Design Of Large-scale Systems Research Articles

Design of Series-Connected Novel Large-Scale Offshore Wind Power All-DC System with Fault Blocking Capability

The utilization of wind power all-DC systems with DC collection and transmission is an effective solution for the extensive development of wind power in deep-sea areas. However, in the event of faults occurring in wind power all-DC systems, the fault propagation speed is extremely rapid, with a wide-ranging impact, and to date, there are no complete DC engineering references available. It is crucial to research the topology and fault isolation methods applicable to large-scale offshore wind power all-DC systems in deep-sea areas. This paper proposes a novel series-connected all-DC system topology and presents corresponding fault isolation methods for internal faults in wind turbine units and faults in high-voltage DC transmission lines. The system simulation model was constructed using PSCAD/EMTDC (v4.6.3), and simulations were conducted for internal faults in the wind turbine units and DC transmission line short-circuit faults. The simulation results demonstrate that the proposed system can isolate various DC faults while maintaining stable operation, thereby validating the effectiveness of the control strategies and fault isolation methods proposed in this paper.

Effect of geothermal heat transfer on performance of the adiabatic compressed air energy storage systems with the salt cavern gas storage

The temperature and pressure of compressed air influence the output performance of the adiabatic compressed air energy storage system with salt cavern gas storage. However, current studies often overlook the impact of geothermal heat transfer on system performance. In this paper, a mathematical model of the energy storage system with salt cavern gas storage is developed, considering the geothermal heat transfer in wellbore and salt carven. The model is used to illustrate the effects of geothermal heat transfer on the system performance through a sensitivity analysis of some key parameters. The results show that geothermal heat transfer will significantly reduce the gas and energy storage capacity, with a maximum reduction of 15.3 % and 11.1 % at a depth of 2000 m, respectively. Furthermore, this reduction would increase with the depth of the salt cavern. The geothermal energy reduces the round-trip efficiency from 68.7 % to 66.26 % and the energy storage density from 5.18 kWh·m−3 to 4.32 kWh·m−3. For common salt cavern gas storage at a depth of about 600 m ∼ 1200 m, the negative impacts of geothermal heat transfer on round-trip efficiency and energy storage density are less than 2.2 % and 0.5 kWh·m−3, respectively. Sensitivity analysis also demonstrates that the primary factors influencing the system’s performance are the thermal conductivity of the surrounding rock, the volume of the salt cavern, and the length of the wellbores. The secondary factors include the heat transfer coefficient on the inner wall of the salt cavern, the inner pipe radius, and the roughness of the inner pipe. Irrelevant factors include the thermal conductivity of the protective fluid and the thermal conductivity of the cement layer. The proposed comprehensive mathematical model of the system and simulation findings aid in forecasting the performance of storage systems and offer theoretical guidance for the design of large-scale compressed air energy storage systems.

EFraS: Emulated framework to develop and analyze dynamic Virtual Network Embedding strategies over SDN infrastructure

The integration of Software-Defined Networking (SDN) into Network Virtualization (NV) significantly enhances network management, isolation, and troubleshooting capabilities. However, it brings forth the intricate challenge of allocating Substrate Network (SN) resources for various Virtual Network Requests (VNRs), a process known as Virtual Network Embedding (VNE). It encompasses solving two intractable sub-problems: embedding Virtual Machines (VMs) and embedding Virtual Links (VLs). While the research community has focused on formulating embedding strategies, there has been less emphasis on practical implementation at a laboratory scale, which is crucial for comprehensive design, development, testing, and validation policies for large-scale systems. However, conducting tests using commercial providers presents challenges due to the scale of the problem and associated costs. Moreover, current simulators lack accuracy in representing the complexities of communication patterns, resource allocation, and support for SDN-specific features. These limitations result in inefficient implementations and reduced adaptability, hindering seamless integration with commercial cloud providers. To address this gap, this work introduces EFraS (Emulated Framework for Dynamic VNE Strategies over SDN). The goal is to aid developers and researchers in iterating, testing, and evaluating VNE solutions seamlessly, leveraging a modular design and customized reconfigurability. EFraS offers various functionalities, including generating real-world SN topologies and VNRs. Additionally, it integrates with a diverse set of evaluation metrics to streamline the testing and validation process. EFraS leverages Mininet, Ryu controller, and OpenFlow switches to closely emulate real-time setups. Moreover, we integrate EFraS with various state-of-the-art VNE schemes, ensuring the effective validation of embedding algorithms.

