Andreas Adelmann

Last Name

Adelmann

First Name

Andreas

ORCID

0000-0002-7230-7007

Organisational unit

01259 - Lehre Informatik

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Publications1 - 10 of 18

Fast uncertainty quantification of spent nuclear fuel with neural networks
Albà, Arnau; Adelmann, Andreas; Münster, Lucas; et al. (2024)
Annals of Nuclear Energy
The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards. State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics with reduced computational costs compared to physics-based models. An NN is trained using data generated from CASMO5 lattice calculations. The trained NN accurately predicts decay heat and nuclide concentrations of SNF, as a function of key input parameters, such as enrichment, burnup, cooling time between cycles, mean boron concentration and fuel temperature. The model is validated against physics-based decay heat simulations and measurements of different uranium oxide fuel assemblies from two different pressurized water reactors. In addition, the NN is used to perform sensitivity analysis and uncertainty quantification. The results are in very good alignment to CASMO5, while the computational costs (taking into account the costs of generating training samples) are reduced by a factor of 10 or more. Our findings demonstrate the feasibility of using NNs as surrogate models for fast characterization of SNF, providing a promising avenue for improving computational efficiency in assessing nuclear fuel behavior and associated risks.
Bayesian Optimisation for Fast and Safe Parameter Tuning of SwissFEL
Kirschner, Johannes; Nonnenmacher, Manuel; Mutný, Mojmír; et al. (2019)
FEL2019, Proceedings of the 39th International Free-Electron Laser Conference
Parameter tuning is a notoriously time-consuming task in accelerator facilities. As tool for global optimization with noisy evaluations, Bayesian optimization was recently shown to outperform alternative methods. By learning a model of the underlying function using all available data, the next evaluation can be chosen carefully to find the optimum with as few steps as possible and without violating any safety constraints. However, the per-step computation time increases significantly with the number of parameters and the generality of the approach can lead to slow convergence on functions that are easier to optimize. To overcome these limitations, we divide the global problem into sequential subproblems that can be solved efficiently using safe Bayesian optimization. This allows us to trade off local and global convergence and to adapt to additional structure in the objective function. Further, we provide slice-plots of the function as user feedback during the optimization. We showcase how we use our algorithm to tune up the FEL output of SwissFEL with up to 40 parameters simultaneously, and reach convergence within reasonable tuning times in the order of 30 minutes (< 2000 steps).
Saliency-Enhanced Content-Based Image Retrieval for Diagnosis Support in Dermatology Consultation: Reader Study
Gassner, Mathias; Garcia, Javier Barranco; Tanadini-Lang, Stephanie; et al. (2023)
JMIR Dermatology
Background: Previous research studies have demonstrated that medical content image retrieval can play an important role by assisting dermatologists in skin lesion diagnosis. However, current state-of-the-art approaches have not been adopted in routine consultation, partly due to the lack of interpretability limiting trust by clinical users. Objective: This study developed a new image retrieval architecture for polarized or dermoscopic imaging guided by interpretable saliency maps. This approach provides better feature extraction, leading to better quantitative retrieval performance as well as providing interpretability for an eventual real-world implementation. Methods: Content-based image retrieval (CBIR) algorithms rely on the comparison of image features embedded by convolutional neural network (CNN) against a labeled data set. Saliency maps are computer vision–interpretable methods that highlight the most relevant regions for the prediction made by a neural network. By introducing a fine-tuning stage that includes saliency maps to guide feature extraction, the accuracy of image retrieval is optimized. We refer to this approach as saliency-enhanced CBIR (SE-CBIR). A reader study was designed at the University Hospital Zurich Dermatology Clinic to evaluate SE-CBIR’s retrieval accuracy as well as the impact of the participant’s confidence on the diagnosis. Results: SE-CBIR improved the retrieval accuracy by 7% (77% vs 84%) when doing single-lesion retrieval against traditional CBIR. The reader study showed an overall increase in classification accuracy of 22% (62% vs 84%) when the participant is provided with SE-CBIR retrieved images. In addition, the overall confidence in the lesion’s diagnosis increased by 24%. Finally, the use of SE-CBIR as a support tool helped the participants reduce the number of nonmelanoma lesions previously diagnosed as melanoma (overdiagnosis) by 53%. Conclusions: SE-CBIR presents better retrieval accuracy compared to traditional CBIR CNN-based approaches. Furthermore, we have shown how these support tools can help dermatologists and residents improve diagnosis accuracy and confidence. Additionally, by introducing interpretable methods, we should expect increased acceptance and use of these tools in routine consultation.
Cyclotrons as Drivers for Precision Neutrino Measurements
Adelmann, Andreas; Alonso, Jose R.; Barletta, William A.; et al. (2014)
Advances in High Energy Physics
As we enter the age of precision measurement in neutrino physics, improved flux sources are required. These must have a well defined flavor content with energies in ranges where backgrounds are low and cross-section knowledge is high. Very few sources of neutrinos can meet these requirements. However, pion/muon and isotope decay-at-rest sources qualify. The ideal drivers for decay-at-rest sources are cyclotron accelerators, which are compact and relatively inexpensive. This paper describes a scheme to produce decay-at-rest sources driven by such cyclotrons, developed within the DAEδALUS program. Examples of the value of the high precision beams for pursuing Beyond Standard Model interactions are reviewed. New results on a combined DAEδALUS - Hyper-K search for CP violation that achieve errors on the mixing matrix parameter of 4° to 12° are presented.
A novel approach for classification and forecasting of time series in particle accelerators
Li, Sichen; Zacharias, Mélissa; Snuverink, Jochem; et al. (2021)
