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Turntable ladders are flexible large-scale manipulators, so an active oscillation damping is highly valuable. Aiming to support the development and parametrization of the controller, a precise dynamic model of the vehicle is desired. Previous work considering solely the dynamic oscillations of the ladder parts is extended by a dynamic model of the chassis and support structure that includes flexible deformation of the chassis. All contact forces to the ground are calculated because they provide potential for determining dynamic outreach limits in future. As it can happen in practice, the outriggers are able to become fully unloaded in the simulation model. This behaviour is validated by measurements. A sensitivity analysis is used to establish a suitable approach for identification of model parameters. Dynamical measurements illustrate that the combination of two dynamic models of ladder and chassis improves representation of the dynamic oscillations that are crucial for the active oscillation damping control.

Abstract The magnet-less switched reluctance motor (SRM) speed-torque characteristics are ideally suited for traction motor drive characteristics and its advantage to minimize the overall cost of on-road EVs. The main drawbacks are torque and flux ripple, which have produced high in low-speed operation. However, the emerging direct torque control (DTC) operated magnitude flux and torque estimation with voltage vectors (VVs) gives high torque ripples due to the selection of effective switching states and sector partition accuracy. On the other hand, the existing model predictive control (MPC) with multiple objective and optimization weighting factors produces high torque ripples due to the system dynamics and constraints. Therefore, existing DTC and MPC can result in high torque ripples. This paper proposed a finite set (FS)-MPC with a single cost function objective without weighting factor: the predicted torque considered to evaluate VVs to minimize the ripples further. The selected optimal VV minimizes the SRM drive torque and flux ripples in steady and dynamic state behaviour. The classical DTC and proposed model were developed, and simulation results were verified using MATLAB/Simulink. The proposed model operated in SRM drives experimental results to prove the effective minimization of torque and flux ripples compared to the existing DTC.

Despite emerging as a green solution for power systems across various fields, fuel cell systems still face challenges that hinder their adoption due to difficulties in accurately characterising subsystems and complex phenomena, as well as the lack of effective computational models. This work utilises advanced AI technology to develop a fuel cell power system dynamic model with significantly enhanced computational speed. Three key milestones are achieved. First, a mechanistic/semi-empirical fuel cell model is established based on parameters with direct physical meaning. This model effectively illustrates the internal mechanisms of the fuel cell, providing deeper insights into its operation. Second, a complete dynamic model of a fuel cell power system is developed, comprising all necessary components and being capable of independently powering an external load or interacting with other systems. Third, by employing a Nonlinear Autoregressive model with External Input (NARX), a metamodel of the fuel cell system is created, achieving significantly improved computational efficiency while retaining essential knowledge of key phenomena. When comparing the simulation results of the NARX metamodel with those from the original mathematical model, the coefficient of determination (R2) exceeds 0.98 in post-startup conditions. Moreover, the computational speed increases at least 90-fold. The resulting metamodel demonstrates substantial potential for resolving the existential obstacles in fuel cell modelling, helping to foster the adoption of the system in real-world decarbonisation.

Steam condensers are critical for maintaining efficiency and stability in thermal power plants by converting exhaust steam back into water. Controlling the pressure within the condenser is essential for optimal performance and operational safety. This paper presents a dynamic model of a steam condenser, with pressure control achieved through a proportional-integral (PI) controller. The PI controller parameters are optimized using the gazelle optimization algorithm (GOA), which is applied for the first time to this system in the literature. Additionally, novel enhancements, the logarithmic spiral (Ls) search with greedy selection, are integrated into GOA to further enhance control effectiveness. The performance of the optimized PI controller is evaluated using nonlinear simulations, which include statistical analysis and fitness function assessments. To validate the effectiveness of the proposed approach, comparisons are made with other widely used optimization techniques. The results reveal that the GOA notably enhances control accuracy and stability, with the enhanced GOA (Ls-GOA) achieving the most significant improvements. This underscores its potential for enhancing steam condenser efficiency in thermal power plants.

Abstract Integrating non‐conventional renewable energy sources into distribution systems, alongside data science and enabling technological infrastructures, presents significant challenges, particularly in managing active demand. The rapid evolution of the electric energy system and increasing electricity demand highlight the need for reliable tracking and predictive methods to manage Distributed Energy Resources and digital infrastructure. These methods are essential for advancing carbon neutrality, democratizing environmental sustainability, and improving energy efficiency. Effective active demand monitoring requires understanding the transactional system concept, including digital infrastructure and decentralized demand. Although metaheuristic techniques are increasingly important in demand response integration, much research focuses on specific techniques rather than providing a comprehensive view of dynamic transaction integration for active demand. Technological advancements, like smart meters and communication systems, are shifting from basic consumption measurement to active customer participation. This article reviews key concepts in electrical distribution systems, such as active demand, DERs, and transactive systems. It examines prevalent metaheuristic techniques, emphasizing their role in integrating and predicting active demand and DER behaviors. Additionally, the study presents a methodology serving as a roadmap for efficient DER integration and the transition to active demand and transactive electricity systems, addressing gaps in the current literature.

The robust rotor structure and fault-tolerance characteristics of the Switched Reluctance Motors (SRMs) are the best choice for next-generation Electric Vehicle (EV) applications. This machine has few restraints like high torque and flux ripples. However, the existing Model Predictive Control (MPC) using multiple control objectives and maximum sectors in the switching table results in high torque ripples due to the improper sector partition, Voltage Vectors (VVs) and weight factor (K1) selection. This paper proposes a Finite Set-Model Predictive Control (FS-MPC) for an analytical model of a non-linearity SRM machine to analyze the torque ripple performance. The proposed VVs are derived using sector partition based on the rotor position. The control is designed as a single cost function with the weighting factor contributing to smooth torque by selecting optimal control signals. Simulation studies and experiments with a four-phase 8/6 SRM drive verifies the enhanced FS-MPC for real-time implementation. The dynamic speed and ripple values of SRM Drives are measured using a mixed signal oscilloscope and the sensor probes. The laboratory outcomes calculate the analytical equations to validate the findings. The calculated value of torque ripple is 9 % through this FS-MPC. The study reveals that the proposed method is well suited for torque ripple reduction than flux ripples.