A new method for model reduction and controller design of large-scale dynamical systems

A new method for model reduction and controller design of large-scale dynamical systems

Deep photonic network platform enabling arbitrary and broadband optical functionality

Expanding applications in optical communications, computing, and sensing continue to drive the need for high-performance integrated photonic components. Designing these on-chip systems with arbitrary functionality requires beyond what is possible with physical intuition, for which machine learning-based methods have recently become popular. However, computational demands for physically accurate device simulations present critical challenges, significantly limiting scalability and design flexibility of these methods. Here, we present a highly-scalable, physics-informed design platform for on-chip optical systems with arbitrary functionality, based on deep photonic networks of custom-designed Mach-Zehnder interferometers. Leveraging this platform, we demonstrate ultra-broadband power splitters and a spectral duplexer, each designed within two minutes. The devices exhibit state-of-the-art experimental performance with insertion losses below 0.66 dB, and 1-dB bandwidths exceeding 120 nm. This platform provides a tractable path towards systematic, large-scale photonic system design, enabling custom power, phase, and dispersion profiles for high-throughput communications, quantum information processing, and medical/biological sensing applications.

Microwave dielectric characterization and loss mechanism of biowaste during pyrolysis

The effectiveness of microwave heating of biomass depends on the changes in dielectric properties. This study systematically investigated the microwave dielectric properties and loss mechanism of biowaste at different frequencies (915 and 2,450 MHz) and temperatures (25–800 °C) using the cavity perturbation method. The gas emissions and properties of the biochar produced during pyrolysis were characterised by SEM, XRD, and TG-FTIR. The findings revealed that the dielectric properties and penetration depth of the biomass and biochar changed significantly during microwave pyrolysis. The dielectric constant and dielectric loss coefficient first increased and then decreased during the drying stage, and they decreased continuously with the volatile’s generation in the pyrolysis stage, while the formation and high conductivity of biochar promoted a sharp increase in dielectric properties during the carbonisation stage. The loss tangent angle is 0.01–0.05 in the first two stages, while it increased to 0.10–0.25 in the carbonization stage, demonstrated that the biowaste is a low-loss material. However, the microwave absorption capacity was enhanced for pyrolyzed biochar. The loss mechanism revealed that dipole polarisation primarily contributes to the dielectric loss during the drying and pyrolysis stages, whereas interfacial polarisation plays an important role during the carbonisation stage. The data provided in this paper contribute to the evaluation of microwave penetration depth in biomass and the design of large-scale microwave-based heating systems. Furthermore, this study offers a theoretical basis for modelling and simulating the microwave heating of biomass and achieving efficient energy conversion.

Adaptivity, Structure, and Objectives in Sequential Decision-Making

Sequential decision-making algorithms are ubiquitous in the design and optimization of large-scale systems due to their practical impact. The typical algorithmic paradigm ignores the sequential notion of these problems: use a historical dataset to predict future uncertainty and solve the resulting offline planning problem.

Intelligent optimization design of large-scale three-dimensional ultrasonic vibration system

Large-scale three-dimensional ultrasonic vibration systems are susceptible to the influence of coupled vibration, resulting in a series of problems such as increased energy loss, small longitudinal displacement amplitude of the radiation surface, and uneven distribution of longitudinal displacement amplitude, which can seriously affect the working efficiency of ultrasonic processing system. How to effectively control the coupled vibration of large-scale three-dimensional ultrasonic transducer systems and optimize their performance has become an urgent problem in the field of power ultrasound. The research has found that some phononic crystal slots and point defect structures can suppress the lateral vibration of large-scale transducer systems, improve the uniformity of system amplitude distribution, and artificially regulate the performance of large-scale three-dimensional ultrasonic vibration systems by changing the configuration parameters of phononic crystal structures. However, excessive design parameters will inevitably increase the complexity of system design, and, currently, the optimization design of large-scale three-dimensional ultrasonic transducer systems relies on empirical trial and error methods which has low design efficiency and low success rate, and cannot guarantee the system performance. Therefore, in the study, homogeneous dislocations and point defect structures are introduced to optimize the design of large-scale three-dimensional ultrasonic vibration systems. Data analysis techniques are used to evaluate the influences of the configuration of homogeneous dislocations and point defect structures on the longitudinal displacement amplitude, amplitude distribution uniformity, radiated sound power, working bandwidth of the system’s radiation surface. And a predictive model for the performance of large-scale ultrasonic transducer system with homogeneous dislocation structure and near periodic defect structure is established, which can achieve intelligent design of large-scale power ultrasonic transducer system, improve design efficiency and success rate, and reduce the design cost.

