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Reynolds-stress models (EARSM) from high-fidelity data is developed building on the frozen-training SpaRTA algorithm of [1]. Corrections for the Reynolds stress tensor and the production of transported turbulent quantities of a baseline linear eddy viscosity model (LEVM) are expressed as functions of tensor polynomials selected from a library of candidate functions. The CFD-driven training consists in solving a blackbox optimization problem in which the fitness of candidate EARSM models is evaluated by running RANS simulations. The procedure enables training models against any target quantity of interest, computable as an output of the CFD model. Unlike the frozen-training approach, the proposed methodology is not restricted to data sets for which full fields of high-fidelity data, including second flow order statistics, are available. However, the solution of a high-dimensional expensive blackbox function optimization problem is required. Several steps are then undertaken to reduce the associated computational burden. First, a sensitivity analysis is used to identify the most influential terms and to reduce the dimensionality of the search space. Afterwards, the Constrained Optimization using Response Surface (CORS) algorithm, which approximates the black-box cost function using a response surface constructed from a limited number of CFD solves, is used to find the optimal model parameters. Model discovery and cross-validation is performed for three configurations of 2D turbulent separated flows in channels of variable section using different sets of training data to show the flexibility of the method. The discovered models are then applied to the prediction of an unseen 2D separated flow with higher Reynolds number and different geometry. The predictions of the discovered models for the new case are shown to be not only more accurate than the baseline LEVM, but also of a multi-purpose EARSM model derived from purely physical arguments. The proposed deterministic symbolic identification approach constitutes a promising candidate for building accurate and robust RANS models customized for a given class of flows at moderate computational cost. ©2022 Elsevier Inc. All rights reserved.

The loss factor is often determined in building acoustics by measuring the structure-borne reverberation time. To do this, elements under test are excited either by a hammer or by a shaker. In several experiments with sand-lime brick walls, measured loss factors turned out to be significantly larger when hammer excitation was used instead of shaker excitation. A thorough investigation of this effect was then performed using hammer blows of different strengths and a 250-kg shaker. This way, forces are in the same order of magnitude for both excitations. Measurement results lead to the conclusion that large forces may create a nonlinear structural response. The nonlinearity is observed for a sand-lime brick wall without plastering but not for a lightweight composite wall and also not for a monolithic concrete wall. The assumption of nonlinear behaviour is furthermore supported by an additional investigation where alarm pistol shots were used to excite a sand-lime brick wall with very large airborne sound pressure levels. The airborne sound insulation of the wall turned out to be nonlinear, that is, it increased with increasing sending room levels.

In this short note we apply the recently proposed data-driven RANS closure modelling framework of Schmelzer et al. (2020) to fully three-dimensional, high Reynolds number flows: namely wall-mounted cubes and cuboids at Re=40,000, and a cylinder at Re=140,000. For each flow, a new RANS closure is generated using sparse symbolic regression based on LES or DES reference data. This new model is implemented in a CFD solver, and subsequently applied to prediction of the other flows. We see consistent improvements compared to the baseline $k-\omega$ SST model in predictions of mean-velocity in the complete flow domain., Comment: Submitted Computers & Fluids

Objectives Intermittent as well as prolonged fasting are receiving considerable attention and appear favorable in conditions such as metabolic syndrome, type 2 diabetes, and rheumatic diseases. Fasting for individuals with type 1 diabetes (T1D) is generally considered too risky. However, the ability and possibility to change from carbohydrate- to ketone-based fuel supply might be relevant for individuals with T1D. The aim of this patient-led research was to investigate the feasibility, benefit, and safety of a 7-d multimodal fasting intervention in individuals with T1D. Methods This was a non-randomized controlled pilot study, with 20 participants with T1D and 10 without the disease. Data acquisition took place before, after, and 4 mo after the intervention and daily during intervention. Results Of the individuals with T1D, 19 finished fasting. A mean β-hydroxybutyrate as representative ketone body increased to 2.8 ± 1.9 mmol/L on day 7; whereas average glucose remained between 4.9 (±1.5) and 7.5 (±2.3) mmol/L (89 ± 27 and 136 ± 40 mg/dL). Mean daily insulin dose was adjusted from 24.4 (3–50) IU on the day before fasting to 7.6 (0–26.7) IU on day 7. Quality of life (WHO-5) normalized from 54 (±4.4) to 68.8 (±15; P = 0.01) after fasting. There was a decrease from before until the follow-up 4 mo later of weight from 77.6 (±20.4) to 76.6 (±20.9) kg (P = 0.023) and for body mass index from 27.68 (±7.04) to 26.74 (±7.15) kg/m2 (P = 0.008). Diastolic blood pressure increased from 69.75 (±11.41) to 75.74 (±8.42) mm Hg (P = 0.028) and stayed in a healthy range on average. Fasting-related side effects were all temporary, and slightly more prevalent in those with type 1 diabetes compared with the reference group. Conclusions This study demonstrated the feasibility, benefits, and safety aspects of a 7-d fast in adults with T1D.

