient in FM industry. However, Cu powders/fibers are responsible for metallic wear debris generation, which has harmful effects on aquatic organisms. Hence to get rid of a problem of metallic wear debris generation and to keep the positive effect of TC improvement, incorporation of Cu fabric in NAO brake-pads can be an innovative solution. Keeping this in view, two realistic multi-ingredient FM composites with identical formulations were developed in the form of brake-pads. Out of which one composite series consisted of a single layer of Cu fabric in the body of brake-pad and designated as C1 while double layer of Cu fabric was incorporated in another brake-pad series with designation of C2. Distance of Cu fabric layer from the back-plate was kept constant for C1 and C2. One more composite (C0) was developed without Cu fabric for the sake of comparison. Developed composites were characterized for physical properties. Tribological performance was evaluated on full scale inertia dynamometer by following JASO C 406 testing standard. It was concluded that Cu fabric successfully improved fade resistance by increasing conductivity of the composite and also showed slight improvement in wear resistance. Worn surfaces of pads and disc were analyzed by SEM and EDAX to study wear mechanism.

Keywords: brake inertia dynamometer, copper fabric, non-asbestos organic (NAO) friction materials, thermal conductivity enhancement

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Authors: Desmond Young

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In building services electrical design, a key element of any project will be assessing the electrical load requirements. This needs to be done early in the design process to allow the selection of infrastructure that would be required to meet the electrical needs of the type of building. The type of building will define the type of assessment made, and the values applied in defining the maximum demand for the building, and ultimately the size of supply or infrastructure required, and the application that needs to be made to the distribution network operator, or alternatively to an independent network operator. The fact that this assessment needs to be undertaken early in the design process provides limits on the type of assessment that can be used, as different methods require different types of information, and sometimes this information is not available until the latter stages of a project. A common method applied in the earlier design stages of a project, typically during stages 1,2 & 3, is the use of benchmarks. It is a possibility that some of the benchmarks applied are excessive in relation to the current loads that exist in a modern installation. This lack of accuracy is based on information which does not correspond to the actual equipment loads that are used. This includes lighting and small power loads, where the use of more efficient equipment and lighting has reduced the maximum demand required. The electrical load can be used as part of the process to assess the heat generated from the equipment, with the heat gains from other sources, this feeds into the sizing of the infrastructure required to cool the building. Any overestimation of the loads would contribute to the increase in the design load for the heating and ventilation systems. Finally, with the new policies driving the industry to decarbonise buildings, a prime example being the recently introduced London Plan, loads are potentially going to increase. In addition, with the advent of the pandemic and changes to working practices, and the adoption of electric heating and vehicles, a better understanding of the loads that should be applied will aid in ensuring that infrastructure is not oversized, as a cost to the client, or undersized to the detriment of the building. In addition, more accurate benchmarks and methods will allow assessments to be made for the incorporation of energy storage and renewable technologies as these technologies become more common in buildings new or refurbished.

Keywords: energy, ADMD, electrical load assessment, energy benchmarks

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Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

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Authors: K. Anas, M. Selvakumar, Samson David, R. R. Babu, S. Chattopadhyay

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The dynamic deformation of a visco elastic material can cause heat generation. This heat generation is aspect energy dissipation. The present work investigates the contribution of various factors like; elastomer structure, cross link type and density, filler networking, reinforcement potential and temperature at energy dissipation mechanism. The influences of these elements are investigated using very high frequency analyzer (VHF ) and dynamical mechanical analysis(DMA).VHF follows transmissibility and vibration isolation principle whereas DMA works on dynamical mechanical deformation principle. VHF analysis of different types of elastomers reveals that elastomer can act as a transmitter or damper of energy depending on the applied frequency ratio (ω/ωn). Dynamic modulus (G') of low damping rubbers like natural rubber does not varies rapidly with frequency but vice-versa for high damping rubber like butyl rubber (IIR). VHF analysis also depicts that polysulfidic linkages has high damping ratio (ζ) than mono sulfidic linkages due to its dissipative nature. At comparable cross link density, mono sulfidic linkages shows higher glass transition temperature (Tg) than poly sulfidic linkages. The intensity and location of loss modulus (G'') peak of different types of carbon black filled natural rubber compounds suggests that segmental relaxation at glass transition temperature (Tg) is seldom affected by filler particles, but the filler networks can influence the cross link density by absorbing the curatives. The filler network breaking and reformation during a dynamic strain is a thermally activated process. Thus, stronger aggregates are highly dissipative in nature. Measurements indicate that at lower temperature regimes polymeric chain friction is highly dissipative in nature.