Architecture Design and Optimization of Large-scale Data Processing Systems in Cloud Computing Environments

Architecture Design and Optimization of Large-scale Data Processing Systems in Cloud Computing Environments

Decomposition of PFOA in IEX regeneration wastewater: Comparison of UV/sulfur-based processes, key parameters and submicellar aggregates on degradation kinetics

The ultraviolet/sulfite (UV/S) destruction of perfluorooctanoic acid (PFOA) in seven solutions, representing ion exchange (IEX) regeneration wastes, was investigated, where Na2SO4, Na2SO3 and NaCl were the most promising regenerants in degrading PFOA. A complete degradation and 94.7 % defluorination of 10 ppm PFOA were achieved in 10 % (1.7 M) NaCl regenerant containing waste within fluence of 248 J.cm−2. Among the four investigated key parameters (pH, sulfite dose, temperature, and initial PFOA concentration), an unexpected trend was observed for the effect of PFOA initial concentration on degradation efficiency. At outset of process, higher initial PFOA concentrations led to substantial decrease in PFOA concentration, but this trend reversed as the process continued. This drop in concentration is explained based on the fast aggregation of PFOA as revealed in molecular dynamic simulations and thermodynamic calculation. The reactivity of aggregates toward eaq- was three orders of magnitude less than that of monomers. Incorporating aggregation into the kinetic model led to a noteworthy improvement in the model prediction of PFOA concentration profiles, closely aligned with the experimental data. Neglecting aggregation kinetics in models can lead to an overestimation of the required fluence for PFOA degradation, with significant cost implications for large-scale treatment system design. This study underscores the critical and often underestimated issue of PFAS aggregation and its profound implications for the effectiveness of UV/S treatment in addressing IEX regeneration wastes.

A Review of Optimal Design for Large-Scale Micro-Irrigation Pipe Network Systems

Micro-irrigation pipe network systems are commonly utilized for water transmission and distribution in agricultural irrigation. They effectively transport and distribute water to crops, aiming to achieve water and energy conservation, increased yield, and improved quality. This paper presents a model for the scaled micro-irrigation pipeline network system and provides a comprehensive review of the fundamental concepts and practical applications of optimization techniques in the field of pipeline network design. This paper is divided into four main sections: Firstly, it covers the background and theoretical foundations of optimal design for scaled micro-irrigation pipeline network systems. Secondly, the paper presents an optimal design model specifically tailored for scaled micro-irrigation pipeline networks. And then, it discusses various optimization solution techniques employed for addressing the design challenges of scaled micro-irrigation pipeline networks, along with real-world case studies. Finally, this paper concludes with an outlook on the ongoing research and development efforts in the field of scaled micro-irrigation pipeline network systems. In addition, this paper establishes a fundamental model for optimizing pipeline networks, to achieve minimum safe operation and total cost reduction. It considers constraints such as pipeline pressure-bearing capacity, maximum flow rate, and diameter. The decision-making variables include pipeline diameter, length, internal roughness, node pressure, future demand, and valve placement. Additionally, this paper provides an extensive overview of deterministic methods and heuristic algorithms utilized in the optimal design of micro-irrigation pipeline networks. Finally, this paper presents future research directions for pipeline network optimization and explores the potential for algorithmic improvements, integration of machine learning techniques, and wider adoption of EPANET 2.0 software. These endeavors aim to lay a strong foundation for effectively solving complex and challenging optimization problems in micro-irrigation pipeline network systems in the future.

Decentralized Controller Design for Large-Scale Uncertain Discrete-Time Systems with Non-Block-Diagonal Output Matrix

This research paper addresses the challenge of designing a decentralized controller for a discrete-time uncertain polytopic system with a linear large-scale (LSS) structure. Specifically, we investigate this problem in cases where the subsystem’s output matrix lacks a decentralized structure. Firstly, the proposed novel procedure of a decentralized controller design transforms the LSS model to have a fully decentralized structure (both input and output matrices are block-diagonal). Then, the robust stability boundary parameter is calculated for the open-loop system. This stability boundary parameter is considered in robust decentralized controller design where an appropriate controller design method is used. The entire process of designing a robust decentralized controller takes place at the subsystem level, and the influence of interaction is considered through the robust stability boundary parameter. Lastly, we present an example of a five-order system comprising two subsystems to show the effectiveness of the new method.

