Stability estimates for radial basis function methods applied to linear scalar conservation laws

We derive stability estimates for three commonly used radial basis function (RBF) methods to solve hyperbolic time-dependent PDEs: the RBF generated finite difference (RBF-FD) method, the RBF partition of unity method (RBF-PUM) and Kansa's (global) RBF method. We give the estimates in the discrete l(2)-norm 2-norm intrinsic to each of the three methods. The results show that Kansa's method and RBF-PUM can be l(2)-stable 2-stable in time under a sufficiently large oversampling of the discretized system of equations. The RBF-FD method in addition requires stabilization of the spurious jump terms due to the discontinuous RBF-FD cardinal basis functions. Numerical experiments show an agreement with our theoretical observations

A case study of railway curve squeal radiated from both the outer and inner wheel

This case study is presented after observations were made that partially contrast the prevailing picture of railway curve squeal given in the literature. These are observations with significance both for modelling and condition monitoring. The current case study will be followed by a subsequent investigation focused on the validation and application of a simulation model to investigate root-causes for curve squeal at the Stockholm metro. Curve squeal in a 213 m radius curve on the Stockholm metro is studied. Data includes track characteristics (rail profile, rail roughness and gauge width) and noise measured at the trackside as well as by two vehicles equipped with an on-board mounted noise monitoring system. The current case study contrasts field measurements of curve squeal reported in the literature by a relative shift of emitted noise towards higher frequencies. Wayside noise measurements in the studied curve during a few hours showed squeal generation for all vehicle passages with dominating 1/3 octave band centre frequencies in the range between 6.3–15.8  kHz. Noise data measured during one year of regular traffic of two vehicles equipped with a monitoring system were obtained. The occurrence of curve squeal was analysed through an implementation of the curve squeal detection algorithm in operation at the Stockholm metro. This algorithm was also applied to search for events of squeal noise radiation from the outer wheel. Results show emissions of squeal noise from the inner and outer wheel for 65 % and 8 % of the vehicle passages through the studied curve, respectively. Further, the occurrence of curve squeal radiated from the inner wheel was found to increase by 10 % after rail grinding. In the literature, squeal radiated from the outer wheel is described as having an intermittent character with magnified spectral components in the frequency range between 5–10  kHz. In contrast, the current work presents sustained tonal squeal generated from the outer wheel with similar noise characteristics as typically related to ordinary curve squeal. Curve squeal – Influence of track design and maintenance statu

Multiobjective energy management of multi-source offshore parks assisted with hybrid battery and hydrogen/fuel-cell energy storage systems

With the recent advancements in the development of hybrid offshore parks and the expected large-scale implementation of them in the near future, it becomes paramount to investigate proper energy management strategies to improve the integrability of these parks into the power systems. This paper addresses a multiobjective energy management approach using a hybrid energy storage system comprising batteries and hydrogen/fuel-cell systems applied to multi-source wind-wave and wind-solar offshore parks to maximize the delivered energy while minimizing the variations of the power output. To find the solution of the optimization problem defined for energy management, a strategy is proposed based on the examination of a set of weighting factors to form the Pareto front while the problem associated with each of them is assessed in a mixed-integer linear programming framework. Subsequently, fuzzy decision making is applied to select the final solution among the ones existing in the Pareto front. The studies are implemented in different locations considering scenarios for electrical system limitation and the place of the storage units. According to the results, applying the proposed multiobjective framework successfully addresses the enhancement of energy delivery and the decrease in power output fluctuations in the hybrid offshore parks across all scenarios of electrical system limitation and combinational storage locations. Based on the results, in addition to the increase in delivered energy, a decrease in power variations by around 40 % up to over 80 % is observed in the studied cases

Assessing the impact of degree of fusion and muscle fibre twitch shape variation on the accuracy of motor unit discharge time identification from ultrasound images

Objective: Ultrasound (US) images during a muscle contraction can be decoded into individual motor unit (MU) activity, i.e., trains of neural discharges from the spinal cord. However, current decoding algorithms assume a stationary mixing matrix, i.e. equal mechanical twitches at each discharge. This study aimed to investigate the accuracy of these approaches in non-ideal conditions when the mechanical twitches in response to neural discharges vary over time and are partially fused in tetanic contractions. Methods: We performed an in silico experiment to study the decomposition accuracy for changes in simulation parameters, including the twitch waveforms, spatial territories, and motoneuron-driven activity. Then, we explored the consistency of the in silico findings with an in vivo experiment on the tibialis anterior muscle at varying contraction forces. Results: A large population of MU spike trains across different excitatory drives, and noise levels could be identified. The identified MUs with varying twitch waveforms resulted in varying amplitudes of the estimated sources correlated with the ground truth twitch amplitudes. The identified spike trains had a wide range of firing rates, and the later recruited MUs with larger twitch amplitudes were easier to identify than those with small amplitudes. Finally, the in silico and in vivo results were consistent, and the method could identify MU spike trains in US images at least up to 40% of the maximal voluntary contraction force. Conclusion: The decoding method was accurate irrespective of the varying twitch-like shapes or the degree of twitch fusion, indicating robustness, important for neural interfacing applications

