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The energy industry has decades of experience with modelling the multiphase flow of gas, oil, and water through wells and pipelines. This has led to state-of-the art design tools like OLGA and LedaFlow. Currently, the industry is working on upgrading the multiphase flow tools, to make them reliable for the design of CO2 transport. A particular challenge is modeling CO2 transport under two-phase flow conditions, which occurs during transient operations and even during steady-state operation for the case of injection into depleted gas reservoirs. This paper presents an accurate numerical method to solve the Homogeneous Equilibrium Model for single component CO2 transport under single-phase and two-phase flow conditions. The model performance is demonstrated through several test cases featuring fast transients. The same test cases are also simulated with OLGA, and a few of them also with LedaFlow and WANDA. The model and the test cases can be used as a basis for further developing, testing, and optimizing the numerical methods used to model CO2 transport. Our study shows that state-of-the art simulation models can provide numerically reliable solutions for fast transients with two-phase CO2 flow in pipes.

Modern high-performance analytical query engines follow one of two execution paradigms. Vectorized engines implement an interpreter for relational algebra operators that operates on batches of tuples to maximize performance. Compiling engines, on the other hand, generate optimized and specialized code for every query. This paper unifies these two approaches. We present Incremental Fusion, a novel execution paradigm for modern, high-performance query engines. An Incremental Fusion engine performs operator-fusing code generation -with a twist: The compiling engine generates its own vectorized interpreter. The engine uses a finite set of building blocks below relational algebra for code generation. It can enumerate each building block and generate a vectorized primitive for it. The vectorized interpreter becomes a free byproduct of carefully choosing the right abstraction for code generation. This allows an Incremental Fusion engine to dynamically switch between vectorized interpretation and operator-fusing code generation. We demonstrate Incremental Fusion in our open-source prototype engine InkFuse. We measure InkFuse against the state-of-the-art vectorized and compiling engines DuckDB and Umbra. InkFuse is able to achieve competitive performance both for low-latency processing, and compute-intensive long-running queries.

The present work presents a stable proper orthogonal decomposition (POD)-Galerkin based reduced-order model (ROM) for two-dimensional Rayleigh-Bénard convection in a square geometry for three Rayleigh numbers: 104 (steady state), 3×105 (periodic), and 6×106 (chaotic). Stability is obtained through a particular (staggered-grid) full-order model (FOM) discretization that leads to a ROM that is pressure-free and has skew-symmetric (energy-conserving) convective terms. This yields long-time stable solutions without requiring stabilizing mechanisms, even outside the training data range. The ROM's stability is validated for the different test cases by investigating the Nusselt and Reynolds number time series and the mean and variance of the vertical temperature profile. In general, these quantities converge to the FOM when increasing the number of modes, and turn out to be a good measure of accuracy. However, for the chaotic case, convergence with increasing numbers of modes is relatively difficult and a high number of modes is required to resolve the low-energy structures that are important for the global dynamics.

In this paper we present a complete framework for the energy-stable simulation of stratified incompressible flow in channels, using the one-dimensional two-fluid model. Building on earlier energy-conserving work on the basic two-fluid model, our new framework includes diffusion, friction, and surface tension. We show that surface tension can be added in an energy-conserving manner, and that diffusion and friction have a strictly dissipative effect on the energy. We then propose spatial discretizations for these terms such that a semi-discrete model is obtained that has the same conservation properties as the continuous model. Additionally, we propose a new energy-stable advective flux scheme that is energy-conserving in smooth regions of the flow and strictly dissipative where sharp gradients appear. This is obtained by combining, using flux limiters, a previously developed energy-conserving advective flux with a novel first-order upwind scheme that is shown to be strictly dissipative. The complete framework, with diffusion, surface tension, and a bounded energy, is linearly stable to short wavelength perturbations, and exhibits nonlinear damping near shocks. The model yields smoothly converging numerical solutions, even under conditions for which the basic two-fluid model is ill-posed. With our explicit expressions for the dissipation rates, we are able to attribute the nonlinear damping to the different dissipation mechanisms, and compare their effects.

