Search

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.

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.

This article proposes a method to decentralize the navigation burden and improve the fault tolerance for a spacecraft constellation. The constellation body reference system is introduced, which is the perifocal frame of one satellite in the constellation. The structure of the proposed navigation method is constructed to enable each spacecraft to estimate its own orbit in this body reference system. This step is essentially the relative orbit determination (OD) based on inter-satellite range measurements. Thereafter, the approach to transfer an orbit from the constellation body reference system to an inertial reference system is developed. The essential requirements on absolute measurements to realize the coordinate transfer are presented. By dividing the absolute OD into relative OD and coordinate transfer, each navigation subsystem operated in a spacecraft can be independent with others, and the absolute measurements collected by any spacecraft can contribute to the absolute OD of the whole constellation. The proposed method applies to constellations in any geometric configuration. A Walker constellation is taken as an example for numerical simulations. The results show that the proposed method has a lower computation burden compared to an integrated navigation system. With the same type of absolute measurements, the proposed method has higher accuracy and convergence velocity than conventional decentralized algorithms. When a spacecraft occurs with fault, the orbit results of other spacecraft are not affected using the proposed method, which is beyond the ability of conventional methods.

Communication between people is commonly thought to play a major role in determining the success or failure of collaborative design projects. We conducted eight weeks of observation and 62 interviews in three design companies. Our findings indicate that communication is blamed for causing many problems which are not of an obviously technical nature. However, such problems are more often symptomatic of underlying factors related to information, representation, individual, team, and organisational issues. Examples of factors influencing communication at interfaces are people's understanding of the ‘sequence of tasks’ or ‘goals and objectives’ in the design process. With a system-theory informed conceptualisation of communication, we propose that awareness of such factors could assist design practitioners in targeting interventions and researchers in generating hypotheses about communication and design performance.

Vertical axis wind turbine (VAWT) array design requires adequate modelling of the turbine wakes to model the flow throughout the array and, therefore, the power output of turbines in the array. This paper investigates how accurately different turbulence models using 2D computational fluid dynamics (CFD) simulations can estimate near and far wakes of VAWTs to determine an approach towards accurate modelling for array design. Three experiments from the literature are chosen as baselines for validation, with these experiments representing the near to far wake of the turbine. Five URANS turbulence models were chosen due to their common and potential usage for VAWT CFD: models k–ω SST, k–ω SST LRN, k–ω SSTI, transition SST, and k–kl–ω. In addition, the lifting line-free vortex wake (LLFWV) model was tested as an alternative to CFD for the far turbine wake where it was appropriate for use. The results for turbulent kinetic energy and vorticity were compared for the first experiment, whilst streamwise and cross-stream velocity were used for the other two experiments. It was found that none of the turbulence models tested or LLFVW produced adequate estimations within the methodology tested, however, transition SST produced the closest estimations. Further adjustments to the methodology are required to improve accuracy due to their large impact on results including use of 3D CFD, adjustment of surface roughness, and inlet flow characteristics.

This paper discusses the aerodynamic analysis and design of a novel, hubless, contra-rotating, rim-driven fan (RDF) device. Rim-driven thruster technology has been used for marine propulsion for decades, yet RDF technology has not been comparably developed for aerospace applications. This paper demonstrates that an innovative two-stage contra-rotating RDF configuration provides the potential for a significant increase in the available thrust and efflux velocity when compared with existing technologies. The analysis was conducted for an RDF device with a relatively small fan diameter of 120 mm. However, the findings of this study can be equally applied to the performance of much larger and more powerful RDF propulsion units. This would also include devices having multiple contra-rotating paired stages, for example, four, six, or more rotors to permit higher-pressure ratios and exhaust airspeeds suitable for large modern aircraft propulsion.

PurposeThis research identifies the forms of tacit knowledge frequently requested in the job descriptions of knowledge workers in a multinational automotive manufacturer's product development department. It then explores how and why the most requested forms of tacit knowledge are used in practice to achieve organizational goals.Design/methodology/approachThis study follows a sequential mixed-methods approach to quantify the most frequently requested forms of tacit knowledge within internal job descriptions and then explores how and why this tacit knowledge is used. The first stage applies manifest content analysis to internal job descriptions to highlight the epitomes of tacit knowledge to identify the most frequently requested forms of tacit knowledge. The second stage employs semi-structured interviews to explore the use of the most frequently requested forms of tacit knowledge in practice.FindingsThe research indicated that the organization most frequently requested tacit knowledge in the form of skills and experience in the job descriptions of knowledge workers in the product development department. When the use and application of tacit knowledge in the form of skills were further explored in practice, it was found that tacit knowledge-based socially-focused skills were used, which was underpinned by the need to bring people together and align them towards a common goal to make things work; by enabling people to work together as a team; by developing and using networks; and acting as a required resource to support the development and integration in product development. Tacit knowledge in the form of experience was applied through the application of personally obtained experience to enhance development work by acting as a pacemaker for increasing efficiency and a sense of upcoming issues.Originality/valueThis work addresses the paucity of studies identifying tacit knowledge in large organizations and meets calls to investigate the processes and activities related to tacit knowledge in specific contexts.

Isogrid is a highly efficient stiffened plate structure which was developed in the aerospace industry for use in rocketry and space structures. Its current form is unviable outwith these applications, as the available production methods are expensive due to excessive machining time in addition to considerable material wastage. The method detailed in this body of work was developed to manufacture Isogrid in a more efficient manner, so that its weight-saving properties may become more widely accessible. This novel Isogrid manufacturing process uses a rolling mill with patterned rollers to imprint a 3D structure of ribs into the surface of a billet material. To validate this method, a patterned roller was designed, manufactured and fitted to a rolling mill to produce sheets of aluminium AA1050 Isogrid. This process successfully created Isogrid in a sustainable, rapid manner. The samples produced were tested in 3-point bending and compared against flat plate of the same material. They were found to be 100% stronger in bending compared to a neutral flat plate with a strength shape factor of 1.6 after discounting the effect of cold work.

