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Wang, Hong

Publications and source records attributed to Wang, Hong.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

π-Extended Porphyrin–Phthalocyanine Heterojunction Devices Exhibiting High Ammonia Sensitivity with a Remarkable Light Effect

π-Extended porphyrins represent an attractive class of organic compounds because of their unique photophysical, optoelectronic, and physicochemical properties. Herein, cross-conjugated (Ace-PQ-Ni) and linear-conjugated (AM6) porphyrins are used to build double-layer heterojunction devices by combining them with a lutetium bisphthalocyanine complex (LuPc 2 ). The heterojunction effect at the porphyrin–phthalocyanine interface plays a key role in the charge transport properties. Both devices exhibit exceptionally high ammonia sensitivity at room temperature and under ambient relative humidity, with limit of detection values of 156 and 115 ppb for Ace-PQ-Ni/LuPc 2 and AM6/LuPc 2 sensors, respectively. Interestingly, the Ace-PQ-Ni/LuPc 2 and AM6/LuPc 2 sensors display opposite effects upon light illumination. While the former sensors show largely decreased ammonia sensitivity under light illumination, the current variation of the latter under ammonia is remarkably enhanced with a multiplication factor of 13 and a limit of detection (LOD) of 83 ppb. Here, the striking difference in their sensing properties upon light illumination is attributed to their different π-conjugation pathways (cross-conjugation versus linear conjugation).

conductometric transducer↗
DOE OSTI2024

Stochastic Distribution Control Theory-Its Potential Application in Risk Management in Financial Systems

Stochastic Distribution Control (SDC) theory [1], originated by the author in 1996, aims at developing modeling and control strategies for dynamic and non-Gaussian stochastic systems by controlling the shape of the probability density functions of some concerned variables and parameters in stochastic systems. It generalizes the capability of standard stochastic differential equations and can therefore be applied to generic non-Gaussian systems. Since it was established in 1996, it has found a wide spectrum of applications in non-Gaussian stochastic system control, data mining, filtering and optimization for uncertain systems. In this short opinion article, discussions will be made on potential applications of SDC theory to financial systems in terms of risk analysis and management.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High‐Throughput Study of Amorphous Stability and Optical Properties of Superlattice‐Like Ge–Sb–Te Thin Films

A high‐throughput ion beam sputtering system is used to synthesize compositional gradient superlattice‐like (SLL) thin film libraries of Ge–Sb–Te alloys over the entire phase diagram. Here, the optical properties and structural evolution of the Ge–Sb–Te combinatorial SLL thin film are investigated. A systematic screening over the annealing temperature, annealing time, and modulation period has elucidated the critical factors that affect the stability of the metastable phase and optical properties. It is found that amorphous stability and optical constant are highly dependent on the modulation period and chemical composition of the thin film. This data‐driven approach offers new perspectives for accelerating the development of new materials with excellent optical and amorphous stability and for exploring their mechanisms, by greatly expanding the dataset of Ge–Sb–Te alloys with SLL structures through high‐throughput experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Mobile Edge Computing Framework for Traffic Optimization At Urban Intersections Through Cyber-Physical Integration