Keywords: damping ratio, natural frequency, crosslinking density, segmental motion, surface activity, dissipative, polymeric chain friction

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Authors: Khaled Al-Badani, Andrew Norbury, Essam Elmshawet, Glynn Rotwell, Ian Jenkinson , James Ren

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Micro welding procedures are widely used for joining materials, developing duplex components or functional surfaces, through various methods such as Micro Discharge Welding or Spot Welding process, which can be found in the engineering, aerospace, automotive, biochemical, biomedical and numerous other industries. The relationship between the material properties, structure and processing is very important to improve the structural integrity and the final performance of the welded joints. This includes controlling the shape and the size of the welding nugget, state of the heat affected zone, residual stress, etc. Nowadays, modern high volume productions require the welding of much versatile shapes/sizes and material systems that are suitable for various applications. Hence, an improved understanding of the micro welding process and the digital tools, which are based on computational numerical modelling linking key welding parameters, dimensional attributes and functional performance of the weldment, would directly benefit the industry in developing products that meet current and future market demands. This paper will introduce recent work on developing an integrated experimental and numerical modelling code for micro welding techniques. This includes similar and dissimilar materials for both ferrous and non-ferrous metals, at different scales. The paper will also produce a comparative study, concerning the differences between the micro discharge welding process and the spot welding technique, in regards to the size effect of the welding zone and the changes in the material structure. Numerical modelling method for the micro welding processes and its effects on the material properties, during melting and cooling progression at different scales, will also be presented. Finally, the applications of the integrated numerical modelling and the material development for the digital manufacturing of welding, is discussed with references to typical application cases such as sensors (thermocouples), energy (heat exchanger) and automotive structures (duplex steel structures).

Keywords: computer modelling, droplet formation, material distortion, materials forming, welding

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Authors: Bobuchi Ken-Opurum

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The impacts of climate change on urban settlements is growing. In the global south, communities are even more vulnerable and suffer there is an increased vulnerability from due to climate change disasters such as flooding and high temperatures. This is primarily due to high intensity rainfall, low-lying coasts, inadequate infrastructure, and limited resources. According to the Emergency Events Database, floods were the leading cause of disaster -based deaths in the global south between 2006 and 2015. This includes deaths from heat stress related health outcomes. Adapting to climate vulnerabilities is paramount in reducing the significant redevelopment costs from climate disasters. Governments and urban planners provide top-down approaches such as evacuation, and disaster and emergency communication. While they address infrastructure and public services, they are not always able to address the immediate and critical day to day needs of poor and vulnerable populations. There is growing evidence that some bottom-up strategies and grassroots initiatives of self-build housing such as in urban informal settlements are successful in coping and adapting to hydroclimatic impacts. However, these research findings are not consolidated and the evaluation of the resilience outcomes of the bottom-up strategies are limited. Using self-build housing as a model for sustainable and resilient urban planning, this research aimed to consolidate the flood and heat stress resilient design solutions, analyze the effectiveness of these solutions, and develop guidelines and methods for adopting these design solutions into mainstream housing in megathermal climates. The methodological approach comprised of analyses of over 40 ethnographic based peer reviewed literature, white papers, and reports between the years 2000 and 2019 to identify coping strategies and grassroots initiatives that have been applied by occupants and communities of the global south. The results of the research provide a consolidated source and prioritized list of the best bottom-up strategies for communities in megathermal climates to improve the lives of people in some of the most vulnerable places in the world.

