Events Calendar
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[ Janurary 06, 2020]
Dynamic Latent Variable Regression for Inferential Sensor Modeling and Supervised Monitoring
| This seminar will be on system modeling and process monitoring with multivariate statistical methods. Specifically, the presentation will (1) review some existing multivariate statistical approaches for process modeling and monitoring; and (2) introduce a novel latent variable regression algorithm and its dynamic counterpart. -
[ Janurary 06, 2020]
| The possibility to model and design metamaterials tailored to aeroacoustic applications is one of the hottest topics in aeronautical engineering research. The possibilities disclosed by aeroacoustic metamaterials for the mitigation of the community noise of aircraft are virtually endless. Metamaterials for scattering cancellation (cloaking), noise hyper-focusing, and noise trapping could be exploited to develop a new generation of low-noise devices capable to modify substantially the acoustic impact of aircraft. The theoretical and numerical modelling of metamaterials capable to operate within a flow is here addressed in a comprehensive fashion, starting from the modelling of a metacontinuum with unconventional mechanical properties, to eventually attain a generalized formulation of the equations governing the propagation of an acoustic disturbance within the unconventional medium. The results of numerical simulations and experimental campaigns are also shown and commented. -
[ December 25, 2019]
Exact theory of dispersion for nonlinear wave propagation and extension to phononic materials
| Wave motion lies at the heart of many disciplines in the physical sciences and engineering. For example, problems and applications involving light, sound, heat or fluid flow are all likely to involve wave dynamics at some level. In this work, we consider strongly nonlinear wave propagation in elastic solids, although the theory presented is in principle applicable to other types of waves such as waves in fluids, gases, and plasma.
We investigate a thick elastic rod admitting longitudinal motion. In the linear limit, this rod is dispersive due to the effect of lateral inertia. The nonlinearity is introduced through either the stress-strain relation and/or the strain-displacement gradient relation. Using a theory we have developed earlier and demonstrated on thin rods and beams [1], we derive an exact nonlinear dispersion relation for the thick rod.
The derived relation is validated by direct time-domain simulations, examining both instantaneous dispersion (by direct observation) and short-term, pre-breaking dispersion (by Fourier transformations). The study is then extended to a continuous thin rod with a periodic arrangement of resonators (nonlinear elastic metamaterial) [2] or material properties (nonlinear phononic crystal) [3]. For this problem we introduce a new method that is based on a standard transfer matrix augmented with a nonlinear enrichment at the constitutive material level. This method yields an approximate band structure that accounts for the finite wave amplitude. Finally, we present an analysis on the condition required for the existence of spatial invariance in the wave profile. -
[ December 23, 2019]
Bio-inspired smart soft materials
| Smart soft materials, capable of changing optical properties and/or demonstrating shape reconfiguration as exposed to external stimuli, have attracted tremendous research interest. Since mother nature has demonstrated numerous intriguing samples in this field, three studies on smart materials learned from natural life will be present in this talk. (1) Inspired by the display tactics in marine life, we developed a deformation-controlled surface-engineering approach via strain-dependent micro-cracks and folds to realize a broad range of mechanochromic devices with high sensitivity and reversibility. (2) Mimicing the wrinkling on the wet finger, we also realized three types of moisture-responsive wrinkled devices through a single film–substrate system. (3) Inspired by the remarkable shape adaptivity demonstrated in various biological systems, a stretchable, 3D tubular structure formed due to processing-induced wrinkles is proposed as a platform for adaptive stretchable electronics. -
[ December 13, 2019]
Autoignition of CH4 and H2/CO in CO2 and Ar Diluent at High Pressure Conditions
| The directly fired supercritical carbon dioxide (sCO2) power cycle has high efficiency while allowing nearly complete carbon dioxide (CO2) capture. The operating condition of sCO2 power cycle (10 MPa to 30 MPa) combustors is dramatically different from conventional gas turbine combustors. However, combustion properties, e.g., autoigntion delays, have never been reported under sCO2 conditions. An urgent question to be answered for supercritical carbon dioxide oxy-combustion is how well existing chemical kinetic models perform as no experimental data exists at relevant conditions. In this talk, autoignition delays at sCO2 condition are reported for CH4/O2/ and H2/CO/O2 mixtures in CO2 and Ar diluents above their critical pressures (approximately 100 bar). Experiments reveal that a widely used kinetic model, GRI 3.0, underpredicts the ignition delay by a factor of 3 for CH4. However, kinetic models Aramco 2.0, USC Mech II and HP-Mech are capable of predicting autoignition delays though not validated at these conditions. For H2/CO mixture, all the tested kinetic models could reasonably predict the autoigition delays at supercritical conditions. Detailed kinetic analysis is conducted. Underlying kinetic processes controlling ignition and the effect of CO2 as diluent is revealed for different fuels. -
[ December 13, 2019]
Novel approaches for molecular and 3-dimensional imaging using unconventional tools in biophotonics