The stop-and-go traffic pattern on urban roads often results in excessive energy consumption because of unnecessary vehicle braking, idling, and accelerations. With the widespread and increased use of automobiles, this traffic pattern creates many negative impacts (e.g., delayed travel time, air pollution, and additional carbon emission) on the sustainability of our cities. Taking advantage of the recent emerging Internet of Things (IoT) and edge computing paradigms, we propose a mobile edge computing framework that integrates the capability of real-time vehicle-to-infrastructure communication and intelligent speed optimization algorithms into a mobile app to optimize individual vehicles' driving speed at signalized intersections. The optimization aims to mitigate the stop-and-go traffic pattern and its undesirable consequences in urban transportation systems. The framework consists of (1) a cyberinfrastructure-enabled dynamic messaging system for retrieving and delivering real-time traffic and signal phase and timing information from IoT-connected signal controllers and sensors, (2) a real-time speed optimization algorithm for generating intelligent speed advisory using vehicle's information (e.g., GPS and driving directions from mobile sensing) and corresponding signal and traffic information, and (3) an ad-hoc mobile computing environment that converts drivers' smartphones into edge devices to host the speed optimization algorithms for enabling intelligent advisory on the vehicle's driving speed within signalized corridors. The paper presents the design and implementation of the proposed framework. Finally, we demonstrate the feasibility, usefulness, and energy-saving benefits of our proposed framework and its prototyping mobile app on urban transportation systems through traffic simulation, real-vehicle laboratory experiments, an evaluative survey, and field communication tests. The simulation-based energy evaluation results show that the 100% usage of the mobile app can achieve 24% energy savings in the transportation system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Stability study of aqueous foams under high-temperature and high-pressure conditions relevant to Enhanced Geothermal Systems (EGS)

In Enhanced Geothermal System (EGS) hydraulic fracturing is carried out by injecting cold water into deep Hot Dry Rocks (HDR) under carefully controlled conditions to create new or reopen existing fractures. Water-based fracturing fluids demonstrate some challenges including immense quantity of water usage, water sensitivity of the formations, water blocking, and lack of proppant carrying capacity and transportation. Thus, an alternative is to use foam-based fracturing fluid which offers potential advantage over conventional water-based fracturing fluid such as minimum water usage, reduced wellbore damage, high proppant carrying capacity, and less environmental damage. However, foams are complex mixture of gaseous phase and liquid phase which are thermodynamically unstable at downhole conditions, and their stability can decrease over time due to liquid drainage, bubble coarsening, and coalescence. This paper shows laboratory experiments executed to study foam stability at high temperature (200°C) and high pressure (6.9MPa) conditions which simulates the geothermal environment. Foam stability was characterized by half-life of foam, which is defined as the time taken by the foam to decreases by 50% of its original height due to drainage. In this paper, two types of gaseous phases, nitrogen (N 2 ) and carbon dioxide (CO 2 ) were investigated. Also, based on successful practice of foam-based fracturing fluid in oil and gas industries, four surfactants, including Alpha olefin sulfonate (AOS), Sodium dodecyl sulfonate (SDS), Tergitol™ (NP – 40), and Cetyltrimethylammonium chloride (CTAC) at optimum concentration of 1wt.% were tested for best stability performance. In addition, different stabilizing agents including guar gum, bentonite clay, crosslinker, silicon dioxide nanoparticles (SiO 2 ), graphene oxide (GO) were also studied. Experimental results showed that N 2 foams were more stable than CO 2 foams. It was observed that foam half-life decreased with the increase in temperature. Among all the surfactants, AOS foams showed the most promising thermal stability at high temperatures. Moreover, with the addition of stabilizing agents, foam's half-life was enhanced. Stabilizing agents such as crosslinker and GO dispersion showed the most stable foams with half-life recorded at 20min and 17min, respectively, at 200°C and 6.9MPa. Finally, pressure also showed a positive effect on foam stability; with increased pressure, foam half-life was increased. Based on the experimental data, analytical models for the effect of temperature and pressure were developed, considering foam degradation is a first-order kinetic reaction that linearly depends on the foam drainage mechanism. The effect of temperature on foam half-life was studied as an exponential decay model. In this model, foam half-life is a function of drainage rate constant (D A ) and activation energy (E a ) of the foam system. The effect of pressure on foam half-life was found to obey a power-law model where an increase in pressure showed an increase in foam half-life. Furthermore, a linear relation was studied for the effect of pressure on foam activation energy and drainage rate. Then the combined effects of temperature and pressure were studied, which yielded an analytical model to predict the foam stabilities in terms of half-life for different foam compositions. In conclusion, this research indicates that with an appropriate selection of surfactants and stabilizing agents, it is possible to obtain stable foams, which could replace conventional water fracturing fluid under EGS conditions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Optimal Siting of EV Fleet Charging Station Considering EV Mobility and Microgrid Formation for Enhanced Grid Resilience