Many failures of semiconductor-based oxide confined vertical cavity surface emitting lasers (VCSELs) are closely related to the generation and expansion of defects in the device structure. However, existing research has predominantly focused on the static study of defect morphology, with little attention given to analyzing the dynamic process of defect expansion, which limited our ability to predict device random failures due to lack of understandings on defect generation and expansion. To address this issue, we present a macroscopic phenomenological evolution model that describes the dynamic expansion of defects in VCSELs, in which the expansion of defects is treated as an anisotropic lattice strain diffusion process. We further exploit a diffusion-limited aggregation (DLA) method in solving the diffusion equation, which describes the random propagation and aggregation of strain in the vicinity of highly strained areas, resulting in defect formation when the stress from accumulated strain surpasses the bonding force of the atoms in the lattice. Our simulation result manages to replicate the dendritic expansion morphology of defects, aligning with experimental observations very well. Our model also predicts an accelerated defect expansion process, which is again consistent with the experimental result. This model finds relevance in applications such as random failure prediction through device aging, burn-in condition setting in device screening, and device structural and/or material design improvement for mitigating defect expansion.

The demand for cement has significantly increased, growing by 8% in the year 2022 and by a further 12% in 2023. It is highly anticipated that this trend will continue, and it will result in significant growth by 2030. However, cement production is highly energy-intensive, with 70 to 80% of the total energy consumed during the clinker formation, which is the main cement production process. Minimising energy losses requires a radical approach that includes optimising the performance of the kilns and significantly improving their energy efficiency. One of the most efficient approaches to optimise the performance of the kilns and reduce energy losses is by integrating process re-engineering models, which leverage process data analytics, machine learning, and computational methods. This study employed a model-based integration approach to improve energy efficiency during clinker formation. Energy consumption data were collected from two semi-automated cement production plants. The data were analysed using a regression model in Minitab (Minitab 21.1.0) statistical software. The analysis resulted in a linear energy consumption equation that links energy consumption to both production and energy loss. Dynamic simulations and modelling using Simulink in MATLAB were performed based on a proportional–integral–derivative (PID)-controlled system. The dynamic behaviour of the model was evaluated using data from Plant A and validated with data from Plant B. The energy efficiency equation was established as a mathematical model that explains energy improvements based on incorporating parameters for the cement kiln system and disturbances from the environment.

Undoubtedly, wind turbines are currently one of the most significant contributors to clean energy. Therefore, it is crucial to enhance the capability of wind turbines, which in turn leads to an increase in their dimensions. Nevertheless, only advanced control systems can guarantee the optimal and secure operation of these huge machines. On the other hand, the precise control of these modern wind turbines is only achievable through the use of highly specialised actuators. Despite their importance, actuators have historically been overlooked and seen as secondary components in control systems. However, in modern machines, actuators are required to manipulate multiple tonnes or manage thousands of volts and amperes within short times. Consequently, greater emphasis must be placed on their handling and operation. This study aims to review actuators for modern large wind energy converters from a control engineering perspective, using a tutorial approach.

A simple proportional integral (PI) controller with scheduled gain has been developed and implemented in a catalytic membrane reactor (CMR) to obtain pure hydrogen from a methanol steam reforming process. The controller is designed to track the setpoint of the pure hydrogen flow rate in the permeate side of the CMR via the manipulation of the fuel inlet flow rate. Therefore, the controller actuator is the liquid pump that provides the mixture of methanol and water to the reactor. Within the CMR, the catalytic pellets of PdZn/ZnAl2O4/Al2O3 have been used to facilitate the methanol steam-reforming reaction under stoichiometric conditions (S/C = 1), and Pd–Ag metallic membranes have been employed to simultaneously separate the generated hydrogen. The PI controller design is based on a mathematical model constructed using transfer functions acquired from dynamic experiments conducted with the CMR. The controller has been successfully implemented, and experimental validation tests have been carried out at 450 °C and relative pressures of 6, 8, 10, and 12 bar.

With the growing urgency of addressing climate change it is increasingly important that decision makers at all levels are equipped to take efficient mitigation actions. This research evaluates the potential of four mitigation strategies to reduce greenhouse gas emissions from the building stock based on a case study, and these are further evaluated in terms of alignment with the remaining global emission budget and the planetary boundary for global warming. The results cover annual emissions from 2023 to 2050 across 18 impact categories, thus highlighting possible impact burden-shifting that may occur as a result of the mitigation strategies. The results show that decarbonisation of the electricity grid delivers a substantial reduction across impact categories. However, in absolute terms, this is counteracted by the increase in building stock. The results also show that current estimates for mitigation potentials are insufficient to comply with the remaining global emission budget. Thus, mitigation strategies should be even more ambitious: constructing 80% fewer new buildings and reducing operational energy demand by 80%. These findings highlight the urgency of taking multiple actions. The increase in demand for new buildings needs to be challenged. Practice relevance Present urban mitigation strategies are inadequate for meeting the stated GHG targets. To enable informed decisions it is important to quantify the effects of different strategies. Traditional life cycle assessments have static inventories and impacts can therefore not be temporarily differentiated and transparency cannot be provided on long-term potentials. This study suggests combining material flow analysis and life cycle assessment to enable integration of dynamic parameters into the life cycle inventory. This shows how the long-term effectiveness of different mitigation strategies can be evaluated. To ensure unintended burden-shifting does not occur, results cover annual emissions from 2023 to 2050 across 18 impact categories. Furthermore, this study showcases how strategies can be designed to align with global commitments such as those set by the Paris Agreement.

