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Following the publication of the above article, the authors contacted the Editorial Office to explain that they had identified a pair of duplicate images in the control (Vehicle) group of mouse images in Fig. 1A on p. 1792. Specifically, the same image (corresponding correctly to the 'Day 5' experiment) was inadvertently chosen to represent the cutaneous manifestations of mice in the Vehicle group on 'Day 3' and 'Day 5' in Fig. 1A. This error arose as a consequence of repetitive application and duplication procedures within the image set, resulting in the inadvertent reuse of the same photo. Additionally, due to minimal alterations observed in the skin condition of mice from the control group following treatment, each mouse exhibited a similar appearance; this similarity further contributed to the delayed identification of this error during the paper revision stage. Consequently, this duplication of the same image was made as a result of insufficient scrutiny. The revised version of Fig. 1, showing the correct image for the 'Day 3' experiment in Fig. 1A, is shown on the next page. The authors can confirm that the error associated with the assembly of this figure did not have any significant impact on either the results or the conclusions reported in this study, and all the authors agree with the publication of this Corrigendum. The authors are grateful to the Editor of International Journal of Molecular Medicine for allowing them the opportunity to publish this; furthermore, they apologize to the readership of the Journal for any inconvenience caused. [International Journal of Molecular Medicine 43: 1789‑1805, 2019; DOI: 10.3892/ijmm.2019.4098].

As wind turbines (WTs) become more prevalent, there is an increasing interest in actively controlling their power output to participate in the frequency regulation for the power grid. Conventional frequency regulation controllers use fixed gains, making it difficult for the WT to adjust its kinetic energy uptake to its operating conditions and to collaborate effectively with other WTs in the wind farm. In addition, the design of conventional frequency controllers does not consider their impacts on mechanical structure. To address these issues, we model the cooperative frequency control problem for all WTs in a wind farm as a decentralised partially observable Markov decision process (Dec-POMDP) and use a multi-agent deep reinforcement learning (MADRL) algorithm to solve it. We also develop a grid-connected wind farm simulation model based on MATLAB/Simulink and OpenFAST, which can reflect the detailed interactions between the electrical and mechanical components of WTs. Simulation results show that the proposed strategy is effective in reducing frequency drops and has less impact on mechanical structure deflections compared with traditional methods.

Prior research in management and economics has predominantly focused on how managers or policymakers can shape workers’ allocation of effort using output-based or effort-based incentives. In many settings, however, managers may seek to influence workers’ effort choices through resource allocation—that is, changing the cost of securing resources for different projects or activities. In this paper, we develop a formal model to investigate how a worker changes the allocation of a fixed amount of effort across different projects in response to changes in the cost of securing resources for each project. Our model shows how cutting resources available to one project, under certain circumstances, can inadvertently reduce the share of effort allocated to other projects and vice versa. We use the insights from the model to explore the effectiveness of funding strategies designed to influence the research direction of academic scientists. We specifically examine how U.S. scientists working in stem cell research responded to a 2001 policy change that restricted access to federal funding for research in the human embryonic stem cell (hESC) area. In line with our model’s predictions, we find that cutting resources for hESC research inadvertently reduced U.S. scientists’ output in non-hESC areas of stem cell research—an effect that is strongest among the highest-ability scientists. Our findings highlight the complexities of incentivizing effort allocation using resource-based incentives. In particular, we show how altering resource-based incentives in one area can have unforeseen spillover effects on effort allocation in other areas. Funding: Financial support from the London Business School is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1565 .

The network identification plays a very prominent role for the network operator to accomplish the various objectives such as state-estimation, monitoring, control, planning, and real-time analytics. The network structure varies from time-to-time and its details are often not available with the network operator. To address this issue, an alternating direction method of multipliers (ADMM) based framework is presented herein to identify the network topology and line parameters using smart meter (SM) and micro phasor measurement unit (micro-PMU) measurements. The presented algorithm is divided into two sections, 1) approximate parameter evaluation through regression, to extract the partial topology information, and 2) complete network topology identification through the ADMM framework. This algorithm accomplishes the objectives of identifying the network configuration, branch parameters (e.g. conductance and susceptance), and change in branch parameters. Simulation results demonstrate the effectiveness of the presented algorithm on the benchmarked IEEE 13-bus and IEEE 123-bus feeders under various operating scenarios. Furthermore, the presented framework illustrates excellent network identification even with the presence of the stochastic nature of renewable power generation. The presented algorithm exhibits an excellent performance even with the consideration of noise in both measurements. In addition, the comparative performance is carried out on the benchmarked unbalanced IEEE 13-bus and balanced IEEE 33-bus feeders to highlight the efficacy of the presented framework over the state-of-art framework.