Coordinating infrastructure planning for transportation and the power grid is essential for enhanced reliability and resilience during operation and disaster management. This paper presents a two-stage stochastic model to optimize the location of electric vehicle fleet charging stations (FEVCSs) to enhance the resilience of a distribution network. The first stage of this model deals with the decision to place an FEVCS at the most favorable and optimized location, whereas the second stage aims to minimize the weighted sum of the value of lost load in multiple potential scenarios with different faults. Indeed, the second stage is a joint grid restoration scheme with network reconfiguration and microgrid formation using available distributed generators and fleet electric vehicles. The proposed model is tested on a modified IEEE-33 node distribution network and a four-node transportation network. Case studies demonstrate the effectiveness of the proposed model.

25 ENERGY STORAGE↗
DOE OSTI2023

Excited-State Charge Transfer in Push–Pull Platinum(II) π-Extended Porphyrins Fused with Pentacenequinone

Platinum(II) π-extended porphyrins fused with pentacenequinone and dihydropentacene have been successfully synthesized. These porphyrins were investigated using various techniques including absorption, steady-state, and time-resolved phosphorescence spectroscopy and differential pulse voltammetry. UV–vis absorption spectra of pentacenequinone-fused porphyrins (SW-Pt1 and SW-Pt2) showed unusually broad and nontypical absorption patterns. Phosphorescence spectra of SW-Pt1, SW-Pt2, and SW-Pt3 displayed similar emissions in the 704–706 nm region indicating electronic transitions of similar origin; however, the triplet lifetimes were found to be quenched in the case of both SW-Pt1 and SW-Pt2, suggesting the occurrence of excited-state events. Facile reductions were obtained for both the pentacene-quinone-fused monomer (SW-Pt2) and dimer (SW-Pt1) and were identified to be located at the pentacenequinone components. Here, the observed orbital segregations for SW-Pt2 and SW-Pt1 from DFT calculations supported the possibility of charge transfer in these push–pull systems. Interestingly, the established energy level diagram revealed that the charge transfer from the triplet excited Pt porphyrin is thermodynamically an uphill process. Systematic studies involving both femtosecond and nanosecond transient absorption techniques revealed that the singlet excited Pt porphyrins undergo an intermediate charge transfer state prior to populating the triplet state, providing a plausible explanation for phosphorescence quenching. The lifetime of the intermediate charge transfer states was found to be 25.9 and 5.68 ps, respectively, for SW-Pt1 and SW-Pt2.

Charge transfer↗
DOE OSTI2023

Machine Learning-Based Regression Models for Ironmaking Blast Furnace Automation

Computational fluid dynamics (CFD)-based simulation has been the traditional way to model complex industrial systems and processes. One very large and complex industrial system that has benefited from CFD-based simulations is the steel blast furnace system. The problem with the CFD-based simulation approach is that it tends to be very slow for generating data. The CFD-only approach may not be fast enough for use in real-time decisionmaking. To address this issue, in this work, the authors propose the use of machine learning techniques to train and test models based on data generated via CFD simulation. Regression models based on neural networks are compared with tree-boosting models. In particular, several areas (tuyere, raceway, and shaft) of the blast furnace are modeled using these approaches. The results of the model training and testing are presented and discussed. The obtained R 2 metrics are, in general, very high. The results appear promising and may help to improve the efficiency of operator and process engineer decisionmaking when running a blast furnace.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System