Purpose As wind power generation increases globally, there will be a substantial number of wind turbines that need to be decommissioned in the coming years. It is crucial for wind farm developers to design safe and cost-effective decommissioning plans and procedures for assets before they reach the end of their useful life. Adequate financial provisions for decommissioning operations are essential, not only for wind farm owners but also for national governments. Economic analysis approaches and cost estimation models therefore need to be accurate and computationally efficient. Thus, this paper aims to develop an economic assessment framework for decommissioning of offshore wind farms using a cost breakdown structure (CBS) approach. Methods In the development of the models, all the cost elements and their key influencing factors are identified from literature and expert interviews. Similar activities within the decommissioning process are aggregated to form four cost groups including: planning and regulatory approval, execution, logistics and waste management, and post-decommissioning. Some mathematical models are proposed to estimate the costs associated with decommissioning activities as well as to identify the most critical cost drivers in each activity group. The proposed models incorporate all cost parameters involved in each decommissioning phase for more robust cost assessment. Results and discussion A case study of a 500 MW baseline offshore wind farm is proposed to illustrate the models’ applicability. The results show that the removal of wind turbines and foundation structures is the most costly and lengthy stage of the decommissioning process due to many requirements involved in carrying out the operations. Although inherent uncertainties are taken into account, cost estimates can be easily updated when new information becomes available. Additionally, further decommissioning cost elements can be captured allowing for sensitivity analysis to be easily performed. Conclusions Using the CBS approach, cost drivers can be clearly identified, revealing critical areas that require attention for each unique offshore wind decommissioning project. The CBS approach promotes adequate management and optimisation of identified key cost drivers, which will enable all stakeholders involved in offshore wind farm decommissioning projects to achieve cost reduction and optimal schedule, especially for safety-critical tasks.

This paper investigates the possibility to navigate one or more spacecraft in the proximity of an asteroid binary system using only optical measurements. The approach proposed in this paper assumes only limited prior knowledge of the shape and orbital parameters of the two asteroids. In particular it is shown that the knowledge of the semi-major axis of the orbit of the secondary with respect to the primary is critical to guarantee a sufficient level of navigation accuracy. A method is proposed to allow for the inclusion of the orbital parameters of the secondary asteroid in the estimation process. It is shown that the inclusion of the estimation of the orbital parameters of the secondary provides the required information to allow navigating in the proximity of the binary system at the Lagrange point L4. The paper then demonstrates that the same method can be used to navigate a small formation of two spacecraft at L4 provided that intersatellite position and velocity vectors can be measured with sufficient accuracy. In this case the formation can be controlled even if the optical sensor on board one of the spacecraft partially fails.

Energy density of current generation battery packs is insufficient for next generation electric vehicles nor the electrification of the aerospace industry. Currently, approximately a third of energy density is lost due to ancillary demands (e.g., cooling and instrumentation) within a pack, relative to cell energy density. Smart cells, instrumented cells with sensors and circuitry, offer a means to monitor cell performance (e.g. temperature, voltage, current data). Uniquely here we demonstrate our 21700 cells instrumented with internal thermistor sensing arrays with custom miniature interface circuitry including data acquisition and communication components. This circuitry including a power line communication (PLC) system, enables sensor data to be collected and transmitted to a master controller without requiring additional wiring, and can achieve an excellent

The transformers architecture and transfer learning have radically modified the Natural Language Processing (NLP) landscape, enabling new applications in fields where open source labelled datasets are scarce. Space systems engineering is a field with limited access to large labelled corpora and a need for enhanced knowledge reuse of accumulated design data. Transformers models such as the Bidirectional Encoder Representations from Transformers (BERT) and the Robustly Optimised BERT Pretraining Approach (RoBERTa) are however trained on general corpora. To answer the need for domain specific contextualised word embedding in the space field, we propose Space Transformers, a novel family of three models, SpaceBERT, SpaceRoBERTa and SpaceSciBERT, respectively further pre-trained from BERT, RoBERTa and SciBERT on our domain-specific corpus. We collect and label a new dataset of space systems concepts based on space standards. We fine-tune and compare our domain-specific models to their general counterparts on a domain-specific Concept Recognition (CR) task. Our study rightly demonstrates that the models further pre-trained on a space corpus outperform their respective baseline models in the Concept Recognition task, with SpaceRoBERTa achieving significant higher ranking overall.

Positive displacement expanders are widely investigated and theirs models commonly use the REFPROP database to evaluate fluid properties. However, estimating procedures in CFD models using REFPROP result in heavy use of CPU time. Therefore, a study has been carried out to determine if simpler methods for fluid property estimation are suitable. The ANSYS CFX solver used includes a number of cubic equations of state, namely, the Redlich Kwong, Soave Redlich Kwong, Aungier Redlich Kwong and Peng Robinson. To determine their suitability, performance simulations were carried out with each of them, on a twin screw expander with a 4/5 rotor configuration and an “N” rotor profile, operating with R245fa, R290, R1336mzz(Z) and R1233zd(E). For each considered equation, the expander's performance results deviation from those obtained using REFPROP is evaluated. These results show that the ideal gas equation of state gives predictions of flow and power that deviate significantly from those determined with REFPROP. The alternatively available cubic equations give far better agreement. Of these, the RK equation has the highest deviation, although it differs by only 2.2% maximum from the REFPROP values. The PR equation yields the closest agreement with a deviation of the order of only 0.8%, while the deviation with both SRK and ARK equations is less than 1.1% maximum, practically negligible. Apart from the good agreement obtained, the use of the real gas equations, in place of REFPROP, resulted in a saving of approximately 43% in CPU operating time to obtain same level of convergence of the solver.

Conventional technologies are largely powered by fossil fuel exploitation and have ultimately led to extensive environmental concerns. Hydrogen is an excellent carbon-free energy carrier, but its storage and long-distance transportation remain big challenges. Ammonia, however, is a promising indirect hydrogen storage medium that has well-established storage and transportation links to make it an accessible fuel source. Moreover, the notion of ‘green ammonia’ synthesised from renewable energy sources is an emerging topic that may open significant markets and provide a pathway to decarbonise a variety of applications reliant on fossil fuels. Herein, a comparative study based on the chosen design, working principles, advantages and disadvantages of direct ammonia fuel cells is summarised. This work aims to review the most recent advances in ammonia fuel cells and demonstrates how close this technology type is to integration with future applications. At present, several challenges such as material selection, NOx formation, CO2 tolerance, limited power densities and long term stability must still be overcome and are also addressed within the contents of this review.