Brexit has potentially wide-ranging implications for UK policy, although little is known about what these are yet. Now, post the transition period, is a good time to consider its actual impacts as opposed to what was expected by academics, and by proponents of Brexit. In the absence of any established theory of EU-exit, and drawing on insights from (de-)Europeanisation, Brexit energy and climate policy studies, and political economy, this article develops a framework to identify the impact of EU-exit on UK energy policy. This is applied to sustainable energy, an area in urgent need of policy development to meet legally binding national targets. We conclude that, so far, despite leaving various EU bodies there has been relatively little divergence from Europeanised policy; that new UK energy and climate policies, required to replace EU membership benefits, are relatively less effective; and that hard-pressed civil servants have been drawn away from other important policymaking tasks.

With increasing the number of wind turbines approaching the end of their service life, it has become crucial for businesses to understand and assess safety and security issues related to the decommissioning phase of wind farm asset lifecycle. This paper aims to develop, for the first time, a Bayesian Network (BN) model for the safety assessment of offshore wind farm decommissioning operations. The most critical safety incidents are identified and their corresponding risk-influencing factors (RIF) are determined. The impacts of human errors as well as procedural and mechanical/electrical failures on the safety and efficiency of decommissioning operations are thoroughly analysed. The findings of the study revealed that the most critical RIFs during offshore wind decommissioning operations include: visibility, crew fatigue, number of personnel per operation, proper safety procedures, crane integrity, number of lifts available in the wind farm, inspection frequency, as well as equipment design.

This paper is devoted to real-time optimal attitude reorientation control of rigid spacecraft control. Particularly, two typical practical problems - actuator misalignment and forbidden pointing constraints are considered. Within the framework of adaptive dynamic programming (ADP), a novel constrained optimal attitude control scheme is proposed. In this design, a special reward function is developed to characterize the environment feedback and deal with the pointing constraints. Notably, a novel argument term is introduced to the reward function for overcoming the inevitable difficulty in actuator misalignment. By virtue of the Lyapunov stability theory, the ultimate boundedness of state error and the optimality of the proposed method can be guaranteed. Finally, the effectiveness and performance of the developed ADP-based controller are evaluated by not only numerical simulations but also experimental tests with a hardware-in-loop platform.

This paper is the second in a two-part study on lubrication in wind turbine main bearings. Where Part 1 provided an introductory review of elastohydrodynamic lubrication theory, this paper will apply those ideas to investigate lubrication in the double-row spherical roller main bearing of a 1.5 MW wind turbine. Lubrication is investigated across a “contact conditions dataset” generated by inputting main-bearing applied loads, estimated from hub loads generated using aeroelastic simulation software, into a Hertzian contact model of the main bearing. From the Hertzian model is extracted values of roller load and contact patch dimensions, along with the time rate of change of contact patch dimensions. Also included in the dataset are additional environmental and operational variable values (e.g. wind speeds and shaft rotational speeds). A suitable formula for estimating film thickness within this particular bearing is then identified. Using lubricant properties of a commercially available wind turbine grease, specifically marketed for use in main bearings, an analysis of film thickness across the generated dataset is undertaken. The analysis includes consideration of effects relating to temperature, starvation, grease thickener interactions and possible non-steady effects. Results show that the studied main bearing is at risk of operating under mixed lubrication conditions for a non-negligible proportion of its operational life, indicating that further work is required to better understand lubrication in this context and implications for main-bearing damage and operational lifetimes. Key sensitivities and uncertainties within the analysis are discussed, along with recommendations for future work.

Decline of rotating inertia due to the high share of renewable sources cause challenges in controlling grid frequency. With recent grid codes, large-scale wind turbines (WTs) are required to provide frequency support. Existing stepwise inertial control methods suggest immediate incremental power injection by WTs, followed by the abrupt over-production termination to avoid over-deceleration of the rotor speed. Unfortunately, these methods are not practically desirable as they impose severe secondary-frequency-drops (SFD) or considering unrealistic constant wind speed for tens of seconds. This paper proposes a novel inertial control scheme that can improve frequency nadir without rotor speed over-deceleration. Upon detecting a power imbalance, WT increases the output power with an incremental power and declines it following a Gaussian distribution trajectory controlled by a standard deviation parameter, to ensure convergence to an equilibrium point. The proposed scheme can be practically implemented for fast frequency response. This scheme is tested on the wind-integrated IEEE 9-bus system and IEEE 39-bus system and compared with other methods reported in literature. Furthermore, experimental tests are conducted to verify the performance of the proposed scheme in practice. Blade fatigue is studied using FAST Code. Results show reliable operation during abrupt wind speed changes or cascade events.