A novel hyper-reduction method is proposed that conserves kinetic energy and momentum for reduced order models of the incompressible Navier-Stokes equations. The main advantage of conservation of kinetic energy is that it endows the hyper-reduced order model (hROM) with a nonlinear stability property. The new method poses the discrete empirical interpolation method (DEIM) as a minimization problem and subsequently imposes constraints to conserve kinetic energy. Two methods are proposed to improve the robustness of the new method against error accumulation: oversampling and Mahalanobis regularization. Mahalanobis regularization has the benefit of not requiring additional measurement points. Furthermore, a novel method is proposed to perform energy- and momentum-conserving temporal localization with the principle interval decomposition: new interface conditions are derived such that energy and momentum are conserved for a full time-integration instead of only during separate intervals. The performance of the new energy- and momentum-conserving hyper-reduction methods and the energy- and momentum-conserving temporal localization method is analysed using three convection-dominated test cases; a shear-layer roll-up, two-dimensional homogeneous isotropic turbulence and a time-periodic inviscid flow consisting of a vortex in a uniform background flow. Our main finding is that energy conservation in combination with oversampling or regularization leads to a robust method with excellent long time stability properties. When any of these two ingredients is missing, accuracy and/or stability is significantly impaired.

Recently, we saw a trend toward using physiological signals in interactive systems. These signals, offering deep insights into users' internal states and health, herald a new era for HCI. However, as this is an interdisciplinary approach, many challenges arise for HCI researchers, such as merging diverse disciplines, from understanding physiological functions to design expertise. Also, isolated research endeavors limit the scope and reach of findings. This workshop aims to bridge these gaps, fostering cross-disciplinary discussions on usability, open science, and ethics tied to physiological data in HCI. In this workshop, we will discuss best practices for embedding physiological signals in interactive systems. Through collective efforts, we seek to craft a guiding document for best practices in physiological HCI research, ensuring that it remains grounded in shared principles and methodologies as the field advances.

A novel dynamic economic model-predictive control strategy is presented that improves wind farm power production and reduces the additional demands of wake steering on yaw actuation when compared to an industry state-of-the-art reference controller. The novel controller takes a distributed approach to yaw control optimisation using a free-vortex wake model. An actuator-disc representation of the wind turbine is employed and adapted to the wind farm scale by modelling secondary effects of wake steering and connecting individual turbines through a directed graph network. The economic model-predictive control problem is solved on a receding horizon using gradient-based optimisation, demonstrating sufficient performance for realising real-time control. The novel controller is tested in a large-eddy simulation environment and compared against a state-of-the-art look-up table approach based on steady-state model optimisation and an extension with wind direction preview. Under realistic variations in wind direction and wind speed, the preview-enabled look-up table controller yielded the largest gains in power production. The novel controller based on the free-vortex wake produced smaller gains in these conditions while yielding more power under large changes in wind direction. Additionally, the novel controller demonstrated potential for a substantial reduction in yaw actuator usage.

In turbulence modeling, we are concerned with finding closure models that represent the effect of the subgrid scales on the resolved scales. Recent approaches gravitate towards machine learning techniques to construct such models. However, the stability of machine-learned closure models and their abidance by physical structure (e.g. symmetries, conservation laws) are still open problems. To tackle both issues, we take the ‘discretize first, filter next’ approach. In this approach we apply a spatial averaging filter to existing fine-grid discretizations. The main novelty is that we introduce an additional set of equations which dynamically model the energy of the subgrid scales. Having an estimate of the energy of the subgrid scales, we can use the concept of energy conservation to derive stability. The subgrid energy containing variables is determined via a data-driven technique. The closure model is used to model the interaction between the filtered quantities and the subgrid energy. Therefore the total energy should be conserved. Abiding by this conservation law yields guaranteed stability of the system. In this work, we propose a novel skew-symmetric convolutional neural network architecture that satisfies this law. The result is that stability is guaranteed, independent of the weights and biases of the network. Importantly, as our framework allows for energy exchange between resolved and subgrid scales it can model backscatter. To model dissipative systems (e.g. viscous flows), the framework is extended with a diffusive component. The introduced neural network architecture is constructed such that it also satisfies momentum conservation. We apply the new methodology to both the viscous Burgers' equation and the Korteweg-De Vries equation in 1D. The novel architecture displays superior stability properties when compared to a vanilla convolutional neural network.