Keywords: resilient, design, megathermal, climate change

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Authors: Nwachukwu Ifeanyi

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Wind energy is a vital component of the global renewable energy portfolio, with wind turbines serving as the primary means of harnessing this abundant resource. However, the efficiency and stability of wind turbines remain critical challenges in maximizing energy output and ensuring long-term operational viability. This study proposes a comprehensive approach utilizing computational aerodynamics and aeromechanics to optimize wind turbine performance across multiple objectives. The proposed research aims to integrate advanced computational fluid dynamics (CFD) simulations with structural analysis techniques to enhance the aerodynamic efficiency and mechanical stability of wind turbine blades. By leveraging multi-objective optimization algorithms, the study seeks to simultaneously optimize aerodynamic performance metrics such as lift-to-drag ratio and power coefficient while ensuring structural integrity and minimizing fatigue loads on the turbine components. Furthermore, the investigation will explore the influence of various design parameters, including blade geometry, airfoil profiles, and turbine operating conditions, on the overall performance and stability of wind turbines. Through detailed parametric studies and sensitivity analyses, valuable insights into the complex interplay between aerodynamics and structural dynamics will be gained, facilitating the development of next-generation wind turbine designs. Ultimately, this research endeavours to contribute to the advancement of sustainable energy technologies by providing innovative solutions to enhance the efficiency, reliability, and economic viability of wind power generation systems. The findings have the potential to inform the design and optimization of wind turbines, leading to increased energy output, reduced maintenance costs, and greater environmental benefits in the transition towards a cleaner and more sustainable energy future.

Keywords: computation, robotics, mathematics, simulation

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Authors: A. Soria-Salinas, M.-P. Zorzano, J. Martín-Torres, J. Sánchez-García-Casarrubios, J.-L. Pérez-Díaz, A. Vakkada-Ramachandran

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The current state-of-the-art methods of mass gauging of Electric Propulsion (EP) propellants in microgravity conditions rely on external measurements that are taken at the surface of the tank. The tanks are operated under a constant thermal duty cycle to store the propellant within a pre-defined temperature and pressure range. We demonstrate using computational fluid dynamics (CFD) simulations that the heat-transfer within the pressurized propellant generates temperature and density anisotropies. This challenges the standard mass gauging methods that rely on the use of time changing skin-temperatures and pressures. We observe that the domes of the tanks are prone to be overheated, and that a long time after the heaters of the thermal cycle are switched off, the system reaches a quasi-equilibrium state with a more uniform density. We propose a new gauging method, which we call the Improved PVT method, based on universal physics and thermodynamics principles, existing TRL-9 technology and telemetry data. This method only uses as inputs the temperature and pressure readings of sensors externally attached to the tank. These sensors can operate during the nominal thermal duty cycle. The improved PVT method shows little sensitivity to the pressure sensor drifts which are critical towards the end-of-life of the missions, as well as little sensitivity to systematic temperature errors. The retrieval method has been validated experimentally with CO₂ in gas and fluid state in a chamber that operates up to 82 bar within a nominal thermal cycle of 38 °C to 42 °C. The mass gauging error is shown to be lower than 1% the mass at the beginning of life, assuming an initial tank load at 100 bar. In particular, for a pressure of about 70 bar, just below the critical pressure of CO₂, the error of the mass gauging in gas phase goes down to 0.1% and for 77 bar, just above the critical point, the error of the mass gauging of the liquid phase is 0.6% of initial tank load. This gauging method improves by a factor of 8 the accuracy of the standard PVT retrievals using look-up tables with tabulated data from the National Institute of Standards and Technology.

Keywords: electric propulsion, mass gauging, propellant, PVT, xenon

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Authors: Nora Arbaoui, Rachid Tadili

Abstract:

This study focuses on a solar system designed to heat an agricultural greenhouse. This solar system is based on the heating of a transfer fluid that circulates inside the greenhouse through a solar copper coil integrated into the roof of the greenhouse. The thermal energy stored during the day will be released during the night to improve the microclimate of the greenhouse. This system was tested in a small agricultural greenhouse in order to ameliorate the different operational parameters. The climatic and agronomic results obtained with this system are significant in comparison with a greenhouse with no heating system.

Keywords: solar system, agricultural greenhouse, heating, storage, drying

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Authors: Timine Suoware, Sylvester Edelugo, Charles Amgbari

Abstract:

