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All structures experience vibrations due to external dynamic force excitations, such as earthquakes and wind loadings. At resonance, the impact of this natural dynamic force on structures may lead to structural failures. Hence, an absorber is mounted to absorb vibrations from the primary system. Unfortunately, passive tuned mass absorbers can only target a single frequency. Since structural buildings possess multiple modes, an adaptive or tune-able vibration absorber is needed to attenuate the vibration in a multi-degree of freedom (MDOF) system. In this work, an adaptive electromagnetic vibration absorber (AEMVA) is proposed to eliminate the effects of vibrations and is dynamically tuned using electromagnets. By varying the current supplied to the coil, the stiffness of the AEMVA can be adjusted, resulting in a varying absorber frequency. A mathematical description of the AEMVA on a three-story prototype model building is also presented. The three-story benchmark model was used to demonstrate the effectiveness of AEMVA in absorbing multiple vibration modes, both analytically and experimentally. It is shown that 68.81 %, 50.49 %, and 33.45 % of vibration amplitude reductions were achieved at the first, second, and third modes, respectively. [ABSTRACT FROM AUTHOR]

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The biomechanical responses of the driver-truck system in a dynamic environment have become a significant concern in the design and control of trucks. When evaluating the riding comfort of the seat-cab system, it is necessary to predict the biomechanical responses of the driver's different parts and directions. However, there is no reliable model and method for effectively predicting the response characteristics of the driver-seat-cab system. Aiming at such problem, firstly, based on the 7 DOF (degree-of-freedom) seated human biodynamic model established previously, a 10 DOF non-linear dynamic model of the driver-seat-cab system was created, and its vibration differential equations were established. Secondly, the vibration signals for simulation and verification were collected through the road test using a truck. Thirdly, based on the Newmark-ß integration method, the specific solution process of the model was given. The non-linear damping coefficients of the front and rear dampers for the cab suspensions were measured with a bench test. Moreover, the simulations were conducted based on the measured model parameters, taking the collected frame vibration signals as the inputs. The results show that the simulation results agree with the test results, proving that the dynamic model can effectively predict the driver's biomechanical responses. Finally, some useful conclusions were obtained through the simulation analysis. The established model and conclusions can provide technical support for comfort evaluation, optimization design, and control of the seat-cab suspension system. [ABSTRACT FROM AUTHOR]

Over the past decade, unmanned aerial vehicles (UAVs) have received a significant attention due to their diverse capabilities for non-combatant and military applications. The primary aim of this study is to unveil a clear categorization overview for more than a decade worth of substantial progress in UAVs. The paper will begin with a general overview of the advancements, followed by an up-to-date explanation of the different mechanical structures and technical elements that have been included. The paper will then explore and examine various vertical take-off and landing (VTOL) configurations, followed by expressing the dynamics, applicable simulation tools and control strategies for a Quadrotor. In conclusion to this review, the dynamic system presented will always face limitations such as internal and/or external disturbances. Hence, this can be minimised by the choice of introducing appropriate control techniques or mechanical enhancements. [ABSTRACT FROM AUTHOR]

This paper describes a lumped dynamic model for a high pressure PEM water electrolyzer. Since the electrolyzer under analysis is characterized by unbalanced pressure configuration with high cathodic working pressure, the model focuses on the cathode side to adequately predict the electrolyzer performance, analyzing and highlighting the importance of the cathodic activation overpotential term. The model is calibrated using experimental data from a 5.6 kW PEM water electrolyzer stack. A very good fit can be observed between the model and the experimental data, not only at different temperatures, but also at different pressures. It is found that the rise of temperature affects mainly the ohmic overpotential, while increasing the cathode pressure leads to an increment in the cathode activation overpotential that is not negligible for the electrolyzer performance. By rising the operating current, the cathode activation overpotential becomes 63% of that of the anode (at 70 bar, 1.2 A/cm2 and 50 °C). • Dynamic model of PEM electrolyzer stack with unbalance pressure configuration. • Model calibrated with experimental data from a 5.6 kW PEM water electrolyzer stack. • Modeling of pressure influence on the activation overpotential term. • Non negligible contribution of cathodic activation overpotential at high pressure. • Voltage increase when increasing pressure mainly due to activation overpotential. [ABSTRACT FROM AUTHOR]

Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem. [ABSTRACT FROM AUTHOR]

The aim of applying vibration-based condition monitoring in gear systems is to detect the initiation and development of degradation before the occurrence of failure. Eventually, degradation will result in a malfunction of the gearing system, which will affect the availability of the whole system. Early detection allows proper scheduled shutdown to prevent catastrophic failure and consequently results in a safer operation and higher cost reduction. The current report with crack modelling from a condition monitoring perspective and focuses on the early detection of cracks propagating in gear teeth using vibration signal analysis. The research approach is based on crack modelling and gear mesh stiffness calculation, dynamic modelling and simulation, and methods for fault detection by means of dynamic response and vibration analysis. The time-varying gear mesh stiffness has been studied for cracked gears, and different crack progression scenarios have been applied. Different gear dynamic models have been used for simulating the dynamic response of the studied gear system. The gyroscopic effect of the gear disc has been studied by introducing a 12 DOF gear dynamic model. Signal processing techniques have been used in vibration signal analysis for detecting any impact of the crack. Moreover, the changes in the system dynamic response with varying mesh stiffness have been investigated. The possibility of detecting cracks has then been studied using the changes in the dynamic response of the system due to a stiffness reduction of the cracked tooth. [ABSTRACT FROM AUTHOR]

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.

On the battlefield of today, it has become an important requirement to hit moving or fixed targets by using tank or artillery ammunition with high precision. However, while there are many articles on guiding tactical missiles, it cannot found sufficient scientific study for guiding tank ammunition in the related literature. In this study, the laser BR-guidance method is offered to the classic tank ammunition with a diameter of 155 mm in order to give the tank a precision strike capacity, as different from the literature. First of all, an ammunition model is created with coefficients of the mass, inertia, and surface area and friction. In addition, an autopilot dynamic is modelled for the pitch and roll axes of the ammunition. Also, the atmosphere model and environmental factors are added to the model. In order to control this nonlinear model, a lead-compensator and a PD-controller are designed. In order for the results to be transferred to a real application, the accelerations obtained must basically be produced by the electric motors that will drive the control surfaces to be designed. At the end of the study, it is seen that both controllers can produce lateral accelerations within limits without reaching high saturation. [ABSTRACT FROM AUTHOR]

A mechanistic model for tumbling mills was developed based on breakage characteristics and tumbling mill operational features. The concept was presented at the IMPC (International Mineral Processing Congress) 2014, followed by progress in a sub-process of the model presented at the IMPC 2016. Additionally, a number of papers on the sub-models and breakage function have been published. This paper provides a consolidated summary of the outcomes and status of the model. The overall model structure is presented along with the sub-models such as appearance functions, breakage rate functions, energy distribution, transport, and dual component grinding interaction model. The strengths and capabilities of the model structure as achieved to date are presented. The approach developed can be used as a platform for building multicomponent models. The modelling work can be done quicker by using an existing structure such as the one presented in this paper. It is recommended that researchers assess compatibility prior to embarking on model development work if the intention is to use this model structure. [Display omitted] • The evolution of the different version of dynamic models for tumbling mills is presented. • Each type of the mechanistic models are validated with plant data. • The strengths and capabilities of each version of the model are compared. • The gaps in knowledge and sub-processes mechanistic modelling are presented. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical study of the dynamic behaviour of a wind turbine consisting of a wind rotor, a speed increaser with fixed axes, and a counter-rotating electric generator, operating in variable wind conditions. In the first part, the dynamic analytical model of the wind turbine mechanical system is elaborated based on the dynamic equations associated with the component rigid bodies and the linear mechanical characteristics associated with the direct current (DC) generator and wind rotor. The paper proposes a method for identifying the coefficients of the wind rotor mechanical characteristics depending on the wind speed. The numerical simulations performed in Simulink-MATLAB by MathWorks on a case study of a 10 kW wind turbine highlight the variation with the time of the kinematic parameters (angular speeds and accelerations), torques and powers for wind system shafts, as well as the mechanical efficiency, both in transient and steady-state regimes, considering variable wind speed. The analytical and numerical results are helpful for researchers, designers, developers, and practitioners of wind turbines aiming to optimise their construction and functionality through virtual prototyping. [ABSTRACT FROM AUTHOR]