This wind tunnel study investigates the aerodynamic effects of Mini Gurney flaps (MGFs) and their combination with vortex generators (VGs) on the performance of airfoils and wind turbine rotor blades. VGs are installed on the suction side aiming at stall delay and increased maximum lift. MGFs are thin angle profiles that are attached at the trailing edge in order to increase lift at pre-stall operation. The implementation of both these passive flow control devices is accompanied by a certain drag penalty. The wind tunnel tests are conducted at the Hermann- Föttinger Institut of the Technische Universität Berlin. Lift is determined with a force balance and drag with a wake rake for static angles of attack from −5° to 17° at a constant Reynolds number of 1.5 million. The impact of different MGF heights including 0.25 %, 0.5 % and 1.0 % and an uniform VG height of 1.1 % of the chord length are tested on three airfoils that are characteristic for different sections of large rotor blades. Furthermore, the clean and the tripped baseline cases are considered. In the latter, leading edge transition is forced by means of Zig Zag (ZZ) turbulator tape. The preferred configurations are the smallest MGF on the NACA63(3)618 and the AH93W174 (mid to tip blade region) and the medium sized MGF combined with VGs on the DU97W300 (root to mid region). Next, the experimental lift and drag polar data is imported into the software QBlade in order to design a generic rotor blade. The blade performance is simulated with and without the add-ons based on two case studies. In the first case, the retrofit application on an existing blade mitigates the adverse effects of the ZZ tape. Stall is delayed and the aerodynamic efficiency is partly recovered leading to an improvement of the power curve. In the second case, the new design application allows for the design of a more slender blade while maintaining the power output. Moreover, the alternative blade appears to be more resistant against forced leading edge transition.

In this article, a deep reinforcement learning (RL)-based control approach with enhanced learning efficiency and effectiveness is proposed to address the wind farm control problem. Specifically, a novel composite experience replay (CER) strategy is designed and embedded in the deep deterministic policy gradient (DDPG) algorithm. CER provides a new sampling scheme that can mine the information of stored transitions in-depth by making a tradeoff between rewards and temporal difference (TD) errors. Modified importance-sampling weights are introduced to the training process of neural networks (NNs) to deal with the distribution mismatching problem induced by CER. Then, our CER-DDPG approach is applied to optimizing the total power production of wind farms. The main challenge of this control problem comes from the strong wake effects among wind turbines and the stochastic features of environments, rendering it intractable for conventional control approaches. A reward regularization process is designed along with the CER-DDPG, which employs an additional NN to handle the bias of rewards caused by the stochastic wind speeds. Tests with a dynamic wind farm simulator (WFSim) show that our method achieves higher rewards with less training costs than conventional deep RL-based control approaches, and it has the ability to increase the total power generation of wind farms with different specifications.\ud

Needle biopsy is an essential part of modern clinical medicine. The puncture accuracy and sampling success rate of puncture surgery can be effectively improved through virtual surgery. There are few three-dimensional puncture (3D) models, which have little significance for surgical guidance under complicated conditions and restrict the development of virtual surgery. In this paper, a 3D simulation of the muscle tissue puncture process is studied. Firstly, the mechanical properties of muscle tissue are measured. The Mooney-Rivlin (M-R) model is selected by considering the fitting accuracy and calculation speed. Subsequently, an accurate 3D dynamic puncture model is established. The failure criterion is used to define the breaking characteristics of the muscle, and the bilinear cohesion model defines the breaking process. Experiments with different puncture speeds are carried out through the built in vitro puncture platform. The experimental results are compared with the simulation results. The experimental and simulated reaction force curves are highly consistent, which verifies the accuracy of the model. Finally, the model under different parameters is studied. The simulation results of varying puncture depths and puncture speeds are analyzed. The 3D puncture model can provide more accurate model support for virtual surgery and help improve the success rate of puncture surgery.

This paper aims to address the wind-farm power tracking problem, which requires the farm's total power generation to track time-varying power references and therefore allows the wind farm to participate in ancillary services such as frequency regulation. A novel preview-based robust deep reinforcement learning (PR-DRL) method is proposed to handle such tasks which are subject to uncertain environmental conditions and strong aerodynamic interactions among wind turbines. To our knowledge, this is for the first time that a data-driven model-free solution is developed for wind-farm power tracking. Particularly, reference signals are treated as preview information and embedded in the system as specially designed augmented states. The control problem is then transformed into a zero-sum game to quantify the influence of unknown wind conditions and future reference signals. Built upon the $H_\infty$ control theory, the proposed PR-DRL method can successfully approximate the resulting zero-sum game's solution and achieve wind-farm power tracking. Time-series measurements and long short-term memory (LSTM) networks are employed in our DRL structure to handle the non-Markovian property induced by the time-delayed feature of aerodynamic interactions. Tests based on a dynamic wind farm simulator demonstrate the effectiveness of the proposed PR-DRL wind farm control strategy.

In diesel engines, double-layer multi-holes nozzles contribute significantly in making spray injection uniform in both the circumferential and axial directions; they further ensure that minimal or no interactions are encountered among the spray jets emerging from the nozzle holes and positively affect fuel atomisation and enhance mixing during engine operation. In this study, the variation in internal flow characteristics and spray patterns from the upper and the lower layer nozzle holes were investigated experimentally and computationally. A double-layer 8-hole heavy-duty diesel engine injector nozzle was utilised for the characterisation of hole-to-hole variation on spray formation. The actual nozzle geometry was derived from X-ray scans obtained at the third generation X-ray imaging and biomedical beamline station in SSRF, revealing all geometrical differences between the individual injection holes. The momentum fluxes from each holes were obtained together with spray tip penetration under non-evaporating conditions. These data were used to validate the computational fluid dynamics (CFD) model suitable to describe the relevant flow processes. Initially, an Eulerian-Eulerian two-phase flow model was utilised to predict the internal nozzle flow under cavitating conditions. This model was weakly coupled with a Lagrangian spray model predicted the subsequent atomisation and penetration of all individual spray plumes. The results show that cavitation development within the upper layer holes is more intense than those formed within the lower layer nozzle holes; this is leading to higher injection rates from the lower layer nozzle holes that they also exhibit less cycle-to-cycle variations in the observed spray patterns.

The majority of facilities installed in offshore oil and gas fields during the 1980s and 1990s were designed to operate in ‘normal’ conditions. However, during the operational life of the fields, some new high pressure/high temperature (HPHT) wells may be discovered and tied back to older facilities. Operating these facilities beyond their design parameters in harsh environments may lead to catastrophic failures, resulting in significant economic losses and environmental problems. Managing the risks associated with failure of ageing subsea facilities in HPHT environments is considered as a very complex and critical task. To overcome such challenge, there is a need for development of decision-making methods that are capable of estimating precisely the risks associated with HPHT conditions as well as prioritising the risk mitigation and remediation strategies. This paper aims to propose an integrated risk management framework – based on Failure Mode and Effects Analysis (FMEA) approach and a hybrid Multi-Criteria Decision Analysis (MCDA) model – for evaluating the risks and prioritising mitigation strategies over the extended lifetime of subsea facilities in HPHT environments. For the purpose of illustrating the model, a case study of subsea manifold and flowlines is provided and the results are evaluated and discussed. Our findings indicate that the proposed approach offers significant improvement to the classical risk management processes applied to subsea oil and gas facilities as it can assist asset managers, risk analyst, regulators and policy makers with a decision model which considers both subjective (qualitative) judgements and objective (quantitative) evaluation measures.