P.F. Strawson and his Philosophical Legacy aims to bring out the continuing relevance of Sir Peter Frederick Strawson’s (1919–2006) work for current philosophical debates. It is the first collection of essays published after Strawson’s death that covers the full range of his work. The focus in contemporary work on Strawson is often on his relation to Kant or his paper ‘Freedom and Resentment’. While this volume gives due attention to these topics, it also includes essays on Strawson’s lasting contributions to the philosophy of language, metaphysics, epistemology, perception, moral philosophy, and philosophical methodology. The authors of this volume seek a balance between exegesis of Strawson’s work, critical engagement, and the study of subsequent developments.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0‐5 and 5‐15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1‐km² pixels (summarized from 8519 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10° degrees C (mean = 3.0 +/‐ 2.1° degrees C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 +/‐2.3° degrees C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (‐0.7 +/‐ 2.3° degrees C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological

Tiivistelmä. Kuorma-autojen voimansiirto eroaa huomattavasti henkilöautojen voimansiirrosta. Tässä kandidaatintyössä lukijalle esitellään erilaisia kuorma-autojen voimansiirtojärjestelmän osia ja tapoja, miten kuorma-autojen voimansiirtoa ohjataan. Työssä perehdytään perinteisten polttomoottorilla toimivien kuorma-autojen voimansiirtoon, sekä käsitellään myös markkinoilla yleistyvien hybridivoimanlähteitä käyttävien kuorma-autojen voimansiirron osia. Työn tavoitteena on luoda lukijalle laaja käsitys siitä, millaisia ratkaisuja raskaan kaluston voimansiirrossa voidaan käyttää, ja miten eri ratkaisut vaikuttavat kuorma-auton voimansiirtoon ja muihin voimansiirron komponentteihin. Työn luettuaan lukijalla on myös ymmärrystä voimansiirron mekaanisesta, sähköisestä, pneumaattisesta ja hydraulisesta ohjauksesta. Työssä havaitaan, että esimerkiksi kuorma-autoihin tarkoitettuja kytkinratkaisuja on olemassa useita, johtuen erilaisista käyttökohteista joihin kuorma-autoja voidaan käyttää. Kuorma-autoissa voi myös olla useita vetäviä akseleita, joihin sovelletaan omanlaista kytkinratkaisua. Hybridivoimanlähteisiin joudutaan suunnittelemaan täysin omanlaisia voimansiirtojärjestelmiä eri voimanlähteiden yhdistämisen vuoksi. Lisäksi erilaisten hybridijärjestelmien voimansiirron rakenne voi olla täysin erilainen.Heavy-duty truck drivetrain systems. Abstract. In this thesis, the reader is presented with different parts of heavy-duty truck drivetrain systems and the ways in which the drivetrains of heavy-duty trucks are controlled. This thesis introduces drivetrains of traditional combustion engine powered trucks and looks into drivetrains of trucks with hybrid power sources that are becoming more common on the market. The goal of the thesis is to create a broad understanding for the reader of what kind of solutions can be used in the drivetrains of heavy-duty vehicles, and how different solutions affect the vehicle’s drivetrain components. After reading this thesis, the reader also