Trickle bed reactors (TBR), historically utilized in petroleum processing and wastewater treatment, now extend their applicability in biological gas conversions, including syngas biomethanation for renewable methane production. Despite operational benefits, optimizing TBR design and scale-up demands a good understanding of the reactor's hydraulic behavior and mass transfer phenomena. This study's novelty is TBR's hydraulic characterization in respect to liquid and gas flowrates and simulation of the gas-liquid interaction by a dynamic gas-liquid transfer model. Residence time distribution (RTD) experiments in 220 mL lab- and 5000 mL pilot-scale TBRs with co-current flow of gas and liquid were conducted to determine the liquid and gas working volume and the number of tanks for a dynamic tank-in-series (TIS) model. RTD experiments revealed 2–6 tanks for varying liquid flowrate (constant gas flowrate) and 5–8 tanks for varying gas flowrate (constant liquid flowrate). A dimensionless equation, fitted to RTD experimental data, predicted the liquid working volume of a TBR at various liquid flowrates and reactor configurations. TBR simulation by TIS model considering 8 well mixed gas and liquid phase tanks connected in series, resulted in an excellent model validation with an R2 at 0.99. Finally, the model simulated the mass transfer kinetics of H 2 , CO, and CO 2 in water under varying gas and liquid flowrates for lab- and pilot-scale TBRs. Simulation results showed increased dissolved gas concentration with higher gas flowrate (constant liquid flowrate) for both scales TBR. It was also noticeable that under constant gas flowrate, the dissolved gas concentration initially decreased (0.01 < Re L < 17), then increased (17 < Re L < 34), and again decreased (34 < Re L < 105). This behavior was the same for both scales TBR and revealed an optimum Re L value irrespective of TBR size. [Display omitted] • Hydraulic characterization of trickle bed reactor by residence time distribution. • Liquid holdup as a function of reactor geometry, packing material, liquid flowrate. • Tank in series dynamic model for mass transfer in TBR was developed and validated. • Model can be used for liquid Reynolds number between 0.1 and 105. • Model can be used for gas Reynolds number between 0.02 and 1. [ABSTRACT FROM AUTHOR]

Integrated energy systems can improve flexibility on future energy grids with one option being Nuclear-Solar hybrid systems. Integrating solar generated heat from parabolic troughs into the feedwater line allows the plant to alleviate turbine bled steam and transiently power boost 15% above nominal power for a nominal small modular PWR cycle. This article presents a parametric study of the design of such a system and provides a full system dynamic model written in the Modelica language to analyse the dispatch in transient load following operation. The control of the system is presented, and the transient analysis is shown to help inform sizing of the concrete storage and parabolic trough arrays. The trade off in design between improved degrees of power boosting and system nominal efficiency is investigated with the work suggesting that higher steam generator entry temperatures offer improved opportunities of up to 42% for power boosting flexibility. • An open-source model of the full proposed energy system is written in Modelica. • The model allows a high time fidelity simulation of all key system variables. • Solar feedwater heating allows power boosting without changing reactor conditions. • Transient analysis informs sizing the concrete thermal storage and solar array. [ABSTRACT FROM AUTHOR]

