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Poorte, V.K. (author), Bergsma, O.K. (author), van Campen, J.M.J.F. (author), Alderliesten, R.C. (author)
Hydrogen is being investigated as aviation fuel, with the objective to achieve an energy transition for the aviation sector. Effective storage solutions are crucial to mitigate the aerodynamic penalty caused by its low volumetric energy density. The focus of this study is the integration of a cryo-compressed vacuum-insulated storage vessel into...
conference paper 2024
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Anand, S. (author), Alderliesten, R.C. (author), Castro, Saullo G.P. (author)
Carbon emissions from commercial aircraft are expected to reach more than twice as much as the current levels by 2050. Unconventional aircraft, such as the Flying-V, are projected to achieve more than 20% fuel savings when compared to conventional configurations. However, these unconventional aircraft configurations pose a unique set of design...
conference paper 2024
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Pascoe, J.A. (author), Tu, W. (author), Biagini, D. (author), Alderliesten, R.C. (author)
Fibre reinforced polymer composites have found increasing use in aircraft structures. This means that fleet managers need damage assessment tools for such materials, in order to decide on appropriate sustainment strategies. Developing such tools is hindered by the difficulty of generalising from lab tests to predict the behaviour of full-scale...
conference paper 2023
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Tu, W. (author), Pascoe, J.A. (author), Alderliesten, R.C. (author)
Delamination growth is a key damage mode threatening the structural integrity of fibre reinforced polymer composite structures. To guide design and damage management of composite structures, research efforts have been made to understand delamination behaviours and establish standardized evaluation methods based mainly on one-dimensional...
conference paper 2023
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Biagini, D. (author), Pascoe, J.A. (author), Alderliesten, R.C. (author)
Impacts on carbon fiber reinforced composites (CFRP) can produce a complex internal damage comprising multiple delaminations, which is hard to detect from visual inspection. This situation is known as barely visible impact damage (BVID). Considering that every airplane faces several impacts during its operational life, and that the majority of...
conference paper 2023
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Chebykin, Alexander (author), Dushatskiy, A. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
In this work, we show that simultaneously training and mixing neural networks is a promising way to conduct Neural Architecture Search (NAS). For hyperparameter optimization, reusing the partially trained weights allows for efficient search, as was previously demonstrated by the Population Based Training (PBT) algorithm. We propose PBT-NAS,...
conference paper 2023
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Guijt, Arthur (author), Thierens, Dirk (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
In a parallel EA one can strictly adhere to the generational clock, and wait for all evaluations in a generation to be done. However, this idle time limits the throughput of the algorithm and wastes computational resources. Alternatively, an EA can be made asynchronous parallel. However, EAs using classic recombination and selection operators...
conference paper 2023
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Harrison, Joe (author), Virgolin, Marco (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
The aim of Symbolic Regression (SR) is to discover interpretable expressions that accurately describe data. The accuracy of an expression depends on both its structure and coefficients. To keep the structure simple enough to be interpretable, effective coefficient optimisation becomes key. Gradient-based optimisation is clearly effective at...
conference paper 2023
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Deist, Timo M. (author), Grewal, M. (author), Dankers, Frank J.W.M. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Real-world problems are often multi-objective, with decision-makers unable to specify a priori which trade-off between the conflicting objectives is preferable. Intuitively, building machine learning solutions in such cases would entail providing multiple predictions that span and uniformly cover the Pareto front of all optimal trade-off...
conference paper 2023
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Anand, S. (author), Alderliesten, R.C. (author), Castro, Saullo G.P. (author)
This paper reviews analytical models proposed by Abramowicz et al.[1, 2] and Stefan et al.[3] for the axial crushing of metallic tubular structures with square and circular cross-sections. First, a database of experiments for square and circular tubes was created based on the literature. Subsequently, the predictions obtained using these...
conference paper 2023
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Smeets, E.T.B. (author), Rans, C.D. (author), Alderliesten, R.C. (author), Castro, Saullo G.P. (author), Villegas, I.F. (author)
Ultrasonic spot welding is a joining technique for thermoplastic composites with great potential regarding processing speed and cost. To investigate the damage tolerance and possible inherent damage arresting behavior of multi-spot welded joints, a technique is necessary to measure damage growth in the joints under cyclic loading. Visual...
conference paper 2022
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Alderliesten, R.C. (author), den Ouden, H.J. (author)
Delamination growth in fibre reinforced polymer composites is generally evaluated with experiments that have been standardized for quasi-static load conditions. These tests characterize unidirectional delamination growth in mode I (DCB), mode II (ELS or ENF) of mixed mode conditions (MMB). However, little attention is paid in literature to the...
conference paper 2022
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Bhangale, J.A. (author), Alderliesten, R.C. (author), Benedictus, R. (author), Bersee, H.E.N. (author)
Prediction models for fatigue in engineering applications are developed within a fatigue analysis framework, deliberately selected in some cases, but mostly chosen without substantiation. The proposition of this paper is that selecting the most appropriate framework can only be done with the knowledge and a complete overview of existing...
conference paper 2022
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Scholman, R.J. (author), Bouter, Anton (author), Dickhoff, Leah R.M. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find solutions well spread over all locally optimal approximation sets of a Multi-modal Multi-objective Optimization Problem (MMOP), there is a risk that the found set of solutions is not smoothly navigable because the solutions belong to various niches,...
conference paper 2022
document
Harrison, J. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Genetic Programming (GP) can make an important contribution to explainable artificial intelligence because it can create symbolic expressions as machine learning models. Nevertheless, to be explainable, the expressions must not become too large. This may, however, limit their potential to be accurate. The re-use of subexpressions has the...
conference paper 2022
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Dushatskiy, A. (author), Lowe, Gerry (author), Bosman, P.A.N. (author), Alderliesten, T. (author)
Deep learning algorithms have become the golden standard for segmentation of medical imaging data. In most works, the variability and heterogeneity of real clinical data is acknowledged to still be a problem. One way to automatically overcome this is to capture and exploit this variation explicitly. Here, we propose an approach that improves...
conference paper 2022
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Bosma, Martijn M.A. (author), Dushatskiy, A. (author), Grewal, M. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cases human-level, performance, they have become the standard approach in this field. The design of the best possible medical image segmentation DNNs, however, is task...
conference paper 2022
document
Chebykin, Alexander (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
To achieve excellent performance with modern neural networks, having the right network architecture is important. Neural Architecture Search (NAS) concerns the automatic discovery of task-specific network architectures. Modern NAS approaches leverage super-networks whose subnetworks encode candidate neural network architectures. These...
conference paper 2022
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Liu, D. (author), Virgolin, Marco (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Genetic programming (GP) is one of the best approaches today to discover symbolic regression models. To find models that trade off accuracy and complexity, the non-dominated sorting genetic algorithm II (NSGA-II) is widely used. Unfortunately, it has been shown that NSGA-II can be inefficient: in early generations, low-complexity models over...
conference paper 2022
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Guijt, Arthur (author), Thierens, Dirk (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Model-Based Evolutionary Algorithms (MBEAs) can be highly scalable by virtue of linkage (or variable interaction) learning. This requires, however, that the linkage model can capture the exploitable structure of a problem. Usually, a single type of linkage structure is attempted to be captured using models such as a linkage tree. However, in...
conference paper 2022
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