The continuous cost reduction of wind turbines has consolidated the competitiveness of wind energy. With the increasing installation of wind turbines in seismic-prone regions, it is likely that earthquakes will strike farms in operation. A practical approach to predict the dynamic behavior of a wind turbine under simultaneous seismic and operational wind loads is investigated in this work. The combined action can be determined by analyzing the wind and the seismic-induced responses separately. However, an accurate definition of the aerodynamic damping is required for this purpose and there are few experimental studies on the additional damping source. A 1/100-scaled wind turbine model was designed and the aerodynamic damping of the model was identified. Subsequently, the ground motion was applied in the model by means of a shake table and the combined wind/earthquake response that was measured experimentally was compared with the response predicted by several combination rules. A numerical study using the FAST analysis package for wind turbines was also conducted to complement the experiments with fully-coupled simulations that include aeroelastic effects. This work provides a necessary experimental reference for structural engineers to use adequate aerodynamic damping and load combination methods for the seismic analysis of wind turbines in operation.

Owing to the increasing penetration of renewable power generation, the modern power system faces great challenges in frequency regulations and reduced system inertia. Hence, renewable energy is expected to take over part of the frequency regulation responsibilities from the gas or hydro plants and contribute to the system inertia. In this article, we investigate the feasibility of frequency regulation by the offshore hydrostatic wind turbine (HWT). The simulation model is transformed from NREL (National Renewable Energy Laboratory) 5-MW gearbox-equipped wind turbine model within FAST (fatigue, aerodynamics, structures, and turbulence) code. With proposed coordinated control scheme and the hydrostatic transmission configuration of the HWT, the `continuously variable gearbox ratio' in turbulent wind conditions can be realised to maintain the constant generator speed, so that the HWT can be connected to the grid without power converters in-between. To test the performances of the control scheme, the HWT is connected to a 5-bus grid model and operates with different frequency events. The simulation results indicate that the proposed control scheme is a promising solution for offshore HWT to participated in frequency response in the modern power system.\ud

Fast and accurate in-line areal surface topography measuring instruments are required to control the quality of microscale manufactured components, without significantly slowing down the production process. Full-field areal optical surface topography measurement instruments are promising for in-line or on-machine measurement applications due to their ability to measure quickly, to access small features and to avoid surface damage. This paper presents the development and integration of a compact optical focus variation sensor for on-machine surface topography measurement mounted on to a hybrid ultraprecision machine tool. The sensor development is described and a case study involving the on-machine dimensional measurement of the depth of hydrophobic microscale features, including microchannels and micro-dimples, is presented. Comparisons of results between the on-machine measurements obtained by the developed sensor and a desktop focus variation microscope are presented and discussed. The comparison results show that the developed focus variation sensor is able to perform on-machine dimensional measurement of microscale features within sub-micrometre accuracy.

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.

This paper investigates the effects power oscillation damping (POD) controller could have on a wind turbine structural system. Most of the published work in this area has been done using relatively simple aerodynamic and structural models of a wind turbine which cannot be used to investigate the detailed interactions between electrical and mechanical components of the wind turbine. Therefore, a detailed model that combines electrical, structural and aerodynamic characteristics of a grid-connected Doubly Fed Induction Generator (DFIG) based wind turbine has been developed by adapting the NREL (National Renewable Energy Laboratory) 5 MW wind turbine model within FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code. This detailed model is used to evaluate the effects of POD controller on the wind turbine system. The results appear to indicate that the effects of POD control on the WT structural system are comparable or less significant as those caused by wind speed variations. Furthermore, the results also reveal that the effects of a transient three-phase short circuit fault on the WT structural system are much larger than those caused by the POD controller.

Integrated energy carriers in the framework of energy hub system (EHS) have an undeniable role in reducing operating costs and increasing energy efficiency as well as the system's reliability. Nowadays, power-to-gas (P2G), as a novel technology, is a great choice to intensify the interdependency between electricity and natural gas networks. The proposed strategy of this study is divided into two parts: (i) a conditional value-at-risk-based stochastic model is presented to determine the optimal day-ahead scheduling of the EHS with the coordinated operating of P2G storage and tri-state compressed air energy storage (CAES) system. The main objective of the proposed strategy is to indicate the positive impact of P2G storage and tri-state CAES on lessening the system uncertainties including electricity market price, power generation of the wind turbine, and even electrical, gas, and thermal demands. (ii) A demand response program focusing on day-ahead load shifting is applied to the multiple electrical loads according to the load's activity schedule. The proposed strategy is successfully applied to an illustrative example and is solved by general algebraic modeling system software. The obtained results validate the proposed strategy by demonstrating the considerable diminution in the operating cost of the EHS by almost 4.5%.

Hard ballistic body armour plates are designed to withstand the impact of a bullet and protect the wearer, if this happens the armour is clearly damaged and so is retired from service. Mishandling, however, such as dropping the armour, may cause minor and difficult to detect damage which compromises the effectiveness of the plate. Current methods of inspection involve shipping the plates to a central location, performing a thorough inspection and returning them to service if uncompromised; this is costly and requires redundancy of equipment for when not in service. Acousto-Ultrasonics is a method of structural health monitoring in which ultrasonic waves are excited in a structure by a transducer and receivers record the response, any deviation from a baseline measurement give an indication of damage within the structure. Within this paper the development and testing of a novel handheld prototype device is presented, which gives a simple yes/no answer to if there is damage on the plate. This inspection is quick and easy to perform by unskilled personnel. Low profile sensors have been utilised combined with a novel flexible circuitry with built in memory, which does not compromise the effectiveness of the armour.

The focus of this research is the potential of biomethane in Britain's gas grid. It examines its relative ability to address Britain's sustainability and energy security challenges from an economic perspective. Such research is important because UK is wedded to gas for heat production and power generation and is increasingly dependent on imported gas, in line with shrinking domestic production, and uncertain future trading relationships. Also, dependency on natural gas, threatens Britain achieving its legally-binding carbon budgets. The study included a thorough literature review, primary research to finally uncover the views of key UK market participants plus analytical modelling. The findings reveal that the market is cautiously optimistic, despite reservations regarding feedstock availability and the impending cessation of subsidy approvals. Investors are in greater need of long-term certainty, however, and the challenge of decarbonising heat and heavy-duty transport warrants this. Retail price premiums are polarised but, in line with wholesale costs, relatively high compared to electricity. The key recommendation is for the policymakers to follow precedents in renewable electricity and liquid biofuels, by mandating that energy suppliers, owners of heavy-duty road fleets and occupiers of new buildings purchase biomethane. In tandem, feedstock and grid-entry restrictions must be tackled creatively.