Information
The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High-Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also uses the advances of image classification techniques. Our best-performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71±0.01 compared to 0.65±0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5±0.2 s per interlock.
Time series forecasting methods and their applications to particle accelerators
Li, Sichen; Adelmann, Andreas (2023)
Physical Review Accelerators and Beams
Particle accelerators are complex facilities that produce large amounts of structured data and have clear optimization goals as well as precisely defined control requirements. As such they are naturally amenable to data-driven research methodologies. The data from sensors and monitors inside the accelerator form multivariate time series. With fast preemptive approaches being highly preferred in accelerator control and diagnostics, the application of data-driven time series forecasting methods is particularly promising. This review formulates the time series forecasting problem and summarizes existing models with applications in various scientific areas. Several current and future attempts in the field of particle accelerators are introduced. The application of time series forecasting to particle accelerators has shown encouraging results and promise for broader use, and existing problems such as data consistency and compatibility have started to be addressed.
Parallel general purpose multiobjective optimization framework with application to electron beam dynamics
Neveu, Nicole; Spentzouris, Linda; Adelmann, Andreas; et al. (2019)
Physical Review Accelerators and Beams
Particle accelerators are invaluable tools for research in the basic and applied sciences, such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a nontrivial task, due to the large number of control parameters and the complex interplay of several conflicting design goals. The Argonne Wakefield Accelerator facility has some unique challenges resulting from its purpose to carry out advanced accelerator R&D. Individual experiments often have challenging beam requirements, and the physical configuration of the beam lines is often changed to accommodate the variety of supported experiments. The need for rapid deployment of different operational settings further complicates the optimization work that must be done for multiple constraints and challenging operational regimes. One example of this is an independently staged two-beam acceleration experiment which requires the construction of an additional beam line (this is now in progress). The high charge drive beam, well into the space charge regime, must be threaded through small aperture (17.6 mm) decelerating structures. In addition, the bunch length must be sufficiently short to maximize power generation in the decelerator. We propose to tackle this problem by means of multiobjective optimization algorithms which also facilitate a parallel deployment. In order to compute solutions in a meaningful time frame, a fast and scalable software framework is required. In this paper, we present a general-purpose framework for simulation-based multiobjective optimization methods that allows the automatic investigation of optimal sets of machine parameters. Using evolutionary algorithms as the optimizer and opal as the forward solver, validation experiments and results of multiobjective optimization problems in the domain of beam dynamics are presented. Optimized solutions for the new high charge drive beam line found by the framework were used to finish the design of a two beam acceleration experiment. The selected solution along with the associated beam parameters is presented.
Constrained multiobjective shape optimization of superconducting rf cavities considering robustness against geometric perturbations
Kranjčević, Marija; Zadeh, Shahnam G.; Adelmann, Andreas; et al. (2019)
Physical Review Accelerators and Beams
High current storage rings, such as the Z-pole operating mode of the FCC-ee, require accelerating cavities that are optimized with respect to both the fundamental mode and the higher order modes. Furthermore, the cavity shape needs to be robust against geometric perturbations which could, for example, arise from manufacturing inaccuracies or harsh operating conditions at cryogenic temperatures. This leads to a constrained multiobjective shape optimization problem which is computationally expensive even for axisymmetric cavity shapes. In order to decrease the computation cost, a global sensitivity analysis is performed and its results are used to reduce the search space and redefine the objective functions. A massively parallel implementation of an evolutionary algorithm, combined with a fast axisymmetric Maxwell eigensolver and a frequency-tuning method is used to find an approximation of the Pareto front. The computed Pareto front approximation and a cavity shape with desired properties are shown. Further, the approach is generalized and applied to another type of cavity.
Multi-level preconditioning and eigenvector approximation for axisymmetric Maxwell eigenproblems
Kranjčević, Marija; Arbenz, Peter; Adelmann, Andreas (2018)
Proceedings in Applied Mathematics and Mechanics
OPAL-MITHRA: Self-consistent Software for Start-to-End Simulation of Undulator-based Facilities
Albà, Arnau; Adelmann, Andreas; Fallahi, Arya (2020)
2020 33rd International Vacuum Nanoelectronics Conference (IVNC)
We present the recently started effort and progress towards the development of a software for start-to-end simulation of accelerator facilities employing undulator radiation. The core of this effort aims at the combination of two numerical solvers, the Object Oriented Parallel Accelerator Library (OPAL), a parallel open source tool for charged-particle optics in linear accelerators and rings, including 3D space charge, and the full-wave simulation tool for free electron lasers MITHRA, which solves the electromagnetic potential equations in free-space for radiating particles propagating along an undulator using an FDTD/PIC scheme. As an example, we present the application of the OPAL-MITHRA platform for the simulation of an experiment that will take place at the Argonne Wakefield Accelerator, in which the capability of a magnetic wiggler to induce energy modulation starting from a micro-bunched electron beam will be investigated. © 2020 IEEE.

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Andreas Adelmann

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