A cyber-physical approach to the optimal design of civil structures using boundary layer wind tunnels and mechatronic models

The threat of wind-related hazards to vulnerable coastal locations necessitates the development of economical approaches to design and construct resilient buildings. This study investigates using a cyber-physical systems (CPS) approach as a replacement for traditional trial-and-error methods for civil infrastructure design for wind loads. The CPS approach combines the accuracy of boundary layer wind tunnel (BLWT) testing with the efficiency of numerical optimization algorithms. The approach is autonomous: experiments are executed in a BLWT, sensor feedback is monitored and analyzed, and optimization algorithms dictate physical changes to the model through actuators. The cyberinfrastructure for this project was developed with the collaboration of multiple researchers at the University of Florida Experimental Facility (UFEF) under the Natural Hazard Engineering Research Infrastructure (NHERI) program. A proof-of-concept was developed to optimally design the parapet wall of a low-rise building. Parapet walls nominally reduce suction loads on the roof but lead to an increase in positive roof pressure and base shear. A mechatronic low-rise building model was created with a parapet wall of adjustable height for BLWT testing. Various single-objective optimization algorithms were implemented to minimize the magnitude of roof wind pressures. Multi-objective optimization was used to simultaneously minimize both the magnitude of roof suction pressures and building base shear. A multi-objective procedure can consider the competing objectives of multiple stakeholders often present in engineering design. The CPS approach was extended to optimize the performance of a landmark tall building for wind loads. A 1:200 multi-degree-of-freedom (MDOF) aeroelastic model was created to represent the building in a BLWT. Aeroelastic models directly simulate the scaled dynamic behavior of the building including effects of aerodynamic damping, vortex shedding, coupling within modes, and higher modes. The model was equipped with a series of variable stiffness devices (adjustable leaf springs) in the base to enable quick adjustments to the model’s dynamics. Additionally, the model was equipped with an active fin system (AFS) consisting of individually controllable fins installed at the four corners to modify the building aerodynamics and suppress vortex-induced vibrations. Multiple design problems were explored where the model’s dynamics and aerodynamics were refined using heuristic optimization algorithms to minimize costs while satisfying acceleration and drift limits. The traditional design process for wind requires lengthy collaboration between designers and wind tunnel operators. This process may include the construction of a limited set of building models, leading to a non-exhaustive exploration of potential designs. Using mechatronic models guided by optimization algorithms enables optimum designs to be attained quicker than conventional methods. In future work, the proposed cyber-physical framework can be expanded to integrate machine learning and other computational tools to improve efficiency and reduce the reliance on experimental testing

Joint operation of pressure reducing valves and pumps for improving the efficiency of water distribution systems

[EN] New environmental paradigms imposed by climate change and urbanization processes are leading cities to rethink urban management services. Propelled by technological development and the internet of things, an increasingly smart management of cities has favored the emergence of a new research field, namely, the smart city. Within this new way of considering cities, smart water systems are emerging for the planning, operation, and management of water distribution networks (WDNs) with maximum efficiency derived from the application of data analysis and other information technology tools. Considering the possibility of improving WDN operation using available demand data, this work proposes a hybrid and near-real-time optimization algorithm to jointly manage pumps working with variable speed drives and pressure-reducing valves for maximum operational efficiency. A near-real-time demand forecasting model is coupled with an optimization algorithm that updates in real time the water demand of the hydraulic model and can be used to define optimal operations. The D-town WDN is used to validate the proposal. The number of control devices in this WDN makes real time control especially complex. Warm solutions are proposed to cope with this feature as they reduce the computational effort needed if suitably tuned. In addition to energy savings of around 50%, the methodology proposed in this paper enables an efficient system pressure management, leading to significant leakage reduction.Brentan, BM.; Meirelles, G.; Luvizotto, E.; Izquierdo Sebastián, J. (2018). Joint operation of pressure reducing valves and pumps for improving the efficiency of water distribution systems. Journal of Water Resources Planning and Management. 144(9):04018055-1-04018055-12. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000974S04018055-104018055-12144

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Ion Channel Modeling beyond State of the Art: A Comparison with a System Theory-Based Model of the Shaker-Related Voltage-Gated Potassium Channel Kv 1.1

The mathematical modeling of ion channel kinetics is an important tool for studying the electrophysiological mechanisms of the nerves, heart, or cancer, from a single cell to an organ. Common approaches use either a Hodgkin–Huxley (HH) or a hidden Markov model (HMM) description, depending on the level of detail of the functionality and structural changes of the underlying channel gating, and taking into account the computational effort for model simulations. Here, we introduce for the first time a novel system theory-based approach for ion channel modeling based on the concept of transfer function characterization, without a priori knowledge of the biological system, using patch clamp measurements. Using the shaker-related voltage-gated potassium channel Kv1.1 (KCNA1) as an example, we compare the established approaches, HH and HMM, with the system theory-based concept in terms of model accuracy, computational effort, the degree of electrophysiological interpretability, and methodological limitations. This highly data-driven modeling concept offers a new opportunity for the phenomenological kinetic modeling of ion channels, exhibiting exceptional accuracy and computational efficiency compared to the conventional methods. The method has a high potential to further improve the quality and computational performance of complex cell and organ model simulations, and could provide a valuable new tool in the field of next-generation in silico electrophysiology