This study proposes a novel, eco-friendly design for a deep-sea floating mining vehicle multi-body system (DFMVMS), incorporating a floating mining vehicle (FMV) as its central mining apparatus. The comprehensive system architecture encompasses a surface mother ship, a main pipe cable, an ore transit station, an umbilical pipe cable (UPC), and the FMV, engineered for deep-sea extraction activities while preserving benthic ecosystems. To investigate the interactions among the multi-body system's components and ascertain the collective motion dynamics during operational states, initial steps involved developing mathematical models for each individual component. Subsequently, a holistic model for the entire DFMVMS was formulated by integrating these component models via coupling boundary conditions, with the model's accuracy subsequently affirmed. Analytical scenarios, including three foundational motion states of the FMV, were structured to explore the influence of FMV dynamics on the DFMVMS's overall behaviour. Moreover, specific operational scenarios were tailored to reflect two principal functions of the DFMVMS—production and transfer. The simulation results significant interplay among the movements of the system's components, highlighting UPC tension as a critical consideration in devising transfer routes. • Introduced an eco-friendly deep-sea mining system with a floating vehicle as the main equipment. • Developed a dynamic model for this complex multi-body system, including a transit station. • Validated the model through simulations of three fundamental floating vehicle operations. • Designed and simulated two real-world operational scenarios, providing practical insights. [ABSTRACT FROM AUTHOR]

Benchmarking coal seam gas (CSG) production from Brownfield to Greenfield is an important step for assessing the development plan for Greenfield. However, the observed Brownfield production data is impacted by many factors besides reservoir properties, e.g., well spacing, well efficiency factor (WEFAC), surface pipeline network. This paper presents a case study in the Surat Basin to discuss how to benchmark CSG production from Brownfield to Greenfield. The selected Brownfield study area includes 143 wells and the Greenfield 450 proposed wells. Various tools for static modelling, decline curve analysis, and dynamic modelling were used in this study. Results show that the peak gas rate decreases quickly and the time to peak gas rate increases quickly when the WEFAC is less than 0.65. The peak gas rate is reached when the cumulative water production is between 39% and 46% of water-in-place for the selected box model. After introducing the wells' online time ratio or WEFAC from the daily production data and the ratio of the observed cumulative water production to the expected cumulative water production, gas production rates can be predicted based on the modified Morse potential energy equation. Artificial neural networks (ANN) can be used to estimate the peak gas rate, the time to peak gas rate and the decline rate based on the reservoir properties; while the ramp-up rate can be calculated from the time to peak gas rate. • 143 wells with seven years of production data were used in this study. • This study integrated static modelling, dynamic modelling and decline curve analysis. • Artificial neural networks can be used to estimate the peak gas rate, the time to peak gas rate and the decline rate based on the reservoir properties. • The well efficiency factor impacts the wells' peak gas rate, the time to peak gas rate, and the decline rate. [ABSTRACT FROM AUTHOR]

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.

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.

In this work, we present a new mathematic model for the flight of a bird-scale flapping-wing aerial vehicle, in which the impacts of the wing inertia and its deformations are considered. Based on this model, the longitudinal and later-directional orbit stability of the vehicle at uncontrolled condition are proved to be steady via Floquet Theory combined with the untethered flight experiment. However, both simulation and experiment show that the vehicle presents a periodical motion state which is similar to spiral flight at uncontrolled condition. At this spiral-like flight state, the yaw angle of the vehicle varies constantly, which makes it difficult to meet the requirements of the general flight mission. In this case, two independent PID controllers are designed to stabilize the vehicle attitude based on the approximate linear model in the vicinity of the equilibrium flight condition. And the controlled flight of the vehicle prototype is also proved to be stable through numerical calculation and physical experiment. [ABSTRACT FROM AUTHOR]