Pollinator decline is one of the gravest challenges facing the world today, and the overuse of pesticides may be among its causes. Here, we studied whether glyphosate, the world’s most widely used pesticide, affects the bumblebee gut microbiota. We exposed the bumblebee diet to glyphosate and a glyphosate-based herbicide and quantified the microbiota community shifts using 16S rRNA gene sequencing. Furthermore, we estimated the potential sensitivity of bee gut microbes to glyphosate based on previously reported presence of target enzyme. Glyphosate increased, whereas the glyphosate-based herbicide decreased gut microbiota diversity, indicating that negative effects are attributable to co-formulants. Both glyphosate and the glyphosate-based herbicide treatments significantly decreased the relative abundance of potentially glyphosate-sensitive bacterial species Snodgrasella alvi. However, the relative abundance of potentially glyphosate-sensitive Candidatus Schmidhempelia genera increased in bumblebees treated with glyphosate. Overall, 50% of the bacterial genera detected in the bee gut microbiota were classified as potentially resistant to glyphosate, while 36% were classified as sensitive. Healthy core microbiota have been shown to protect bees from parasite infections, change metabolism, and decrease mortality. Thus, the heavy use of glyphosate-based herbicides may have implications on bees and ecosystems.

Purpose: SUPER-Finland is a large Finnish collection of psychosis cases. This cohort also represents the Finnish contribution to the Stanley Global Neuropsychiatric Genetics Initiative, which seeks to diversify genetic sample collection to include Asian, Latin American and African populations in addition to known population isolates, such as Finland. Participants: 10 474 individuals aged 18 years or older were recruited throughout the country. The subjects have been genotyped with a genome-wide genotyping chip and exome sequenced. A subset of 897 individuals selected from known population sub-isolates were selected for whole-genome sequencing. Recruitment was done between November 2015 and December 2018. Findings to date: 5757 (55.2%) had a diagnosis of schizophrenia, 944 (9.1%) schizoaffective disorder, 1612 (15.5%) type I or type II bipolar disorder, 532 (5.1 %) psychotic depression, 1047 (10.0%) other psychosis and for 530 (5.1%) self-reported psychosis at recruitment could not be confirmed from register data. Mean duration of schizophrenia was 22.0 years at the time of the recruitment. By the end of the year 2018, 204 of the recruited individuals had died. The most common cause of death was cardiovascular disease (n=61) followed by neoplasms (n=40). Ten subjects had psychiatric morbidity as the primary cause of death. Future plans: Compare the effects of common variants, rare variants and copy number variations (CNVs) on severity of psychotic illness. In addition, we aim to track longitudinal course of illness based on nation-wide register data to estimate how phenotypic and genetic differences alter it.

Objective: The COVID-19 pandemic posed new challenges for integrated health care worldwide. Our study aimed to describe newly implemented structures and procedures of psychosocial consultation and liaison (CL) services in Europe and beyond, and to highlight emerging needs for co-operation. Methods: Cross-sectional online survey from June to October 2021, using a self-developed 25-item questionnaire in four language versions (English, French, Italian, German). Dissemination was via national professional societies, working groups, and heads of CL services. Results: Of the participating 259 CL services from Europe, Iran, and parts of Canada, 222 reported COVID-19 related psychosocial care (COVID-psyCare) in their hospital. Among these, 86.5% indicated that specific COVID-psyCare co-operation structures had been established. 50.8% provided specific COVID-psyCare for patients, 38.2% for relatives, and 77.0% for staff. Over half of the time resources were invested for patients. About a quarter of the time was used for staff, and these interventions, typically associated with the liaison function of CL services, were reported as most useful. Concerning emerging needs, 58.1% of the CL services providing COVID-psyCare expressed wishes for mutual information exchange and support, and 64.0% suggested specific changes or improvements that they considered essential for the future. Conclusion: Over 80% of participating CL services established specific structures to provide COVID-psyCare for patients, their relatives, or staff. Mostly, resources were committed to patient care and specific interventions were largely implemented for staff support. Future development of COVID-psyCare warrants intensified intra- and inter-institutional exchange and co-operation.

The flows-to-equity method is commonly used in leveraged buyouts and other highly leveraged transactions. These flows are hybrid flows, mixing expected operating cash flows with promised debt payments under a planned debt schedule. Because of this, it is difficult to accurately estimate the appropriate discount rate, a difficulty that is compounded by the typically changing leverage over time under the planned debt schedule. We show how the flows-to-equity approach works and discuss its benefits and drawbacks as compared with other, “more standard” methods.

