Stochastic Crashworthiness Optimization Accounting for Simulation Noise

Finite element-based crashworthiness optimization is extensively used to improve the safety of motor vehicles. However, the responses of crash simulations are characterized by a high level of numerical noise, which can hamper the blind use of surrogate-based design optimization methods. It is therefore essential to account for the noise-induced uncertainty when performing optimization. For this purpose, a surrogate, referred to as Non-Deterministic Kriging (NDK), can be used. It models the noise as a non-stationary stochastic process, which is added to a traditional deterministic kriging surrogate. Based on the NDK surrogate, this study proposes an optimization algorithm tailored to account for both epistemic uncertainty, due to the lack of data, and irreducible aleatory uncertainty, due to the simulation noise. The variances are included within an extension of the well-known expected improvement infill criterion referred to as Modified Augmented Expected Improvement (MAEI). Because the proposed optimization scheme requires an estimate of the aleatory variance, it is approximated through a regression kriging, which is iteratively refined. The proposed algorithm is tested on a set of analytical functions and applied to the optimization of an Occupant Restraint System (ORS) during a crash.

Structural Response of a Single-Stage Centrifugal Compressor to Fluid-Induced Excitations at Low-Flow Operating Condition: Experimental and Numerical Study

The article describes an assessment of possible changes in constant fatigue life of a medium flow-coefficient centrifugal compressor impeller subject to operation at close-to-surge point. Some aspects of duct acoustics are additionally analyzed. The experimental measurements at partial load are presented and are primarily used for validation of unidirectionally coupled fluid-structural numerical model. The model is based on unsteady finite-volume fluid-flow simulations and on finite-element transient structural analysis. The validation is followed by the model implementation to replicate the industry-scale loads with reasonably higher rotational speed and suction pressure. The approach demonstrates satisfactory accuracy in prediction of stage performance and unsteady flow field in vaneless diffuser. The latter is deduced from signal analysis relying on continuous wavelet transformations. On the other hand, it is found that the aerodynamic incidence losses at close-to-surge point are underpredicted. The structural simulation generates considerable amounts of numerical noise, which has to be separated prior to evaluation of fluid-induced dynamic strain. The main source of disturbance is defined as a stationary region of static pressure drop caused by flow contraction at volute tongue and leading to first engine-order excitation in rotating frame of reference. Eventually, it is concluded that the amplitude of excitation is too low to lead to any additional fatigue.

Numerical Simulations Using Eulerian Schemes for the Vlasov–Poisson Model

The Vlasov equation describes the temporal evolution of the distribution function of particles in a collisionless plasma and, if magnetic fields are negligible, the mean electric field is prescribed by Poisson equation. Eulerian numerical methods discretize and directly solve the Vlasov equation on a mesh in phase space and can provide high accuracy with low numerical noise. In this paper, we present a comprehensive analysis and comparison between the most used Eulerian methods for the two-dimensional Vlasov–Poisson system, including finite-differences, finite-volumes and semi-Lagrangian ones. The schemes are evaluated and compared through classical problems and conclusions are drawn regarding their accuracy and performance.

Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics

Machine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes. Such models often rely on low-dimensional maps generated by complex processing pipelines. However, the numerical instabilities inherent to pipelines limit the fidelity of these maps and introduce computational bias. Monte Carlo Arithmetic, a technique for introducing controlled amounts of numerical noise, was used to perturb a structural connectome estimation pipeline, ultimately producing a range of plausible networks for each sample. The variability in the perturbed networks was captured in an augmented dataset, which was then used for an age classification task. We found that resampling brain networks across a series of such numerically perturbed outcomes led to improved performance in all tested classifiers, preprocessing strategies, and dimensionality reduction techniques. Importantly, we find that this benefit does not hinge on a large number of perturbations, suggesting that even minimally perturbing a dataset adds meaningful variance which can be captured in the subsequently designed models.

Factors Influencing Ensemble Sensitivity–Based Targeted Observing Predictions at Convection-Allowing Resolutions

Ensemble sensitivity analysis (ESA) is applied to select types of observations, in various locations and in advance of forecast convection, to systematically evaluate the effectiveness of ESA-based observation targeting for 10 convection forecasts. To facilitate the analysis, observing system simulation experiments and perfect models are utilized to generate synthetic targeted observations of temperature and pressure for future assimilation with an ensemble prediction system. Various observation assimilation experiments are carried out to assess the impacts of nonlinearity, covariance localization, and numerical noise on ESA-based observation-impact predictions. It is discovered that localization applied during data assimilation restricts targeted-observation increments onto the forecast responses of composite reflectivity and 3-hourly accumulated precipitation, making impact predictions poor. In addition, numerical noise introduced by nonlinear perturbation evolution tends to reduce the correlations between observed and predicted impacts; small, random-perturbation experiments often yielded similar impacts on forecasts as targeted observations. Nonlinearity also manifests in the observation impacts when comparing targeted observations with nontargeted, randomly chosen observations; random observations have seemingly the same impact on forecasts as targeted observations. The results, under idealized conditions and simplified ensemble configurations, demonstrate that ESA-based targeting for nonlinear convection forecasts may be most applicable at short time scales. Important implications for ESA-based targeting methods employed with real-time ensemble systems are also discussed.

