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Liping Wang

Publications and source records attributed to Liping Wang.

At least 73 records · Page 4Linked to original sources
arXiv2020

Enhanced Infrared Emission by Thermally Switching the Excitation of Magnetic Polariton with Scalable Microstructured VO2 Metasurfaces

Dynamic radiative cooling attracts fast-increasing interest due to its adaptability to changing environment and promises for more energy-savings than the static counterpart. Here we demonstrate enhanced infrared emission by thermally switching the excitation of magnetic polariton with microstructured vanadium dioxide (VO2) metasurfaces fabricated via scalable and etch-free processes. Temperature-dependent infrared spectroscopy clearly shows that the spectral emittance of fabricated tunable metasurfaces at wavelengths from 2 to 6 um is significantly enhanced when heated beyond its phase transition temperature, where the magnetic polariton is excited with metallic VO2. The tunable emittance spectra are also demonstrated to be insensitive to incidence and polarization angles such that the VO2 metasurface can be treated as a diffuse infrared emitter. Numerical optical simulation and analytical inductance-capacitance model elucidate the suppression or excitation of magnetic polariton with insulating or metallic VO2 upon phase transition. The effect of enhanced thermal emission with the tunable VO2 metasurface is experimentally demonstrated with a thermal vacuum test. For the same heating power of 0.2 W, the steady-state temperature of the tunable VO2 metasurface emitter after phase transition is found to be 20degC lower than that of a reference V2O5 emitter whose static spectral emittance is almost the same as that of the VO2 metasurface before phase transition. The radiative thermal conductance for the tunable metasurface emitter is found to be 3.96 W/m2K with metallic VO2 at higher temperatures and 0.68 W/m2K with insulating VO2 at lower temperatures, clearly demonstrating almost six-fold enhancement in radiative heat dissipation.

physics.app-ph↗
arXiv2020

Spectrally-Selective Vanadium Dioxide Based Tunable Metafilm Emitter for Dynamic Radiative Cooling

Dynamic radiative cooling with variable emissive power is experimentally demonstrated in this study by a wavelength-selective tunable metafilm emitter, which consists of an opaque aluminum film, a sputtered silicon spacer, and a thermochromic vanadium dioxide (VO2) layer fabricated by a furnace oxidation method. The temperature-dependent spectral emittance, experimentally obtained from spectral reflectance measurements, clearly shows a pronounced emission peak around 10 um wavelength when the VO2 experiences an insulator-to-metal phase transition near 65C. The tunable metafilm emitter achieves a significant total emittance increase from 0.14 at room temperature to 0.6 at 100C. Theoretical modeling based on thin-film optics indicates that the emission enhancement at high temperatures is realized by Fabry-Perot cavity resonance with the metallic VO2 film. Moreover, a calorimetry-based thermal vacuum experiment was conducted and the enhanced thermal emission of the fabricated tunable metafilm sample was experimentally demonstrated at temperatures higher than the phase transition temperature, compared to black, aluminum and doped silicon samples, whose emittance changes little near room temperatures. The developed tunable metafilm emitter with variable spectrally-selective emittance in the mid-infrared holds great promise for both terrestrial and extraterrestrial dynamic radiative cooling applications.

physics.optics↗
arXiv2020

Super-Planckian Radiative Heat Transfer between Metallic Surfaces Due to Near-Field and Thin-Film Effects

In this Letter we experimentally demonstrate that the radiative heat transfer between metallic planar surfaces exceeds the blackbody limit by employing the near-field and thin-film effects. Nanosized polystyrene particles were used to create a nanometer gap between aluminum thin-films of different thicknesses coated on 5x5 mm2 diced silicon chips while the gap spacing is fitted from the near-field measurement with bare Si chips. The experimental results are validated by theoretical calculation based on fluctuational electrodynamics. The near-field radiative heat flux between 13-nm Al thin-film samples at 215 nm gap distance is measured to be 6.4 times over the blackbody limit and 420 times compared to the far-field radiative heat transfer between metallic surfaces with a temperature difference of 65 K. In addition, the theoretical prediction suggests a near-field enhancement of 122 times relative to the blackbody limit and 8000 times over far-field one at 50-nm vacuum gap between 20-nm Al thin-film samples, under the same temperature difference of 65 K. This work will facilitate the understanding and application of near-field radiation to thermal power conversion, noncontact cooling, heat flow management, and optical storage where metallic materials are involved.

