Experimental Study of Lean Blowout with Hydrogen Addition in a Swirl-stabilized Premixed Combustor

Lean premixed combustion is widely used to achieve a better compromise between nitric oxides emissions and combustion efficiency. However, combustor operation near the lean blowout limit can render the flame unstable and lead to oscillations, flashback, or extinction, thereby limiting the potential range of lean combustion. Recent interest in integrated gasification combined cycle plants and syngas combustion requires an improved understanding of the role of hydrogen on the combustion process. Therefore, in present study, combustion of pure methane and blended methane-hydrogen has been conducted in a swirl stabilized premixed combustor. The measurement techniques implemented mainly include particle image velocimetry, CH*/OH* chemiluminescence imaging, planar laser-induced fluorescence imaging of OH radical. By investigating the flow field, heat release, flow-flame interaction, and flame structure properties, the fundamental controlling processes that limit lean and hydrogen-enriched premixed combustion with and without confinement have been analyzed and discussed. As equivalence ratio decreases, for unconfined flames, the reduced flame speed leads flame shrinking toward internal recirculation zone (IRZ) and getting more interacted with inner shear layer, where turbulence level and vorticity are higher. The flame fronts therefore experience higher hydrodynamic stretch rate, resulting in local extinction, and breaks along the flame fronts. Those breaks, in turn, entrain the unburnt fuel air mixture into IRZ passing through the shear layer with the local vortex effect, further leading to reaction within IRZ. In methane-only flames, the width of IRZ decreases, causing flames to straddle the boundary of the IRZ and to be unstable. High speed imaging shows that periodic flame rotating with local extinction and re-light events are evident, resulting in high RMS of heat release rate, and therefore a shorter extinction time scale. With hydrogen addition, flames remain in relatively axisymmetric burning structure and stable with the aid of low minimum ignition energy and high molecular diffusivity associated with hydrogen, leading to lower heat release fluctuation and a longer extinction time scale. For confined flames, however, the hydrogen effect on the extinction transient is completely opposite due to spiraling columnar burning structure, in comparison of a relatively stable conical shape in methane flames

Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching

Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems

Distributed resource allocation for data center networks: a hierarchical game approach

The increasing demand of data computing and storage for cloud-based services motivates the development and deployment of large-scale data centers. This paper studies the resource allocation problem for the data center networking system when multiple data center operators (DCOs) simultaneously serve multiple service subscribers (SSs). We formulate a hierarchical game to analyze this system where the DCOs and the SSs are regarded as the leaders and followers, respectively. In the proposed game, each SS selects its serving DCO with preferred price and purchases the optimal amount of resources for the SS's computing requirements. Based on the responses of the SSs' and the other DCOs', the DCOs decide their resource prices so as to receive the highest profit. When the coordination among DCOs is weak, we consider all DCOs are noncooperative with each other, and propose a sub-gradient algorithm for the DCOs to approach a sub-optimal solution of the game. When all DCOs are sufficiently coordinated, we formulate a coalition game among all DCOs and apply Kalai-Smorodinsky bargaining as a resource division approach to achieve high utilities. Both solutions constitute the Stackelberg Equilibrium. The simulation results verify the performance improvement provided by our proposed approaches

The correlated expression of COX-2 and keratin 15 in radicular cysts

The expression of cyclooxygenase-2 (COX-2) and Keratin-15 (K15) in radicular cysts (RCs) is poorly understood. Identifying the expression of these two markers may modify the current treatment of RC. The objective of this study was to evaluate the express

Observationally constrained modeling of atmospheric oxidation capacity and photochemical reactivity in Shanghai, China

16 pags., 8 figs., 2 tabs.An observation-based model coupled to the Master Chemical Mechanism (V3.3.1) and constrained by a full suite of observations was developed to study atmospheric oxidation capacity (AOC), OH reactivity, OH chain length and HOx (=OHCHO) budget for three different ozone (O3) concentration levels in Shanghai, China. Five months of observations from 1 May to 30 September 2018 showed that the air quality level is lightly polluted or worse (Ambient Air Quality Index, AQI, of > 100) for 12 d, of which ozone is the primary pollutant for 10 d, indicating ozone pollution was the main air quality challenge in Shanghai during summer of 2018. The levels of ozone and its precursors, as well as meteorological parameters, revealed the significant differences among different ozone levels, indicating that the high level of precursors is the precondition of ozone pollution, and strong radiation is an essential driving force. By increasing the input JNO value by 40 %, the simulated O3 level increased by 30 %-40 % correspondingly under the same level of precursors. The simulation results show that AOC, dominated by reactions involving OH radicals during the daytime, has a positive correlation with ozone levels. The reactions with non-methane volatile organic compounds (NMVOCs; 30 %-36 %), carbon monoxide (CO; 26 %-31 %) and nitrogen dioxide (NO; 21 %-29 %) dominated the OH reactivity under different ozone levels in Shanghai. Among the NMVOCs, alkenes and oxygenated VOCs (OVOCs) played a key role in OH reactivity, defined as the inverse of the OH lifetime. A longer OH chain length was found in clean conditions primarily due to low NO in the atmosphere. The high level of radical precursors (e.g., O3, HONO and OVOCs) promotes the production and cycling of HOx, and the daytime HOx primary source shifted from HONO photolysis in the morning to O3 photolysis in the afternoon. For the sinks of radicals, the reaction with NO dominated radical termination during the morning rush hour, while the reactions of radical-radical also contributed to the sinks of HOx in the afternoon. Furthermore, the top four species contributing to ozone formation potential (OFP) were HCHO, toluene, ethylene and m/p-xylene. The concentration ratio (∼ 23 %) of these four species to total NMVOCs is not proportional to their contribution (∼ 55 %) to OFP, implying that controlling key VOC species emission is more effective than limiting the total concentration of VOC in preventing and controlling ozone pollution.This research has been supported by the National Key Research and Development Program of China (grant nos. 2017YFC0210002, 2016YFC0200401 and 2018YFC0213801), the National Natural Science Foundation of China (grant nos. 41775113, 21777026 and 21607104), the Shanghai Pujiang Talent Program (grant no. 17PJC015), and the Shanghai Rising-Star Program (grant no. 18QA1403600). This work was also funded by The Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning and Shanghai Thousand Talents Program