This work investigates the effect of liquid fuel viscosity, as specific by the European Committee for Standardization 2009 (European Norm) for all automotive fuels, on the predicted cavitating flow in micro-orifice flows. The wide range of viscosities allowed leads to a significant variation in orifice nominal Reynolds numbers for the same pressure drop across the orifice. This in turn, is found to affect flow detachment and the formation of large-scale vortices and microscale turbulence. A pressure-based compressible solver is used on the filtered Navier-Stokes equations using the multifluid approach; separate velocity fields are solved for each phase, which share a common pressure. The rates of evaporation and condensation are evaluated with a simplified model based on the Rayleigh-Plesset equation; the coherent structure model is adopted for the subgrid scale modeling in the momentum conservation equation. The test case simulated is a well-reported benchmark throttled flow channel geometry, referred to as "I-channel"; this has allowed for easy optical access for which flow visualization and laser-induced fluorescence measurements allowed for validation of the developed methodology. Despite its simplicity, the I-channel geometry is found to reproduce the most characteristic flow features prevailing in high-speed flows realized in cavitating fuel injectors. Subsequently, the effect of liquid viscosity on integral mass flow, velocity profiles, vapor cavity distribution, and pressure peaks indicating locations prone to cavitation erosion is reported.

Creativity’s importance to organizations and businesses is now recognized to be a precondition for both design and innovation. One strategy is to introduce new forms of information system that support human creative thinking by their employees. Most successful uses have been in professional disciplines in the creative industries such as design and theatre. This paper reports the design and evaluation of a new information system that was researched and developed to support human creativity in a non-creative industry – health-and-safety in a manufacturing plant. An established risk detection and resolution process in one plant was extended with the new system to support plant employees to think creatively about resolutions to health-and-safety risks. The new system was used in a manufacturing plant for over 3 months. Results revealed that a subset of the risk resolutions generated with the new system were more creative and more complete than risk resolutions generated without the system in a corresponding period. However, the employees needed more time than was available to generate more complete risk resolutions. The evaluation results led to coordinated changes to both the information system and work practices associated with it.

Stent graft devices for the treatment of abdominal aortic aneurysms (AAAs) are being increasingly used worldwide. Yet, during modelling and optimization of these devices, as well as in clinical practice, vascular sections are idealized, possibly compromising the effectiveness of the intervention. In this study, we challenge the commonly used approximation of the circular cross-section of the aorta and identify the implications of this approximation to the mechanical assessment of stent grafts. Using computed tomography angiography (CTA) data from 258 AAA patients, the lumen of the aneurysmal neck was analysed. The cross-section of the aortic neck was found to be an independent variable, uncorrelated to other geometrical aspects of the region, and its shape was non-circular reaching elliptical ratios as low as 0.77. These results were used to design a finite element analysis (FEA) study for the assessment of a ring stent bundle deployed under a variety of aortic cross-sections. Results showed that the most common clinical approximations of the vascular cross-section can be a source of significant error when calculating the maximum stent strains (underestimated by up to 69%) and radial forces (overestimated by up to 13%). Nevertheless, a less frequently used average approximation was shown to yield satisfactory results (5% and 2% of divergence respectively).

Structural integrity plays an important role in any industrial activity, due to its capability of assessing complex systems against sudden and unpredicted failures. The work here presented investigates an unexpected new mechanism occurring in structures subjected to monotonic and cyclic loading at high temperature creep condition. An unexpected accumulation of plastic strain is observed to occur, within the high-temperature creep dwell. This phenomenon has been observed during several full inelastic finite element analyses. In order to understand which parameters make possible such behaviour, an extensive numerical study has been undertaken on two different notched bars. The notched bar has been selected due to its capability of representing a multiaxial stress state, which is a practical situation in real components. Two numerical examples consisting of an axisymmetric v-notch bar and a semi-circular notched bar are considered, in order to investigate different notches severity. Two material models have been considered for the plastic response, which is modelled by both Elastic-Perfectly Plastic and Armstrong-Frederick kinematic hardening material models. The high-temperature creep behaviour is introduced using the time hardening law. To study the problem several results are presented, as the effect of the material model on the plastic strain accumulation, the effect of the notch severity and the mesh element type and sensitivity. All the findings further confirm that the phenomenon observed is not an artefact but a real mechanism, which needs to be considered when assessing off-design condition. Moreover, it might be extremely dangerous if the cyclic loading condition occurs at such a high loading level.

The efficiency of automatic underground tunneling is significantly depends on the localization accuracy and reliable for the Boom-type roadheader. In comparison with other underground equipment positioning methods, vision-based measurement has gained attention for its advantages of non-contact and no accumulated error. However, the harsh underground environment, especially the geometric errors brought by the vibration of the machine body to the underground camera model, has a certain influence on the accuracy and stability for the vision-based underground localization. In this paper, a laser beams-based localization methods for the machine body of Boom-type roadheader is presented, which can tackle the dense-dust, low illumination environment with the stray lights interference. Taking mining vibration into consideration, an underground camera non-uniform blur model that incorporate the two-layer glasses refraction effect was established to eliminate vibration errors. The blur model explicitly reveals the change of imaging optical path under the influence of vibration and double layer explosion-proof glass. On the basis of this, the underground laser beams extraction and positioning are presents, which is with well environmental adaptability, and the improved 2P3L (two-points-three-lines) localization model from line correspondences are developed. Experimental evaluation are designed to verify the performance of the proposed method, and the deblurring algorithm is investigated and evaluated. The results show that the proposed methods is effective to restore the blurred laser beams image that caused by the vibration, and can meet the precision need of roadheader body localization for roadway construction in coal mine.

The black soil of Northeast China is sticky and agglomerates easily, which often adheres to the surface of a traditional furrow opener during the furrowing process. In this paper, biomimetic design principles in resistance-reducing, anti-adhesion and resistance-reducing mechanism of biomimetic disc furrow opener were studied. Nine kinds of singular convex hull, nine kinds of singular wedge and nine kinds of mixed convex hull and wedge structural biomimetic disc furrow opener were designed, and the furrowing process with the soil simulated by finite element method (FEM). Three types of biomimetic disc furrow opener with less resistance were manufactured by laser processing for comparative test in soil bin based on the simulation results. The test results showed that the resistance of the biomimetic disc furrow opener was less than that of the ordinary disc. The soil-disc stress, influence of biomimetic structures, moisture content and furrow speeds on resistance were discussed. The resistance-reducing rate of D-BC-3 reached the maximum value 15.36% at the furrow speed of 0.6 m/s and the soil moisture content of 20%. It is believed that the biomimetic design principles can provide the significant inspirations for the future design of disc furrow opener with drag reduction.