This paper discusses cooperative path-planning and tracking controller for autonomous vehicles using a distributed model predictive control approach. Mixed-integer quadratic programming approach is used for optimal trajectory generation using a linear model predictive control for path-tracking. Cooperative behaviour is introduced by broadcasting the planned trajectories of two connected automated vehicles. The controller generates steering and torque inputs. The steering and drive motor actuator constraints are incorporated in the control law. Computational simulations are performed to evaluate the controller for vehicle models of varying complexities. A 12-degrees-of-freedom vehicle model is developed and is subsequently linearised to be used as the plant model for the linearised model predictive control-based tracking controller. The model behaviour is compared against the kinematic, bicycle and the sophisticated high-fidelity multi-body dynamics CarSim model of the vehicle. Vehicle trajectories used for tracking are longitudinal and lateral positions, velocities and yaw rate. A cooperative obstacle avoidance manoeuvre is performed at different speeds using a co-simulation between the controller model in Simulink and the high-fidelity vehicle model in CarSim. The simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Power-to-gas is a pathway for the storage of intermittent renewable energy in a chemical form; energy surpluses can be exploited to produce hydrogen (via electrolysis) that can then react with carbon dioxide to produce synthetic methane by using catalytic cooled reactors. Methanation unit has been designed and optimized, defining the number of involved reactors, the number of parallel tubes for each reactor and the staged CO 2 injection to moderate the maximum temperature throughout the reactors. The produced synthetic natural gas (SNG) must have a methane content at least equal to 95 mol.-%, achieved by involving a series of 3 cooled reactors. Dynamic behavior of the as-designed system has been investigated for two cases that can occur when a power-to-gas system is operated intermittently: start-up from reactor hot standby and system operated at partial load. Methanation system requires about 130 s to reach the targeted methane content when it is started up from hot standby. In order to broaden the system rangeability (the minimum percentage of full load), different CO 2 staged injection should be carried out: part of carbon dioxide bypasses first and second reactor and it is directly conveyed to the third one. [Display omitted] • Design of CO 2 methanation unit based on fixed-bed cooled reactors. • Dynamic behavior: start up after hot standby and partial load operation. • Synthetic natural gas quality is achieved after about 130 s from start up. • Carbon dioxide staging should be modified to widen the rangeability (45–100%). [ABSTRACT FROM AUTHOR]

Featured Application: Achievement of proper thrust for BAUVs based on the correct application of control when parallel mechanisms are incorporated in propulsion systems. Incorporation of parallel mechanisms inside propulsion systems in biomimetic autonomous underwater vehicles (BAUVs) is a novel approach for motion generation. The vehicle to which the studied propulsion system is implemented presents thunniform locomotion, and its thrust depends mainly on the oscillation from its caudal fin. This paper describes the kinematic and dynamic modeling of a 3-DOF spherical 3UCU-1S parallel robotic system to which the caudal fin of a BAUV is attached. Lagrange formalism was employed for inverse dynamic modeling, and its derivation is detailed throughout this paper. Additionally, the implementation of control strategies to compute forces required to actuate limbs to change platform's flapping frequencies was developed. Four controllers: classic PD, a feedforward plus feedback PD, an adaptive Fuzzy-PD, and a feedforward plus Fuzzy-PD were compared in different simulations. Results showed that augmenting oscillating frequencies (from 0.5 to 5 Hz) increased the complexity of the path tracking task, where the classic control strategy (i.e., PD) was not sufficient, reaching percentage errors above 9%. Control strategies using feedforward terms combined with adaptive feedback techniques reduced tracking error below 2% even during the presence of external disturbances. [ABSTRACT FROM AUTHOR]