A Numerical Investigation of an Eddy Current Sensor for Detecting Small Defects in Metal Additive Manufacturing

This paper presents a parametric study of an eddy current (EC) sensor that measures the magnetic flux density (MFD) for detecting lack-of-fusion defects commonly found in metal additive manufacturing (Metal-AM). In this study, the EC-sensing system is simulated using a two-stage method that decomposes the EC-based detection of a defective conductor into two subproblems; the first analytically solves for the EC assuming no defects, and the second solves for the EC perturbation in the focused regions near the defects using a distributed current source method. Based on the proposed EC model, the effects of geometrical parameters on the sensitivity of an EC-sensing system were analyzed and verified by comparing with finite-element analysis (FEA). The study leads to the identification of two key parameters that significantly affect the sensitivity and accuracy of an EC sensor for detecting small defects, which are the locations and axes of the MFD sensors relative to the coil. The ECD distributions are simulated for two EC-sensor design scenarios: fixed at specified locations, and scanning over the entire specimen. Both DCS-based and FEA results match excellently well when images are at a fixed senor location. When scanning, DCS-based images are much smoother and require significantly less time to scan as compared to FEA that requires remeshing between steps and exhibits significant numerical noise, demonstrating the accuracy and efficiency of the proposed numerical model.

An Evaluation of a Hybrid, Terrain-Following Vertical Coordinate in the WRF-Based RAP and HRRR Models

A new hybrid, sigma-pressure vertical coordinate was recently added to the Weather Research and Forecasting (WRF) Model in an effort to reduce numerical noise in the model equations near complex terrain. Testing of this hybrid, terrain-following coordinate was undertaken in the WRF-based Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) models to assess impacts on retrospective and real-time simulations. Initial cold-start simulations indicated that the majority of differences between the hybrid and traditional sigma coordinate were confined to regions downstream of mountainous terrain and focused in the upper levels. Week-long retrospective simulations generally resulted in small improvements for the RAP, and a neutral impact in the HRRR when the hybrid coordinate was used. However, one possibility is that the inclusion of data assimilation in the experiments may have minimized differences between the vertical coordinates. Finally, analysis of turbulence forecasts with the new hybrid coordinate indicate a significant reduction in spurious vertical motion over the full length of the Rocky Mountains. Overall, the results indicate a potential to improve forecast metrics through implementation of the hybrid coordinate, particularly at upper levels, and downstream of complex terrain.

A coupled heat and moisture tracking framework to assess water cycle changes in a warming climate

<p>The expected intensification of the global water cycle in a warming climate comes along with an increase in the frequency and intensity of extreme events, such as droughts and floods. From a drought perspective, local limitations of terrestrial evaporation can cause a reduction of water vapor in the atmosphere and thus further induce local and remote precipitation deficits. Despite the existing myriad of tools and models to assess the origin of precipitation, trends and uncertainties in such source–sink relationships remain largely unexplored. The main reason is the scarcity of observations to explore these relationships and validate moisture-tracking models, which are commonly subject to assumptions that limit their reliability and applicability. Lagrangian models, for example, typically establish source–sink relationships based on moisture changes along air parcel trajectories, yet tend to be heavily affected by numerical noise. Moreover, they do not assess the plausibility of a given moisture change by considering the increasing saturation point of air with increasing temperatures, which hampers reliable assessments of trends under global warming. </p><p>Here, we present a holistic framework for the process-based evaluation of atmospheric trajectories to infer source–sink relationships of moisture. Building upon previous process-based evaluations of trajectories, we extend the analysis to a coupled heat and moisture diagnosis that includes physics-based limits for the detection of evaporation and precipitation from humidity changes along each trajectory. The framework comprises three steps: (i) the coupled moisture and heat diagnosis of fluxes from Lagrangian trajectories using multi-objective criteria, (ii) the attribution of sources following a mass- and energy-conserving algorithm, and (iii) the bias correction of diagnosed fluxes and the corresponding source–sink relationship. Applying this framework to simulations from the Lagrangian particle dispersion model FLEXPART, driven with ERA-Interim reanalysis data, allows us to quantify errors and uncertainties associated with the resulting source–sink relationships. A comparison to alternative methodologies illustrates the benefit of our coupled heat and moisture tracking approach. Moreover, the multivariate character of this framework paves the way for a cohesive assessment of the spatial dependencies that cause water cycle changes in a warming climate.</p>