The susceptibility of natural fibre polymer composites to flame has necessitated research to improve and develop flame retardant (FR) to delay the escape of combustible volatiles. Previous approaches relied mostly on FR such as aluminium tri-hydroxide (ATH) and ammonium polyphosphate (APP) to improve fire performances of wood sawdust polymer composites (WSPC) with emphasis on non-structural building applications. In this paper, APP was modified with gum Arabic powder (GAP) and then hybridized with ATH at 0, 12 and 18% loading ratio to form new FR species; WSPC12%APP-GAP and WSPC18%ATH/APP-GAP. The FR species were incorporated in wood sawdust waste reinforced in polyester resin to form panels for fireproof doors. The panels were produced using hand lay compression moulding technique and cured at room temperature. Specimen cut from panels were then tested for tensile strength (TS), flexural strength (FS) and impact strength (IS) using universal testing machine and impact tester; thermal stability using (TGA/DSC 1: Metler Toledo); time-to-ignition (Tig), heat release rates (HRR); peak HRR (HRRp), average HRR (HRRavg), total HRR (THR), peak mass loss rate (MLRp), average smoke production rate (SPRavg) and carbon monoxide production (COP ) were obtained using the cone calorimeter apparatus. From the mechanical properties obtained, improvements of IS for the panels were not noticeable whereas TS and FS for WSPC12%APP-GAP respectively stood at 12.44 MPa and 85.58 MPa more than those without FR (WSPC0%). For WSC18%ATH/APP-GAP TS and FS respectively stood at 16.45 MPa and 50.49 MPa more compared to (WSPC0%). From the thermal analysis, the panels did not exhibit any significant change as early degradation was observed. At 900 OC, the char residues improved by 15% for WSPC12%APP-GAP and 19% for WSPC18%ATH/APP-GAP more than (WSC0%) at 5%, confirming the APP-GAP to be a good FR. At 50 kW/m2 heat flux (HF), WSPC12%APP-GAP improved better the fire behaviour of the panels when compared to WSC0% as follows; Tig = 46 s, HRRp = 56.1 kW/2, HRRavg = 32.8 kW/m2, THR = 66.6 MJ/m2, MLRp = 0.103 g/s, TSR = 0.04 m2/s and COP = 0.051 kg/kg. These were respectively more than WSC0%. It can be concluded that the new concept of modifying FR with GAP in WSC could meet the requirement of a fireproof door for building applications.

Keywords: composite, flame retardant, wood sawdust, fireproof doors

Procedia PDF Downloads 111

Authors: Welder A. Baldassini, Jon J. Ramsey, Marcos R. Chiaratti, Amália S. Chaves, Renata H. Branco, Sarah F. M. Bonilha, Dante P. D. Lanna

Abstract:

With increased production costs there is a need for animals that are more efficient in terms of meat production. In this context, the role of mitochondrial DNA (mtDNA) on physiological processes in liver, muscle and adipose tissues may account for inter-animal variation in energy expenditures and heat production. The purpose this study was to investigate if the amounts of mtDNA in liver, muscle and adipose tissue (subcutaneous and visceral depots) of Nellore bulls are associated with residual feed intake (RFI) and estimated heat production (EHP). Eighteen animals were individually fed in a feedlot for 90 days. RFI values were obtained by regression of dry matter intake (DMI) in relation to average daily gain (ADG) and mid-test metabolic body weight (BW). The animals were classified into low (more efficient) and high (less efficient) RFI groups. The bulls were then randomly distributed in individual pens where they were given excess feed twice daily to result in 5 to 10% orts for 90 d with diet containing 15% crude protein and 2.7 Mcal ME/kg DM. The heart rate (HR) of bulls was monitored for 4 consecutive days and used for calculation of EHP. Electrodes were fitted to bulls with stretch belts (POLAR RS400; Kempele, Finland). To calculate oxygen pulse (O2P), oxygen consumption was obtained using a facemask connected to the gas analyzer (EXHALYZER, ECOMedics, Zurich, Switzerland) and HR were simultaneously measured for 15 minutes period. Daily oxygen (O2) consumption was calculated by multiplying the volume of O2 per beat by total daily beats. EHP was calculated multiplying O2P by the average HR obtained during the 4 days, assuming 4.89 kcal/L of O2 to measure daily EHP that was expressed in kilocalories/day/kilogram metabolic BW (kcal/day/kg BW0.75). Blood samples were collected between days 45 and 90th after the beginning of the trial period in order to measure the concentration of hemoglobin and hematocrit. The bulls were slaughtered in an experimental slaughter house in accordance with current guidelines. Immediately after slaughter, a section of liver, a portion of longissimus thoracis (LT) muscle, plus a portion of subcutaneous fat (surrounding LT muscle) and portions of visceral fat (kidney, pelvis and inguinal fat) were collected. Samples of liver, muscle and adipose tissues were used to quantify mtDNA copy number per cell. The number of mtDNA copies was determined by normalization of mtDNA amount against a single copy nuclear gene (B2M). Mean of EHP, hemoglobin and hematocrit of high and low RFI bulls were compared using two-sample t-tests. Additionally, the one-way ANOVA was used to compare mtDNA quantification considering the mains effects of RFI groups. We found lower EHP (83.047 vs. 97.590 kcal/day/kgBW0.75; P < 0.10), hemoglobin concentration (13.533 vs. 15.108 g/dL; P < 0.10) and hematocrit percentage (39.3 vs. 43.6 %; P < 0.05) in low compared to high RFI bulls, respectively, which may be useful traits to identify efficient animals. However, no differences were observed between the mtDNA content in liver, muscle and adipose tissue of Nellore bulls with high and low RFI.