The construction energy consumption (CEC) is about 35% of entire energy consumption (EC), of which 50% is for cooler air-conditioner. The practical cooling EC is relevant to the dynamic thermal performance of cooler air-conditioner. Analyzing the difference in cooling efficiency and energy-saving potential (ESP) of the same equipment is significant to cutting down building EC. Using the concept of thermodynamic perfectness, a thermal model of refrigeration equipment is established to analyze the deviation from the ideal refrigeration cycle. At the same time, to explore the internal connection between its energy conservation and climate adaptability, five typical cities in different thermal climate zones were selected. Dynamic load model of an office building is established and the cooling EC throughout the year of each city is simulated separately. Preliminary research results show that the thermodynamic perfectness does not have a single-valued function relationship with their cooling efficiency; Guangzhou has the highest cooling demand, with a total cooling load 14.3% higher than Wuhan, and its cooling EC is lower than Wuhan. Through the establishment of a thermodynamic model and preliminary application, the calculation of the cooling ESP in different climatic regions in Chinese summer is greatly important to the usage of air conditioner.

Pneumatic artificial muscles (PAMs) possess compliant properties desirable for certain applications such as prosthetics and robotic structures. However, this compliance along with their inherent nonlinear dynamics make them difficult to accurately model and as such accurately control under certain control architectures. Common approaches to this problem include measuring the actuator’s physical properties and approximating a model based on these parameters or using deep learning methods to train a model with the actuator’s behaviours. This paper introduces an optimisation-based modelling approach based on a particle swarm optimisation (PSO) algorithm using a mass–spring–damper approximation for the PAM, as well as a piecewise modelling method that accounts for nonlinear dynamics. The use of optimisation to estimate model parameters removes the need to measure physical properties, and the three-element approximation allows for fast model generation with low computational complexity and training data requirements. Through multiple tests comparing model behaviour with real PAM motion, the accuracy of these models is confirmed to be promising for future work. Dynamic nonlinearities are properly accounted for using the piecewise modelling method, including both hysteresis and disproportionate input/output relationship across the stroke length of the actuator. Compared with other PAM modelling techniques, this method has improved generation time, lower computational load requirements, and complexity and can be applied to actuators for which the phenomenological mass–spring–damper model is a good approximation.

Phytoremediation can be effective for the removal, immobilization, mineralization, and/or detoxification of various pollutants in soils and water, including inorganic and organic pollutants, and radioisotopes. Although the feasibility of phytoremediation has been proven in the last decades, its performance is uncertain due to the complex interactions among soil, water, plants, weather, microorganisms, and pollutants, leading to its underutilization globally. This paper aims to review the representations and methods for quantifying key phytoremediation processes via modelling. We examine the structures, methods and ability of phytoremediation models that characterize the biogeochemical, hydrological, and phenological processes accountable for phytoremediation dynamics, along with discussions about their advantages and limitations. Then, we identify the knowledge gaps and challenges in incorporating biogeochemical, hydrological, and phenological processes into phytoremediation models in contaminated sites and representing spatial heterogeneity and temporal variability in large-scale applications. The existing phytoremediation models are difficult to predict the phytoremediation period under real environmental conditions but it is a key assessment of phytoremediation performance and cost. Finally, we explore the opportunities to integrate the current knowledge from other disciplines, such as soil, agriculture, ecology, and plant research in a competition-based model. We highlight the key research priorities for effective integration of knowledge based on physical, chemical, and biological processes in modelling phytoremediation, including biogeochemical processes, soil amendments and agro-practices. Further studies need to consider the immobilization, mineralization and detoxification processes of pollutants in contaminated sites.

Sustainble hydrogen production for use as a renewable combustible fuel and clean chemical feedstock is an important objective as the world moves towards a renewable energy future. High temperature steam electrolysis is a promising hydrogen production technology due to its reduced electric input that is offset by heat input into steam generation and steam superheating. An option to provide this heat is to use concentrating solar thermal technology that can sustainably provide heat input while renewable electricity is used for the electrolysis reaction. In this work, a solar-to-hydrogen high temperature steam electrolysis plant is designed and dynamically modeled, showing continuous hydrogen production by utilizing supplemental heating and efficient recuperative heating from the electrolysis product streams. Through this design, over 90% of the required heat input for the process can by met by a combination of solar and recuperative heat. Additionally, the plant can flexibility operate by ramping down hydrogen production and through flexible heat integration, which intelligently integrates solar heat based on solar conditions. Smooth operation with flexible hydrogen production is demonstrated which decreases electrical input during on-peak grid times and also decreases the total supplemental heat load over the course of a day from 26.1% to 24.5%. In addition, by using flexible heat integration, the plant can increase its solar heat usage by 4.1% relative to a base case. Both options for flexibility show efficient use of solar thermal energy to sustainably and continuously produce hydrogen.

Dynamic designs for ship propulsion shafting can be categorised as complex multi-disciplinary coupling systems. The traditional single disciplinary optimisation design method has become a bottleneck, restricting the further improvement of shafting design. In this paper, taking a complex propulsion shafting as the object, a dynamic analysis model of the propeller-shafting-hull system was established. In order to analyse the coupling effect of propeller hydrodynamics on shafting dynamics, the propeller’s hydrodynamic force in the wake flow field was calculated as the input for shafting alignment and vibration analysis. On this basis, the discipline decomposition and analysis of the subdisciplines in design of shafting dynamics were carried out. The coupling relationships between design variables in the subdisciplines were studied and the Multi-disciplinary Design Optimisation (MDO) framework of shafting dynamics was established. Finally, taking the hollowness of the shaft segments and the vertical displacement of bearings as design variables, combined with the optimal algorithm, the MDO of shafting dynamics, considering the coupling effect of the propeller-shafting-hull system, was realised. The results presented in this paper can provide a beneficial reference for improving the design quality of ship shafting.