A hosting capacity based approach toward distribution system planning for high PV penetration

This paper presents an approach to estimate the hosting capacity for distribution networks considering the impact of PV penetration at different voltage levels. The estimation and the method were selected such that the results were most suitable for distribution system planning. A time-series based method was used as it covers significant aspects needed for prioritising network reinforcement. The MV background voltage was modelled varying in time, assuming the same penetration level in the other LV networks supplied by the same MV system. The hosting capacity is defined as the maximum acceptable PV size per customer for a given PV penetration. Based on the different possible combinations of PV location, the probability of overvoltage and overloading is used as a performance index. The planning risk is used as a limit for the performance criterion. The method can be automated for a large number of networks due to using an IEC 61970-based input format. It also enables linking DSO network models to customer smart metre databases. The severity and risks of limit violations are analysed with different metrics from the time-series simulations. The change in background voltage with increasing penetration is shown to impact the results significantly. When considering it, the estimated hosting capacity was reduced by 32 %, on average.Validerad;2024;Nivå 2;2024-10-17 (sarsun);Full text license: CC BY 4.0;Funder: Swedish Energy Agency; Skellefteå Kraft;</p

Influence of TEMPO on preparation of softwood nanofibrils and their hydrogel network properties

From an economic and environmental perspective, the use of less chemicals in the production of cellulose nanofibrils (CNFs) is advantageous. In this study, we investigated the oxidation (TEMPO/NaClO2/NaClO, pH 6.8) of softwood (SW) particles with varying amounts of TEMPO (16, 8 or 0 mg g−1 of wood). Following, TEMPO-oxidized SW nanofibrils (TO-SWNFs) were obtained by nanofibrillation and their size, morphology, and crystallite size were assessed. Hydrogel networks of TO-SWNFs were prepared and mechanical properties were measured in dH2O and phosphate buffered saline (PBS) to compare their performance for possible biomedical applications such as wound dressings. The results reveal that the presence of TEMPO is of importance for TO-SWNF network properties, presenting higher eq. H2O absorption (≈2500 %) and elongation at break (≈10 %) with good wet strength (≈180 kPa). In addition, a decrease in use of TEMPO catalyst from 16 to 8 mg g−1 of wood is possible, without detrimental effects on hydrogel network properties (dH2O absorption ≈ 2000 %, elongation at break ≈ 13 %, wet strength ≈ 190 kPa) related to applications as wound dressings.Full text license: CC BY 4.0; Funder: Swedish Foundation for Strategic Research (RMX18-0039); Stiftelsen Gunnar Sundblads forskningsfond; </p

Deep networks for system identification : A survey

Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results in many application areas that reveal essential but hidden properties of the observations. System identification learns mathematical descriptions of dynamic systems from input-output data and can thus benefit from the advances of deep neural networks to enrich the possible range of models to choose from. For this reason, we provide a survey of deep learning from a system identification perspective. We cover a wide spectrum of topics to enable researchers to understand the methods, providing rigorous practical and theoretical insights into the benefits and challenges of using them. The main aim of the identified model is to predict new data from previous observations. This can be achieved with different deep learning-based modelling techniques and we discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks. Their parameters have to be estimated from past data to optimize the prediction performance. For this purpose, we discuss a specific set of first-order optimization tools that have emerged as efficient. The survey then draws connections to the well-studied area of kernel-based methods. They control the data fit by regularization terms that penalize models not in line with prior assumptions. We illustrate how to cast them in deep architectures to obtain deep kernel-based methods. The success of deep learning also resulted in surprising empirical observations, like the counter-intuitive behaviour of models with many parameters. We discuss the role of overparameterized models, including their connection to kernels, as well as implicit regularization mechanisms which affect generalization, specifically the interesting phenomena of benign overfitting and double-descent. Finally, we highlight numerical, computational and software aspects in the area with the help of applied examples

Invasive ventilation at the boundary of viability : A respiratory pathophysiology study of infants born between 22 and 24 weeks of gestation

