Model Reduction for Large-Scale Systems with High Dimensional Parametric Input Space

A model-constrained adaptive sampling methodology is proposed for reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a reduced basis approach, which requires the computation of high-fidelity solutions at a number of sample points throughout the parametric input space. A key challenge that must be addressed in the optimization, control, and probabilistic settings is the need for the reduced models to capture variation over this parametric input space, which, for many applications, will be of high dimension. We pose the task of determining appropriate sample points as a PDE-constrained optimization problem, which is implemented using an efficient adaptive algorithm that scales well to systems with a large number of parameters. The methodology is demonstrated for examples with parametric input spaces of dimension 11 and 21, which describe thermal analysis and design of a heat conduction fin, and compared with statistically-based sampling methods. For this example, the model-constrained adaptive sampling leads to reduced models that, for a given basis size, have error several orders of magnitude smaller than that obtained using the other methods

Computational chemo-thermo-mechanical coupling phase-field model for complex fracture induced by early-age shrinkage and hydration heat in cement-based materials

In this paper, we present a new multi-physics computational framework that enables us to capture and investigate complex fracture behavior in cement-based materials at early-age. The present model consists of coupling the most important chemo-thermo-mechanical processes to describe temperature evolution, variation of hydration degree, and mechanical behavior. The changes of material properties are expressed as a function of the hydration degree, to capture the age effects. Fracture analysis of these processes are then accommodated by a versatile phase field model in the framework of smeared crack models, addressing the influence of cracks on hydration and thermal transfer. We additionally describe a stable and robust numerical algorithm, which aims to solve coupled problems by using a staggered scheme. The developed approach is applied to study the fracture phenomena at both macroscopic and mesoscopic scales, in which all microstructural heterogeneities of sand and cement matrix are explicitly accounted. Nucleation, initiation, and propagation of complex crack network are simulated in an efficient way demonstrating the potential of the proposed approach to assess the early-age defects in concrete structures and materials

Role of interfacial transition zone in phase field modeling of fracture in layered heterogeneous structures

Mechanical behavior of layered materials and structures greatly depends on the mechanical behavior of interfaces. In the past decades, the failure in such layered media has been studied by many researchers due to their critical role in the mechanics and physics of solids. This study aims at investigating crack-interface interaction in two-dimensional (2-D) and three-dimensional (3-D) layered media by a phase field model. Our objectives are fourfold: (a) to better understand fracture behavior in layered heterogeneous systems under quasi-static load; (b) to introduce a new methodology for better describing interfaces by a regularized interfacial transition zone in the context of varia-tional phase field approach, exploring its important role; (c) to show the accuracy , performance and applicability of the present model in modeling material failure at the interfaces in both 2-D and 3-D bodies; and (d) to quantitatively validate computed crack path with respect to experimental data. Phase field models with both perfectly and cohesive bonded interfaces are thus derived. A regularized interfacial transition zone is introduced to capture characteristics of material mismatch at the interfaces. Numerical examples for 2-D and 3-D layered systems with experimental validation provide fundamentals of fracture behavior in layered structures. The obtained results shed light on the behavior of crack paths, which are drastically affected by the elastic modulus mismatch between two layers and interface types, and reveal the important role of the proposed interfacial transition zone in phase field modeling of crack interface interactions

Identification of nonlinear heat transfer laws from boundary observations

We consider the problem of identifying a nonlinear heat transfer law at the boundary, or of the temperature-dependent heat transfer coefficient in a parabolic equation from boundary observations. As a practical example, this model applies to the heat transfer coefficient that describes the intensity of heat exchange between a hot wire and the cooling water in which it is placed. We reformulate the inverse problem as a variational one which aims to minimize a misfit functional and prove that it has a solution. We provide a gradient formula for the misfit functional and then use some iterative methods for solving the variational problem. Thorough investigations are made with respect to several initial guesses and amounts of noise in the input data. Numerical results show that the methods are robust, stable and accurate