Multi-Criteria Performance Evaluation and Control in Power and Energy Systems

The role of intuition and human preferences are often overlooked in autonomous control of power and energy systems. However, the growing operational diversity of many systems such as microgrids, electric/hybrid-electric vehicles and maritime vessels has created a need for more flexible control and optimization methods. In order to develop such flexible control methods, the role of human decision makers and their desired performance metrics must be studied in power and energy systems. This dissertation investigates the concept of multi-criteria decision making as a gateway to integrate human decision makers and their opinions into complex mathematical control laws. There are two major steps this research takes to algorithmically integrate human preferences into control environments: MetaMetric (MM) performance benchmark: considering the interrelations of mathematical and psychological convergence, and the potential conflict of opinion between the control designer and end-user, a novel holistic performance benchmark, denoted as MM, is developed to evaluate control performance in real-time. MM uses sensor measurements and implicit human opinions to construct a unique criterion that benchmarks the system\u27s performance characteristics. MM decision support system (DSS): the concept of MM is incorporated into multi-objective evolutionary optimization algorithms as their DSS. The DSS\u27s role is to guide and sort the optimization decisions such that they reflect the best outcome desired by the human decision-maker and mathematical considerations. A diverse set of case studies including a ship power system, a terrestrial power system, and a vehicular traction system are used to validate the approaches proposed in this work. Additionally, the MM DSS is designed in a modular way such that it is not specific to any underlying evolutionary optimization algorithm

Comparative Study of P&O and Fuzzy MPPT Controllers and Their Optimization Using PSO and GA to Improve Wind Energy System

Many academics have recently focused on wind energy installations. WECS (wind energy conversion system) is a renewable energy source that has seen significant development in recent years. Furthermore, compared to the use of power grid supply, the use of the WECS in the water pumping field is a cost-free option (economically). The purpose of this study is to demonstrate a wind-powered pumping mechanism. To obtain the best option, it considers and contrasts four distinct approaches. This research aims to improve the system\u27s performance and the quality of the generated power. The objective of the control of WECS with a permanent magnet synchronous generator (PMSG) is to carefully maximize power generation. Finally, this research employed the fuzzy logic control (FLC) and particle swarm optimization (PSO) algorithms improved using a genetic algorithm (GA). The proposed system\u27s performance was tested using the generated output voltage, current, and power waveforms, as well as the intermediate circuit voltage waveform and generator speed. The provided data show that the control technique used in this study was effective

Optimal Control of Power Quality in Microgrids Using Particle Swarm Optimisation

Driven by environmental protection, economic factors, conservation of energy resources, and technical challenges, the microgrid has emerged as an innovative small-scale power generation network. Microgrids consist of a cluster of Distributed Generation units that encompass a portion of an electric power distribution system and may rely on different energy sources. Functionally, the microgrid is required to provide adequate levels and quality of power to meet load demands. The issue of power quality is significant as it directly affects the characteristics of the microgrid’s operation. This problem can be defined as an occurrence of short to long periods of inadequate or unstable power outputs by the microgrid. In a stand-alone operation mode, the system voltage and frequency must be established by the microgrid, otherwise the system will collapse due to the variety in the microgrid component characteristics. The harmonic distortion of the output power waveforms is also a serious problem that often occurs because of the high speed operation of the converter switches. The long transient period is a critical issue that is usually caused by changing the operation mode or the load demand. Power sharing among the Distributed Generation units is also an important matter for sharing the load appropriately, particularly given that some renewable energy resources are not available continuously. In a utility connected microgrid, the reliable power quality mainly depends on the regulation of both active and reactive power, because the microgrid’s behaviour is mostly dominated by the bulk power system. Therefore, an optimal power control strategy is proposed in this thesis to improve the quality of the power supply in a microgrid scenario. This controller comprises an inner current control loop and an outer power control loop based on a synchronous reference frame and conventional PI regulators. The power control loop can operate in two modes: voltage-frequency power control mode and active-reactive power control mode. Particle Swarm Optimisation is an intelligent searching algorithm that is applied here for real-time self-tuning of the power control parameters. The voltage-frequency power controller is proposed for an inverter-based Distributed Generation unit in an autonomous operation mode. The results show satisfactory system voltage and frequency, high dynamic response, and an acceptable harmonic distortion level. The active-reactive power controller is adopted for an inverter-based Distributed Generation unit in a utility operation mode. This controller provides excellent regulation of the active and reactive power, in particular when load power has to be shared equally between the microgrid and utility. The voltage-frequency and active-reactive power control modes are used for a microgrid configured from two DG units in an autonomous operation mode. The proposed control strategy maintains the system voltage and frequency within acceptable limits, and injects sustained output power from one DG unit during a load change. The reliability of the system’s operation is investigated through developing a small-signal dynamic model for the microgrid. The results prove that the system was stable for the given operating point and under the proposed power controller. Consequently, this research reveals that the microgrid can successfully operate as a controllable power generation unit to support the utility, thus reducing the dependency on the bulk power system and increasing the market penetration of the micro-sources

Data-driven-based vector space decomposition modeling of multiphase induction machines

For contemporary variable-speed electric drives, the accuracy of the machine's mathematical model is critical for optimal control performance. Basically, phase variables of multiphase machines are preferably decomposed into multiple orthogonal subspaces based on vector space decomposition (VSD). In the available literature, identifying the correlation between states governed by the dynamic equations and the parameter estimate of different subspaces of multiphase IM remains scarce, especially under unbalanced conditions, where the effect of secondary subspaces sounds influential. Most available literature has relied on simple RL circuit representation to model these secondary subspaces. To this end, this paper presents an effective data-driven-based space harmonic model for n-phase IMs using sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover the IM governing equations. Moreover, the proposed approach is computationally efficient, and it precisely identifies both the electrical and mechanical dynamics of all subspaces of an IM using a single transient startup run. Additionally, the derived model can be reformulated into the standard canonical form of the induction machine model to easily extract the parameters of all subspaces based on online measurements. Eventually, the proposed modeling approach is experimentally validated using a 1.5 Hp asymmetrical six-phase induction machine