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Network Clustering for Distribution System with Photovoltaic System and Electric Vehicles

Network Clustering for Distribution System with Photovoltaic System and Electric Vehicles

Primary author: Lusha Wang
Faculty sponsor: Noel Schulz

Primary college/unit: Voiland College of Engineering and Architecture
Campus: Pullman

Abstract:

The penetration of both Photovoltaic (PV) system and electric vehicles (EVs) are increasing rapidly in distribution systems, which brings challenges to system operation. The distribution system should meet some requirement to operate safely, the most important one being that the voltage magnitude should be within the desired range. Compared with the traditional centralized voltage control where all the information of the system is obtained and an optimization problem is solved in the control center, the decentralized voltage control is more flexible and consumes less computation time, making it suitable for large-size distribution system. To realize the decentralized voltage control, proper division of the system and choice of regional agents should be well determined. The rapid change of system configuration and DG output as well as EV movement brings the need of rapid and frequent determination of network clusters. We proposed a new algorithm to cluster a distribution system with PVs and EVs. The modularity index is used to evaluate the clustering result. The original modularity index is purely based on system structure, so we add the information of PV generation and EV driving distance into the index to accommodate power system properties. The Louvain algorithm with the aim of maximizing the modified modularity index is used to cluster the distribution system, which shows great computation speed and reasonable results. An IEEE 123 node system is used to demonstrate the clustering result, with comparison of network clustering based on solely structure, structure with DG output and the three together.

“The Bed We Made For You”: Earth’s Average Surface Temperature as a Baby Quilt

“The Bed We Made For You”: Earth’s Average Surface Temperature as a Baby Quilt

Primary author: Lisa Waananen Jones

Primary college/unit: Edward R. Murrow College of Communication
Campus: Pullman

Abstract:

The use of textiles as a form of storytelling and documentation extends to the earliest human civilizations. “Cloth relates to humanity is its mortality and transience—both cloth and our body can be cut, stitched, age, and decay. … It evokes memory. The child clings to their comfort blanket, and in times of crisis we too still reach for cloth and its human connections” (Nickell, 2015). As numerical data has grown as a form of documentation, recent textile projects shared and popularized on social media have incorporated climate and weather data.
This work of data art visualizes a widely used NASA dataset of Earth’s annual average surface temperature, 1880-2018, in this textile tradition as a half-square triangle baby quilt with color encoding. A diverging blue-red color scheme is common in temperature visualizations for a public audience, such as annually published news graphics using this dataset by The New York Times and Bloomberg News. This project makes use of the dual symbolism of blue and pink for temperature data and the symbolic colors used for babies. Each year in the dataset is represented by a half-square of fabric, with color representing the degree to which that year was warmer or cooler than the preindustrial average. The entirely hand-stitched quilt shows the distinct pattern of rising temperatures and invites contemplation about the role of generational traditions and heirlooms in a changing world.
Karen Nickell (2015) “Troubles Textiles”: Textile Responses to the Conflict in Northern Ireland, TEXTILE, 13:3, 234-251, DOI: 10.1080/14759756.2015.1084693

Metallic Aerogel As Electrocatalysts In Oxygen Evolution And Hydrogen Evolution Reactions for Water Splitting

Metallic Aerogel As Electrocatalysts In Oxygen Evolution And Hydrogen Evolution Reactions for Water Splitting

Primary author: Hangyu Tian
Faculty sponsor: Yuehe Lin

Primary college/unit: Voiland College of Engineering and Architecture
Campus: Pullman

Abstract:

The emergent demands for a green and efficient energy resource driving the development of new energy conversion and storage systems. Among numerous energy resources, hydrogen is one of promising candidates for the next generation energy due to its zero carbon emission, high energy density and no pollutants. However, the sluggish oxygen evolution reaction that require a large overpotential over standard potential (1.23V vs RHE) has hindered water splitting for hydrogen production and made it hard to compete with fossil fuel in cost and efficiency. And due to its shortage, exorbitant price and poor durability, current commercial noble metal electrocatalysts still hindered the electrochemical production of the hydrogen. In past decades, significant efforts have been made on structure and composition design to improve the performance and efficiency of the electrocatalysts. Among various structure, Aerogel stands out for its ultra-high porosity, low apparent density and high specific area. We focus on the facile synthesis, composition optimization and defect engineering to increase both the number and activity of the reaction sites. By optimization of these parameters, our metallic aerogels exhibited excellent OER and HER performance.

