Advanced Control Research Articles

The impact of AI on boosting renewable energy utilization and visual power plant efficiency in contemporary construction

The integration of Artificial Intelligence (AI) in the renewable energy sector has revolutionized the efficiency and utilization of renewable energy sources in contemporary construction, significantly impacting visual power plant operations. AI technologies, including machine learning and predictive analytics, optimize energy production, enhance system performance, and streamline maintenance processes, thereby driving the adoption and efficiency of renewable energy systems. AI-powered predictive maintenance tools analyze vast amounts of data from renewable energy installations, such as solar panels and wind turbines, to predict and prevent equipment failures. This proactive approach reduces downtime and maintenance costs, ensuring continuous and efficient energy generation. Additionally, AI algorithms optimize energy storage and distribution by accurately forecasting energy production and demand. This optimization helps balance supply and demand, reducing reliance on non-renewable energy sources and enhancing grid stability. In visual power plants, AI enhances operational efficiency through advanced monitoring and control systems. Real-time data from sensors and IoT devices are processed by AI to provide actionable insights, enabling operators to make informed decisions promptly. AI-driven automation in visual power plants ensures optimal performance by adjusting parameters in real-time, based on environmental conditions and energy demand, thus maximizing energy output. Moreover, AI facilitates the design and construction of smart buildings and infrastructure, integrating renewable energy systems seamlessly. AI-driven energy management systems in buildings analyze consumption patterns, optimize energy use, and promote energy-saving practices, contributing to overall energy efficiency. This integration not only reduces the carbon footprint of construction projects but also aligns with global sustainability goals. The impact of AI on boosting renewable energy utilization and visual power plant efficiency is profound. By leveraging AI technologies, contemporary construction can achieve higher energy efficiency, lower operational costs, and increased sustainability. As AI continues to evolve, its role in the renewable energy sector will likely expand, further enhancing the capabilities of renewable energy systems and paving the way for a more sustainable future in construction and beyond. This paper underscores the transformative potential of AI in renewable energy and its critical role in shaping the future of sustainable construction practices.

Sourcing renewable energy components: building resilient supply chains, reducing dependence on foreign suppliers, and enhancing energy security

The transition to renewable energy is essential for mitigating climate change and ensuring long-term energy security. As global demand for renewable energy technologies grows, establishing resilient supply chains becomes crucial. This paper examines strategies to build robust supply chains, reduce reliance on foreign suppliers, and enhance energy security. A major challenge in renewable energy is the dependence on a limited number of international suppliers, which risks supply disruptions due to geopolitical tensions, trade restrictions, or natural disasters. Diversifying suppliers can mitigate these risks by encouraging alternative suppliers and fostering competition (Smith, 2020). Local manufacturing is another critical strategy, promoting supply chain resilience, economic growth, and job creation. Countries like the United States and Germany have incentivized local manufacturing of solar panels and wind turbine components (Jones, 2021), ensuring a steady supply of parts and aligning with broader industrial policies. Maintaining strategic reserves of critical materials, such as rare earth elements and lithium, is essential for buffering against supply disruptions and price volatility, ensuring continuous production of renewable energy technologies (Brown, 2019). Strategic planning and investment in reserves are vital for a comprehensive energy security strategy. Technological innovation enhances supply chain resilience. Advances in materials science, manufacturing processes, and recycling technologies can reduce dependence on scarce materials and improve renewable energy system efficiency. Developing alternative materials for battery storage, for example, can reduce reliance on lithium and cobalt sourced from unstable regions (White, 2022). Public-private partnerships (PPPs) are crucial for advancing the renewable energy sector. By leveraging both sectors' strengths, PPPs can accelerate technology deployment, improve infrastructure, and foster innovation. Successful examples include government-private collaborations on offshore wind farms and smart grid technologies (Green & Associates, 2021). Favorable trade policies facilitate the global exchange of renewable energy technologies and components. Reducing tariffs, streamlining customs, and fostering international cooperation can enhance supply chain efficiency and reliability (Lee, 2020). This study advocates for trade agreements tailored to the renewable energy sector's needs. Integrated energy systems and smart grid technologies optimize renewable energy use and distribution. Advanced monitoring and control systems improve grid stability, reduce energy waste, and enhance renewable source integration (Adams, 2019). Investment in smart grid infrastructure is crucial for a resilient energy strategy. Robust policy frameworks are necessary to support these initiatives. Governments must enact policies providing clear guidance, incentives, and support for resilient renewable energy supply chains, including ambitious renewable energy targets, financial incentives for R&D, and regulatory stability (Martinez, 2021).