Background: Invasive ventilation of infants born before 24 weeks of gestation is critical for survival and long-term respiratory outcomes, but currently there is a lack of evidence to guide respiratory management. We aimed to compare respiratory mechanics and gas exchange in ventilated extremely preterm infants born before and after 24 weeks of gestation. Methods: Secondary analysis of two prospective observational cohort studies, comparing respiratory mechanics and indices of gas exchange in ventilated infants born at 22-24 weeks of gestation (N=14) compared to infants born at 25-27 weeks (N=37). The ventilation/perfusion ratio (V-A/Q), intrapulmonary shunt, alveolar dead space (V-Dalv) and adjusted alveolar surface area (S-A) were measured in infants born at the Neonatal Unit of King's College Hospital NHS Foundation Trust, London, UK. Results: Compared to infants of 25-27 weeks, infants of 22-24 weeks had higher median (IQR) intrapulmonary shunt [18 (4 - 29) % vs 8 (2 - 12) %, p=0.044] and higher VDalv [0.9 (0.6 - 1.4) vs 0.6 (0.5 - 0.7) ml/kg, p=0.036], but did not differ in VA/Q. Compared to infants of 25-27 weeks, the infants of 22-24 weeks had a lower adjusted S-A [509 (322- 687) vs 706 (564 - 800) cm(2), p=0.044]. The infants in the two groups did not differ in any of the indices of respiratory mechanics. Conclusion: Ventilated infants born before 24 completed weeks of gestation exhibit abnormal gas exchange, with higher alveolar dead space and intrapulmonary shunt and a decreased alveolar surface area compared to extreme preterms born after 24 weeks of gestation

First-year university students’ mathematics capital and identities

Based on a survey of 150 respondents and 16 timeline interviews with first-year university mathematics students in Sweden, we explore how the material resources and conditions available to them —mathematics capital— connect to their mathematical identities. We found that mathematics capital has bearing on how early in life students start to consider doing mathematics. We also found individually different trajectories among students with low mathematics capital into university mathematics. The study expands both existing theoretical and methodological ways of researching the material bearings of identity and opens up for new ways of understanding and exploring the conditions that may facilitate access to participation and success in university mathematics. It contributes to understanding on the social and cultural resources that students bring with them to start mathematics, thus complementing the insights that Simon Goodchild’s work had provided on the context of access to university mathematics.IMMPAC

Heterogeneous Bonded Particle Modelling of Rock Fracture

The dynamic fracture process of rock materials is of importance for several industries, such as the rock drilling process in geothermal and mining applications. Gaining knowledge and understanding of dynamic rock fracture through numerical simulations can enhance the rock drilling process, for example by optimising the drill bit geometry and drilling parameters. In order for a numerical simulation of rock fracture processes to be accurate, the model needs to be able to capture key aspects of rock materials. Generally, rock materials are said to be britle and heterogeneous. The heterogeneity is partly due to the varying mechanical properties of constituent minerals, and partly due to the varying sizes, shapes, and directions of these minerals. The main objective of this thesis is the development of a heterogeneous rock model to be used for dynamic drilling processes. In the first article in this thesis, a heterogeneous bonded particle model is developed. Here, the heterogeneity is introduced in two steps – a geometrical heterogeneity using statistically distributed grain shapes and sizes, and a mechanical heterogeneity by distributing bonding parameters using a Weibull distribution. The model is applied to the quasi-static Brazilian disc test and a parametric study is conducted on the heterogeneity index and intergranular cement strength. The results show that crack initiation and propagation are highly dependent on the degree of heterogeneity. In general, the model was found to replicate typical phenomena associated with britle heterogeneous materials, for example unpredictability of macroscopic strength and crack properties. In the second paper of this thesis, an extensive dynamic experimental characterization of two igneous rock materials – Kuru grey granite and Kuru black diorite – is conducted. Here, a Split-Hopkinson configuration together with high-speed photography and digital image correlation is utilized to obtain the compressive and indirect tensile behavior of the rock materials. By using a significantly high frame rate of 671,000 fps in the digital image correlation analysis, it is shown that the point in time for crack initiation in the Brazilian disc can be estimated. From this, it is shown that the main splititng crack in the Brazilian disc occurs at 70 and 77 % for the two rock materials. In the third paper of this thesis, the heterogeneous bonded particle model from the first paper is further developed and calibrated using the dynamic experimental data for Kuru black diorite from the second paper. In contrast to the first paper, where one Weibull distribution is used, three Weibull distributions are used here. The first distribution is used for assigning average bonding parameters of the grains, the second for the intragranular bonding parameters and the third for the bonding parameters of the intergranular cementing. First, a homogeneous bonded particle model, i.e., without heterogeneous grains and no statistical distribution of bonding parameters, is calibrated so that the average experimental results are replicated. Then, using this homogeneously calibrated model, the heterogeneous model is activated, and a parametric study is conducted on the heterogeneity index for the average grain properties and the intergranular cement strength. The results show that this modelling approach is able to capture key phenomena of dynamic rock fracture, such as stochastic crack initiation and propagation, as well as peak stress, overloading, strain rate and crack propagation time. In the fourth paper, the proposed heterogeneous bonded particle model from previous papers is validated using a laboratory rock drilling experiment. The rock material is dynamically characterized using the methodology from the second paper and the grain structure is obtained from a scan of the rock surface. Three of the constituent minerals are represented in the model in terms of their size, occurrence, and mechanical properties. Furthermore, the model is calibrated in both compression and tension, where both the peak stress values and fracture behavior are captured. The model is then used to simulate the laboratory rock drilling experiment, where crater depth, load and rock fragment sizes are compared with the experiments. The results show that the simulation is able to capture peak load values and the rock fragment sizes are similar to that of the experiments