Keywords: bioenergetics, Bos indicus, feed efficiency, mitochondria

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Authors: Mila Fantinelli

Abstract:

Cairo is the perfect modern representation of a living hell between rising temperatures and failing architecture. The works of Egyptian writer Mohamed Makhzangi may prove to add more depth to the debate that surrounds the topic of pollution in Arab literature. This Egyptian writer, who studied medicine and psychiatry before dedicating his life to writing, sheds indeed further light on the ecological condition of Cairo. In relation to this, the short story “The breeze hunter” (2018) tackles the topic of “environmental architecture”, citing the research of Hassan Fathy in “Architecture for the Poor: An Experiment in Rural Egypt” which provides examples from history of inventions to counter the effects of the hot weather. Specifically, the main character of the story is a man living in Cairo in a small apartment, which turns into a living hell in the summer season because of the high temperatures. Catastrophic urban planning, therefore, prevents the mitigation of the torrid climate but rather worsens it. Makhzangi indeed refers to the environmental issue of pollution caused by the excessive number of air conditioners, which transform Cairo into an infernal island of heat while our country becomes warmer with the passing of time. His description of the city already intersects ecocriticism and environmental issues, tackling the topic of pollution inside of cities and the impact of climate change, of which Cairo is a perfect example. History has indeed provided ways in order to reduce the heat inside houses. Yet, these have all been ignored. As a result, through the teachings of Hassan Fathy, the narrator of the story builds an opening in the house to catch the natural breeze coming from the north. He, therefore, becomes the breeze hunter of Mansoura. However, polluted waves interrupt this brief rest, thus leading to a worsening of his conditions, leading to him suffering from the effects of crowding and the consequences of climate change and pollution. Makhzangi, therefore, reflects on how architecture and urbanism affect the psychological sanity of people and how the situation is worsened by the catastrophic consequences of climate change and pollution.

Keywords: ecocriticism, Arabic literature, pollution, environmental architecture, crowding

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Authors: Ranadhir Roy

Abstract:

Inverse problems arise in medical (molecular) imaging. These problems are characterized by large in three dimensions, and by the diffusion equation which models the physical phenomena within the media. The inverse problems are posed as a nonlinear optimization where the unknown parameters are found by minimizing the difference between the predicted data and the measured data. To obtain a unique and stable solution to an ill-posed inverse problem, a priori information must be used. Mathematical conditions to obtain stable solutions are established in Tikhonov’s regularization method, where the a priori information is introduced via a stabilizing functional, which may be designed to incorporate some relevant information of an inverse problem. Effective determination of the Tikhonov regularization parameter requires knowledge of the true solution, or in the case of optical imaging, the true image. Yet, in, clinically-based imaging, true image is not known. To alleviate these difficulties we have applied the penalty/modified barrier function (PMBF) method instead of Tikhonov regularization technique to make the inverse problems well-posed. Unlike the Tikhonov regularization method, the constrained optimization technique, which is based on simple bounds of the optical parameter properties of the tissue, can easily be implemented in the PMBF method. Imposing the constraints on the optical properties of the tissue explicitly restricts solution sets and can restore uniqueness. Like the Tikhonov regularization method, the PMBF method limits the size of the condition number of the Hessian matrix of the given objective function. The accuracy and the rapid convergence of the PMBF method require a good initial guess of the Lagrange multipliers. To obtain the initial guess of the multipliers, we use a least square unconstrained minimization problem. Three-dimensional images of fluorescence absorption coefficients and lifetimes were reconstructed from contact and noncontact experimentally measured data.