A novel development of advanced control approach for battery-fed electric vehicle systems

In today’s context, there is a clear preference for DC microgrids over AC microgrids due to their better compatibility with generating sources, loads, and battery energy storage systems (BESS). However, the intermittent nature of renewable resources disrupts the balance between power generation and load demand. It raises concerns regarding power management and quality in the power system. Control strategies are essential to address these challenges. This article focuses on developing a novel control strategy to ensure stability in microgrid systems. The proposed control structure utilizes a second-order multi-agent system (MAS) to enhance the power-sharing and coordination in the microgrid network. For effective control of battery energy storage units, a Voltage–Power (V-P) reference-based droop control and leader–follower consensus method is employed. The control approach consists of primary and secondary control layers. The primary layer uses a V-P reference-based droop control strategy to allocate load components to storage units. The secondary control layer aims to restore DC bus voltage using a MAS-based consensus protocol. The MAS approach offers greater flexibility and requires less computational power than other strategies such as Model Predictive Control (MPC). The enhanced control structure incorporates a current ratio modification loop to adjust the current ratio between the converters, thereby modifying gain and improving the voltage profile. This novel control optimizes the reliability and stability of the proposed DC microgrid system. The effectiveness of the enhanced consensus-based secondary control strategy is demonstrated using the MATLAB/Simulink platform.

An Alternative Reinforcement Learning (ARL) control strategy for data center air-cooled HVAC systems

Energy efficiency of data center is of great concern globally due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. Advanced control strategies are needed to reduce energy consumption. However, the optimization of data center HVAC control is a challenge task due to the complexity of the thermal dynamic models of buildings and uncertainties associated with both server loads and outdoor temperature. In this paper, we propose a new control strategy called Alternately-Reinforcement Learning-control(ARL), which realizes the alternating control of RL and proportional, integral and derivative (PID) for optimizing the control of air-cooled HVAC systems in data centers. The control object of the ARL is the speed set of the compressor and the condensing fan, and the control goal is to minimize energy consumption while maintaining temperature stability. The applied RL algorithm is Deep deterministic policy gradient (DDPG) and Proximal policy optimization (PPO). We pre-train the ARL strategy in offline environment firstly and deploy it in real data center environment for online testing. The test results show that the ARL has significant advantages in maintaining temperature stability while reducing energy consumption, and the PPO algorithm performs better than the DDPG algorithm. Compared with the PID algorithm, the PPO algorithm can save energy by 5.27%, and the temperature control effect can be improved by 3.27%,which indicates the feasibility of the ARL for implementation in real data centers.

Usability of personalized thermal control systems by people with intellectual disabilities in energy poverty

This study assessed the usability of three readily available Personalized Thermal Control Systems (PECS)—an electric blanket, a small personal fan, and a large pedestal fan—among individuals with intellectual disabilities living independently in energy poverty conditions in Chile. The research aimed to identify the primary usability challenges that affect the adoption and operational effectiveness of these technologies and, consequently, their potential to enhance thermal comfort. Results indicated that devices with more advanced control features, i.e. the large pedestal fan, presented the most significant usability challenges, followed by the electric blanket and the small personal fan. Key usability issues included poor visibility, inadequate material choice, ineffective communication, bad affordance, and inadequate levels of touch sensitivity of the control interface in these PECS. The study also showed a large variance in the level of adoption of the PECS among participants, thereby indicating that users have different individual attitudes, ranging from passive acceptance to proactive exploration and use. To conclude, this study advocates for the necessity of developing easily operable PECS that cater to the specific needs of individuals with intellectual disabilities, thereby supporting their autonomy and improving their quality of life in thermally comfortable environments.