AGV Trajectory Control Based on Laser Sensor Navigation

Autonomous Guided Vehicle Systems (AGVs) are used to transport goods and products in manufacturing fields where navigation can be done in a structured environment. In order to track the given trajectory, a tracking control based on Lyapunov stability theory is introduced. The use of the nonlinear Lyapunov technique provides robustness for load disturbance and sensor noise. To apply Lyapunov\u27s theorem, the kinematic model of AGV is given. To recognize its position in indoor environment, in this paper, a laser sensor device NAV200 is used to detect the AGV position in real-time. For simulation and experiment, software and hardware are described. The AGV consists of 4 wheels with two passive wheels and two driving wheels. A controller is developed based on industrial computer. The effectiveness of the proposed controller is proved by simulation and experimental results.[AGV Trajectory Control, Laser Sensor Navigation

Satellites May Underestimate Rice Residue and Associated Burning Emissions in Vietnam

In this study, we estimate rice residue, associated burning emissions, and compare results with existing emissions inventories employing a bottom-up approach. We first estimated field-level post-harvest rice residues, including separate fuel-loading factors for rice straw and rice stubble. Results suggested fuel-loading factors of 0.27 kg/sq m (+/-0.033), 0.61 kg/sq m (+/-0.076), and 0.88 kg/sq m (+/-0.083) for rice straw, stubble, and total post-harvest biomass, respectively. Using these factors, we quantified potential emissions from rice residue burning and compared our estimates with other studies. Our results suggest total rice residue burning emissions as 2.24 Gg PM2.5, 36.54 Gg CO and 567.79 Gg CO2 for Hanoi Province, which are significantly higher than earlier studies. We attribute our higher emission estimates to improved fuel-loading factors; moreover, we infer that some earlier studies relying on residue-to-product ratios could be underestimating rice residue emissions by more than a factor of 2.3 for Hanoi, Vietnam. Using the rice planted area data from the Vietnamese government, and combining our fuel-loading factors, we also estimated rice residue PM2.5 emissions for the entirety of Vietnam and compared these estimates with an existing all-sources emissions inventory, and the Global Fire Emissions Database (GFED). Results suggest 75.98 Gg of PM2.5 released from rice residue burning accounting for 12.8% of total emissions for Vietnam. The GFED database suggests 42.56 Gg PM2.5 from biomass burning with 5.62 Gg attributed to agricultural waste burning indicating satellite-based methods may be significantly underestimating emissions. Our results not only provide improved residue and emission estimates, but also highlight the need for emissions mitigation from rice residue burning

Comparison of some Reduced Representation Approximations

In the field of numerical approximation, specialists considering highly complex problems have recently proposed various ways to simplify their underlying problems. In this field, depending on the problem they were tackling and the community that are at work, different approaches have been developed with some success and have even gained some maturity, the applications can now be applied to information analysis or for numerical simulation of PDE's. At this point, a crossed analysis and effort for understanding the similarities and the differences between these approaches that found their starting points in different backgrounds is of interest. It is the purpose of this paper to contribute to this effort by comparing some constructive reduced representations of complex functions. We present here in full details the Adaptive Cross Approximation (ACA) and the Empirical Interpolation Method (EIM) together with other approaches that enter in the same category

Multichannel Photon Counting Lidar Measurements Using USB-based Digital Storage Oscilloscope

We present a simple method of making multichannel photon counting measurements of weak lidar signal from large ranges, using commonly available USB-based digital storage oscilloscopes. The single photon pulses from compact photomultiplier tubes are amplified and stretched so that the pulses are large and broad enough to be sampled efficiently by the USB oscilloscopes. A software interface written in Labview is then used to count the number of photon pulses in each of the prescribed time bins to form the histogram of LIDAR signal. This method presents a flexible alternative to the modular multichannel scalers and facilitate the development of sensitive lidar systems

Class based Influence Functions for Error Detection

Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets. However, they are unstable when applied to deep networks. In this paper, we provide an explanation for the instability of IFs and develop a solution to this problem. We show that IFs are unreliable when the two data points belong to two different classes. Our solution leverages class information to improve the stability of IFs. Extensive experiments show that our modification significantly improves the performance and stability of IFs while incurring no additional computational cost.Comment: Thang Nguyen-Duc, Hoang Thanh-Tung, and Quan Hung Tran are co-first authors of this paper. 12 pages, 12 figures. Accepted to ACL 202