Flexible link manipulators (FLM) are widely preferred in applications that require faster operation, high maneuverability, and less energy consumption. However, their flexibility is associated with undesired vibrations, making accurate positioning challenging. As most of the existing controllers are model-based, their performances are affected by uncertainties and often require the feedback of all the states. This makes such control challenging and expensive. Interestingly, despite its poor position tracking, the input shaping control (ISC) proved to be effective in oscillation suppression of flexible structures. In this paper, precise automation of the FLM is presented by hybridizing an improved input shaping control (iISC) with a model-free fuzzy logic control (FLC), and the model-based sliding mode control (SMC). The single feedback FLC, and SMC were designed for the position control while the iISC provided the tip deflection control. Three parameters of the FLM, namely, the length, mass, and spring stiffness of the link were used to assess the sensitivity of the controllers. With the maximum velocity of 94 deg/s and the maximum r.m.s of 0.33 deg, ISC+FLC has increased the velocity of the iISC by at least 77% and improved the poor oscillation suppression of the FLC by at least 64%. Thus, the analysis demonstrated that the iISC+FLC could provide precise automation of the FLM by measuring the output of only one state (position), making it cost-effective and reduces the complexity and computation time of the full-state feedback controllers.

District Heating Network is identified as a promising technology for decarbonizing urban areas. Thanks to the surplus of heat available from distributed renewable energy plants, a typical heat consumer of the network could become an energy producer during the day (typically referred to as a “prosumer”). Most of the models for thermal grids developed during past years usually assumed a centralized production of the consumed heat. The increasing presence of prosumers will require accurate dynamic modelling to monitor the changes induced in the thermohydraulic parameters of the network. To fill this knowledge gap, this paper aims at developing a model of a thermal grid with prosumers in the TRNSYS environment. The model allows for the dynamic monitoring of the main thermohydraulic parameters of the network. To show these capabilities, a ring-shaped network serving a cluster of 10 residential users located in Palermo (Italy) was assumed as the case study. Different scenarios are investigated based on the presence of solar collectors, prosumers along the network, and cooling by an absorption chiller. The achievable energy and emissions savings are calculated. The results of the study show that even only decreasing the operating temperature can significantly reduce heat losses via the network pipes. In particular, a temperature drop from 100 °C to 80 °C can reduce heat losses by 27.1%. Furthermore, the heat losses can be decreased by up to 52.8% when the network temperature is lowered from 100 °C to 60 °C. Additionally, the presence of prosumers and the solar field could lead to a 31.3% reduction in the energy produced by the centralized plant and a 17.6% reduction in energy consumed for pumping.

This work presents a solar-driven air conditioning system based on ejector technology with closed-loop vertical geothermal boreholes. Modeling and simulation are carried out using Dymola software for dynamic transient analysis, with the refrigerant R134a. Condenser temperature regulation produces a 267% improvement of performance compared to a solar-only configuration. Solar fraction increases with increasing solar collector area, which benefits the system up to the limit of 27 m2 for a cooling load of 9 kW. The reservoir volume ensuring high solar fraction is 14 m3. Optimal generator saturation temperature ensures a seasonal coefficient of performance value of 0.772 and a maximum overall efficiency of 39%. When the system encounters deviations from the optimum set point, the overall efficiency becomes 37.4%.

The growth of populations is of interest in a broad variety of fields, such as epidemiology, economics or biology. Although a large variety of growth models are available in the scientific literature, their application usually requires advanced knowledge of mathematical programming and statistical inference, especially when modelling growth under dynamic environmental conditions. This article presents the biogrowth package for R, which implements functions for modelling the growth of populations. It can predict growth under static or dynamic environments, considering the effect of an arbitrary number of environmental factors. Moreover, it can be used to fit growth models to data gathered under static or dynamic environmental conditions. The package allows the user to fix any model parameter prior to the fit, an approach that can mitigate identifiability issues associated to growth models. The package includes common S3 methods for visualization and statistical analysis (summary of the fit, predictions, . . . ), easing result interpretation. It also includes functions for model comparison/selection. We illustrate the functions in biogrowth using examples from food science and economy.

Erosion, soil loss and consequent nutrient fluxes impair water quality and can degrade arable soils. Erosion rates in Sweden are generally low but episodic losses of suspended solids (SS) can affect water quality. Identifying critical source areas (CSAs) and “hot moments” is essential to reduce erosive losses from arable land. Here we use a distributed, dynamic high-resolution erosion model that simulates the sum of all transported material, i.e., erosion within the soil profile, on the soil surface and transport through drainage systems. We simulate monthly SS transport in six small agricultural catchments with varying soil texture over 8 years. Kling-Gupta Efficiency (KGE) was used as model performance statistics, and calibration (KGE = 0.45–0.78) and validation (KGE = 0.64–0.83) showed acceptable model performance for all catchments, with mean annual SS losses between 2.1 and 31.5 t km-2yr-1. Equifinality could be minimised by using more precise initial parameter values. We suggest that the model can be applied to comparable unmonitored catchments to identify erosion-sensitive periods and CSAs.

High-speed pick-and-place industrial applications often use parallel kinematic robots due to their high stiffness and dynamic performance; furthermore, the latter not only depends on the mechanical characteristics of the robots but also on the control algorithm. The literature shows several theoretical contributions to such controllers, mainly tested at the simulation level or on simple proof-of-concept laboratory equipment that execute low-speed and simple trajectories. This paper presents an experimental investigation of the dynamic performance of an industrial high-speed 4-DOF 5R parallel robot designed for pick-and-place applications on moving objects. The inverse dynamics control in the task space is used as a control algorithm. The results show the contribution of all the components of the control algorithm to the motor torque, and the inverse dynamics controller performances are discussed also in comparison to those achievable with simpler PD or PID controllers in a joint space. Moreover, the paper shows the controller synthesis from a modern mechatronic point of view, and the effectiveness of the proposed solution for the tracking of complex high-speed trajectories in an industrial application.