Innovations in Aquaponics Technology and Building Sustainable Infrastructure for Agriculture

Aquaponics, a symbiotic integration of aquaculture and hydroponics, presents a promising approach to sustainable agriculture by maximizing resource utilization and minimizing environmental impact. This study explores recent innovations in aquaponics technology and the development of sustainable infrastructure to support its widespread adoption. A systematic literature review is conducted, showing the fundamentals of aquaponics and its key components such as fish tanks, grow beds, and biological filters. Innovations in technology are examined, including advanced monitoring and control systems, integration of renewable energy sources, automation, and novel growing techniques. Sustainable infrastructure for aquaponics is analyzed, focusing on water management strategies, energy efficiency measures, material selection, and integration with urban agriculture. Furthermore, the alignment of aquaponics technology and sustainable infrastructure development with the United Nations Sustainable Development Goals (SDGs) is studied, particularly in achieving goals related to zero hunger, clean water and sanitation, responsible consumption and production, and climate action.

Finite control set model predictive current control for three phase grid connected inverter with common mode voltage suppression

This research introduces an advanced finite control set model predictive current control (FCS-MPCC) specifically tailored for three-phase grid-connected inverters, with a primary focus on the suppression of common mode voltage (CMV). CMV is known for causing a range of issues, including leakage currents, electromagnetic interference (EMI), and accelerated system degradation. The proposed control strategy employs a system model that predicts the inverter’s future states, enabling the selection of optimal switching states from a finite set to achieve dual objectives: precise current control and effective CMV reduction, a meticulously designed cost function evaluates the potential switching states, balancing the accuracy of current tracking against the necessity to minimize CMV. The approach is grounded in a comprehensive mathematical model that captures the dynamics of CMV within the system, and it utilizes an optimization process that functions in real-time to determine the most suitable control action at each interval, Experimental validations of the proposed FCS-MPCC scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters, Experimental validations of the proposed (MPC with CMV) scheme have demonstrated its effectiveness in significantly improving the performance and durability of three-phase grid-connected inverters. The proposed method achieved substantial reductions in CMV, notable improvements in current tracking accuracy, and extended system lifespan compared to conventional control methods.

Large-scale energy cost optimization and performance analysis for dedicated outdoor air system: simulation results from ASHRAE RP-1865

This is the first journal article from the ASHRAE research project RP 1865, “Optimizing Supply Air Temperature Control for Dedicated Outdoor Air Systems,” focusing on the large-scale energy performance evaluation of the optimal control of dedicated outdoor air systems (DOAS). DOAS is a type of air handling system that is installed in buildings to ensure proper ventilation for the built environment. It is acknowledged that DOAS with proper operation strategies can provide sufficient ventilation and dehumidification while achieving energy efficiency. In this regard, there have been efforts to develop advanced control sequences (e.g., optimal control) for DOAS. Still, limited studies have demonstrated the effectiveness of such DOAS controls on a larger scale under varying climate zones with different DOAS configurations. To fill the research gap, this study aims to quantify the energy-saving potentials of applying an optimal supply air temperature (SAT) control strategy for DOAS considering diverse configurations of HVAC systems in various climate conditions across the United States. For this study, we first developed a total of 180 EnergyPlus models accommodating different types of buildings (i.e., detailed medium-sized office and primary school) and HVAC systems (i.e., DOAS types, DOAS sizes, and terminal units) under varying ASHRAE climate zones (i.e., 2A, 3B, 4A, 4C, 6A, and 6B). Next, genetic algorithm (GA)-based optimizations were conducted to determine the optimal DOAS SAT control that would minimize the energy cost of the entire HVAC system operation. Our study of comparing the optimal control to a typical rule-based control (i.e., outdoor air temperature-based reset control) revealed approximately 4–34% of energy cost saving potential by optimizing the DOAS SAT control sequence.

Digital: accelerating the pathway.