Near-wake effects of wind turbine models using the free-vortex wake have been studied extensively, but there is a lack of validation for such predictions in the mid to far wake. This paper presents a novel validation study using three free-vortex wake models of increasing complexity: an actuator disc, an actuator disc with rotation, and a lifting-line model. We emphasise the application for dynamic wind farm flow control optimisation with a focus on wake redirection using yaw misalignment. For this purpose, wake models should provide sufficiently accurate power predictions at a low computational expense to enable real-time control optimisation. Three sets of wind tunnel data are used for validation: flow measurements under steady yaw misalignment, time-resolved flow measurements for a step change in yaw, and turbine output measurements with yaw control and simulated wind direction variation. Results indicate that the actuator-disc model provides the best balance between computational cost and accuracy in power predictions for the mid to far wake, which is not significantly improved upon by the addition of rotation. In the near wake, the added complexity of the lifting-line model may provide value as it models blade loading and individual tip vortices. Altogether, this study provides important validation for further studies into optimisation of wake steering under time-varying conditions and suggests that the actuator-disc model is a suitable candidate for use in a model-predictive wind farm flow control framework.

The description of concurrent systems as a network of interacting processes helps to reduce the complexity of the specification. The same principle applies for the description of cyber-physical systems as a network of interacting components. We introduce a transition system based specification of cyber-physical components whose semantics is compositional with respect to a family of algebraic products. We give sufficient conditions for execution of a product of cyber-physical components to be correctly implemented by a lazy runtime expansion of the product construction. Our transition system algebra is implemented in the Maude rewriting logic system. As an example, we show that, under a coordination protocol, a set of autonomous energy-aware robots can self-sort themselves on a shared physical grid.

This work presents a novel reduced-order model (ROM) for the incompressible Navier-Stokes equations with time-dependent boundary conditions. This ROM is velocity-only, i.e. the simulation of the velocity does not require the computation of the pressure, and preserves the structure of the kinetic energy evolution. The key ingredient of the novel ROM is a decomposition of the velocity into a field with homogeneous boundary conditions and a lifting function that satisfies the mass equation with the prescribed inhomogeneous boundary conditions. This decomposition is inspired by the Helmholtz-Hodge decomposition and exhibits orthogonality of the two components. This orthogonality is crucial to preserve the structure of the kinetic energy evolution. To make the evaluation of the lifting function efficient, we propose a novel method that involves an explicit approximation of the boundary conditions with POD modes, while preserving the orthogonality of the velocity decomposition and thus the structure of the kinetic energy evolution. We show that the proposed velocity-only ROM is equivalent to a velocity-pressure ROM, i.e., a ROM that simulates both velocity and pressure. This equivalence can be generalized to other existing velocity-pressure ROMs and reveals valuable insights in their behaviour. Numerical experiments on test cases with inflow-outflow boundary conditions confirm the correctness and efficiency of the new ROM, and the equivalence with the velocity-pressure formulation.

The present work presents a stable POD-Galerkin based reduced-order model (ROM) for two-dimensional Rayleigh- Bénard convection in a square geometry for three Rayleigh numbers: 104 (steady state), 3 × 105 (periodic), and 6 × 106 (chaotic). Stability is obtained through a particular (staggered-grid) FOM discretization that leads to a ROM that is pressure-free and has skew-symmetric (energy-conserving) convective terms. This yields long-time stable solutions without requiring stabilizing mechanisms, even outside the training data range. The ROM’s stability is validated for the different test cases by investigating the Nusselt and Reynolds number time series and the mean and variance of the vertical temperature profile. In general, these quantities converge to the FOM when increasing the number of modes convergence, and turn out to be a good measure of accuracy for the non- chaotic cases. However, for the chaotic case, convergence with increasing numbers of modes is not evident, and additional measures are required to represent the effect of the smallest (neglected) scales.