Signal detection of large scale MIMO physical layer space optical communication system based on genetic algorithm

In order to understand the signal detection design of large-scale MIMO physical layer spatial optical communication systems, the author proposes a research on signal detection of large-scale MIMO physical layer spatial optical communication systems based on genetic algorithms. Firstly, the author introduces the basic principles of large-scale MIMO physical layer spatial optical communication systems, analyzes signal detection issues and their challenges. Secondly, a nitty gritty acquaintance was given with hereditary calculation and its application in signal recognition. A genetic algorithm-based signal detection technology scheme was developed for MIMO-OFDM systems. The GA algorithm was contrasted with the ML algorithm and the BLAST algorithm to comprehend its performance. The following parameters served as the foundation for the outcomes of the simulation: The quantity of subcarriers in OFDM is K = 18, and the quantity of images sent per transporter is 165; Additionally, simulation was used to demonstrate how the genetic algorithm affected the MIMO-OFDM system's signal detection. Subsequent to concentrating on the location calculations in MIMO-OFDM frameworks, hereditary calculations are applied to flag discovery in MIMO-OFDM frameworks, to accomplish a decent split the difference among execution and computational intricacy.

Optimization of the thermal management system of battery thermal network model based on coupled liquid cooling of phase change materials

Battery optimization models can extend battery life, improve safety, and maximize battery performance. Previous optimization methods focus more on (Computational Fluid Dynamics) CFD and data mining. However, the two optimization methods apply only to specific battery module structures. In addition, previous studies have focused more on improving optimization algorithms to improve performance, but the computational design of large-scale battery thermal management systems still requires large loads. This paper presents a new optimization method based on the thermal equivalent circuit model (TECM) of battery thermal management system (BTMS) for the efficient design of liquid-cold coupled phase change material BTMS (PCM). The method can easily change and optimize the battery module structure according to different application scenarios. Compared with the reference structure, the TECM Parto solution optimized combined with multi-objective particle swarm optimization (MOPSO) saved 39.51 % of the liquid cooling energy consumption, reduced the maximum temperature rise of BTMS by 0.38 °C, and reduced the maximum temperature difference by 19.81 %. The method adopted can improve the efficiency and quality of BTMS design, reduce the cost of trial and error, and speed up the design process.

Large-scale solar system design, optimal sizing and techno-economic-environmental assessment

Malaysia targets to achieve an energy mix that is inclusive of at least 20% of renewable energies by the year 2025. Large-scale solar photovoltaic system (LSS-PV) emerged as the most preferable choice in Malaysia. Energy Commission (EC) Malaysia has launched competitive bidding on LSS since 2016 with a capacity of 500 MW in Peninsular Malaysia and targets to add the solar capacity in Peninsula Malaysia to 500 MW by 2021. Solar energy is a very intermittent source which causes voltage variation. This project aims to overcome the shortcomings of the intermittency of solar energy by identifying an optimum PV panel sizing and configuration that reduces the intermittency of the supply. The project was carried out in three distinctive stages; first suitable sites were selected based on the EC proposed locations and further scrutinized to locations near the Main Transmission intake. The second stage was to size of PV panels and the inverters. The optimization was carried out at the PV module level. Suitable inverters for the said configuration was identified with a tool that evaluates the technical aspects of renewable energy power systems. The final stage was the economic analysis and environmental assessment. The optimization method has shown improvement in the system production from a range of 1.7–3.9% in the 30 MW plants. Countries with similar climates as Malaysia like Indonesia, Philippines and Thailand can adopt this optimizing method to size PV farms.

Pyrolysis characteristics and kinetic parameters assessment of typical agricultural residues using high heating photothermal TGA

Understanding the complexity of thermal degradation characteristics, kinetic behavior, and thermodynamic parameters is pertinent for the efficient design and process optimization of large-scale conversion systems. Comparative assessment of pyrolysis characteristics and thermodynamic parameters of corn cobs (CC), peanut shells (PS) and sugarcane bagasse (SB) are investigated using high heating rates (100–1000 ℃/min) TGA results. Despite the effect of the heating rate increase on the degradation process of individual samples, increasing heating rates affect pyrolysis characteristics due to compositional differences. With increasing heating rates, the comprehensive pyrolysis index increased sporadically for CC (2.54–44.09 ×104%2/min2 ℃3) and PS (1.36–31.13 ×104%2/min2 ℃3), indicating easier release of volatiles at higher pyrolysis heating rates. The pyrolysis stability index (Rw), which relates to thermal degradation stability, presented an increasing trend (CC>PS>SB) with a heating rate increase (≥106%). Kinetic parameters estimated using model-free methods showed that the activation energies of CC and PS were significantly lower than that of SB. Comparative evaluation of fractional conversions and kinetic parameters using traditional and modified parallel models showed that the double random pore model (DRPM) fitted the best for simulating the experimental results at higher heating rates than other models. The change in Gibbs free energy (ΔG) trend for PS and SB showed a deviation, suggesting reaction difficulties due to higher temperature demands.

