Measuring muon tracks in Baikal-GVD using a fast reconstruction algorithm

The Baikal Gigaton Volume Detector (Baikal-GVD) is a km$$^3$$ 3 -scale neutrino detector currently under construction in Lake Baikal, Russia. The detector consists of several thousand optical sensors arranged on vertical strings, with 36 sensors per string. The strings are grouped into clusters of 8 strings each. Each cluster can operate as a stand-alone neutrino detector. The detector layout is optimized for the measurement of astrophysical neutrinos with energies of $$\sim $$ ∼ 100 TeV and above. Events resulting from charged current interactions of muon (anti-)neutrinos will have a track-like topology in Baikal-GVD. A fast $$\chi ^2$$ χ 2 -based reconstruction algorithm has been developed to reconstruct such track-like events. The algorithm has been applied to data collected in 2019 from the first five operational clusters of Baikal-GVD, resulting in observations of both downgoing atmospheric muons and upgoing atmospheric neutrinos. This serves as an important milestone towards experimental validation of the Baikal-GVD design. The analysis is limited to single-cluster data, favoring nearly-vertical tracks.

Fast Reconstruction Method of the Stress Field for the Steam Turbine Rotor Based On Deep Fully Convolutional Network

Since it is difficult to directly measure the transient stress of a steam turbine rotor in operation, a rotor stress field reconstruction model based on deep fully convolutional network for the start-up process is proposed. The stress distribution in the rotor can be directly predicted based on the temperature of a few measurement points. First, the finite element model is used to accurately simulate the temperature and stress field of the rotor start-up process, generating training data for the deep learning method. Next, data of only 15 temperature measurement points are arranged to predict the stress distribution in critical area of the rotor surface, with the accuracy (R2-score) reaching 0.997. The time cost of the trained neural network model at a single case is 1.42s in CPUs and 0.11s in GPUs, shortened by 97.3% and 99.8% with comparison to finite element analysis, respectively. In addition, the influence of the number of temperature measurement points and the training size are discussed, verifying the stability of the model. With the advantages of fast calculation, high accuracy and strong stability, the fast reconstruction model can effectively realize the stress prediction during start-up processes, resulting in the possibility of real-time diagnosis of rotor strength in operation.

Fast Reconstruction Method of the Stress Field for the Steam Turbine Rotor Based on Deep Fully Convolutional Network

Since it is difficult to directly measure the transient stress of a steam turbine rotor in operation, a rotor stress field reconstruction model based on deep fully convolutional network for the start-up process is proposed. The stress distribution in the rotor can be directly predicted based on the temperature of a few measurement points. First, the finite element model is used to accurately simulate the temperature and stress field of the rotor start-up process, generating training data for the deep learning method. Next, data of only 15 temperature measurement points are arranged to predict the stress distribution in critical area of the rotor surface, with the accuracy (R2-score) reaching 0.997. The time cost of the trained neural network model at a single case is 1.42s in CPUs and 0.11s in GPUs, shortened by 97.3% and 99.8% with comparison to finite element analysis, respectively. In addition, the influence of the number of temperature measurement points and the training size are discussed, verifying the stability of the model. With the advantages of fast calculation, high accuracy and strong stability, the fast reconstruction model can effectively realize the stress prediction during start-up processes, resulting in the possibility of real-time diagnosis of rotor strength in operation.

Deterministic Sparse Sublinear FFT with Improved Numerical Stability

In this paper we extend the deterministic sublinear FFT algorithm in Plonka et al. (Numer Algorithms 78:133–159, 2018. 10.1007/s11075-017-0370-5) for fast reconstruction of M-sparse vectors $${\mathbf{x}}$$ x of length $$N= 2^J$$ N = 2 J , where we assume that all components of the discrete Fourier transform $$\hat{\mathbf{x}}= {\mathbf{F}}_{N} {\mathbf{x}}$$ x ^ = F N x are available. The sparsity of $${\mathbf{x}}$$ x needs not to be known a priori, but is determined by the algorithm. If the sparsity M is larger than $$2^{J/2}$$ 2 J / 2 , then the algorithm turns into a usual FFT algorithm with runtime $${\mathcal O}(N \log N)$$ O ( N log N ) . For $$M^{2} < N$$ M 2 < N , the runtime of the algorithm is $${\mathcal O}(M^2 \, \log N)$$ O ( M 2 log N ) . The proposed modifications of the approach in Plonka et al. (2018) lead to a significant improvement of the condition numbers of the Vandermonde matrices which are employed in the iterative reconstruction. Our numerical experiments show that our modification has a huge impact on the stability of the algorithm. While the algorithm in Plonka et al. (2018) starts to be unreliable for $$M>20$$ M > 20 because of numerical instabilities, the modified algorithm is still numerically stable for $$M=200$$ M = 200 .