In turbulence modeling, and more particularly in the Large-Eddy Simulation (LES) framework, we are concerned with finding closure models that represent the effect of the unresolved subgrid scales on the resolved scales. Recent approaches gravitate towards machine learning techniques to construct such models. However, the stability of machine-learned closure models and their abidance by physical structure (e.g. symmetries, conservation laws) are still open problems. To tackle both issues, we take the `discretize first, filter next' approach, in which we apply a spatial averaging filter to existing energy-conserving (fine-grid) discretizations. The main novelty is that we extend the system of equations describing the filtered solution with a set of equations that describe the evolution of (a compressed version of) the energy of the subgrid scales. Having an estimate of this energy, we can use the concept of energy conservation and derive stability. The compressed variables are determined via a data-driven technique in such a way that the energy of the subgrid scales is matched. For the extended system, the closure model should be energy-conserving, and a new skew-symmetric convolutional neural network architecture is proposed that has this property. Stability is thus guaranteed, independent of the actual weights and biases of the network. Importantly, our framework allows energy exchange between resolved scales and compressed subgrid scales and thus enables backscatter. To model dissipative systems (e.g. viscous flows), the framework is extended with a diffusive component. The introduced neural network architecture is constructed such that it also satisfies momentum conservation. We apply the new methodology to both the viscous Burgers' equation and the Korteweg-De Vries equation in 1D and show superior stability properties when compared to a vanilla convolutional neural network.

A novel dynamic economic model-predictive control strategy is presented that improves wind farm power production and reduces the additional demands of wake steering on yaw actuation when compared to an industry state-of-the-art reference controller. The novel controller takes a distributed approach to yaw control optimisation using a free-vortex wake model. An actuator-disc representation of the wind turbine is employed and adapted to the wind-farm scale by modelling secondary effects of wake steering and connecting individual turbines through a directed graph network. The economic model-predictive control problem is solved on a receding horizon using gradient-based optimisation, demonstrating sufficient performance for realising real-time control. The novel controller is tested in a large-eddy simulation environment and compared against a state-of-the-art look-up table approach based on steady-state model optimisation. Under realistic variations in wind direction and wind speed, the novel controller yields additional gains in power production during transients as well as a reduction in yaw actuator usage.

In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, the Markov Chain Generative Adversarial Neural Network (MCGAN), to alleviate the computational costs associated with solving the Bayesian inference problem. GANs pose a very suitable framework to aid in the solution of Bayesian inference problems, as they are designed to generate samples from complicated high-dimensional distributions. By training a GAN to sample from a low-dimensional latent space and then embedding it in a Markov Chain Monte Carlo method, we can highly efficiently sample from the posterior, by replacing both the high-dimensional prior and the expensive forward map. This comes at the cost of a potentially expensive offline stage in which training data must be simulated or gathered and the GAN has to be trained. We prove that the proposed methodology converges to the true posterior in the Wasserstein-1 distance and that sampling from the latent space is equivalent to sampling in the high-dimensional space in a weak sense. The method is showcased in two test cases where we perform both state and parameter estimation simultaneously and it is compared with two conventional approaches, polynomial chaos expansion and ensemble Kalman filter, and a deep learning-based approach, deep Bayesian inversion. The method is shown to be more accurate than alternative approaches while also being computationally faster, in multiple test cases, including the important engineering setting of detecting leaks in pipelines.

In this paper we present a complete framework for the energy-stable simulation of stratified incompressible flow in channels, using the one-dimensional two-fluid model. Building on earlier energy-conserving work on the basic two-fluid model, our new framework includes diffusion, friction, and surface tension. We show that surface tension can be added in an energy-conserving manner, and that diffusion and friction have a strictly dissipative effect on the energy. We then propose spatial discretizations for these terms such that a semi-discrete model is obtained that has the same conservation properties as the continuous model. Additionally, we propose a new energy-stable advective flux scheme that is energy-conserving in smooth regions of the flow and strictly dissipative where sharp gradients appear. This is obtained by combining, using flux limiters, a previously developed energy-conserving advective flux with a novel first-order upwind scheme that is shown to be strictly dissipative. The complete framework, with diffusion, surface tension, and a bounded energy, is linearly stable to short wavelength perturbations, and exhibits nonlinear damping near shocks. The model yields smoothly converging numerical solutions, even under conditions for which the basic two-fluid model is ill-posed. With our explicit expressions for the dissipation rates, we are able to attribute the nonlinear damping to the different dissipation mechanisms, and compare their effects.