Multipopulation methods are highly effective in solving dynamic optimization problems. Three factors affect this significantly: 1) the exclusion mechanisms to avoid the convergence to the same peak by multiple subpopulations; 2) the resource allocation mechanism that assigns the computational resources to the subpopulations; and 3) the control mechanisms to adaptively adjust the number of subpopulations by considering the number of optima and available computational resources. In the existing exclusion mechanisms, when the distance (i.e., the distance between their best found positions) between two subpopulations becomes less than a predefined threshold, the inferior one will be removed/reinitialized. However, this leads to incapability of algorithms in covering peaks/optima that are closer than the threshold. Moreover, despite the importance of resource allocation due to the limited available computational resources between environmental changes, it has not been well studied in the literature. Finally, the number of subpopulations should be adapted to the number of optima. However, in most existing adaptive multipopulation methods, there is no predefined upper bound for generating subpopulations. Consequently, in problems with large numbers of peaks, they can generate too many subpopulations sharing limited computational resources. In this article, a multipopulation framework is proposed to address the aforementioned issues by using three adaptive approaches: 1) subpopulation generation; 2) double-layer exclusion; and 3) computational resource allocation. The experimental results demonstrate the superiority of the proposed framework over several peer approaches in solving various benchmark problems.

This work considers the characteristics and drivers of the loads experienced by wind turbine main-bearings. Simplified load response models of two different hub and main-bearing configurations are presented, representative of both inverting direct-drive and four-point mounted geared drivetrains. The influences of deterministic wind field characteristics, such as wind speed, shear, yaw offset and veer, on the bearing load patterns are then investigated for similarity scaled 5, 7.5 and 10 MW reference wind turbine models. Main-bearing load response in cases of deterministic gusts and extreme changes in wind direction are also considered for the 5 MW model. Perhaps surprisingly, veer is identified as an important driver of main-bearing load fluctuations. Upscaling results indicate that similar behaviour holds as turbines become larger, but with mean loads and load fluctuation levels increasing at least cubically with the turbine rotor radius. Strong links between turbine control and main-bearing load response are also observed.

This paper aims to handle the optimal attitude tracking control tasks for rigid bodies via a reinforcement learning-based control scheme, in which a constrained parameter estimator is designed to compensate system uncertainties accurately. This estimator guarantees the exponential convergence of estimation errors and can strictly keep all instant estimates always within pre-determined bounds. Based on it, a critic-only adaptive dynamic programming (ADP) control strategy is proposed to learn the optimal control policy with respect to a user-defined cost function. The matching condition on reference control signals, which is commonly employed in relevant ADP design, is not required in the proposed control scheme. We prove the uniform ultimate boundedness of the tracking errors and critic weight's estimation errors under finite excitation conditions by Lyapunov-based analysis. Moreover, an easy-to-implement initial control policy is designed to trigger the real-time learning process. The effectiveness and advantages of the proposed method are verified by both numerical simulations and hardware-in-loop experimental tests.

Electroplastic rolling was employed to produce nanostructured (NS), near-equiatomic NiTi alloy from a coarse grained NiTi nugget (ingot), which was produced using vacuum induction melting, followed by quenching in water from a temperature of 800°C. The microstructure of NS NiTi was characterized using X-ray Diffraction (XRD) and transmission electron microscopy (TEM). XRD analysis revealed that the NS NiTi is predominantly martensitic at room temperature, with less than ≈10 % of the austenite phase. The NS NiTi alloy has an average grain size of ≈36 nm. TEM investigation confirmed the presence of grains that are less than 10 nm in size and no amorphous zones were detected. The NS martensitic NiTi alloy specimens were tested in tension at two different strain rates (10−2 and 10−1 s−1). In contrast to a stress-strain profile expected in a martensitic NiTi alloy, the stress-strain curves show conventional tensile behaviour. The observed UTS was high, around ≈1800 MPa, with a less than usual elongation to failure of ≈6 %. The presence of dimples on the fracture surfaces can be seen in scanning electron microscopy (SEM) images, which is indicative of ductile fracture. The role of grain size in the observed deformation and fracture features is also discussed.

The United Nations Economic Commission for Europe (UN ECE) has developed new aspects of its WP.29 agreement for harmonising vehicle regulations, focusing on the regulation of vehicle manufacturers’ approaches to ensuring vehicle cyber security by requiring implementation of an approved cyber security management system (CSMS). This paper investigates the background, framework and content of WP.29’s cyber security regulation. We provide an overall description of the processes required to become certified, discuss key gaps, issues and the impacts of implementation on stakeholders, and provide recommendations for manufacturers and the authorities who will oversee the operation. Putting the discussion into a broader theoretical framework on risk certification, we explore to the role of non-academic sources to shape public risk perception and to drive, for better or worse, legislative responses.