Half of the global final energy consumption is related to heat, which accounts for 40% of global energy-related carbon emissions. Particularly in the UK, nearly 24 million end-users rely on gas boilers to provide heat, leading to 37% of the country’s carbon emissions. Quick and successful heat decarbonisation is critical if ambitious climate goals need to be met by the mid-21st century. Heat pumps have been recognised as a key solution to reduce carbon emissions. This would instead draw carbon-reduced electricity from the electrical grid to produce heat more efficiently. Reciprocatively, the potential flexibility offered by the growing adoption of heat pumps on a (gigawatt) scale could also potentially support the deep decarbonisation of power and reduce the costs involved in the power system balancing and upgrades. By reviewing the technical development and barriers to creating high-efficiency and high-flexibility heat pumps, this study discusses how heat pumps could support the decarbonisation of heat and power with a focus on the UK system and market, which is also useful to countries in which heating is currently relying on fossil fuels. The major hurdle that mitigates the use of heat pumps – the high cost involved – and the methods to improve the economics of the technology are also discussed. If these techno-economic challenges can be overcome, electrification of heat using heat pumps could provide a route towards decarbonising heat and power and give the two communities and industries a collaborative way of tackling climate change at the root.

The present paper aims to develop an Artificial Neural Network (ANN) formula to predict the lateral-torsional buckling (LTB) resistance of slender steel cellular beams. A finite element model is developed and validated through experimental tests followed by a parametric study. 768 models are employed to train the ANN formula. The results are compared with the analytical models as well as the equation predicted by ANN. It was concluded that the ANN model with seven neurons can accurately predict the LTB resistance of cellular beams as well as the LTB combined with web-post buckling or web distortional buckling modes. Hence, the ANN-based formula can be adopted as design tool.

Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

This paper proposes a reinforcement learning (RL)-based six-degree-of-freedom (6-DOF) control scheme for the final phase proximity operations of spacecraft. The main novelty of the proposed method are from two aspects: 1) the closed-loop performance can be improved in real-time through the RL technique, achieving an online approximate optimal control subject to the full 6-DOF nonlinear dynamics of spacecraft; 2) Nontrivial motion constraints of proximity operations are considered and strictly obeyed during the whole control process. As a stepping stone, the dual-quaternion formalism is employed to characterize the 6-DOF dynamics model and motion constraints. Then, an RL-based control scheme is developed under the dual-quaternion algebraic framework to approximate the optimal control solution subject to a cost function and a Hamilton-Jacobi-Bellman equation. In addition, a specially designed barrier function is embedded in the reward function to avoid motion constraint violations. The Lyapunov-based stability analysis guarantees the ultimate boundedness of state errors and the weight of NN estimation errors. Besides, we also show that a PD-like controller under dual-quaternion formulation can be employed as the initial control policy to trigger the online learning process. The boundedness of it is proved by a special Lyapunov strictification method. Simulation results of prototypical spacecraft missions with proximity operations are provided to illustrate the effectiveness of the proposed method.

This paper investigates from a structural reliability assessment (SRA) perspective the fatigue reliability using the S-N curve approach compared with the fracture mechanics (FM) approach for a typical welded offshore wind turbine (OWT) jacket support structure. A non-intrusive formulation was developed for an OWT jacket support structure in 50 m deep water, consisting of a sequence of steps. First, stochastic parametric 3D (three-dimensional) Finite Element Analysis (FEA) simulations are performed, taking into account stochastic variables such as wind loads, wave loads and soil properties using facilities within the software package ANSYS. Secondly, the FEA results are post-processed using an Artificial Neural Network (ANN) response surface modelling technique deriving the performance functions expressed in terms of stochastic variables. Finally, the First Order Reliability Method (FORM) is applied in calculating the reliability index values of components. The developed framework was applied to elucidate the fatigue damage process, including the small to long crack transition amongst other stages, for structural steels used for OWT jacket applications. The FM formulation investigated includes a crack growth formulation based on the bilinear crack growth law, considering both segments of the crack growth law as non-correlated and correlated in calculating the reliability index (RI). Sensitivity analysis results showed a strong dependence of the structure's reliability levels on the uncertainties of the crack growth law constants measured in terms of coefficient of variation (COV). Also investigated, was the reliability of the structure reassessed and updated in the presence of assumed structural health monitoring/ condition monitoring (SHM/CM) data. The results from the case study revealed that fracture reliability is highly sensitive to the initial crack size. It is recommended to apply the S-N curve method at the design stage while the FM approach applied towards the end of the design life as the structure approaches failure.

2021-03-J060716.R1. Some of the materials in this paper has been presented at 23rd International Congress on Acoustics, Aachen, Germany, 9-13 September 2019. Copyright © 2023 The Author(s). Experimental studies of a NACA 65(12)-10 airfoil with a sinusoidal leading-edge undulation (LEU) were carried out to simultaneously optimize its aerodynamic and aeroacoustic performances by considering the attached as well as the separated flow at the effective Reynolds number of 106, where the maximum lift was increased without sacrificing drag or overall noise at near- and poststall angles. Further aerodynamic and aeroacoustic tests indicated that a combination of LEU wavelength λ/c=30% and amplitude h/c=6% gave an optimum LEU by considering the aerodynamic performance as well as the noise reduction. Particle image velocimetry measurements of the flow over the optimized airfoil showed biperiodic velocity fluctuations downstream of the LEU peaks that were associated with unsteady stall cell structure near the trailing edge. EPSRC (Grant Number EP/N018486/1).

Breast cancer is one of the leading forms of cancer. Breast lesion detection is a prerequisite for description of its characteristics and ultimately correct diagnosis of the lesion type. Compared to object detection in natural images, lesion detection in ultrasound images is a challenging task due to the inherent nature of the ultrasound images. This paper is concerned with adopting Faster Regions with Convolutional Neural Network (Faster RCNN) method for detecting breast lesions in 2D ultrasound images. Faster RCNN shows great promise in this application with very few misses of the actual lesion leading to high recall. However, the overall performance of this method suffers from high number of false positives. Therefore, we investigate different modelling hyperparameters of Faster RCNN to find an optimal configuration to improve the overall performance by essentially reducing false positives without significant increase in the number of misdetected lesions. Our empirical study using a total of 1183 images in three datasets shows that the optimal detection model outperformed original Faster RCNN due to significant reductions in false positives, resulting in 15% to 28% higher precision with only 3% to 11% drop in recall. Our optimal model also outperformed an existing breast lesion detection method by 3.4% in F1-score and provides better balance in precision and recall. Our study demonstrates that the optimal hyperparameter selection for Faster RCNN is a promising direction for breast lesion detection in ultrasound images.