physics.app-ph↗
arXiv2020

Cognitive computation of brain disorders based primarily on ocular responses

The present review presents multiple techniques in which ocular assessments may serve as a noninvasive approach for the early diagnoses of various cognitive and psychiatric disorders, such as Alzheimer's disease (AD), autism spectrum disorder (ASD), schizophrenia (SZ), and major depressive disorder (MDD). Real-time ocular responses are tightly associated with emotional and cognitive processing within the central nervous system. Patterns seen in saccades, pupillary responses, and blinking, as well as retinal microvasculature and morphology visualized via office-based ophthalmic imaging, are potential biomarkers for the screening and evaluation of cognitive and psychiatric disorders. Additionally, rapid advances in artificial intelligence (AI) present a growing opportunity to use machine-learning-based AI, especially deep-learning neural networks, to shed new light on the field of cognitive neuroscience, which may lead to novel evaluations and interventions via ocular approaches for cognitive and psychiatric disorders.

q-bio.NC↗
arXiv2020

Advances in Bayesian Probabilistic Modeling for Industrial Applications

Industrial applications frequently pose a notorious challenge for state-of-the-art methods in the contexts of optimization, designing experiments and modeling unknown physical response. This problem is aggravated by limited availability of clean data, uncertainty in available physics-based models and additional logistic and computational expense associated with experiments. In such a scenario, Bayesian methods have played an impactful role in alleviating the aforementioned obstacles by quantifying uncertainty of different types under limited resources. These methods, usually deployed as a framework, allows decision makers to make informed choices under uncertainty while being able to incorporate information on the the fly, usually in the form of data, from multiple sources while being consistent with the physical intuition about the problem. This is a major advantage that Bayesian methods bring to fruition especially in the industrial context. This paper is a compendium of the Bayesian modeling methodology that is being consistently developed at GE Research. The methodology, called GE's Bayesian Hybrid Modeling (GEBHM), is a probabilistic modeling method, based on the Kennedy and O'Hagan framework, that has been continuously scaled-up and industrialized over several years. In this work, we explain the various advancements in GEBHM's methods and demonstrate their impact on several challenging industrial problems.

stat.ML↗
arXiv2020

Bayesian task embedding for few-shot Bayesian optimization

We describe a method for Bayesian optimization by which one may incorporate data from multiple systems whose quantitative interrelationships are unknown a priori. All general (nonreal-valued) features of the systems are associated with continuous latent variables that enter as inputs into a single metamodel that simultaneously learns the response surfaces of all of the systems. Bayesian inference is used to determine appropriate beliefs regarding the latent variables. We explain how the resulting probabilistic metamodel may be used for Bayesian optimization tasks and demonstrate its implementation on a variety of synthetic and real-world examples, comparing its performance under zero-, one-, and few-shot settings against traditional Bayesian optimization, which usually requires substantially more data from the system of interest.

cs.LG↗
arXiv2019

Data-driven discovery of free-form governing differential equations

We present a method of discovering governing differential equations from data without the need to specify a priori the terms to appear in the equation. The input to our method is a dataset (or ensemble of datasets) corresponding to a particular solution (or ensemble of particular solutions) of a differential equation. The output is a human-readable differential equation with parameters calibrated to the individual particular solutions provided. The key to our method is to learn differentiable models of the data that subsequently serve as inputs to a genetic programming algorithm in which graphs specify computation over arbitrary compositions of functions, parameters, and (potentially differential) operators on functions. Differential operators are composed and evaluated using recursive application of automatic differentiation, allowing our algorithm to explore arbitrary compositions of operators without the need for human intervention. We also demonstrate an active learning process to identify and remedy deficiencies in the proposed governing equations.

cs.CE↗
arXiv2019

Solar Thermal Energy Conversion Enhanced by Selective Metafilm Absorber under Multiple Solar Concentrations at High Temperatures

Concentrating solar power, particularly parabolic trough system with solar concentrations less than 50, requires spectrally selective solar absorbers that are thermally stable at high temperatures of 400degC above to achieve high efficiency. In this work, the solar-thermal performance of a selective multilayer metafilm absorber is characterized along with a black absorber for comparison by a lab-scale experimental setup that measures the steady-state absorber temperature under multiple solar concentrations. Heat transfer analysis is employed to elucidate different heat transfer modes and validate the solar-thermal experiment. Due to the superior spectral selectivity and excellent thermal stability, the metafilm absorber deposited on the cost-effective stainless steel foil could achieve the solar-thermal efficiency 57% at a steady-state temperature of 371degC under 10 suns during the lab-scale experiment with losses, while a highest efficiency of 83% was projected under the same conditions for practical solar thermal applications. The results here will facilitate the research and development of novel solar materials for high-efficiency solar thermal energy conversion.