With the increasing deployment of renewable energy-based power generation plants, the power system is becoming increasingly vulnerable due to the intermittent nature of renewable energy, and a blackout can be the worst scenario. The current auxiliary generators must be upgraded to energy sources with substantially high power and storage capacity, a short response time, good profitability, and minimal environmental concern. Difficulties in the power restoration of renewable energy generators should also be addressed. The different energy storage methods can store and release electrical/thermal/mechanical energy and provide flexibility and stability to the power system. Herein, a review of the use of energy storage methods for black start services is provided, for which little has been discussed in the literature. First, the challenges that impede a stable, environmentally friendly, and cost-effective energy storage-based black start are identified. The energy storage-based black start service may lack supply resilience. Second, the typical energy storage-based black start service, including explanations on its steps and configurations, is introduced. Black start services with different energy storage technologies, including electrochemical, thermal, and electromechanical resources, are compared. Results suggest that hybridization of energy storage technologies should be developed, which mitigates the disadvantages of individual energy storage methods, considering the deployment of energy storage-based black start services.

A model-free deep reinforcement learning (DRL) method is proposed in this paper to maximize the total power generation of wind farms through the combination of induction control and yaw control. Specifically, a novel double-network-based DRL approach is designed to generate control policies for thrust coefficients and yaw angles simultaneously and separately. Two sets of critic-actor networks are constructed to this end. They are linked by a central power-related reward, providing a coordinated control structure while inheriting the critic-actor mechanism's advantages. Compared with conventional DRL methods, the proposed double-network-based DRL strategy can adapt to the distinctive and incompatible features of different control inputs, guaranteeing a reliable training process and ensuring superior performance. Also, the prioritized experience replay strategy is utilized to improve the training efficiency of deep neural networks. Simulation tests based on a dynamic wind farm simulator show that the proposed method can significantly increase the power generation for wind farms with different layouts.

The present paper provides a comprehensive comparative analysis of thermochemistry models of various fidelity levels developed in leading research groups around the world. Fully kinetic, hybrid kinetic-continuum, and fully continuum approaches are applied to analyze parameters of hypersonic flows starting from the revision of single-temperature rate constants up to the application in 1-D postshock conditions. Comparison of state-specific and two-temperature approaches shows there are very significant and often qualitative differences in the time-dependent nonequilibrium reaction rates and their ratio to the corresponding single-temperature rates. A major impact of the vibration-dissociation coupling on the temporal relaxation of gas properties is shown. For instance, the legacy Park's model has a strongly nonlinear behavior of nonequilibrium reaction rate with vibrational temperature, while a nearly linear shape exists for all state-specific approaches. Analysis of vibrational level populations in the nonequilibrium region shows a profound impact of the numerical approach and the model on the population ratios, and thus vibrational temperatures inferred from such ratios. The difference in the ultraviolet absorption coefficients, calculated by a temperature-based spectral code using vibrational populations from state-specific and kinetic approaches, is found to exceed an order of magnitude.

Sustainable cities are envisioned to have economic and industrial steps toward reducing pollution. Many real-world applications such as autonomous vehicles, transportation, traffic signals, and industrial automation can now be trained using deep reinforcement learning (DRL) techniques. These applications are designed to take benefit of DRL in order to improve the monitoring as well as measurements in industrial internet of things for automation identification system. The complexity of these environments means that it is more appropriate to use multi-agent systems rather than a single-agent. However, in non-stationary environments multi-agent systems can suffer from increased number of observations, limiting the scalability of algorithms. This study proposes a model to tackle the problem of scalability in DRL algorithms in transportation domain. A partition-based approach is used in the proposed model to reduce the complexity of the environment. This partition-based approach helps agents to stay in their working area. This reduces the complexity of the learning environment and the number of observations for each agent. The proposed model uses generative adversarial imitation learning and behavior cloning, combined with a proximal policy optimization algorithm, for training multiple agents in a dynamic environment. We present a comparison of PPO, soft actor-critic, and our model in reward gathering. Our simulation results show that our model outperforms SAC and PPO in cumulative reward gathering and dramatically improved training multiple agents.

This paper develops a nonlinear mid-fidelity aeroservoelastic model for smart rotor wind turbines and studies the turbulent load alleviation of the wind turbines with trailing edge flaps (TEFs) actuated by a novel proportional-derivative model-free adaptive control (PD-MFAC) algorithm. This nonlinear model contains a structural model for the tower and blades represented by geometrically non-linear composite beams and an aerodynamic model for the rotor using a vortex panel method coupled with a stall delay process. The capability of the new aerodynamic model to deal with flow separations and analyze the detailed flow field enables it to simulate the dynamic response of the wind turbine blade sections with TEFs with arbitrary size and deflection angles. It is shown that the TEF alters the aerodynamic coefficients in a complex manner which could be explained by the evolution of the detailed vortical field. Furthermore, three independent PD-MFAC TEF controllers are designed to alleviate the turbulent load acting on the wind turbines. The effectiveness of the controller in terms of turbulent load alleviation is evaluated by the root mean square value of the blade root-bending moment (RBM). A traditional Gain-scheduled PI (GS-PI) controller is also designed as a comparison to the PD-MFAC controller. Simulation results show that to reduce the RBM and blade tip deflection (BTD) caused by external disturbances, the PD-MFAC flap controller shows more effective performance than the GS-PI flap controllers.\ud \ud