This paper investigates the optimal power generation control for a two-stage horizontal-axis tidal turbine system based on backstepping disturbance rejection control (BDRC), which is a new control framework for high-order nonlinear systems. The tidal turbine system is designed with the main structure being described. The dynamics of the tidal turbine system is then formulated based on the integration of the dynamics of its constitute components. The tidal turbine experiences large uncertainties and unknown dynamics from non-uniform operating thrust and fatigue forces, variations and turbulence in tidal flow velocities induced by waves and wind. The proposed BDRC is a unique control concept which has superior performance in dealing with these large uncertainties without requiring much information about the turbine dynamics. Consequently, the BDRC system is synthesized in two control loops for the control of the turbine dynamics (outer loop) and the q-axis current dynamics (inner loop) respectively to track the optimal tidal turbine speed and hence maintain the optimal power generations. Both the stability and convergence of the two closed control loops are subsequently analyzed and proved. The simulations are conducted in MATLAB/Simulink to compare the performance of the developed BDRC system with a sliding mode control method used in the literature. In addition, the proposed BDRC algorithm is extended to a general nonlinear strict-feedback system with high uncertainties and external/internal disturbances. The proposed BDRC has significantly extended the traditional ADRC and does not need any differential operations, thereby totally eliminating the inherent problems of “explosion of complexity” and repeated differentiations of virtual control variables in traditional backstepping control.

External installation of steel braces is one of the effective approaches to increase the lateral load resistance of the steel moment-resisting frames. However, the effects of existence of steel braces on the robustness of steel moment-resisting frames to resist progressive collapse is still not clear as little study has been carried out. To fill this gap, in this paper, six multi-story steel moment-resisting sub frames (three bare frames and three braced frames) were fabricated and tested. Test results indicated that the specimen with reduced beam section in the connection zone performed best among three types of connections, due to the guaranteed formation of plastic hinges at the location of reduced section and avoiding brittle fracture of weld at the connection. Experimental results proved that steel braces could increase the load resisting capacity by 45.1% and 83.9% of the frame with weld connection and end plate connection, respectively. As the gusset plate restricted the rotation of the plastic hinges in the second story of the braced frames with V-shaped bracing, which decreased its deformation capacity and degraded its catenary action capacity. Actually, the ultimate load of the braced frames with V shaped bracing is only 87.5% of that of the counterpart without any braces. As the compressive braces were severe buckled before the displacement reached 0.4% of the beam span, it has little effects on yield load but increases the initial stiffness of the bare frames. Thus, majority of the benefits of the bracing system were attributed into the tensile braces. Moreover, the analytical results evaluated the differences in load resistance and development of load resisting mechanisms in different stories. Furthermore, the contribution of compressive and tensile braces was de-composed individually by analytical analysis.

Many countries have assigned an indispensable role for carbon capture and storage (CCS) in their national climate change mitigation pathways. However, CCS deployment has stalled in most countries with only limited commercial projects realised mainly in hydrocarbon-rich countries for enhanced oil recovery. If the Paris Agreement is to be met, then this progress must be replicated widely, including hydrocarbon-limited countries. In this study, we present a novel source-to-sink assessment methodology based on a hubs and clusters approach to identify favourable regions for CCS deployment and attract renewed public and political interest in viable deployment pathways. Here, we apply this methodology to Spain, where fifteen emission hubs from both the power and the hard-to-abate industrial sectors are identified as potential CO2 sources. A priority storage structure and two reserves for each hub are selected based on screening and ranking processes using a multi-criteria decision-making method. The priority source-to-sink clusters are identified indicating four potential development regions, with the North-Western and North-Eastern Spain recognised as priority regions due to resilience provided by different types of CO2 sources and geological structures. Up to 68.7 Mt CO2 per year, comprising around 21% of Spanish emissions can be connected to clusters linked to feasible storage. CCS, especially in the hard-to-abate sector, and in combination with other low-carbon energies (e.g., blue hydrogen and bioenergy), remains a significant and unavoidable contributor to the Paris Agreement’s mid-century net-zero target. This study shows that the hubs and clusters approach can facilitate CCS deployment in Spain and other hydrocarbon-limited countries., Funding was provided by the Grup Consolidat de Recerca “Geologia Sedimentària” (2017SGR-824) and the DGICYT Spanish Project PGC2018-093903-B-C22. XS acknowledges funding by the China Scholarship Council for a PhD scholarship (201806450043). JA is funded by MICINN (Juan de la Cierva fellowship - IJC2018-036074-I). EGR acknowledges funding provided by MICINN (“Ramón y Cajal” fellowship RYC2018-026335-I). VV acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program through the Starting Grant GEoREST (www.georest.eu) (Grant agreement No. 801809). IDAEA-CSIC is a Centre of Excellence Severo Ochoa (Spanish Ministry of Science and Innovation, Project CEX2018-000794-S). NH is funded by the Engineering and Physical Sciences Research Council (EPSRC) funded research project “HyStorPor” (EP/S027815/1). SH and AC are funded by EPSRC EP/P026214/1 UKCCSRC 2017, and EU project 837754 - STRATEGY CCUS. DVM is funded by the Spanish Ministry of Science, Innovation and Universities (“Beatriz Galindo Senior” fellowship BEAGAL18/00259). GJ is funded by the University of Strathclyde Faculty of Engineering.

In this work, a physics-informed deep learning model is developed to achieve the reconstruction of the three-dimensional (3-D) spatiotemporal wind field in front of a wind turbine, by combining the 3-D Navier–Stokes equations and the scanning LIDAR measurements. To the best of the authors’ knowledge, this is for the first time that the full 3-D spatiotemporal wind field reconstruction is achieved based on real-time measurements and flow physics. The proposed method is evaluated using high-fidelity large eddy simulations. The results show that the wind vector field in the whole 3-D domain is predicted very accurately based on only scalar line-of-sight LIDAR measurements at sparse locations. Specifically, at the baseline case, the prediction errors for the streamwise, spanwise and vertical velocity fields are 0 . 263 m/s, 0 . 397 m/s and 0 . 361 m/s, respectively. The prediction errors for the horizontal and vertical direction fields are 2 . 84 ° and 2 . 58 ° which are important in tackling yaw misalignment and turbine tilt control, respectively. Further analysis shows that the 3-D wind features are captured clearly, including the evolutions of flow structures, the wind shear in vertical direction, the blade-level speed variations due to turbine rotation, and the speed variations modulated by the turbulent wind. Also, the developed model achieves short-term wind forecasting without the commonly-used Taylor’s frozen turbulence hypothesis. Furthermore it is very useful in advancing other wind energy research fields e.g. wind turbine control & monitoring, power forecasting, and resource assessments because the 3-D spatiotemporal information is important for them but not available with current sensor and prediction technologies.