The Spherical Tokamak for Energy Production (STEP) programme is an ambitious but challenging endeavour to design and deliver a prototype fusion power plant. It is a rapid, fast-moving programme, designing a first of a kind device in a Volatile, Uncertain, Complex and Ambiguous (VUCA) environment, and digital tools play a pivotal role in managing and navigating this space. Digital helps manage the complexity and sheer volume of information. Advanced modelling and simulation techniques provide a platform for designers to explore various scenarios and iteratively refine designs, providing insights into the intricate interplay of requirements, constraints and design factors across physics, technology and engineering domains and aiding informed decision-making amidst uncertainties. It also provides a means of building confidence in the new scientific, technological and engineering solutions, given that a full-scale-integrated precursor test is not feasible, almost by definition. The digital strategy for STEP is built around a vision of a digital twin of the whole plant. This will evolve from the current digital shadow formed by system architecting codes and complex workflows and will be underpinned by developing capabilities in plasma, materials and engineering simulation, data management, advanced control, industrial cybersecurity, regulation, digital technologies and related digital disciplines. These capabilities will help address the key challenges of managing the complexity and quantity of information, improving the reliability and robustness of the current digital shadow and developing an understanding of its validity and performance.This article is part of the theme issue 'Delivering Fusion Energy - The Spherical Tokamak for Energy Production (STEP)'.

Machine learning‐based time series analysis of polylactic acid bead foam extrusion

AbstractUnderstanding the behavior of polymer melts during extrusion is essential for optimizing processes and developing new materials. However, analyzing the continuous data generated by an extruder poses significant challenges. This paper investigates the utility of machine learning in predicting melt pressure at the die plate in polylactic acid (PLA) bead foam extrusion, a critical parameter in the extrusion process. Utilizing a random forest (RF) model, we examine how various processing parameters influence melt pressure. By segmenting the data into time‐delayed intervals, we achieve accurate predictions. We present forecasts of melt pressure at the die for intervals of 5 s, 1 min, and 5 min, demonstrating particularly strong performance for the 5‐min forecast with a Mean Absolute Error (MAE) of 1.88 and the coefficient of determination ( score) of 0.90. By exploring time series data, our study demonstrates the effectiveness of the RF model and provides a foundation for more advanced and precise control strategies in polymer bead extrusion processes.

Comparison of Advanced Multivariable Control Techniques for Axial-Piston Pump

This article is devoted to a comparison of two advanced control techniques applied to the same plant. The plant is a certain type of axial-piston pump. A linear-quadratic (LQR) controller and an H-infinity (H∞) controller were synthesized to regulate the displacement volume of the pump. The classical solution to such a problem is to use a hydro-mechanical controller (by pressure, flow rate, or power) but, in the available sources, there are solutions that implement proportional-integral-derivative (PID), LQR, model predictive control (MPC), etc. Unlike a classical solution, in our case, the hydro-mechanical controller is replaced by an electro-hydraulic proportional valve, which receives a reference signal from an industrial microcontroller. It is used as the actuator of the pump swash plate. The pump swash plate swivel angle determines the displacement volume, respectively, and the flow rate of the pump. The microcontroller is capable of embedding various control algorithms with different structures and complexities. The developed LQR and H∞ controllers are compared in the simulation and real experiment conditions. For this purpose, the authors have developed a laboratory experimental test bench, enabling a real-time function of the control system via USB/CAN communication. Both controllers are compared under different pump loading modes. Also, this paper contributes an uncertain model of an axial-piston pump with proportional valve control that is obtained from experimental data. Based on this model, the robust stability of the closed-loop system is investigated by comparing the structure of a singular value (μ). The investigations show that both control systems achieved robust stability. Moreover, they can tolerate up to four times larger uncertainties than modeled ones. The system with the H∞ controller attenuates approximately at least 30 times the disturbances with frequency up to 1 rad/s while the system with the LQR controller attenuates at least 10 times the same disturbances.