physics.app-ph↗
arXiv2019

Super-Planckian Radiative Heat Transfer between Macroscale Plates with Vacuum Gaps Down to 190 nm Directly Created by SU-8 Posts and Characterized by Capacitance Method

In this work we experimentally demonstrated the near-field thermal radiation enhancement over the blackbody limit by 11 times between highly doped silicon chips with 1x1 cm2 size at a vacuum gap distance of 190 nm under a temperature difference of 74.7 K above room temperature. SU-8 polymer posts, which significantly reduced the conduction less than 6% of the total heat transfer due to its low thermal conductivity, were carefully fabricated with different heights to directly create vacuum gaps from 507 nm down to 190 nm precisely determined in-situ by capacitance measurement. Experimental results were validated by theoretical calculations based on fluctuational electrodynamics, which revealed the enhancement mechanism mainly as coupled surface plasmon polariton. The experimental method developed here will facilitate the potential applications of near-field radiative devices made of electrically conductive materials like metals, graphene, and transparent conductive oxide besides heavily doped semiconductors for thermal energy conversion, radiative thermal rectification, and radiative heat modulation.

physics.app-ph↗
arXiv2019

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty

Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification. Adaptive sampling for multi-fidelity Gaussian process is a changing task due to the fact that not only we seek to estimate the next sampling location of the design variable, but also the level of the simulator fidelity. This issue is often addressed by including the cost of the simulator as an another factor in the searching criterion in conjunction with the uncertainty reduction metric. In this work, we extent the traditional design of experiment framework for the multi-fidelity Gaussian process by partitioning the prediction uncertainty based on the fidelity level and the associated cost of execution. In addition, we utilize the concept of Believer which quantifies the effect of adding an exploratory design point on the Gaussian process uncertainty prediction. We demonstrated our framework using academic examples as well as a industrial application of steady-state thermodynamic operation point of a fluidized bed process

stat.ML↗
arXiv2019

Towards Scalable Gaussian Process Modeling

Numerous engineering problems of interest to the industry are often characterized by expensive black-box objective experiments or computer simulations. Obtaining insight into the problem or performing subsequent optimizations requires hundreds of thousands of evaluations of the objective function which is most often a practically unachievable task. Gaussian Process (GP) surrogate modeling replaces the expensive function with a cheap-to-evaluate data-driven probabilistic model. While the GP does not assume a functional form of the problem, it is defined by a set of parameters, called hyperparameters. The hyperparameters define the characteristics of the objective function, such as smoothness, magnitude, periodicity, etc. Accurately estimating these hyperparameters is a key ingredient in developing a reliable and generalizable surrogate model. Markov chain Monte Carlo (MCMC) is a ubiquitously used Bayesian method to estimate these hyperparameters. At the GE Global Research Center, a customized industry-strength Bayesian hybrid modeling framework utilizing the GP, called GEBHM, has been employed and validated over many years. GEBHM is very effective on problems of small and medium size, typically less than 1000 training points. However, the GP does not scale well in time with a growing dataset and problem dimensionality which can be a major impediment in such problems. In this work, we extend and implement in GEBHM an Adaptive Sequential Monte Carlo (ASMC) methodology for training the GP enabling the modeling of large-scale industry problems. This implementation saves computational time (especially for large-scale problems) while not sacrificing predictability over the current MCMC implementation. We demonstrate the effectiveness and accuracy of GEBHM with ASMC on four mathematical problems and on two challenging industry applications of varying complexity.

stat.ML↗
arXiv2019

Bulk Material Based Selective Infrared Emitter for Sub-Ambient Daytime Radiative Cooling

Through passively emitting excess heat to the outer space, radiative cooling has been demonstrated as an efficient way for energy saving applications. Selective surface with unity emittance only within the atmospheric window as well as zero absorption within the solar spectrum is sought to achieve the best sub-ambient radiative cooling performance during the daytime. In this work, we proposed a bulk radiative cooler consisting of a 1-mm-thick lithium fluoride crystal coated with silver backing, which exhibits a solar absorptance of 4.7% and nearly ideal infrared selectivity with high emission exactly within the atmospheric transmission band (i.e., 8-13 um). Excellent daytime cooling performance was demonstrated in an outdoor test with stagnation temperature below the ambient temperature by 5 K under solar irradiance above 900 W/m2 and a net cooling power of about 60 W/m2 when the cooler is in thermal equilibrium with the ambient. As a bulk material with the highest ultraviolet-visible-near infrared transmittance, lithium fluoride crystal has been widely employed as the optical windows and mirrors in various applications. The proposed simple selective infrared emitter based on lithium fluoride would open up an innovative way to radiatively cool optical systems.