Reinforcement learning entails many intuitive and useful approaches to solving various problems. Its main premise is to learn how to complete tasks by interacting with the environment and observing which actions are more optimal with respect to a reward signal. Methods from reinforcement learning have long been applied in aerospace and have more recently seen renewed interest in space applications. Problems in spacecraft control can benefit from the use of intelligent techniques when faced with significant uncertainties—as is common for space environments. Solving these control problems using reinforcement learning remains a challenge partly due to long training times and sensitivity in performance to hyperparameters which require careful tuning. In this work we seek to address both issues for a sample spacecraft control problem. To reduce training times compared to other approaches, we simplify the problem by discretising the action space and use a data-efficient algorithm to train the agent. Furthermore, we employ an automated approach to hyperparameter selection which optimises for a specified performance metric. Our approach is tested on a 3-DOF powered descent problem with uncertainties in the initial conditions. We run experiments with two different problem formulations—using a ‘shaped’ state representation to guide the agent and also a ‘raw’ state representation with unprocessed values of position, velocity and mass. The results show that an agent can learn a near-optimal policy efficiently by appropriately defining the action-space and state-space. Using the raw state representation led to ‘reward-hacking’ and poor performance, which highlights the importance of the problem and state-space formulation in successfully training reinforcement learning agents. In addition, we show that the optimal hyperparameters can vary significantly based on the choice of loss function. Using two sets of hyperparameters optimised for different loss functions, we demonstrate that in both cases the agent can find near-optimal policies with comparable performance to previously applied methods.

We report a statistical approach to model the resonant peak wavelength (RPW) equation(s) of a photonic crystal fibre (PCF)-based surface plasmon resonance (SPR) sensors in terms of the PCF structural parameters (air-hole diameter, pitch, core diameter and gold layer thickness) at various tolerance levels. Design of experiments (statistical tool) is used to investigate the role played by the PCF structural parameters for sensing performance evaluation—RPW, across three tolerance levels (±2%, ±5% and ±10%). Pitch of the hollow-core PCF was discovered to be the major influencing parameter for the sensing performance (RPW) of the PCF-based SPR sensor while the inner metal (gold) layer thickness and core diameter are the least contributing parameters. This novel statistical method to derive the sensing performance parameter(s) of the PCF-based SPR sensors can be applied effectively and efficiently in the designing, characterisation, tolerance analysis not only at the research level, but also in optical fibre sensor fabrication industry to improve efficiency and lower cost.

Interrupted experimental tests were carried out to investigate the impacts of adhesive layer on the Riv(et)-Bonding processes with different die types. The adhesive layer’s influences on the quality of Riv-Bonded joints with different top sheet thickness ( T t ) were also studied. It was found that the distribution of trapped adhesive was significantly affected by the T t rather than by the die type. Deformation and fracture behaviours of top sheet were apparently influenced by the trapped adhesive. The adhesive layer negatively affected the interlock formation, while its effects on the remaining bottom sheet thickness depended heavily on the T t .

Background:\ud Prosthetic joint infection (PJI) is a devastating complication. Studies have suggested reduction in PJI with the use of ceramic bearings.\ud Methods:\ud Adult patients who underwent total hip arthroplasty (THA) using an uncemented acetabular component with ceramic-on-ceramic (CoC), ceramic-on-polyethylene (CoP), or metal-on-polyethylene (MoP) bearing surfaces between 2002 and 2016 were extracted from the National Joint Registry for England, Wales, Northern Ireland, and the Isle of Man. A competing risk regression model to investigate predictors of each revision outcome was used. Time-to-event was determined by duration of implantation since primary surgery with competing risks being death or revision. The results were adjusted for age, gender, American Association of Anaesthesiologists grade, body mass index, surgical indication, intraoperative complications, and implant data.\ud Results:\ud In total, 456,457 THAs (228,786 MoP, 128,403 CoC, and 99,268 CoP) were identified. Multivariable modeling showed that the risk of revision for PJI was significantly lower with CoC (risk ratio 0.748, P < .001) and CoP (risk ratio 0.775, P < .001) compared to MoP. Significant reduction in risk of aseptic and all-cause revision was also seen. The significant protective effect of ceramic bearing was predominantly seen 2 years after implantation. Aseptic revision beyond 2 years reduced by 18.1% and 24.8% for CoC and CoP (P < .001), respectively. All-cause revision rate beyond 2 years reduced by 21.6% for CoC and 27.1% for CoP (P < .001)\ud Conclusion:\ud This study demonstrates an association between the use of ceramic as part of the bearing, with lower rates of revision for all causes, revision for infection, and revision for aseptic causes, supporting ceramic bearings in THA.

Blade leading edge erosion is acknowledged to significantly reduce the energy yield of wind turbines. The problem is particularly severe for offshore installations, due to faster erosion progression boosted by harsh environmental conditions. This study presents and demonstrates an experimentally validated simulation-based technology for rapidly and accurately estimating wind turbine energy yield losses due to general leading edge erosion. The technology combines the predictive accuracy of two- and three-dimensional Navier-Stokes computational fluid dynamics with the runtime reductions enabled by artificial neural networks and wind turbine engineering codes using the blade element momentum theory. The main demonstration is based on the assessment of the annual energy yield of the National Renewable Energy Laboratory 5 MW reference turbine affected by leading edge erosion damage of increasing severity, considering damages based on available laser scans and previous leading edge erosion analysis. Results also include sensitivity studies of the energy loss to the wind characteristics of the installation site. It is found that the annual energy loss varies between about 0.3 and 4%, depending on the damage severity and the site wind characteristics. The study also illustrates the necessity of resolving the geometry of eroded leading edges rather than accounting for the effects of erosion with surrogate models, since, after an initial increase of distributed surface roughness, erosion yields leading edge geometry alterations causing aerodynamic losses exceeding those due to the loss of boundary layer laminarity consequent to roughness-induced transition. The presented technology enables estimating in a few minutes the amount of energy lost to erosion for many-turbine wind farms, and offers a key tool for predictive maintenance.