This review emphasizes the significance of formulating control strategies for biological and advanced oxidation process (AOP)-based wastewater treatment systems. The aim is to guarantee that the effluent quality continuously aligns with environmental regulations while operating costs are minimized. It highlights the significance of understanding the dynamic behaviour of the process in developing effective control schemes. The most common process control strategies in wastewater treatment plants (WWTPs) are explained and listed. It is emphasized that the proper control scheme should be selected based on the process dynamic behaviour and control goal. This study further discusses the challenges associated with the control of wastewater treatment processes, including inadequacies in developed models, the limitations of most control strategies to the simulation stage, the imperative requirement for real-time data, and the financial and technical intricacies associated with implementing advanced controller hardware. It is discussed that the necessity of the availability of real-time data to achieve reliable control can be achieved by implementing proper, accurate hardware sensors in suitable locations of the process or by developing and implementing soft sensors. This study recommends further investigation on available actuators and the criteria for choosing the most appropriate one to achieve robust and reliable control in WWTPs, especially for biological and AOP-based treatment approaches.

Reef reservoirs are characterised by a complex structure of void space, which is a combination of intergranular porosity, fractures, and vuggy voids distributed chaotically in the carbonate body in different proportions. This causes great uncertainty in the distribution of porosity and permeability properties in the reservoir volume, making field development a complex and unpredictable process associated with many risks. High densities of carbonate secondary alterations can lead to the formation of zones with abnormally high porosity and permeability—high permeability streaks or super-reservoirs. Taking into account super-reservoirs in the bulk of the deposit is necessary in the dynamic modelling of complex-structure reservoirs because it affects the redistribution of filtration flows and is crucial for reservoir management. This paper proposes a method for identifying superreservoirs by identifying enormously high values of porosity and permeability from different-scale study results, followed by the combination and construction of probabilistic curves of superreservoirs. Based on the obtained curves, three probabilistic models of the existence of a superreservoir were identified: P10, P50, and P90, which were further distributed in the volume of the reservoir and on the basis of which new permeability arrays were calculated. Permeability arrays were simulated in a dynamic model of the Alpha field. The P50 probabilistic model showed the best history matching after one iteration.

Regional sensitivity analysis is a part of global sensitivity analysis methods. In this study, a multivariate sensitivity analysis was conducted on a qualitative-phenomenological model. Study results allowed us to pinpoint the most influential parameters. Moreover, important measures or ranking gave more insight into factors ordering according to their relative influence and permitted them to follow parameter sensitivities over time. Lastly, a confirmatory procedure using extensive parameter randomisation that covered a broad range of the parameters space allowed more insight into and certainty of each input parameters’ relative importance.

The paper describes an inverter control scheme which incorporates both a phase-locked loop (PLL) for voltage synchronization and power-frequency droop for load sharing. As such, it is a hybrid grid-forming/following controller and offers beneficial characteristics of both. The model describing the dynamic behaviour of the inverter control scheme is presented and connections with grid-forming and grid-following control strategies are considered. A process for black-starting the PLL-based inverter control scheme is described. Case studies explore controller small- and large-disturbance performance under a variety of system conditions.

Wind turbines are exposed to a variety of faults some of which can cause irreparable economic losses. Therefore, identifying the faults in a short time, ensures the correct operation of the system and prevents the mentioned losses. In this paper, using a dynamic model for wind turbines which includes mechanical and electrical parts with appropriate details, an intelligent fault detection and isolation system is designed utilizing recurrent neural networks. The proposed system can identify the occurred faults in pitch sensors and pitch actuators. Then, in order to consider the robustness of the system, it is suggested to use an adaptive fuzzy threshold in decision making block. Simulation results for the fixed threshold, robust thresholds, and the proposed adaptive fuzzy threshold validate that the suggested adaptive threshold reduces the detection time. In addition, the number of false alarms, and the number of missed ones are reduced by using the intelligent fault detection system.

Abstract The aim of this study is to develop coupled matrix formulations to characterize the dynamic interaction between the vehicle, track, and tunnel. The vehicle–track coupled system is established in light of vehicle–track coupled dynamics theory. The physical characteristics and mechanical behavior of tunnel segments and rings are modeled by the finite element method, while the soil layers of the vehicle–track–tunnel (VTT) system are modeled as an assemblage of 3-D mapping infinite elements by satisfying the boundary conditions at the infinite area. With novelty, the tunnel components, such as rings and segments, have been coupled to the vehicle–track systems using a matrix coupling method for finite elements. The responses of sub-systems included in the VTT interaction are obtained simultaneously to guarantee the solution accuracy. To relieve the computer storage and save the CPU time for the large-scale VTT dynamics system with high degrees of freedoms, a cyclic calculation method is introduced. Apart from model validations, the necessity of considering the tunnel substructures such as rings and segments is demonstrated. In addition, the maximum number of elements in the tunnel segment is confirmed by numerical simulations.

Differential settlement is a common problem in the bridge-roadway transition zone. Approach slabs are often constructed to mitigate the uneven settlement in this problematic zone. An appropriate simulation of the dynamic response of moving loads, approach slab and soil materials is necessary for realistic results. In the present study, the performance of the slab under traffic flow is investigated by a 3D dynamic analysis. For this purpose, the slab is modelled as a plate element while Mohr-Coulomb material model is adopted for base, subbase and subgrade soils. A number of parameters were considered to study the sensitivity of the proposed model to some soil, slab and moving load parameters that contribute to the behaviour of the approach slab. The main variables investigated in this study were the slab thickness, the restriction condition of the slab, the subgrade stiffness, weight of the passing vehicles and the analysis method adopted in such problems. Analysis results including predicted deformations and slab bending moments provide engineers with the necessary engineering knowledge to understand the response of approach slabs under different conditions.

The article researches the direct tax effect on investment share in thirty-five OECD countries for the period from 1996 to 2016 year. The goal of this research is to determine how direct taxes influence on investment level measured by the share in the gross domestic product. The empirical analysis enables the implementation of fundamental econometric procedures as well as different dynamic panel models in order to measure effect of direct taxes. Results of Hausman show that PMG model is appropriate for measuring the effect of tax revenue growth, personal income tax, corporate income tax and property tax on investment share in selected countries. The model results reflect significant effect of tax revenue growth, personal income tax and property tax in the long term, while corporate tax is not significant for investment share in OECD countries. However, direct taxes do not have significant impact on investment share in the short-term, except tax revenue growth has positive effect on the investment in observed period.