Optimizing full-scale MBR performance: A dual-phase approach for real-time air-scouring and permeate flow modifications

This study reports on an advanced fuzzy logic control system designed to optimize air-scour and permeate flow rates in membrane bioreactors, targeting enhanced energy efficiency and reduced fouling. Developed in two stages, the system integrates real-time monitoring of transmembrane pressure and permeability trends to adjust air-scour rates in response to fouling indicators. The initial simulations indicated potential for a 50 % reduction in air-scour rates, suggesting considerable energy savings. The second stage's refinements, based on empirical validations, yielded a more responsive system, albeit with more conservative operational adjustments for energy savings.Over eight months of full-scale testing, the advanced control system implemented in one of eight filtration lines exhibited lower fouling rates and maintained superior permeability values compared to the other lines. This performance translated into reduced chemical cleaning frequency, thereby affirming the system's efficiency in fouling management. These findings validate the application of fuzzy logic in MBR operations and offer a promising approach for energy-efficient and sustainable wastewater treatment.

Connected smart elevator systems for smart power and time saving

Smart elevators provide substantial promise for time and energy management applications by utilizing cutting edge artificial intelligence and image processing technology. In order to improve operating efficiency, this project designs an elevator system that uses the YOLO model for object detection. Compared to traditional methods, our results show a 15% improvement in wait times and a 20% reduction in energy use. Due to the elevator’s increased accuracy and dependability, users’ qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. Due to the elevator’s increased accuracy and dependability, users’ qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. The successful integration of artificial intelligence (AI) and image processing technologies in elevator systems presents a promising foundation for future research and development. Further advancements in object detection algorithms, such as refining YOLO models for even higher accuracy and real-time adaptability, hold potential to enhance operational efficiency. Integrating smart elevators more deeply into IoT networks and building management systems could enable comprehensive energy management strategies and real-time decision-making. Predictive maintenance models tailored to elevator components could minimize downtime and optimize service schedules, enhancing overall reliability. Additionally, exploring adaptive user interfaces and personalized scheduling algorithms could further elevate user satisfaction by tailoring elevator interactions to individual preferences. Sustainable practices, including energy-efficient designs and integration of renewable energy sources, represent crucial avenues for reducing environmental impact. Addressing security concerns through advanced encryption and access control mechanisms will be essential for safeguarding sensitive data in smart elevator systems.

Design of a Hierarchical-Type Control System Based on Smart MBD Approach and its Application to Hydraulic Excavator

Model-based development (MBD), which utilizes system models to design complex products, has received increasing attention. However, an advanced control system design scheme is required to accurately control the developed products under harsh conditions for practical usage. This paper proposes a control system that integrates a data-driven compensator (DDC) with a model-based control (MBC) system design. The proposed method considers a hierarchical control structure comprising an upstream control system based on the MBC design approach and a downstream control system that includes a plant control loop with a DDC. The proposed system can always maintain the desired performance by driving the MBC based on an ideal model because the downstream control system, DDC, maintains the characteristics of the ideal model even if the plant properties change owing to changes in operational and/or environmental conditions. In this study, the design scheme for unifying models and data in the MBD process is called smart MBD (S-MBD), and the proposed control system design scheme is based on the S-MBD approach. The effectiveness of the proposed hierarchical control system is verified by applying it to a hydraulic excavator.

Preface: 6th International Conference on Automatic Control and Mechatronic Engineering (ACME 2024)

2024 6th International Conference on Automatic Control and Mechatronic Engineering (ACME 2024) is to facilitate an exchange of information on best practices for advanced automation, control technology, vehicle engineering, electronic engineering, electrical engineering, mechanical engineering, and mechatronic engineering, addressing the problems and opportunities of a sustainable future. This conference provides a forum for engineers and scientists in academia, industry, and government to address the most innovative research and development including technical challenges and economic issues, and to present and discuss their ideas, results, work in progress and experience in these aspects. ACME 2024 was held during July 13-14, 2024, in Sapporo, Japan. The development of science occurs by utilizing collaboration with other branches of science such as technology, mechanical engineering, and mathematics. The distinctive feature of collaboration that has become an important value. We thank all participants for ACME 2024 who have contributed their research. I hope, with this kind of collaboration, we can jointly improve the development quality in this era. Organizing Committees of ACME Sapporo, Japan