| Here I will discuss my lab’s recent efforts to enable low-cost and highly sensitive molecular imaging using multispectral UV microscopy. Its surprising application to hematology and pathology will be described. In addition, I will present quantitative oblique back-illumination microscopy (qOBM), which enables epi-illumination tomographic quantitative phase imaging in thick scattering samples. qOBM’s unique ability to image thick samples quantitatively, with high image quality, high speed, low-cost and ease-of-use marks a substantial improvement from other 3D imaging technologies. I will describe qOBM in detail, show results from thick samples including blood in storage bags, intact whole mouse brains and organoids, and discuss future directions. -
[ December 06, 2019]
| Lightweight cellular materials are widely used in load-bearing and energy absorption applications, because of favorable mechanical properties such as high compressibility and low relative density. Unlike conventional foams that comprise stochastic cells, cellular structures fabricated by additive manufacturing can be designed by assembling geometrically identical unit cells, specifically tailored to achieve better energy absorption characteristics and efficiencies. Lattice structures comprising cells that are either axial deformation dominated or bending dominated, have been extensively studied, and both exhibit their unique limitations. The present study sets out to combine the geometrical features of these two types of cells via novel designs of hybrid cellular structures. Test samples are fabricated by employing the Fused Deposition Modelling (FDM) technique, and subjected to quasi-static and dynamic compression. The mechanical behavior and energy absorption characteristics of these new designs are compared with those of traditional Octet and Rhombic Dodecahedron lattice structures. To facilitate meso-scale analysis of the deformation mechanisms observed in quasi-static tests, finite element models are also developed. -
[ November 25, 2019]
Efficient Mechanisms for Socio-technical Systems
| Modern society is based on large-scale engineered systems, often at the service of human end-users, e.g., transportation and power networks. While the control of such systems is typically grounded on purely engineering principles, their performance greatly depends on how human users interact with them. A common is- sue arising in these settings is the performance degradation often incurred when the users’ interests are not aligned to the “greater good” (e.g., traffic routing). In this context, a natural question arises: how can we design behavior-influencing mechanisms to incentivize efficient use of the existing infrastructure? In this presentation, I answer this question in relation to the well-studied class of congestion games, of- ten used to model traffic assignment problems. More precisely, I show how to design mechanisms that utilize only local information, and robustly maximize the system efficiency. Surprisingly, optimal mechanisms designed using only local information perform closely to those designed using full information (1% difference for affine latency functions). Additionally, I show how the proposed approach recovers and generalizes a number of well-known results in the literature. Finally, I discuss how the marginal cost mechanism, known to be optimal in the continuous-flow approximation, results in a lower efficiency than that encountered if no mechanism was used. -
[ November 22, 2019]
AI Planning, Learning and Optimization Supported by Model Checking
| Model checking and decision problems, including discrete variables, are often modeled by transition systems and predicate logic. Combining these two frameworks results in a unified model based on modular Petri nets with shared variables. An incremental abstraction method is also introduced for complex temporal logic planning. Uncontrollable events, as well as mu-calculus, are then shown to be interesting alternatives to more common game formulations. Optimization of systems with discrete variables often integrates methods from AI and operation research. An interesting alternative is the model checker Z3, which now also includes optimization. For hybrid systems, a specific method is also presented for energy optimization of robot systems, resulting in up to 30% energy and 50% peak power reduction. Finally, a recent integration of temporal logic and reinforcement learning is discussed. To summarize, methods from model checking are shown to be very useful for AI planning, learning and optimization. -
[ November 18, 2019]
Data Analytics, Forecasting, and Optimization of Smart Power and Energy Systems
| To tackle the challenges of global warming, countries all around the world set aggressive goals to reduce carbon emission in different industries. As a major carbon emitter, the power and energy industry plays a vital role in decarbonization, where increasing renewable energy integration and improve energy efficiency are two effective approaches. However, high penetration of renewable energy integration challenges the reliability, economy, and flexibility of the power and energy systems. Fortunately, these challenges have come hand-in-hand with the advancements of Internet of Things (IoT), communication technology, and data science, which helps build the smart power and energy systems.
This talk will discuss three approaches to explore the flexibility and boost the efficiency of the power and energy systems. In the first part, this talk will introduce the concept of electricity consumer behavior model and then discuss how to make the best use of the fine-grained smart meter data available to process and translate them into actual information and incorporate into consumer behavior modeling and distribution system operations. In the second part, a systematic research on probabilistic load forecasting will be introduced by investigating how to model the uncertainties of the electrical load. In the third part, the talk will discuss how to model and optimize the gas, heat, and power integrated energy systems as whole so that the flexibility beyond power systems can be exploited. Future works for smart power and energy systems will be prospected.