Biological communities are populations of various species interacting in a common location. Microbial communities, which are formed by microorganisms, are ubiquitous in nature and are increasingly used in biotechnological and biomedical applications. They are nonlinear systems whose dynamics can be accurately described by models of ordinary differential equations (ODEs). A number of ODE models have been proposed to describe microbial communities. However, the structural identifiability and observability of most of them—that is, the theoretical possibility of inferring their parameters and internal states by observing their output—have not been determined yet. It is important to establish whether a model possesses these properties, because, in their absence, the ability of a model to make reliable predictions may be compromised. Hence, in this paper, we analyse these properties for the main families of microbial community models. We consider several dimensions and measurements; overall, we analyse more than a hundred different configurations. We find that some of them are fully identifiable and observable, but a number of cases are structurally unidentifiable and/or unobservable under typical experimental conditions. Our results help in deciding which modelling frameworks may be used for a given purpose in this emerging area, and which ones should be avoided.

This study addresses the use of former gas storage facilities as short-term storage for renewable energy through power-to-gas (PtG) technology in Germany. Three test cases with coupled thermal-hydromechanical (THM) modelling were conducted to evaluate short-term injection and production schedules. The operating rates were controlled by the upper and lower limits of the wellbore pressure. The maximum difference in pore pressure and effective stress was 0.6 MPa in all cases. Fault reactivation analysis was performed on the THM models to estimate fault stability. The critical pore pressure for safe reservoir operation was determined to be 1.25 times the original pore pressure, corresponding to a WBHP value of 20.25 MPa. The upper limit of the gas injection rate for safe storage operation was estimated to be between 100,000 and 150,000 m3/day. The thermal stresses were found to be negligible for short-term cases. The storage capacity of PtG technology was reported to be up to 1,322,400 kWh/d of renewable electricity, which can contribute to Germany becoming a greenhouse gas neutral country by 2050. The workflows and results of the study are applicable to all gas storage in a porous medium, including methane, CO2, and hydrogen.

Soil organic carbon (SOC) storage and redistribution across the landscape (through erosion and deposition) are linked to soil physicochemical properties and can affect soil quality. However, the spatial and temporal variability of soil erosion and SOC remains uncertain. Whether soil redistribution leads to SOC gains or losses continues to be hotly debated. These considerations cannot be modelled using conventional soil carbon models and digital soil mapping. This paper presents a coupled-model combining RothPC-1 which considers soil carbon (C) down to 1 m and a soil redistribution model. The soil redistribution component is based on a cellular automata technique using the multi-direction flow (FD8) algorithm. With the optimized input values based on land use, we simulated SOC changes upon soil profiles to 1 m across the Lower Hunter Valley area (11,300 ha) in New South Wales, Australia from the 1970s to 2016. Results were compared to field observations and showed that erosion was predicted mostly in upslope areas and deposition in low-lying areas. We further simulated SOC trends from 2017 until ~2045 in the area under three climate scenarios and five land use projections. The variation in the magnitude and direction of SOC change with different projections shows that the main factors influencing SOC changes considering soil redistribution are climate change which controlled the trend of SOC stocks, followed by land use change. Neglecting soil erosion in carbon models could lead to an overestimation of SOC stocks. This paper provides a framework for incorporating soil redistribution into the SOC dynamics modelling and also postulates the thinking that soil erosion is not just a removal process by surface runoff.

The purpose of vehicle suspension system is to improve ride comfort and road handling.1/4th model of bus suspension system is used to simplify the problem. A open loop response of bus suspension system is developed by using equations of 1/4th model of bus suspension system by using the Matlab/Simulink. The response of open loop bus suspension system is obtained by using equation and state space model.

Abstract Background Antibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mindful of limiting the spread of resistance and safeguarding the efficacy of antibiotics for current and future generations. Policies that strike the right balance must be informed by a transparent rationale that relies on a robust evidence base. Main text One way to generate the evidence base needed to inform policies for managing antibiotic resistance is by using mathematical models. These models can distil the key drivers of the dynamics of resistance transmission from complex infection and evolutionary processes, as well as predict likely responses to policy change in silico. Here, we ask whether we know enough about antibiotic resistance for mathematical modelling to robustly and effectively inform policy. We consider in turn the challenges associated with capturing antibiotic resistance evolution using mathematical models, and with translating mathematical modelling evidence into policy. Conclusions We suggest that in spite of promising advances, we lack a complete understanding of key principles. From this we advocate for priority areas of future empirical and theoretical research.

Abstract This paper demonstrates a methodology for flexible, dynamic systems modelling relevant to urban rail decarbonisation. Decarbonisation of urban rail is a vital component of policy and strategy to minimize anthropogenic emissions. Decarbonisation is a systems problem, however, that needs to reflect the interaction between components and processes. Dynamic computer modelling of systems for decarbonisation involves interfacing multiple models together and running them in parallel in order to observe and predict systems-level effects. This is challenging due to the diverse nature of models, achieving parallel model integration and concerns around intellectual property (IP). One solution is the multi-modelling paradigm, which supports integrated, diverse, secure interfacing of models. This paper demonstrates the application of the multi-modelling approach, using the INTO-CPS tool chain. A multi-model was developed comprising key components required for urban rail decarbonisation problems. This multi-model was tested for power consumption in four different scenarios with an example drawn from the Tyne and Wear Metro in Newcastle-upon-Tyne in the United Kingdom. These scenarios compared combinations of decarbonisation intervention (baseline rolling stock versus lightweight, regenerative braking rolling stock and baseline driving style versus energy-efficient defensive driving style), generating different power consumption profiles for each. As such, this serves as a proof of the application of the multi-modelling approach and demonstrates a number of benefits for flexible and rapid systems modelling. This paper fills a knowledge gap by demonstrating a potentially valuable tool for future systems-level decarbonisation challenges in urban rail.

The lack of equal globally binding GHG’s emission reduction targets is currently leading to a set of diverging GHG’s emission prices across the world (or even no price for GHG’s emission in some regions). This may result in distortions with direct implications on competitiveness of the industries in regions with strict climate policies (as the European Union) and can cause the issue of carbon leakage. Carbon leakage is defined as ‘the increase in emission outside a region as a direct result of the policy to cap emission in this region’.