Keywords: constrained minimization, ill-conditioned inverse problems, Tikhonov regularization method, penalty modified barrier function method

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Authors: Z. Mazrouei-Sebdani, A. Khoddami, H. Hadadzadeh, M. Zarrebini

Abstract:

Silica aerogels are well-known meso-porous materials with high specific surface area (500–1000 m2/g), high porosity (80–99.8%), and low density (0.003–0.8 g/cm3). However, the silica aerogels generally are highly brittle due to their nanoporous nature. Physical and mechanical properties of the silica aerogels can be enhanced by compounding with the fibers. Although some reports presented incorporation of the fibers into the sol, followed by further modification and drying stages, no information regarding the aerogel powders as filler in the polymeric fibers is available. In this research, waterglass based aerogel powder was prepared in the following steps: sol–gel process to prepare a gel, followed by subsequent washing with propan-2-ol, n-Hexane, and TMCS, then ambient pressure drying, and ball milling. Inspired by limited dust releasing, aerogel powder was introduced to the PET electrospinning solution in an attempt to create required bulk and surface structure for the nano fibers to improve their hydrophobic and insulation properties. The samples evaluation was carried out by measuring density, porosity, contact angle, sliding angle, heat transfer, FTIR, BET and SEM. According to the results, porous silica aerogel powder was fabricated with mean pore diameter of 24 nm and contact angle of 145.9º. The results indicated the usefulness of the aerogel powder confined into nano fibers to control surface roughness for manipulating superhydrophobic nanowebs with sliding angle of 5˚ and water contact angle of 147º. It can be due to a multi-scale surface roughness which was created by nanowebs structure itself and nano fibers surface irregularity in presence of the aerogels while a laye of fluorocarbon created low surface energy. The wettability of a solid substrate is an important property that is controlled by both the chemical composition and geometry of the surface. Also, a decreasing trend in the heat transfer was observed from 22% for the nano fibers without any aerogel powder to 8% for the nano fibers with 4% aerogel powder. The development of thermal insulating materials has become increasingly more important than ever in view of the fossil energy depletion and global warming that call for more demanding energy-saving practices.

Keywords: Superhydrophobicity, Insulation, Sol-gel, Surface energy, Roughness.

Procedia PDF Downloads 331

Authors: F. F. Zahra, E. G. Touria, Y. Samia, M. Ahmed, H. Hasna, B. M. Latifa

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The presence of radioactive contaminants in wastewater poses a significant environmental and health risk, necessitating effective treatment solutions. This study investigates the optimization of chitosan-Argan nutshell beads for the removal of radioactive elements from wastewater, utilizing Response Surface Methodology (RSM) to enhance the treatment efficiency. Chitosan, known for its biocompatibility and adsorption properties, was combined with Argan nutshell powder to form composite beads. These beads were then evaluated for their capacity to remove radioactive contaminants from synthetic wastewater. The Box-Behnken design (BBD) under RSM was employed to analyze the influence of key operational parameters, including initial contaminant concentration, pH, bead dosage, and contact time, on the removal efficiency. Experimental results indicated that all tested parameters significantly affected the removal efficiency, with initial contaminant concentration and pH showing the most substantial impact. The optimized conditions, as determined by RSM, were found to be an initial contaminant concentration of 50 mg/L, a pH of 6, a bead dosage of 0.5 g/L, and a contact time of 120 minutes. Under these conditions, the removal efficiency reached up to 95%, demonstrating the potential of chitosan-Argan nutshell beads as a viable solution for radioactive wastewater treatment. Furthermore, the adsorption process was characterized by fitting the experimental data to various isotherm and kinetic models. The adsorption isotherms conformed well to the Langmuir model, indicating monolayer adsorption, while the kinetic data were best described by the pseudo-second-order model, suggesting chemisorption as the primary mechanism. This study highlights the efficacy of chitosan-Argan nutshell beads in removing radioactive contaminants from wastewater and underscores the importance of optimizing treatment parameters using RSM. The findings provide a foundation for developing cost-effective and environmentally friendly treatment technologies for radioactive wastewater.

Keywords: adsorption, argan nutshell, beads, chitosan, mechanism, optimization, radioactive wastewater, response surface methodology

Procedia PDF Downloads 39