This paper evaluates the impacts of the phase-locked loop (PLL) and reactive power control (RPC) on the inertia provision of DFIG wind turbine (WT), by taking a wide range of system loading conditions (i.e., from the reactive injection to absorption) into consideration. First, a linearized model is developed for DFIG WT to describe the motion of its internal voltage phase, and the derived motion equation provides a way for analytical estimation on DFIG WT's inertia provision. After formulating the equivalent inertia, it is then used to conduct a character study on the equivalent inertia subject to different PLL and RPC settings. In this course, the loading condition of DFIG WT changes from the reactive injection to absorption to give a systematic study. Finally, the theoretical outcomes are validated by the simulations on a modified 3-machine, 9-node test system.

An adsorption refrigeration system can be driven by low grade heat and uses natural refrigerant with the advantage of reducing the greenhouse gases emission. However, one of the weaknesses is its low efficiency and more importantly its high cost. The recovery of internal waste heat becomes therefore very important in order to improve the coefficient of performance (COP). Analysis based on pinch technology can be helpful to optimal heat recovery operation. In this paper, temperature-heat diagrams and problem tables for adsorption refrigeration systems are proposed and analyzed using Pinch Technology. The results show that pinch point is located between beds and the main waste heat needs to be recovered between beds. Dynamic characteristic (time factor) of adsorption refrigeration system is the main resistance for heat recovery. The effect of pinch point temperature difference on the system COP is not distinct. Furthermore, when the driving temperature is 90°C, the COP of adsorption refrigeration via optimization of pinch analysis is 0.73 which is fairly comparable to LiBr-water absorption refrigeration system. Pinch Technology can be adopted in different types of adsorption refrigeration systems (two-bed, four-bed, mass recovery, et al.).\ud \ud

Intercalation chemistry has dominated electrochemical energy storage for decades, and storage capacity worldwide has now reached the terawatt-hour level. State-of-the-art intercalation cathodes for Li-ion batteries operate within the limits of transition metal cation electrochemistry, but the discovery of anion-redox processes in recent decades suggests rich opportunities for substantially increasing stored energy densities. The diversity of compounds that exhibit anion redox in the solid state has inspired the exploration of new materials for next-generation cathodes. In this Review, we outline the mechanisms proposed to contribute to anion redox and the accompanying kinetic pathways that can occur in layered transition metal oxides. We discuss the crucial role of structural changes at both the atomic and mesoscopic scales with an emphasis on their impact on electrochemical performance. We emphasize the need for an integrated approach to studying the evolution of both the bulk structure and electrode–electrolyte interphase by combining characterization with computation. Building on the fundamental understanding of electrochemical reaction mechanisms, we discuss engineering strategies such as composition design, surface protection and structural control to achieve stable anion redox for next-generation energy storage devices.

This paper presents a novel hybrid method to design the continuous and accurate multi-gravity-assist trajectory for a high-fidelity dynamics. The gravitational perturbation of the primary body is considered during the gravity assistance. The pseudostate technique is applied to approximate the gravity-assisted trajectory, where the optimal sweepback duration is solved using a trained deep neural network. The major factors that affect the optimal sweepback duration of the approach and departure segments are investigated. The results show that the optimal sweepback duration of the approach segment only relies on the shape of the approach trajectory and is independent of the flight time. Then, a gravity-assisted trajectory patched strategy and a hybrid algorithm combining the particle swarm optimization and the sequential quadratic programming are developed to optimize the multi-gravity-assist trajectory. The proposed hybrid method is applied to the Europa orbiter mission. In comparison with the traditional patched conic method, this method demonstrates outstanding performance on accuracy and significantly reduces the computational time and complexity of the trajectory correction with the high-fidelity dynamics.

The field of artificial intelligence has gained a significant attention in the media. Some counties claim to be the leaders in the field, other countries claim to be winning in the race for leadership in artificial intelligence. This article conducts a statistical (i.e., bibliometric) analysis of research data records on artificial intelligence by year, country, language, and organisation. The results are clearly in favour of the USA on a national level, and English is clearly the dominant language for disseminating results. But in terms of leading organisation in the field of artificial intelligence creates more confusing result – e.g., between the Chinese Academy of Sciences and the University of California - in the leadership race. The forecasts from this study on future evolution of artificial intelligence is that it is unlikely that (in the next 60 years) AI ‘superintelligence’ would trigger a catastrophic event for humanity.