In the development of a digital twin (DT) for hydropower turbines, dynamic modeling of the system (e.g., penstock, turbine, speed control) is crucial, along with all the necessary data interface, virtualization, and dashboard designs. Since the DT must mimic the actual dynamics of the hydropower turbine accurately, adaptive learning is required to train these dynamic models online so that the models in the DT can effectively follow the representation of the actual hydropower turbine dynamics accurately and reliably. This study presents an adaptive learning method for obtaining the hydropower turbine models for DT development of hydropower systems using the recursive least squares algorithm. To simplify the formulation, the hydropower turbine under consideration was assumed to operate near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters. In this context, the well-known six-coefficient model for the Francis turbine was formulated as the starting point to obtain input and output models for the turbine. Then, an adaptive learning mechanism was developed to learn model parameters using real-time data from a hydropower turbine testing system. This led to semi-physical modeling, in which first principles and data-driven modeling are integrated to produce dynamic models for DT development. Applications to a pilot system at the Norwegian University of Science and Technology (NTNU) were made, and the models learned adaptively using the data collected from the university’s pilot system. Desired modeling and validation results were obtained.

13 HYDRO ENERGY↗
DOE OSTI2023

Naphthodithiophene‐Fused Porphyrins: Synthesis, Characterization, and Impact of Extended Conjugation on Aromaticity

The fusion of tetrapyrroles with aromatic heterocycles constitutes a useful tool for manipulating their opto-electronic properties. In this work, the synthesis of naphthodithiophene-fused porphyrins was achieved through a Heck reaction-based cascade of steps followed by the Scholl reaction. The naphthodithiophene-fused porphyrins display a unique set of optical and electronic properties. Fusion of the naphtho[2,1-b:3,4-b’]dithiophene to porphyrin (F2VTP) leads to a ~20% increase in the fluorescence lifetime, which is accompanied, unexpectedly, by a more than two-fold drop in the emission quantum yield (ϕ=0.018). In contrast, fusion of the isomeric naphtho[1,2-b:4,3-b’]dithiophene to porphyrin (F3VPT) results in a ~1.5-fold increase in the fluorescence quantum yield (ϕ=0.13) with a concomitant ~30 % increase in the fluorescence lifetime. This behavior suggests that fusion of the porphyrin with the naphthodithiopheno-system mainly affects the radiative rate constant in the Q-state deactivation pathway, where the effects of the isomeric naphtho[2,1-b:3,4-b’]dithiophene- versus naphtho[1,2-b:4,3-b’]dithiophene-fusion are essentially the opposite. Interestingly, nucleus-independent chemical shifts analysis revealed a considerable difference between the aromaticities of these two isomeric systems. Furthermore, our results demonstrate that subtle structural differences in the fused components of the porphyrin can be reflected in rather significant differences between the photophysical properties of the resulting systems.

Chemistry↗
DOE OSTI2023

Model Predictive Control for Urban Traffic Signals with Stability Guarantees

Traditional traffic signal control focuses more on the optimization aspects whereas the stability and robustness of the closed-loop system are less studied. This paper aims to establish the stability properties of traffic signal control systems through the analysis of a practical model predictive control (MPC) scheme, which models the traffic network with the conservation of vehicles based on a store-and forward model and attempts to balance the traffic densities. More precisely, this scheme guarantees the exponential stability of the closed-loop system under state and input constraints when the inflow is feasible and traffic demand can be fully accessed. Practical exponential stability is achieved in case of small uncertain traffic demand by a modification of the previous scheme. Simulation results of a small-scale traffic network validate the theoretical analysis.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2023

DT-HYDRO

The software solves the time dependent, one dimensional (1D) coupled mass and momentum balance equations governing the elastic flow of water through the penstock, turbine and draft tube in a hydroelectric facility using a high order finite volume based method. The numerical method is based on the Kurganov-Tadmor central method paired with the Monotonic Upstream-centered Scheme for Conservation Laws (MUSCL). This solution method accurately resolves the fast transient behavior of the flow, including water hammer. Additionally, the software estimates the full 3D flow field within the turbine chamber in real time, a feat that is made possible by leveraging pre-computed CFD results by utilizing a reduced order modeling method based on an efficient randomized singular value decomposition (SVD) driven proper orthogonal decomposition (POD) with POD-mode weight regression. The reduced order model of the 3D flow is directly coupled to the 1D elastic flow model so the entire flow field through the penstock and turbine system is resolved quickly and with high fidelity.