Operational Technology resilience in the 2023 draft delegated act on cybersecurity for the power sector—An EU policy process analysis

The EU’s 2020 Cybersecurity Strategy promotes cybersecurity as essential for building a resilient, green, and digital Europe. Cleaner energy sources such as wind and solar are more volatile and thus need digital integration with Industrial Control Systems (ICS) for grid balancing. However, the digitization and the properties of cyberspace provide the ability to coordinate disruptive cyberattacks against power grid infrastructures. Digital weapons may be launched against ICS to start multiple cascading outages with a keystroke, causing large-scale blackouts we have never seen before. To reduce risk, the EU’s Strategy describes three objectives for ICS: Secure-by-design, resilient, and timely patched. In the strategy, the European Commission suggests a ”network code,” i.e. a delegated act for the electric power sector, setting rules for cybersecurity in cross-border electricity flows. The draft delegated act of November 2023 presents security requirements for Information and Communication Technology (ICT) and Network and Information Systems (NIS). Although ICS systems are used directly to manage electricity flows, ICS is only mentioned in one of the delegated act’s recitals as a subcategory of ICT products. Suppose Information Technology (IT) rather than Operational Technology (OT) is the focus of the delegated act. In that case, policymakers may not fulfill the EU cybersecurity strategy’s ICS objectives, thus failing to improve the resilience of power grid infrastructures and cross-border electricity flows. This study is a policy process analysis, and its contribution is threefold. First, a literature review is conducted to understand the extent to which the delegated act covers OT. Second, a framework condition analysis is applied to understand why the delegated act lacks OT-specific security requirements. Third, the analysis is extended to understand whether OT is sufficiently covered to achieve the EU strategy’s ICS objectives. In conclusion, our analysis shows a strong intention to include OT-specific security in the preparatory work of the delegated act, but that a stronger position of the IT communities forced OT onto the sideline. Further, the study shows weak fulfillment of general secure-by-design principles and security patch management. These results indicate that OT coverage in the delegated act is not in line with the expectations of the EU’s cybersecurity strategy and the delegated act’s early preparatory work. Therefore, we have suggested three measures to increase OT resilience focus in the act: (a) Define the expressions NIS, ICT services, ICT processes, and ICT in general as umbrella terms that include OT, (b) The foreseen minimum and advanced cybersecurity controls should require OT-specific measures, including holistic secure-by-design principles and patch management covering all patching phases, (c) Develop an OT implementation guide for the delegated act. Our work can be used by policymakers to optimize cybersecurity policy processes and by researchers studying socio-technical gaps in the cybersecurity domain.

Conversion of a Coaxial Rotorcraft to a UAV—Lessons Learned

A coaxial helicopter with a maximum take-off weight of 600 kg was converted to an unmanned aerial vehicle. A minimally invasive robotic actuator system was developed, which can be retrofitted onto the copilot seat of the rotorcraft in a short period of time to enable automatic flight. The automatic flight control robot includes electromechanical actuators, which are connected to the cockpit inceptors and control the helicopter. Most of the sensors and avionic components were integrated into the modular robotic system for faster integration into the rotorcraft. The mechanical design of the control system, the development of the robot control software, and the control system architecture are described in this paper. Furthermore, the multi-body simulation of the robotic system and the estimation of the linear low-order actuator models from hover-frame flight test data are discussed. The developed technologies in this study are not specific to a coaxial helicopter and can be applied to the conversion of any crewed flight vehicle with mechanical controls to unmanned or fly-by-wire. This agile development of a full-size flying test-bed can accelerate the testing of advanced flight control laws, as well as advanced air mobility-related functions.