Graphical abstract, Environmental structure describes physical structure that can determine heterogenous spatial distribution of biotic and abiotic (nutrients, stressors etc.) components of a microorganism’s microenvironment. This study investigated the impact of micrometre-scale structure on microbial stress sensing, using yeast cells exposed to copper in microfluidic devices comprising either complex soil-like architectures or simplified environmental structures. In the soil micromodels, the responses of individual cells to inflowing medium supplemented with high copper (using cells expressing a copper-responsive pCUP1-reporter fusion) could be described neither by spatial metrics developed to quantify proximity to environmental structures and surrounding space, nor by computational modelling of fluid flow in the systems. In contrast, the proximities of cells to structures did correlate with their responses to elevated copper in microfluidic chambers that contained simplified environmental structure. Here, cells within more open spaces showed the stronger responses to the copper-supplemented inflow. These insights highlight not only the importance of structure for microbial responses to their chemical environment, but also how predictive modelling of these interactions can depend on complexity of the system, even when deploying controlled laboratory conditions and microfluidics.

This article addresses the tracking control problem for a class of nonlinear systems described by Euler-Lagrange equations with uncertain system parameters. The proposed control scheme is capable of guaranteeing prescribed performance from two aspects: 1) a special parameter estimator with prescribed-performance properties is embedded in the control scheme. The estimator not only ensures the exponential convergence of the estimation errors under relaxed excitation conditions but also can restrict all estimates to predetermined bounds during the whole estimation process and 2) the proposed controller can strictly guarantee the user-defined performance specifications on tracking errors, including convergence rate, maximum overshoot, and residual set. More importantly, it has the optimizing ability for the tradeoff between performance and control cost. A state transformation method is employed to transform the constrained optimal tracking control problem to an unconstrained stationary optimal problem. Then, a critic-only adaptive dynamic programming algorithm is designed to approximate the solution of the Hamilton-Jacobi-Bellman equation and the corresponding optimal control policy. Uniformly ultimately bounded stability is guaranteed via a Lyapunov-based stability analysis. Finally, numerical simulation results demonstrate the effectiveness of the proposed control scheme.

Wire arc additive manufacturing (WAAM), also known as directed energy deposition (DED) process, is an efficient additive manufacturing technology, offers high potential to rapidly fabricate large-scale parts with complex geometries layer-by-layer. However, the fundamental understanding of the fatigue behaviour of such parts and the material requirements need to be significantly improved at all levels before this unique technology can be implemented for critical applications. This work aims to investigate the fatigue behaviour of WAAM built ER70S-6 steel under uniaxial, torsion and multiaxial loading conditions. Specimens were extracted in two different orientations: vertical and horizontal, to explore if the orientation direction has any effect on the fatigue results. Scanning Electron Microscopy (SEM) was conducted to examine the fracture surface of broken specimens and identify crack initiation regions and fracture mechanisms. The obtained results were compared with the fatigue data available in the literature on common structural steels fabricated using conventional welding and WAAM technique, showing similar fatigue behaviour with wrought S355 specimens. Moreover, the uniaxial data set on ER70S-6 WAAM specimens was evaluated according to the DNV RP-C203 standard for continuous welds, demonstrating advantageous fatigue resistance in the examined material.

A pneumatic drying model, developed in a previous work, was tested and validated based on a series of 56 experiments of cassava starch drying conducted on a pilot-scale pneumatic dryer. After fitting two parameters, the average diameter of starch particles and a heat loss factor, the model predicted the effects of main operating conditions with a level of accuracy comparable to that of the experimental measurements. Transient phases with sudden variations of product feed rate were also successfully simulated. The model is therefore suitable for equipment design purposes. The experimental results showed that the size of starch particles considerably decreases with increasing air velocity. As a result, at high air velocity, despite the shorter residence time in the drying pipe, the overall drying performance is improved due to the smaller particle size. It is therefore possible to increase air velocity, and hence product feed rate and production capacity, without detrimental effect on the drying efficiency.

Cyber-physical systems (CPS) are increasingly used in manufacturing, transportation, health, and other industries. To develop these complex interdisciplinary systems, highly qualified CPS engineers are required who possess sound engineering knowledge and excellent transferable skills. Academic institutions offer a range of modules and curricula to teach CPS engineering. However, the literature reports a gap between expectations of industry and competencies of CPS graduates. To close this gap, this paper introduces and describes a holistic educational framework (T-CHAT) for teaching CPS engineering at the module level. To evaluate this framework, two use cases were analysed by conducting self-perception surveys and semi-structured interviews with students. Descriptive statistics and t-tests were calculated for the survey data. Interviews were coded and analysed using a General Inductive Approach. The analysis results were discussed by the comparison of the T-CHAT implementations in these two use cases.

This paper deals with the numerical simulation of a ship's manoeuvring performance in regular waves. This is made possible by employing the time-domain code ELIGMOS, which adopts a hybrid formulation to couple seakeeping and manoeuvring contributions. The first and steady second-order wave-induced forces are incorporated, implementing a multidimensional interpolation scheme to account for their dependency on the instantaneous heading and forward speed values. Two methods have been adopted for the calculation of the added resistance regarding the size of the wavelength (short or long wave seas), namely a far and a near-field one, using the hydrodynamic software NEWDRIFT+ and NEWDRIFT v.7. Low-frequency hydrodynamic manoeuvring forces are incorporated by adopting the expressions suggested by the Japanese MMG (Manoeuvring Modelling Group) method. Validation of the suggested methodology is attempted through the comparison of numerical simulations of turning circle tests in calm water and in regular waves with available experimental evidence for the S-175 container ship.