The State Of Charge (SOC) is the most important index in a Battery ManagementSystem (BMS) to regulate charge/discharge decisions and to ensure the battery’s safety,efficiency, and longevity. There are many methods to estimate SOC of a battery and the model based-methods exhibit higher accuracy compared to other methods. Among them the EquivalentCircuit Model (ECM)-based methods are employed in power system applications due to theirflexible nature. These models consist of a voltage source to represent Open Circuit Voltage(OCV) which depends on the SOC of the battery. The accuracy of the SOC estimation highlydepends on the adopted Equivalent Circuit Model. To accomplish accurate battery model,battery SOC should be precisely estimated. This paper investigates various types of SOCestimation methods for lithium-ion batteries in-depth in view point of Battery Energy StorageSystems (BESS). Different SOC estimation methods are compared and evaluated to assess theirsuitability under both static response and dynamic conditions.

The development of dynamic models for control purposes is characterised by the challenge of finding a compromise between the minimum necessary information about the system dynamics contained in the model and a model with a low level of complexity such that the model-based control system design becomes comfortable. To achieve this balance, a modified dynamic model for the drivetrain of a wind turbine is proposed in this contribution. The main idea is to introduce the tip-speed ratio as a state variable so that an interval observer can be designed in such a way that its estimates can be used in the torque control during the partial load operation as well as for the estimation of the effective wind speed. During the runtime, the observer’s matrix gain is recalculated to adapt the behaviour to the current operational state, which changes all the time with the wind speed. Besides the theoretical formulation, a numerical example of a 20 MW reference wind turbine illustrates the utility of the method. The results show good control performance concerning the tip-speed ratio control loop and a satisfactory estimation of the effective wind speed.

In recent years, numerous projects for floating PV systems have been developed. These plants of various sizes have mainly been installed on enclosed lakes or basins characterised by the absence of external forcing related to waves and currents. However, offshore installation would allow the development of such plants in areas where land is not available, such as islands. This paper analyses the state of the art of floating PV, describes the design of a floating PV platform and the development of a numerical model to evaluate the system performance in an offshore environment. The case study of the island of Lampedusa is then analyzed: starting from a single floating foundation with its mooring system, a floating PV system is designed to meet the island’s electricity needs. In order to provide the competitiveness of the system, a techno-economic analysis is carried out, evaluating the main cost items of Capex, Opex and LCOE. Although the LCOE obtained is significantly higher than a traditional solar plant installed on land, this technology is competitive compared to other offshore marine technologies such as offshore wind and wave energy.

In recent years, the lower limb exoskeleton has been more and more widely used in military, medical and other fields. In this paper, the muscle–bone model of the lower limb during the human walking process is analyzed, and a lower limb exoskeleton with the purpose of loadbearing is designed. The exoskeleton is driven by four hydraulic cylinders to the hip and knee joints whose design load is 50 kg. The kinematic and dynamic model of the exoskeleton designed in this paper is established and analyzed, and it is simulated. Finally, the experiments were carried out on the exoskeleton test platform to verify that the stability, bearing capacity, tracking effect and durability of the exoskeleton can meet the requirements.

Film drying is used for the gentle production of casein microparticles. These result from aggregates that have previously formed in solution from micellar caseins in the presence of pectin under neutral pH conditions. The particles are stabilized by hydrophobic interactions and have a spherical shape. Their swelling behavior, which is enhanced with increasing pH, could be of interest for future applications in the field of drug encapsulation and release. The swelling of the microparticles occurs within minutes at pH 11, making it possible to perform a total of 46 consecutive individual tests. The analysis of the results showed that the microparticles keep their spherical shape during swelling. Furthermore, the swelling process proceeds in two steps, the rates of which are independent of the initial size of the casein microparticles. The values of the five parameters of the dynamic swelling model, which is used for the analysis of the kinetics, are examined with the Bravais-Pearson coefficient for pairwise linear correlations. Both swelling steps can be clearly distinguished with regard to rate and characteristic time, whereby the second swelling step at pH 11 consequently has a larger characteristic initial time but also a larger rate. For the first swelling step, the characteristic time and the rate coefficient are uncorrelated, while the rate for swelling step 2, which ends with the disintegration of the casein microparticles, is smaller the later it begins. Since there is a positive correlation between the characteristic times of the two swelling steps, it can be concluded that those particles are most stable for which the swelling process starts the latest. It seems plausible that a different degree of hydration of the microparticles after preparation is the cause of the observed stability differences.

To solve the problem of uncertain parameters in dynamic modelling of upper-limb rehabilitation robots, a dynamic parameter identification method based on variable parameters particle swarm optimisation (PSO) is developed. Based on the dynamic model of the system, the algorithm changes the inertia parameter and learning law of the basic PSO algorithm from the fixed-parameter to the function that changes with the number of iterations. It solves the problems of small search space in the early stage and slow convergence speed in the later stage of the basic PSO algorithm, which greatly improves its identification accuracy. Finally, through the comparison and analysis of the simulation results, compared with those of the least square (LS) and unmodified PSO identification algorithms, a great improvement in the identification accuracy of the algorithm is achieved. The control effect in the actual control system is also much better than those of the LS and PSO algorithms.

Dyssol is a modelling framework for the dynamic flowsheet simulation of processes designed for handling of particulate materials. Main distinctive features of this software are the comprehensive description of multidimensionally distributed particulate materials, the application of transformation matrices and the use of sequential-modular simulation approach. This cross-platform system can be easily extended with new models, applied for calculation of large datasets and coupled to the external programme packages.

