Engineering topics

Meng, Yao

Publications and source records attributed to Meng, Yao.

5 records

DOE OSTI2022

Exogenous Hemin alleviated cadmium stress in maize (Zea mays L.) by enhancing leaf photosynthesis, AsA-GSH cycle and polyamine metabolism

Cadmium (Cd) stress is one of the principal abiotic stresses that inhibit maize growth. The research was to explore (hemin chloride) Hemin (100 μmol L −1 ) on photosynthesis, ascorbic acid (AsA)-glutathione (GSH) cycle system, and polyamine metabolism of maize under Cd stress (85 mg L −1 ) using nutrient solution hydroponics, with Tiannong 9 (Cd tolerant) and Fenghe 6 (Cd sensitive) as experimental materials. The results showed that Hemin can increase leaf photosynthetic pigment content and ameliorate the ratio of Chlorophyll a/chlorophyll b ( Chla / Chlb ) under Cd stress. The values of ribose 1, 5-diphosphate carboxylase/oxygenase (RuBPcase) and phosphoenolpyruvate carboxylase (PEPCase), and total xanthophyll cycle pool [(violoxanthin (V), antiflavin (A) and zeaxanthin (Z)] increased, which enhancing xanthophyll cycle (DEPS) de-epoxidation, and alleviating stomatal and non-stomatal limitation of leaf photosynthesis. Hemin significantly increased net photosynthetic rate ( P n ), stomatal conductance ( g s ), transpiration rate ( T r ), photochemical quenching coefficient ( qP ), PSII maximum photochemical efficiency ( F v /F m ), and electron transfer rate ( ETR ), which contributed to the improvement of the PSII photosynthetic system. Compared with Cd stress, Hemin can reduce thiobartolic acid reactant (TBARS) content, superoxide anion radical (O 2 − ) production rate, hydrogen peroxide (H 2 O 2 ) accumulation, and the extent of electrolyte leakage (EL); decreased the level of malondialdehyde (MDA) content and increased the activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT); slowed the decrease in dehydroascorbic acid reductase (DHAR) and monodehydroascorbate reductase (MDHAR) activity and the increase in glutathione reductase (GR) and ascorbate peroxidase (APX) activity in leaves; promoted the increase in AsA and GSH content, decreased dehydroascorbic acid (DHA) and oxidized glutathione (GSSG), and increased AsA/DHA and GSH/GSSG ratios under Cd stress. Hemin promoted the increase of conjugated and bound polyamine content, and the conversion process speed of free putrescine (Put) to free spermine (Spm) and spermidine (Spd) in maize; decreased polyamine oxidase (PAO) activity and increased diamine oxidase (DAO), arginine decarboxylase (ADC), ornithine decarboxylase (ODC) and S-adenosylmethionine decarboxylase (SAMDC) enzyme activities in leaves under Cd stress.

Piao, Lin↗

DOE OSTI2022

Overestimated Eddy Kinetic Energy in the Eddy‐Rich Regions Simulated by Eddy‐Resolving Global Ocean–Sea Ice Models

Abstract The performance of eddy‐resolving global ocean–sea ice models in simulating mesoscale eddies is evaluated using six eddy‐resolving experiments forced by different atmospheric reanalysis products. Interestingly, eddy‐resolving ocean general circulation models (OGCMs) tend to simulate more (less) energetic eddy‐rich (eddy‐poor) regions with a smaller (larger) spatial extent than satellite observation, which finally shows that larger (smaller) mesoscale energy intensity (EI) is simulated in the eddy‐rich (eddy‐poor) regions. Quantitatively, there is an approximately 27%–60% overestimation of EI in the eddy‐rich regions, which are mainly located in the Kuroshio–Oyashio Extension, the Gulf Stream, and the Antarctic Circumpolar Currents regions, although the global mean EI is underestimated by 25%–45%. Apparently, the eddy kinetic energy in the eddy‐poor region is underestimated. Further analyses based on coherent mesoscale eddy properties show that the overestimation in the eddy‐rich regions is mainly attributed to mesoscale eddies’ intensity and is more prominent when mesoscale eddies are in their growth stage.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Time Series Classification for Locating Forced Oscillation Sources

Here, this article presents a machine learning based time-series classification method for using synchrophasor measurements to locate the source of forced oscillation (FO) for fast disturbance removal. First, multivariate time series (MTS) matrices are constructed by the most informative measurements selected by sequential feature selection from each power plant. Then, the Mahalanobis matrix is trained such that the Mahalanobis distance between the MTSs from the same class (i.e., with the same FO source location) are minimized and from different classes (i.e., with different FO source locations) are maximized. This allows MTSs to be classified by classifiers with class membership corresponding to the location of each FO source. To meet the runtime requirements of online matching, class templates are constructed to reduce data size and improve matching efficiency. To account for uncertainty in identifying the exact beginning of an FO event, dynamic time warping is used to align the out-of-sync MTSs. IEEE 39bus and WECC 179bus systems are used for algorithm development and validation. Simulation results demonstrate that the algorithm meets online operation runtime requirement with high accuracy using misaligned data sets.

42 ENGINEERING↗

DOE OSTI2020

FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets

This paper presents a novel, automated, generative adversarial networks (GAN) based synthetic feeder generation mechanism, abbreviated as FeederGAN. FeederGAN digests real feeder models represented by directed graphs via a deep learning framework powered by GAN and graph convolutional networks (GCN). Information of a distribution feeder circuit is extracted from its model input files so that the device connectivity is mapped onto the adjacency matrix and the device characteristics, such as circuit types (i.e., 3-phase, 2-phase, and 1-phase) and component attributes (e.g., length and current ratings), are mapped onto the attribute matrix. Then, Wasserstein distance is used to optimize the GAN and GCN is used to discriminate the generated graphs from the actual ones. A greedy method based on graph theory is developed to reconstruct the feeder using the generated adjacency and attribute matrices. Furthermore, our results show that the GAN generated feeders resemble the actual feeder in both topology and attributes verified by visual inspection and by empirical statistics obtained from actual distribution feeders.

42 ENGINEERING↗

DOE OSTI2020

A Data-Driven Pivot-Point-Based Time-Series Feeder Load Disaggregation Method

The load profile at a feeder-head is usually known to utility engineers while the nodal load profiles are not. However, the nodal load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder load disaggregation algorithm using smart meter data. The two stages are load profile selection (LPS) and load profile allocation (LPA). In the LPS stage, a random load profile selection process is first executed to meet the load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one load profile at a time for matching the reference load profile at the pivot points. In the LPA stage, the LPS selected load profiles are allocated to each load node on the feeder considering distribution transformer loading limits, load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Finally, simulation results show that the proposed method can generate a unique load shape for each load node while match the shape of their aggregated profile with the actual feeder head load profile.

42 ENGINEERING↗