Gurecky, William [Oak Ridge National Laboratory (O↗
DOE OSTI2023

Predicting the creep-rupture lifetime of a cast austenitic stainless steel using Larson-Miller and Wilshire parametric approaches

An experimental dataset of just over 100 creep tests of a cast austenitic stainless steel, CF8C-Plus, was analyzed by two temperature-compensated parametric models (Larson-Miller, Wilshire et al.) to predict long-term lifetimes as functions of temperature and stress. The dataset and associated regression analyses showed greater scatter than typically found in recent similar studies of wrought Ni-based alloys by the same two models and was attributed to the microstructural inhomogeneity of the cast stainless steel. Qualitatively, the Larson-Miller formalism showed greater lifetime prediction accuracy than the Wilshire approach, with the latter model's predictive ability being particularly degraded by the presence of two very significant outlier results. This observation suggests that the Larson-Miller approach is more robust when treating rupture-time datasets that show particularly wide experimental scatter. Despite the differences in the overall predictive ability, both models yielded similar predictions of the applied stress at which CF8C-Plus would have a creep-limited lifetime of 100,000 h when loaded below the yield point.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Elastic Flow Modeling for Hydropower Digital Twins

This report details the elastic, unsteady, one-dimensional flow equations that are used to model flow through a penstock in a hydropower facility. The elastic flow model is accurate even in cases of fast transients, long penstock length, and high gravitation head. The compressibility of the water and elasticity of the pipe walls are explicitly accounted for in the developed models so that the water hammer phenomena can be accurately captured. The elastic flow model is coupled to a mechanistic turbine model to resolve the dynamic feedbacks between the elastic water column and the turbine rotation rate. A finite volume method is employed to solve the governing equations, and the method is shown to have low numerical diffusion in sharp gradient phenomena, such as those encountered when simulating water hammer. The method is applied to single dimensional linear advection benchmark problem and numerical results are compared with analytic results. This is followed on by an application to a full hydropower system with a coupled turbine. The elastic flow model results are compared against an inelastic model.

13 HYDRO ENERGY↗
DOE OSTI2023

Operational control with set-points tuning - application to mobile robots

This paper proposes a novel method for the optimal tuning of set points for multiple-layered control system structure widely seen in robotics and other complex industrial processes composed of a number of subsystems. The terminal sliding mode control (SMC) is used as the low-level control strategy to ensure the stability of subsystems. When uncertainties exist, it can be shown that the deteriorated system performance will be improved by the outer loop with set points tuning. For this purpose, the learning of the new set point is designed to compensate for the effects caused by uncertainties during the system operation. At the same time, the system is proven to stay with the original set point when the compensation is introduced. A practical application to a holonomic mobile robot system is given to illustrate the presented method. Desired results have been obtained.

42 ENGINEERING↗
DOE OSTI2023

Reducing Urban Traffic Congestion Using Deep Learning and Model Predictive Control

This article proposes a deep learning (DL)-based control algorithm—DL velocity-based model predictive control (VMPC)—for reducing traffic congestion with slowly time-varying traffic signal controls. This control algorithm consists of system identification using DL and traffic signal control using VMPC. For the training process of DL, we established a modeling error entropy loss as the criteria inspired by the theory of stochastic distribution control (SDC) originated by the fourth author. Herein, simulation results show that the proposed algorithm can reduce traffic congestion with a slowly varying traffic signal control input. Results of an ablation study demonstrate that this algorithm compares favorably to other model-based controllers in terms of prediction error, signal varying speed, and control effectiveness.

97 MATHEMATICS AND COMPUTING↗