A novel extended cohesive damage element method is used to develop a numerical snowpack model to study the fundamental damage mechanisms of snowpack under external drivers and to investigate multiple mixed mode damage propagation within snowpack. A new mixed mode damage criterion is introduced to account for tensile and shear fractures as well as compressive crushing together with shear crack for approximating the mixed mode damage initiation and propagation in the weak layer in snowpack. A propagation saw test (PST) is considered to understand basic damage involution in snowpack under self-weight related bending. The nonlinear fracture modelling prediction agrees with the PST sample well. This paper provides an alternative approach as a predictive method using the extended cohesive damage element for potentially forecasting slab avalanches in snow terrain according to weather forecast and planned human activities in the future.

The ability to measure the very high heat fluxes that typically occur during the hypersonic re-entry phase of space vehicles is generally considered a subject of great importance in the aerospace field. Most of the sensors used for these measurements need to be checked periodically and re-calibrated accordingly. Another bottleneck relates to the need to procure thermal sources that are able to generate reliable reference heat fluxes in the range between 100 and 1000 kW/m2 (as order of magnitude). In the present study, a method is presented by which, starting from a calibration system with a capacity of approximately 500 kW/m2 only, heat fluxes in the range of interest for hypersonic applications are generated. The related procedure takes advantage of established standards for the characterization of a radiative heat flux. It also builds on the hybrid radiative-convective nature of typical hypersonic heat fluxes and the yet poorly explored possibility to use convective sources of heat to produce high-intensity fluxes. The reliability of such a strategy has been tested using a high enthalpy supersonic flow facility relying on an electric arc-heater and pure Nitrogen as work gas. Stagnation-point heat fluxes have been successfully measured (with reasonable accuracy) in the range between 600 and 1500 kW/m2 for values of the centerline enthalpy spanning the interval from to 6 to 24 MJ/kg.

The frequency-dependent mass and stiffness matrices of a Timoshenko–Ehrenfest beam are developed through extensive application of symbolic computation. Explicit algebraic expressions for the frequency dependent shape functions and each of the independent elements of the frequency-dependent mass and stiffness matrices are presented concisely. The ensuing frequency-dependent mass and stiffness matrices of the Timoshenko–Ehrenfest beam are applied with particular reference to the Wittrick–Williams algorithm to investigate the free vibration characteristics of an individual Timoshenko–Ehrenfest beam and a framework. The results are discussed with significant conclusions drawn. The proposed method retains the exactness of results like the dynamic stiffness method, but importantly, it opens the possibility of including damping in the analysis.

The effect of deformation heating on microstructure evolution during hot forging of Ti-6Al-4V was established. For this purpose, right-circular cylinders of Ti-6Al-4V with an equiaxed-α preform microstructure were preheated to a temperature between 1148 K (875 °C) and 1223 K (950 °C), and compressed to a 60-pct. height reduction in a screw press, yielding average true strain rates of ~ 5 to 20 s−1. Thermocouple measurements and corroborating finite-element-method (FEM) simulations quantified substantial deformation-heating-induced temperature increases. For all preheat temperatures, the heating transient led to an exposure above the equilibrium β transus temperature. Despite such temperature excursions, the volume fraction of equiaxed primary α in each forged billet was only slightly lower than that in the corresponding preheated condition. The source of such observations was rationalized on the basis of the (hypothesized) solute-concentration fields that develop during the heating and cooling transients experienced in high-rate deformation processing.

Wire arc additive manufacturing (WAAM) is an advanced fabrication technology for the rapid and efficient production of large-scale engineering structures. In order to design WAAM components for a given loading condition, it is essential to characterize the mechanical and failure behavior of the parts. In this study, the performance of ER100S-1 low carbon steel has been investigated by performing fatigue crack growth tests on compact tension specimens extracted from a WAAM built wall. The experimental results have been compared with the recommended trends in the BS7910 standard and with data available in the literature. Metallurgical investigations have been carried out to explore the microstructural effects on the fatigue behavior of the WAAM built components. The specimen location and orientation effects were comprehensively examined, and the results are discussed in terms of the influence of macroscopic and microscopic deformation on the overall response of the WAAM built components under fatigue loading conditions.