Advanced Controller System with Hybrid Renewable Sources Enabling Vehicle-To-Grid and Grid-To-Vehicle Operations

Power demand is rising worldwide, forcing the search for an alternative source to solve the imminent power crisis, which is expected to be accomplished through the deployment of grid synchronized electric vehicles (EV). Wind and solar energy are incorporated into this integration to those concerns. One smart grid innovation that permits energy exchange between the EV and the grid is vehicle to grid (V2G) technology. For the purpose of charging EVs, this research proposes employing hybrid energy. Here, the neighboring household’s linear and non linear demands are powered by the excess electricity from the photovoltaics (PV) panel, wind turbine, and AC generator that is utilized to charge the EV battery. Because of its dependability and independence from the real system model, a proportional integral (PI) controller is used to maximize the DC voltage extracted from the PV panel. The improved DC voltage overall is a result of the SEPIC converter’s improved DC output. Concurrently, an AC generator is incorporated into the system to supply extra power to the grid. An artificial neural network (ANN) controller is used to convert the power generated by the PV panel’s Sepic conversion process into an improved output that is then converted to AC using a 3 phase Voltage Source Inverter (VSI). Harmonic components in the VSI output are corrected with an LC filter to ensure grid compatibility and safe current injection into the grid. This paper discusses the usage of a bidirectional converter to adjust the EV battery’s voltage. MATLAB 2021a / Simulink software is used to simulate and validate the proposed approach.

Clutching inertial system for base-isolated liquid storage tanks: Seismic performance and optimal parameters

Liquid storage tanks (LSTs) are susceptible to significant damage during earthquake events, necessitating their continued functionality and additional protection through isolation techniques. The sloshing response and overall base shear of LSTs are critical factors, particularly in base isolated (BI) tanks. This study aims to enhance the seismic resilience of LSTs by leveraging a newly developed advanced inerter-based vibration control methodology for structures. This innovative approach integrates a supplemental clutching inertial system (CIS) with a laminated rubber isolator (LRB). Utilizing an equivalent linearization technique, the study evaluates the equivalent damping and inertance constants to account for the nonlinear behavior of the CIS. These equivalent parameters are then used to assess the stationary peak response of the isolated tank structure, which encompasses sloshing and bearing displacements, forces within the CIS and the isolator, and the total base shear. Further, the analysis employs the stationary power spectral density function (PSDF) model of earthquake excitation by Kanai-Tajimi and the performance of the inerter-based hybrid control strategy is examined by varying system parameters, such as isolation period, isolation damping, CIS inertance mass ratio, and the tank's aspect ratio (i.e., broad or slender). An optimal inertance of the CIS is identified, leading to the least overall base shear for both isolated tank configurations. The impact of these parameters inertance mass ratio of the CIS is also investigated. Subsequently, the isolated tanks are exposed to eleven near-fault (NF) and far-field (FF) real-time earthquake excitations. The study evaluates the CIS's performance as an auxiliary system during these seismic events and contrasts the outcomes with the seismic response under stochastic excitations. The results highlight the effectiveness of the advanced inerter-based hybrid control strategy in reducing the seismic response of isolated tank configurations.

The Stiffness and Damping Characteristics of a Rubber-Based SMA Composite Shock Absorber with a Hyper-Elastic SMA-Constitutive Model Considering the Loading Rate.

Shock absorbers are essential in enhancing vehicle ride comfort by mitigating vibrations. However, traditional rubber shock absorbers are constrained by their fixed stiffness and damping properties, limiting their adaptability to varying loads and thus affecting the ride comfort, especially under extreme road conditions. Shape Memory Alloys (SMAs), known for their intelligent material properties, offer a unique solution by adjusting stiffness and damping in response to temperature changes or strain rates, making them ideal for advanced vibration control applications. This study builds upon the Auricchio constitutive model to propose an enhanced SMA hyper-elastic constitutive model that accounts for different loading rates. This new model elucidates the impact of loading rates on the stiffness and damping characteristics of SMAs. Additionally, we introduce an innovative circular rubber-based SMA composite vibration reduction structure. Through a parameterized model and finite element simulation, we comprehensively analyze the stiffness and damping properties of the composite damper under various loading rates and harmonic excitations. Our findings suggest a novel approach to improving the vehicle ride comfort, offering significant potential for engineering applications and practical value.