There is much need for autonomous underwater vehicles (AUVs) for inspection and mapping purposes. Most conventional AUVs use torpedo-shaped single-rigid hull, because of which their manoeuvrability is limited. Moreover, any increase in payload results in a larger hull size and the turning diameter, limiting its operation in constrained areas. As a solution to this problem, we develop M-Hull, a subsurface mapping AUV with a modular-split hull design that provides better manoeuvrability than a conventional torpedo-shaped vehicle. At the same time, it has more agility than an unconventional bio-inspired snake-like vehicle though their designs look similar. This approach makes it a hybrid solution between conventional torpedo-shaped AUVs and unconventional bio-inspired vehicles. We focus on improving the turning diameter during the mapping operation, and hence this paper concentrates on the dynamic aspects of the 2D turning motion of the vehicle. It will provide the relationship between turning speed, thrust, and joint torque requirements for the multi-hull underwater vehicle. Different turning modes are compared to choose an optimum turning configuration, and the critical speed is calculated for the vehicle's safe operation. In the end, the modelling is verified using the experimental data. One can follow the method followed here for the 2D motion analysis of similar underwater vehicles. [ABSTRACT FROM AUTHOR]

Since asphalt mortar is highly heterogeneous and occurs as an interfacial bonding material between the asphalt-binder and aggregate, it is very important to have a detailed and adequate understanding of its mechanical properties at a multi-scale (micro and macro) level analysis. In this study, the fundamental properties of the asphalt mortar were evaluated and quantified using molecular dynamic simulations. The tensile strength, stress-separation responses, and adhesive-bonding strength were numerically measured and characterised from a micro perspective. Other mechanical properties such as the complex modulus, low-temperature stiffness modulus, hardness, and elastic modulus were also experimentally measured to verify the dynamic simulations and modelling results. In comparison to the macro experimental test results, it was found that the micro-simulated results were superior in terms of characterising the mechanical properties of the asphalt mortar. Whilst the tensile strength, adhesive-bond strength, and hardness of the asphalt mortar changed significantly as a function of aging, the stress-separation responses and elastic modulus were hardly affected. Overall, the study findings indicated that multi-scale characterisation of the mechanical properties of asphalt mortar is a potentially promising methodology for quantitatively evaluating and understanding the mechanical properties. [ABSTRACT FROM AUTHOR]

This paper presents a numerical investigation on the influence of the mining environment on seismic sources, with a focus on pillar failure mechanisms in tabular mining. We investigate the influence of the mining stope (underground excavation or void) on seismic inversions for the scalar moment, corner frequency, source radius, stress drop and moment tensor using synthetic events created within elastodynamic numerical modelling software, WAVE3D. The main objective is to determine whether the source parameters calculated from the recorded waveforms are due to a combination of the stope source and the pillar sources, rather than being related only to crushing of the pillar or shearing in the pillar footwall. The main finding is that the presence of stopes, and types of pillars, have a significant impact on the seismic moment and other source parameters. This is important since the moment is viewed as a robust parameter on which seismic magnitude is often based; however, this study indicates that moments calculated for pillar failure in a tabular stoping environments are less representative of the shearing or crushing source than originally thought. This article is part of the theme issue 'Fracture dynamics of solid materials: from particles to the globe'. [ABSTRACT FROM AUTHOR]

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.

Abstract Background Data-driven methods that automatically learn relations between attributes from given data are a popular tool for building mathematical models in computational biology. Since measurements are prone to errors, approaches dealing with uncertain data are especially suitable for this task. Fuzzy models are one such approach, but they contain a large amount of parameters and are thus susceptible to over-fitting. Validation methods that help detect over-fitting are therefore needed to eliminate inaccurate models. Results We propose a method to enlarge the validation datasets on which a fuzzy dynamic model of a cellular network can be tested. We apply our method to two data-driven dynamic models of the MAPK signalling pathway and two models of the mammalian circadian clock. We show that random initial state perturbations can drastically increase the mean error of predictions of an inaccurate computational model, while keeping errors of predictions of accurate models small. Conclusions With the improvement of validation methods, fuzzy models are becoming more accurate and are thus likely to gain new applications. This field of research is promising not only because fuzzy models can cope with uncertainty, but also because their run time is short compared to conventional modelling methods that are nowadays used in systems biology.