physics.app-ph↗
arXiv2017

On Delaunay solutions of a biharmonic elliptic equation with critical exponent

We are interested in the qualitative properties of positive entire solutions $u \in C^4 (\mathbb{R}^n \backslash \{0\})$ of the equation \begin{equation} \label{0.0} Δ^2 u=u^{\frac{n+4}{n-4}} \;\;\mbox{in $\mathbb{R}^n \backslash \{0\}$ and 0 is a non-removable singularity of $u(x)$}. \end{equation} It is known from [Theorem 4.2] that any positive entire solution $u$ of \eqref{0.0} is radially symmetric with respect to $x=0$, i.e. $u(x)=u(|x|)$, and equation \eqref{0.0} also admits a special positive entire solution $u_s (x)=\Big(\frac{n^2 (n-4)^2}{16} \Big)^{\frac{n-4}{8}} |x|^{-\frac{n-4}{2}}$. We first show that $u-u_s$ changes signs infinitely many times in $(0, \infty)$ for any positive singular entire solution $u \not \equiv u_s$ in $\mathbb{R}^N \backslash \{0\}$ of \eqref{0.0}. Moreover, equation \eqref{0.0} admits a positive entire singular solution $u(x) \; (=u(|x|)$ such that the scalar curvature of the conformal metric with conformal factor $u^{\frac{4}{n-4}}$ is positive and $v(t):=e^{\frac{n-4}{2} t} u(e^t)$ is $2T$-periodic with suitably large $T$. It is still open that $v(t):=e^{\frac{n-4}{2} t} u(e^t)$ is periodic for any positive entire solution $u(x)$ of \eqref{0.0}.

math.AP↗
arXiv2015

Spectrally enhancing near-field radiative transfer between gold gratings by exciting magnetic polariton in nanometric vacuum gaps

In the present work, we theoretically demonstrate that near field radiative transport between one dimensional periodic grating microstructures separated by nanometer vacuum gaps can be spectrally enhanced by exciting magnetic polariton. Fluctuational electrodynamics that incorporates scattering matrix theory with rigorous coupled wave analysis is employed to exactly calculate the near field radiative flux between two gold gratings. Besides the well known coupled surface plasmon polaritons, the radiative flux can be also spectrally enhanced due to magnetic polariton, which is excited in the gap between gold ridges. The mechanisms of magnetic polariton in the near field radiative transport are elucidated in detail, while the unusual enhancement cannot be predicted by either the Derjaguin or effective medium approximations. The effects of vacuum gap distance and grating geometry parameters between the two gratings are investigated. The findings will open up a new way to control near field radiative transfer by magnetic polariton with micro or nanostructured metamaterials.

cond-mat.mes-hall↗
arXiv2015

Performance Analysis of a Near-Field Thermophotovoltaic Device with a Metallodielectric Selective Emitter and Electrical Contacts for the Photovoltaic Cell

A near-field thermophotovoltaic (TPV) system with a multilayer emitter of alternate tungsten and alumina layer is proposed in this paper. The fluctuational electrodynamics along with the dyadic Green function for a multilayered structure is applied to calculate the spectral heat flux, and the charge transport equations are solved to get the photocurrent generation and electrical power output. The spectral heat flux is much enhanced when plain tungsten emitter is replaced with multilayer emitter. The mechanism of surface plasmon polariton coupling in the tungsten thin film, which is responsible for the heat flux enhancement, is analyzed. In addition, the invalidity of effective medium theory to predict the optical properties of multilayer structure in near-field radiation is discussed. The tungsten and alumina layer thicknesses are optimized to match the spectral heat flux with the bandgap of TPV cell. Practically, with a gold reflector placed on the back of TPV cell, which also acts as the back electrode, and a 5-nm-thick indium tin oxide (ITO) layer as the front contact, when the emitter and receiver temperature are respectively set as 2000 K and 300 K, the conversion efficiency and electrical power output can be achieved to 23.7% and 0.31 MW/m2 at a vacuum gap distance of 100 nm.

cond-mat.mes-hall↗