Multi-fidelity optimization methods promise a high-fidelity optimum at a cost only slightly greater than a low-fidelity optimization. This promise is seldom achieved in practice, due to the requirement that low- and high-fidelity models correlate well. In this article, we propose an efficient bi-fidelity shape optimization method for turbulent fluid-flow applications with Large-Eddy Simulation (LES) and Reynolds-averaged Navier-Stokes (RANS) as the high- and low-fidelity models within a hierarchical-Kriging surrogate modelling framework. Since the LES–RANS correlation is often poor, we use the full LES flow-field at a single point in the design space to derive a custom-tailored RANS closure model that reproduces the LES at that point. This is achieved with machine-learning techniques, specifically sparse regression to obtain high corrections of the turbulence anisotropy tensor and the production of turbulence kinetic energy as functions of the RANS mean-flow. The LES–RANS correlation is dramatically improved throughout the design-space. We demonstrate the effectivity and efficiency of our method in a proof-of-concept shape optimization of the well-known periodic-hill case. Standard RANS models perform poorly in this case, whereas our method converges to the LES-optimum with only two LES samples.

The dynamic stiffness of underlays is a required quantity to predict the reduction of impact and airborne noise transmitted through floating floors. The measurement of the dynamic stiffness is standardized in ISO 9052–1 using a floating floor section of 200 mm edge length. If open pore insulating materials are used for the underlay, the air flow and its flow resistivity have a significant impact on the dynamic stiffness. The standard ISO 9052–1 therefore calculates the dynamic stiffness based on three distinct intervals of the flow resistivity. This contribution analyses and discusses the mathematical origin and practical consequences of these three standardized intervals and proposes an improvement for a revision of the standard. The current physical model is expanded to examine if the results, that are gained from the geometrically reduced systems of the standard, can be transferred to room sized systems.

A multilevel Monte Carlo (MLMC) method for quantifying model-form uncertainties associated with the Reynolds-Averaged Navier-Stokes (RANS) simulations is presented. Two, high-dimensional, stochastic extensions of the RANS equations are considered to demonstrate the applicability of the MLMC method. The first approach is based on global perturbation of the baseline eddy viscosity field using a lognormal random field. A more general second extension is considered based on the work of [Xiao et al.(2017)], where the entire Reynolds Stress Tensor (RST) is perturbed while maintaining realizability. For two fundamental flows, we show that the MLMC method based on a hierarchy of meshes is asymptotically faster than plain Monte Carlo. Additionally, we demonstrate that for some flows an optimal multilevel estimator can be obtained for which the cost scales with the same order as a single CFD solve on the finest grid level., 40 pages

Uncertainties are present in any engineering task. If the predicted result of a numerical simulation agrees with test results or operational data, then uncertainties are typically ignored or simply not even recognized. In case of disagreement, they often become of key interest. This section describes the different types of uncertainties in a product development process and how to handle them in an uncertainty quantification and robust design optimization.

International audience; The lack of an universal modelling approach for turbulence in Reynolds-Averaged Navier–Stokes simulations creates the need for quantifying the modelling error without additional validation data. Bayesian Model-Scenario Averaging (BMSA), which exploits the variability on model closure coefficients across several flow scenarios and multiple models, gives a stochastic, a posteriori estimate of a quantity of interest. The full BMSA requires the propagation of the posterior probability distribution of the closure coefficients through a CFD code, which makes the approach infeasible for industrial relevant flow cases. By using maximum a posteriori (MAP) estimates on the posterior distribution, we drastically reduce the computational costs. The approach is applied to turbulent flow in a pipe at Re= 44,000 over 2D periodic hills at Re=5600, and finally over a generic falcon jet test case (Industrial challenge IC-03 of the UMRIDA project).