Generating simplified ammonia reaction model using genetic algorithm and its integration into numerical combustion simulation of 1 MW test facility

A small reaction model for ammonia is necessary for the design and development of large-scale ammonia combustion systems using computational fluid dynamics (CFD) simulation. The present study generated a simplified reaction model for ammonia using a genetic algorithm (14 species and 45 reactions). Optimization was applied to reproduce the combustion properties of ignition delay times, mole fractions of ammonia and nitric oxide at the exit of a single perfectly stirred reactor (PSR), those of a two-staged PSR, and laminar flame speed. The generated simplified reaction model for ammonia was applied to combustion CFD simulation for a 1 MW boiler test facility. Results of CFD simulation reproduced the characteristics of two-staged combustion well. Nitrous oxide emissions predicted by the simulation agreed well with experimental results.

A machine-learning digital-twin for rapid large-scale solar-thermal energy system design

In many industrialized regions of the world, large-scale photovoltaic systems now contribute a significant part to the energy portfolio during daylight operation. However, as energy demands peak shortly before sunset and persist for several hours afterwards, the integration of solar-thermal systems is extremely advantageous as a green “bridge” energy source. Accordingly, this work develops a digital-twin model to track and optimize the flow of incoming solar power through a complex solar-thermal storage system, consisting of a large array of adaptable mirrors, an optical-receiver and a power distribution system for customers to extract energy. Specifically, the solar power flow is rapidly computed with a reduced order model of Maxwell’s equations, based on a high-frequency decomposition of the irradiance into multiple rays that experience mirror reflections, losses and ultimately receiver absorption and customer delivery. The method allows for rapid testing (in microseconds) of the performance of large numbers of mirror-receiver layout configurations in design space, over extremely long time periods, such as weeks, months and years, using a genetic-based machine-learning digital-twin framework, which integrates submodels for: •optics and tracking of the Fresnel multi-mirror system,•thermal absorption of the optical energy by the receiver and•optimal operating temperatures balancing radiative losses with heat storage. The overall machine-learning digital-twin optimizes the configuration layout to balance meeting customer demands and operational efficiency. Numerical examples are provided to illustrate the approach. Finally, a deep-learning algorithm is developed and applied to the create an Artificial Neural-Net representation, which allows for even further simulation speedup.

A novel robust extended dissipativity state feedback control system design for interval type-2 fuzzy Takagi-Sugeno large-scale systems

Recently, systems have become large in their model and dynamic. To apply control algorithms, serious problems appear that need to be solved. Two significant problems are modelling the dynamics of the large-scale system and reducing effects of perturbations. In this paper, we use the advantage of large-scale systems modelling based on the type-2 fuzzy Takagi–Sugeno model to cover the uncertainties caused by large-scale systems modelling. The advantage of using membership function information is the reduction of conservatism resulting from stability analysis. Also, this paper uses the extended dissipativity robust control performance index to reduce the effect of external perturbations on the large-scale system, which is a generalization of , , passive and dissipativity performance indexes and control gains can be achieved through solving linear matrix inequalities (LMIs). Hence, the whole closed-loop system is asymptotically stable. Finally, the effectiveness of the proposed method is demonstrated by two practical examples.

The quest for game changers - Review of new trends and innovations in the design of large-scale energy systems

This paper presents a subjective review of emerging trends and innovations in the design of large-scale energy systems. The main research question is how societies are responding to serious concerns about climate change, access to resources, access to energy, and economic development. The aim of the study is twofold. Firstly, it aims at the identification of major changes and emerging novelties that may have an impact on the functioning of modern societies and the future shape of energy systems. Secondly, the aim is to present the increasing complexity and multidimensionality of the issues related to the planning and design of energy systems. In order to achieve the objectives, a short development history and the key features of large-scale energy systems are presented in the first place. Then, the AARTT review framework is proposed, which groups trends and innovations into five respective categories, namely: Awareness, Approaches, Resources, Technologies, and Tools. Within each category up to date literature, reports and projects have been reviewed, and as a result, a mind map has been built, which depicts key innovations. In the last part of the paper, a vision of the future energy system is created based on key findings of the literature review. It addresses the identified innovations and development trends as well as the fact that the global energy transition is noticeably accelerating. It is concluded that the fate of our future doesn't depend solely on technologies but rather a holistic approach and concerted multi- and interdisciplinary activities are required.