Aerodynamic Shape Optimisation of a Camber Morphing Airfoil and Noise Estimation

In order to decrease the emitted airframe noise by a two-dimensional high-lift configuration during take-off and landing performance, a morphing airfoil has been designed through a shape design optimisation procedure starting from a baseline airfoil (NLR 7301), with the aim of emulating a high-lift configuration in terms of aerodynamic performance. A methodology has been implemented to accomplish such aerodynamic improvements by means of the compressible steady RANS equations at a certain angle of attack, with the objective of maximising its lift coefficient up to equivalent values regarding the high-lift configuration, whilst respecting the imposed structural constraints to guarantee a realistic optimised design. For such purposes, a gradient-based optimisation through the discrete adjoint method has been undertaken. Once the optimised airfoil is achieved, unsteady simulations have been carried out to obtain surface pressure distributions along a certain time-span to later serve as the input data for the aeroacoustic prediction framework, based on the Farassat 1A formulation, where the subsequent results for both configurations are post-processed to allow for a comparative analysis. Conclusively, the morphing airfoil has proven to be advantageous in terms of aeroacoustics, in which the noise has been reduced with respect to the conventional high-lift configuration for a comparable lift coefficient, despite being hampered by a significant drag coefficient increase due to stall on the morphing airfoil’s trailing edge.

Human-in-the-loop optimization of visual prosthetic stimulation

Retinal prostheses are a promising strategy to restore sight to patients with retinal degenerative diseases. These devices compensate for the loss of photoreceptors by electrically stimulating neurons in the retina. Currently, the visual function that can be recovered with such devices is very limited. This is due, in part, to current spread, unintended axonal activation, and the limited resolution of existing devices. Here we show, using a recent model of prosthetic vision, that optimizing how visual stimuli are encoded by the device can help overcome some of these limitations, leading to dramatic improvements in visual perception. We propose a strategy to do this in practice, using patients' feedback in a visual task. The main challenge of our approach comes from the fact that, typically, one only has access to a limited number of noisy responses from patients. We propose two ways to deal with this: first, we use a model of prosthetic vision to constrain and simplify the optimisation; second, we use preferential Bayesian optimisation to efficiently learn the encoder using minimal trials. As a proof-of concept, we presented healthy subjects with visual stimuli generated by a recent model of prosthetic vision, to replicate the perceptual experience of patients fitted with an implant. Our optimisation procedure led to significant and robust improvements in perceived image quality, that transferred to increased performance in other tasks. Importantly, our strategy is agnostic to the type of prosthesis and thus could readily be implemented in existing implants.

3D printing nanocomposite hydrogels with lattice vascular networks using stereolithography

Hydrogels have emerged as leading candidates to reproduce native extracellular matrix. To provide structures and functions similar to tissues in vivo, controlled porosity and vascular networks are required. However, fabrication techniques to introduce these are still limited. In this study we propose stereolithography as a fabrication technique to achieve 3D vascular networks using water-based solvents only. A 3D printable hydrogel is formulated based on available commercial chemicals such as acrylamide (AAm) and polyethylene glycol diacrylate 700 (PEGDA700), with nanocellulose crystals (CNC) as a nanofiller. An optimisation procedure to increase resolution, tune porosity as well as mechanical properties is developed. The results highlight the importance of photoabsorber addition to improve channel resolution. We demonstrate that with the adequate choice of chemicals and fillers for photocurable formulations, structural and functional properties of the fabricated scaffold can be tailored, opening the path for advanced applications. Graphic abstract

A General Framework for Crankshaft Balancing and Counterweight Design

In the automotive field, the requirements in terms of carbon emissions and improved efficiency are shifting the focus of designers towards reduced engine size. As a result, the dynamic balancing of an engine with strict limitations on the number of cylinders, the weight and the available space becomes a challenging task. The present contribution aims at providing the designer with a tool capable of selecting fundamental parameters needed to correctly balance an internal combustion engine, including the masses and geometry of the elements to be added directly onto the crankshaft and onto the balancing shafts. The relevant elements that distinguish the tool from others already proposed are two. The first is the comprehensive matrix formulation which makes the tool fit for a wide variety of engine configurations. The second is an optimisation procedure that selects not only the position of the mass and centre of gravity of the counterweight but also its complete geometric configuration, thus instantaneously identifying the overall dimensions and weight of the crankshaft.

Simultaneously computing the 3D dip attributes for all the time samples of a seismic trace

The seismic dip attribute is regularly used to aid structural interpretation and is commonly adopted as a compulsory input for computing other seismic geometric attributes. One disadvantage of current dip computation algorithms is that interpreters compute the dip attribute time sample by time sample and do not consider the relationship between dip values of nearby samples. The classic convolution theory suggests one formation boundary should have the corresponding seismic event. However, the seismic wavelet always has a certain time duration. As a result, one formation boundary has a corresponding seismic event that consists of several time samples. Ideally, the time samples, which belong to the same boundary, should have approximately the same dip attributes. In this research, a sample by sample computation procedure is treated as an independent optimisation procedure. Then, simultaneously computing the seismic dip of time samples of one seismic trace can be regarded as a multi-objective optimisation procedure. The proposed method is based on analysing features of seismic waveform within user-defined windows. Considering that nearby time samples should have continuous dip values, we the dynamic time warping to simultaneously compute seismic reflectors’ dip values of a seismic trace. We applied our method to a field seismic data to demonstrate its effectiveness.

Determining jumping performance from a single body-worn accelerometer using machine learning

External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing), sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W·kg-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W·kg-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions.

Minimisation of Pose-Dependent Regenerative Vibrations for 5-Axis Milling Operations

The machining of free-formed surfaces, e.g., dies or moulds, is often affected by tool vibrations, which can affect the quality of the workpiece surface. Furthermore, in 5-axis milling, the dynamic properties of the system consisting of the tool, spindle and machine tool can vary depending on the tool pose. In this paper, a simulation-based methodology for optimising the tool orientation, i.e., tilt and lead angle of simultaneous 5-axis milling processes, is presented. For this purpose, a path finding algorithm was used to identify process configurations, that minimise tool vibrations based on pre-calculated simulation results, which were organised using graph theory. In addition, the acceleration behaviour of the feed drives, which limits the ability of adjusting the tool orientation with a high adaption frequency, as well as potential collisions of the tool, tool chuck and spindle with the workpiece were considered during the optimisation procedure.

A Learning-Based Hybrid Framework for Dynamic Balancing of Exploration-Exploitation: Combining Regression Analysis and Metaheuristics

The idea of hybrid approaches have become a powerful strategy for tackling several complex optimisation problems. In this regard, the present work is concerned with contributing with a novel optimisation framework, named learning-based linear balancer (LB2). A regression model is designed, with the objective to predict better movements for the approach and improve the performance. The main idea is to balance the intensification and diversification performed by the hybrid model in an online-fashion. In this paper, we employ movement operators of a spotted hyena optimiser, a modern algorithm which has proved to yield good results in the literature. In order to test the performance of our hybrid approach, we solve 15 benchmark functions, composed of unimodal, multimodal, and mutimodal functions with fixed dimension. Additionally, regarding the competitiveness, we carry out a comparison against state-of-the-art algorithms, and the sequential parameter optimisation procedure, which is part of multiple successful tuning methods proposed in the literature. Finally, we compare against the traditional implementation of a spotted hyena optimiser and a neural network approach, the respective statistical analysis is carried out. We illustrate experimental results, where we obtain interesting performance and robustness, which allows us to conclude that our hybrid approach is a competitive alternative in the optimisation field.