Abstract Background Dynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i.e. massive search spaces), (ii) convergence to local optima (due to multimodality of the cost function), (iii) overfitting (fitting the noise instead of the signal) and (iv) a lack of identifiability. As a consequence, the use of standard estimation methods (such as gradient-based local ones) will often result in wrong solutions. Overfitting can be particularly problematic, since it produces very good calibrations, giving the impression of an excellent result. However, overfitted models exhibit poor predictive power. Here, we present a novel automated approach to overcome these pitfalls. Its workflow makes use of two sequential optimisation steps incorporating three key algorithms: (1) sampling strategies to systematically tighten the parameter bounds reducing the search space, (2) efficient global optimisation to avoid convergence to local solutions, (3) an advanced regularisation technique to fight overfitting. In addition, this workflow incorporates tests for structural and practical identifiability. Results We successfully evaluate this novel approach considering four difficult case studies regarding the calibration of well-known biological oscillators (Goodwin, FitzHugh–Nagumo, Repressilator and a metabolic oscillator). In contrast, we show how local gradient-based approaches, even if used in multi-start fashion, are unable to avoid the above-mentioned pitfalls. Conclusions Our approach results in more efficient estimations (thanks to the bounding strategy) which are able to escape convergence to local optima (thanks to the global optimisation approach). Further, the use of regularisation allows us to avoid overfitting, resulting in more generalisable calibrated models (i.e. models with greater predictive power).

Batteries offer a combination of balancing and regulation services within a smart grid to improve its resilience and flexibility. Maintaining an acceptable state of health and the highest rate of return requires dynamic modelling of the asset and rigorous optimisation. The authors compare the technical cost and economic benefit of battery employment in dynamic frequency and balancing mechanism actions in a smart grid. They use the services procured by National Grid in the UK as a case study but the methodology is globally applicable, including developing grid infrastructures. Their methodology yields the most optimum scenario of service participation, accounting for the dynamic degradation and considering variable pricing of electricity throughout the day. Additionally, it advises the most optimal despatch schedule and price declarations for the battery over the course a day and a year, employing particle swarm optimisation algorithm and historic data. Their results demonstrate that ordinarily frequency response is preferred due to its lower technical toll and payments for availability rather than despatch. However, the proposed despatch schedule including both services provides the highest profit. They anticipate this methodology to become the basis for more sophisticated battery models that integrate the service despatch optimisation, dynamic lifetime degradation and economic analysis.

Public health education, including mass and interpersonal communication, has been recognized as an effective control of infectious disease. Compared to the well-studied mass communication by dynamic modelling, not much mathematical study has been done on the effect of interpersonal communication. Here, we build a model, to study the overall impact of mass communication and interpersonal communication on disease spread and disease control during the transmission process. By analysing the dynamic behaviour of our model, we find two threshold parameters on which the disease persistence and extinction condition depend. We further prove that the endemic equilibrium, whenever existing, is locally asymptotically stable. Its global stability is also verified. And the impact of public health education on the behaviours of the model is considered by numerical simulation. Our study confirms the value of various education activities and shows that public health education may affect the epidemic threshold.

Weaning is a critical transition phase in swine production in which piglets must cope with different stressors that may affect their health. During this period, the prophylactic use of antibiotics is still frequent to limit piglet morbidity, which raises both economic and public health concerns such as the appearance of antimicrobial-resistant microbes. With the interest of developing tools for assisting health and management decisions around weaning, it is key to provide robustness indexes that inform on the animals’ capacity to endure the challenges associated with weaning. This work aimed at developing a modelling approach for facilitating the quantification of piglet resilience to weaning. A total of 325 Large White pigs weaned at 28 days of age were monitored and further housed and fed conventionally during the post-weaning period without antibiotic administration. Body weight and diarrhoea scores were recorded before and after weaning, and blood was sampled at weaning and 1 week later for collecting haematological data. A dynamic model was constructed based on the Gompertz–Makeham law to describe live weight trajectories during the first 75 days after weaning, following the rationale that the animal response is partitioned in two time windows (a perturbation and a recovery window). Model calibration was performed for each animal. Our results show that the transition time between the two time windows, as well as the weight trajectories are characteristic for each individual. The model captured the weight dynamics of animals at different degrees of perturbation, with an average coefficient of determination of 0.99, and a concordance correlation coefficient of 0.99. The utility of the model is that it provides biologically meaningful parameters that inform on the amplitude and length of perturbation, and the rate of animal recovery. Our rationale is that the dynamics of weight inform on the capability of the animal to cope with the weaning disturbance. Indeed, there were significant correlations between model parameters and individual diarrhoea scores and haematological traits. Overall, the parameters of our model can be useful for constructing weaning robustness indexes by using exclusively the growth curves. We foresee that this modelling approach will provide a step forward in the quantitative characterisation of robustness.

The paper presents the modelling and stabilisation of an unconventional airship. The complexity of such a new design requires both proper dynamic modelling and control. A complete dynamic model is built here. Based on the developed dynamic model, a nonlinear control law is proposed for this airship to evaluate its sensitivity during manoeuvres above a loading area. The proposed stabilisation controller derives its source from a polytopic quasi-Linear Parameter varying (qLPV) model of the nonlinear system. A controller, which takes into account certain modelling uncertainties and the stability of the system, is analysed using Lyapunov’s theory. Finally, to facilitate the design of the controller, we express the stability conditions using Linear Matrix Inequalities (LMIs). Numerical simulations are presented to highlight the power of the proposed controller.

To identify the amino acid residues that are the critical factors contributing to the high catalytic activity and good thermostability of Yersinia enterocolitica subsp. palearctica non-specific nuclease (Y. NSN), in this work, we analyzed the structure and the functional domain of Y. NSN by using bioinformatics software. Disulphide bonds were analyzed by mass spectrometry. Six active site mutants of Y. NSN, including two salt bridge mutants and four disulphide bond mutants, were obtained by site-directed mutagenesis. Their enzyme activity and thermostability were assayed. The results showed that when changing the amino acids of the active site, or breaking the salt bridges and disulphide bonds, the enzyme activity and thermal stability of Y. NSN decreased significantly, compared to those of the wild-type enzyme. It is indicated that the active site, salt bridges and disulphide bonds play an important role in the enzyme activity and thermostability of Y. NSN.