The partial differential equations governing fluid flows, the Navier-Stokes equations, have been known for a long time. However, their solution is highly non-trivial and requires advanced numerical methods. The book Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics by Rozza, Stabile and Ballarin is a timely contribution that describes a state-of-the-art approach to construct such numerical methods, known as reduced order methods.

A new energy-consistent discretization of the viscous dissipation function in incompressible flows is proposed. It is implied by choosing a discretization of the diffusive terms and a discretization of the local kinetic energy equation and by requiring that continuous identities like the product rule are mimicked discretely. The proposed viscous dissipation function has a quadratic, strictly dissipative form, for both simplified (constant viscosity) stress tensors and general stress tensors. The proposed expression is not only useful in evaluating energy budgets in turbulent flows, but also in natural convection flows, where it appears in the internal energy equation and is responsible for viscous heating. The viscous dissipation function is such that a consistent total energy balance is obtained: the 'implied' presence as sink in the kinetic energy equation is exactly balanced by explicitly adding it as source term in the internal energy equation. Numerical experiments of Rayleigh-Bénard convection (RBC) and Rayleigh-Taylor instabilities confirm that with the proposed dissipation function, the energy exchange between kinetic and internal energy is exactly preserved. The experiments show furthermore that viscous dissipation does not affect the critical Rayleigh number at which instabilities form, but it does significantly impact the development of instabilities once they occur. Consequently, the value of the Nusselt number on the cold plate becomes larger than on the hot plate, with the difference increasing with increasing Gebhart number. Finally, 3D simulations of turbulent RBC show that energy balances are exactly satisfied even for very coarse grids; therefore, we consider that the proposed discretization forms an excellent starting point for testing sub-grid scale models.

We extend and consolidate the security justification for the Dilithium signature scheme. In particular, we identify a subtle but crucial gap that appears in several ROM and QROM security proofs for signature schemes that are based on the Fiat-Shamir with aborts paradigm, including Dilithium. The gap lies in the CMA-to-NMA reduction and was uncovered when trying to formalize a variant of the QROM security proof by Kiltz, Lyubashevsky, and Schaffner (Eurocrypt 2018). The gap was confirmed by the authors, and there seems to be no simple patch for it. We provide new, fixed proofs for the affected CMA-to-NMA reduction, both for the ROM and the QROM, and we perform a concrete security analysis for the case of Dilithium to show that the claimed security level is still valid after addressing the gap. Furthermore, we offer a fully mechanized ROM proof for the CMA-security of Dilithium in the EasyCrypt proof assistant. Our formalization includes several new tools and techniques of independent interest for future formal verification results.

Neural closure models have recently been proposed as a method for efficiently approximating small scales in multiscale systems with neural networks. The choice of loss function and associated training procedure has a large effect on the accuracy and stability of the resulting neural closure model. In this work, we systematically compare three distinct procedures: “derivative fitting”, “trajectory fitting” with discretise-then-optimise, and “trajectory fitting” with optimise-then-discretise. Derivative fitting is conceptually the simplest and computationally the most efficient approach and is found to perform reasonably well on one of the test problems (Kuramoto-Sivashinsky) but poorly on the other (Burgers). Trajectory fitting is computationally more expensive but is more robust and is therefore the preferred approach. Of the two trajectory fitting procedures, the discretise-then-optimise approach produces more accurate models than the optimise-then-discretise approach. While the optimise-then-discretise approach can still produce accurate models, care must be taken in choosing the length of the trajectories used for training, in order to train the models on long-term behaviour while still producing reasonably accurate gradients during training. Two existing theorems are interpreted in a novel way that gives insight into the long-term accuracy of a neural closure model based on how accurate it is in the short term.