The current work demonstrates a novel multi-criteria decision-making (MCDM) technique to optimize the process parameters during the wire electric discharge machining of Inconel 718. A hybrid GRA-TOPSIS algorithm using entropy weights is developed to optimize wire electric discharge machining processes parameters for maximizing the cutting speed and simultaneously minimizing the surface roughness and flatness error. Experiments were conducted according to Taguchi L18 orthogonal array considering pulse on time, pulse off time, servo voltage, wire feed rate, and wire electrode types as the input parameters. The weights of each responses were calculated by entropy weights method. The different alternatives were ranked using the hybrid technique, and an optimum parameter combination is found that maximizes the process outcome. The analysis of variance (ANOVA) was performed to study the relative significance of process parameters on the performance index. The results were finally validated by performing confirmation tests. The entropy weighted GRA-TOPSIS method was observed to improve the overall process performance compared to conventional TOPSIS. Microstructural analysis, morphological studies, and EDS analysis were conducted to study the surface integrity of the machined surface under optimal parameter settings.

Background Determination of near-surface residual stresses is challenging for the available measurement techniques due to their limitations. These are often either beyond reach or associated with significant uncertainties. Objective This study describes a critical comparison between three methods of surface and near-surface residual stress measurements, including x-ray diffraction (XRD) and two incremental central hole-drilling techniques one based on strain-gauge rosette and the other based on electronic speckle pattern interferometry (ESPI). Methods These measurements were performed on standard four-point-bend beams of steel loaded to known nominal stresses, according to the ASTM standard. These were to evaluate the sensitivity of different techniques to the variation in the nominal stress, and their associated uncertainties. Results The XRD data showed very good correlations with the surface nominal stress, and with superb repeatability and small uncertainties. The results of the ESPI based hole-drilling technique were also in a good agreement with the XRD data and the expected nominal stress. However, those obtained by the strain gauge rosette based hole-drilling technique were not matching well with the data obtained by the other techniques nor with the nominal stress. This was found to be due to the generation of extensive compressive residual stress during surface preparation for strain gauge installation. Conclusion The ESPI method is proven to be the most suitable hole-drilling technique for measuring near-surface residual stresses within distances close to the surface that are beyond the penetration depth of x-ray and below the resolution of the strain gauge rosette based hole-drilling method.

Wake interactions between wind turbines have a great impact on the overall performance of a wind farm. In this work, a novel deep learning method, called Bilateral Convolutional Neural Network (BiCNN), is proposed and then employed to accurately model dynamic wind farm wakes based on flow field data generated by high-fidelity simulations. Different from the existing machine-learning-based dynamic wake models where dimensionality reduction is essential, the proposed BiCNN is designed to directly process the different types of inputs through a background path and a foreground path, thus avoiding the errors due to dimensionality reduction. Substantial results show that the developed machine learning based wake model can achieve accurate wake predictions in real time, i.e. it captures the spatial variations of the dynamic wakes similarly as high-fidelity wake models and runs as fast as low-fidelity static wake models. The overall prediction error of the developed model is 3.7% with respect to the freestream wind speed. Furthermore, the results for a test farm consisting of 25 turbines show that the developed model can predict the dynamic wind farm wakes within several seconds using

Existing researches about unsupervised cross-domain bearing fault diagnosis mostly consider global alignment of feature distributions in various domains, and focus on relatively ideal diagnosis scenario under the steady speeds. Therefore, unsupervised feature adaptation between all the corresponding subdomains under speed fluctuation remains great challenges. This paper proposes a modified deep subdomain adaptation network (MDSAN) for more practical and challenging cross-domain diagnostic scenarios from the fluctuating speeds to steady speeds. Firstly, to extract the representative features and effectively suppress negative transfer, a novel shared feature extraction module guided by multi-headed self-attention mechanism is constructed. Then, a new trade-off factor is designed to improve the convergence performance and optimization process of MDSAN. The proposed method is used for analyzing experimental bearing vibration data, and the results show that it has higher diagnostic accuracy, faster convergence, better distribution alignment, and is more suitable for unsupervised cross-domain fault diagnosis under speed fluctuation scenario compared with the existing methods.

Metamaterials are a group of materials with artificial engineered structures that exhibits customized properties which are not naturally available in other materials. To accelerate the computational analysis of components made from metamaterials that helps novel engineering product design process, it is crucial to develop an accurate and robust model for these materials in macroscale. The classical approach to drive a material model in continuum level is based on development of a phenomenological model to represent the physical behaviour of the material. However, this approach has specific limitations in including the effect of tailoring design parameters in the model which is a key element for metamaterials. In this study, we have proposed an artificial neural network (ANN) constitutive model to represent the macroscale mechanical behaviour of metamaterials in three-dimensional domain. Because of its extraordinary capabilities to stimulate computational performance in identifying and constructing prospective microstructure model for mechanical metamaterials, the proposed ANN constitutive model provides intriguing advantages over conventional models. The ANN constitutive model has been trained based on strain-stress data which is obtained from microscale simulation of 3D cubic lattice structure under various loading conditions. The trained material model is then validated by measuring the accuracy of material behaviour prediction.