International audience; This work presents developments towards a deterministic symbolic regression method toderive algebraic Reynolds-stress models for the Reynolds-Averaged Navier-Stokes (RANS)equations. The models are written as tensor polynomials, for which optimal coe cients arefound using Bayesian inversion. These coe cient fields are the targets for the symbolic regression.A method is presented based on a regularisation strategy in order to promote sparsityof the inferred models and is applied to high-fidelity data. By being data-driven the methodreduces the assumptions commonly made in the process of model development in order toincrease the predictive fidelity of algebraic models.

In measurements with cryogenic current comparators (CCCs), robustness against mechanical distortions is an issue. Sources of distortions are part of the experimental environment, efficient suppression is difficult. In a test setup, we intentionally applied mechanical vibrations to a CCC probe. In the 10 Hz to 3 kHz range, the frequency response of the SQUID detector's output signal was recorded for an integrative and an H ∞ -controller. When applying vibrations at fixed frequency, the stability of the SQUID's locking depended on the chosen controller, but systematic influence on the result of a 12.9 kΩ vs. 100 Ω resistance comparison was not found.

In der angewandten Arbeitsforschung ist bislang kaum untersucht worden, welchen Anteil das Arbeitsleben bzw. Arbeitsbedingungen im Gesamtgefuge der Entstehung von psychischen Erkrankungen spielt. In diesem Kontext ist von Arbeitswissenschaftlern versucht worden, zusammen mit Fuhrungskraften und (uberbetrieblich tatigen) Betriebsarzten einen Hinweiskatalog fur eine verbesserte Fruhdiagnostik arbeitsbedingter psychischer Erkrankungen zu erstellen. Die aufwendige wissenschaftliche Recherche und Interviews mit Fuhrungskraften, Betriebsarzten und Hausarzten zeigten jedoch, dass die Erstellung eines solchen Hinweiskataloges aufgrund der Komplexitat der Thematik und der vielen vorhandenen „weisen Felder“ in der Ursache-Wirkungs- Kette nicht moglich war. Der Prozess des Scheiterns wird dargestellt. Ferner werden die vorhanden Erklarungsansatze im Kontext eines Prozessmodells zur Entstehung von psychischen Erkrankungen, in dem auch „Burnout als Konstrukt integriert ist, reflektiert. Es wird ein begrundeter Losungsweg aufgezeigt, bei dem der Hausarzt im Vergleich zum Betriebsarzt eine prominente Rolle spielt.

Die Norm DIN 4109 [8] erfordert die Kenntnis der Langsdammung von flankierenden Bauteilen. Da viele Prufstellen nicht uber die erforderlichen Prufstande verfugen, werden dort die Prufobjekte seitlich parallel zur Ausenwand in einen ublichen Wandprufstand eingebaut. Dabei entsteht ein schmales Luftvolumen. Dessen Einflus auf die Langsdammung wurde sowohl experimentell als auch numerisch untersucht. Dies sollte klaren, unter welchen Bedingungen die Messung der Langsdammung auf diese Weise erfolgen kann. Der Beitrag stellt die experimentellen Untersuchungen vor. Measurement of longitudinal sound reduction in standard test facilities. The German standard DIN 4109 requires the knowledge of the longitudinal sound reduction of flanking building components. Many test laboratories do not posses the proper test facilities and therefore install the test objects alongside the flanking wall of the test facility, thereby creating a narrow cavity. The influence of this cavity on the longitudinal sound reduction was examined experimentally and numerically. This should clarify under which conditions the longitudinal sound reduction can be measured using this setup. The article presents the experimental examination.

Experimental studies of physical effects in building acoustics are usually time consuming and expensive. This is mainly caused by the building costs but also by the experimental effort. It is thus desirable to have another method for the investigation of basic effects in building acoustics. Building acoustic problems are characterized by the interaction between airborne and structure‐borne sound fields. It is therefore possible to use scaled models when both sound fields are treated correctly. This means that the wavelengths in the airborne and in the structure‐borne sound fields have to be scaled in the same way. With a scaling factor of typically 1:8, the costs can be reduced drastically and nearly all model parameters can be changed separately. Due to these advantages, this technique is used at PTB's building acoustics group. This contribution gives an overview on the physical background of scaled models, reports on validation experiments and on several applications e.g. investigations of the influence of temperature and static pressure, damping effects, geometry influence on the sound insulation of walls, the measurement of the flanking transmission of walls and the measurement of suspended ceilings.