Customer Baseline Load Estimation for Incentive-Based Demand Response Using Long Short-Term Memory Recurrent Neural Network

The transition to an intelligent, reliable and efficient smart grid with a high penetration of renewable energy drives the need to maximise the utilization of customers demand response potential. The availability of smart meter data means this potential can be more accurately estimated and suitable demand response (DR) programs can be targeted to customers for load shifting, clipping and reduction. In this paper, we focus on estimating customer demand baseline for incentive-based DR. We propose a long short-term memory recurrent neural network framework for customer baseline estimation using previous like days data during DR events period. We test the proposed methodology on the publicly available Irish smart meter data and results shows a significant increase in baseline estimation accuracy when compared to traditional baseline estimation methods

Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning

Battery energy storage systems (BESSs) play a critical role in eliminating uncertainties associated with renewable energy generation, to maintain stability and improve flexibility of power networks. In this paper, a BESS is used to provide energy arbitrage (EA) and frequency regulation (FR) services simultaneously to maximize its total revenue within the physical constraints. The EA and FR actions are taken at different timescales. The multitimescale problem is formulated as two nested Markov decision process (MDP) submodels. The problem is a complex decision-making problem with enormous high-dimensional data and uncertainty (e.g., the price of the electricity). Therefore, a novel co-optimization scheme is proposed to handle the multitimescale problem, and also coordinate EA and FR services. A triplet deep deterministic policy gradient with exploration noise decay (TDD-ND) approach is used to obtain the optimal policy at each timescale. Simulations are conducted with real-time electricity prices and regulation signals data from the American PJM regulation market. The simulation results show that the proposed approach performs better than other studied policies in literature

Person re-identification in the real scene based on the deep learning

Person re-identification aims at automatically retrieving a person of interest across multiple non-overlapping camera views. Because of increasing demand for real-world applications in intelligent video surveillance, person re-identification has become an important computer vision task and achieved high performance in recent years. However, the traditional person re-identification research mainly focus on matching cropped pedestrian images between queries and candidates on commonly used datasets and divided into two steps: pedestrian detection and person re-identification, there is still a big gap with practical applications. Under the premise of model optimization, based on the existing object detection and person re-identification, this paper achieves a one-step search of the specific pedestrians in the whole images or video sequences in the real scene. The experimental results show that our method is effective in commonly used datasets and has achieved good results in real-world applications, such as finding criminals, cross-camera person tracking, and activity analysis.26th International Symposium on Artificial Life and Robotics, AROB 26th 2021, January 21–23, 2021, Beppu, Japan and Onlin

Long-term trends and drivers of aerosol pH in eastern China

Aerosol acidity plays a key role in regulating the chemistry and toxicity of atmospheric aerosol particles. The trend of aerosol pH and its drivers is crucial in understanding the multiphase formation pathways of aerosols. Here, we reported the first trend analysis of aerosol pH from 2011 to 2019 in eastern China, calculated with the ISORROPIA model based on observed gas and aerosol compositions. The implementation of the Air Pollution Prevention and Control Action Plan led to −35.8 %, −37.6 %, −9.6 %, −81.0 % and 1.2 % changes of PM2.5, SO42-, NHx, non-volatile cations (NVCs) and NO3- in the Yangtze River Delta (YRD) region during this period. Different from the drastic changes of aerosol compositions due to the implementation of the Air Pollution Prevention and Control Action Plan, aerosol pH showed a minor change of −0.24 over the 9 years. Besides the multiphase buffer effect, the opposite effects from the changes of SO42- and non-volatile cations played key roles in determining this minor pH trend, contributing to a change of +0.38 and −0.35, respectively. Seasonal variations in aerosol pH were mainly driven by the temperature, while the diurnal variations were driven by both temperature and relative humidity. In the future, SO2, NOx and NH3 emissions are expected to be further reduced by 86.9 %, 74.9 % and 41.7 % in 2050 according to the best health effect pollution control scenario (SSP1-26-BHE). The corresponding aerosol pH in eastern China is estimated to increase by ∼0.19, resulting in 0.04 less NO3- and 0.12 less NH4+ partitioning ratios, which suggests that NH3 and NOx emission controls are effective in mitigating haze pollution in eastern China.</p