Location, year and tree age impact near-infrared (NIR) spectroscopy-based postharvest prediction of dry matter concentration for 58 apple accessions

Location, year and tree age impact near-infrared (NIR) spectroscopy-based postharvest prediction of dry matter concentration for 58 apple accessions

Primary author: Soon Li Teh
Co-author(s): Jamie Coggins; Sarah Kostick; Kate Evans
Faculty sponsor: Kate Evans

Primary college/unit: Agricultural, Human and Natural Resource Sciences
Campus: Wenatchee

Abstract:

In apple breeding, development of cultivars with desirable eating quality and postharvest characteristics is of paramount importance. During each season, fruit are destructively sampled and evaluated for various fruit quality traits. This presents a challenge when young seedling trees do not bear sufficient fruit for destructive sampling. Alternatively, a non-destructive tool can enable prediction of fruit quality indices regardless of fruit count, thus increasing selection efficiency. In this study, near-infrared (NIR) spectroscopy was used as a non-destructive tool to correlate with destructively-derived measurements of dry matter concentration (DMC), a trait touted to be highly linked with fruit quality. The study was aimed at evaluating NIR prediction accuracy for DMC of 2,252 fruit from 58 diverse accessions at three orchard sites belonging to the Washington State University apple breeding program. Results showed that DMC values were generally predicted at high accuracies. In characterizing DMC predictive performance of within- versus between-years, both models were highly predictive and comparable, albeit slightly higher for the former. Further analysis of location × year effects revealed that location was a more important factor than year in influencing predictive performance. Finally, in cultivar-specific models, prediction made using fruit from more established trees as a calibration set consistently yielded higher prediction accuracy. This study provides a framework for understanding the impacts of location, year and tree age on NIR prediction accuracy of DMC in diverse apple breeding accessions. In addition, this work demonstrates the importance of assessing predictive performance using multiple statistical metrics.

Modeling Brook Trout carrying capacity in Owhi Lake, Washington using bioenergetics

Modeling Brook Trout carrying capacity in Owhi Lake, Washington using bioenergetics

Primary author: Timothy Taylor
Co-author(s): Benjamin Cross; Barry Moore
Faculty sponsor: Barry C. Moore

Primary college/unit: Agricultural, Human and Natural Resource Sciences
Campus: Pullman

Abstract:

The management of fish populations often requires an understanding of how density-dependent effects influence population dynamics. In systems where natural populations are supplemented with stocking, the question of ‘how much food is available’ becomes increasingly important. One typical approach for assessing density-dependent interactions is to identifying disparities between fish consumption rates and food availability. The objective of our study was to determine if seasonal lake prey production could support Brook Trout consumption demands in Owhi Lake, Washington at observed abundances. Brook Trout were collected seasonally from 2015 to 2017 to obtain information on length, weight, age, diet, growth, and mortality. Population abundance was estimated in summers using hydroacoustic surveys. Littoral invertebrates and pelagic zooplankton were collected concurrently with fish to enumerate biomass and production. Bioenergetics modeling was used to estimate prey consumption for Brook Trout. In conjunction with supply-demand comparisons, we used growth efficiencies and maximum consumption rates to further identify potential season and annual food limitations. Our results suggest that prey production could support Brook Trout consumption demands for all years, but littoral invertebrate consumption was near to, or exceeded, prey production in summer and fall 2017. Growth efficiency was lowest and maximum consumption rates were highest in summer 2017 compared to all seasons and years. In addition to observed diet switching in summer 2016 and 2017 from littoral invertebrates to zooplankton, we concluded that lower growth efficiencies, lower annual survival rates, and increased consumption rates were influenced by littoral invertebrate production.

razing Impacts of Rotifer Zooplankton in a Seasonally Cyanobacteria-Dominated Lake

Grazing Impacts of Rotifer Zooplankton in a Seasonally Cyanobacteria-Dominated Lake

Primary author: Kathryn Sweeney
Co-author(s): Gretchen Rollwagen-Bollens
Faculty sponsor: Dr. Gretchen Rollwagen-Bollens

Primary college/unit: Arts and Sciences
Campus: Vancouver

Abstract:

Vancouver Lake in western Washington is one of many lakes characterized by annual and often toxic cyanobacteria (harmful algae) blooms. Phytoplankton and cyanobacteria are the primary producers of lake systems, and the foundation on which zooplankton grazers, like copepods or rotifers, are able to survive. Thus, toxic blooms may be controlled top-down by these micrograzers, which is information relevant to resource managers and the public alike. Previous studies have shown copepod grazing to influence bloom formation, and bloom decline to be driven in part by microzooplankton community grazing. However, we don’t understand the individual roles of particular micrograzers such as rotifers. To address the role of rotifers, we are conducting feeding incubations with water collected from Vancouver Lake. Preliminary results show that rotifers have a mild grazing effect on phytoplankton and cyanobacteria only after the peak of a bloom, while the whole microzooplankton community has a large impact both before and after the peak. This seems to suggest that other non-rotifer microzooplankton such as ciliates or dinoflagellates may be responsible for the majority of bloom suppression in both spring and fall. Further microscopical analysis of samples will elucidate which plankton species were present in the lake during each experiment, and which phytoplankton taxa rotifers had been preferentially feeding on. Additionally, due to an unexpected shift in the timing of the 2019 bloom cycle, supplemental experiments will be performed during spring 2020 to complete our understanding of seasonal dynamics related to cyanobacteria blooms.

Functionally antagonistic integrated domains of the Rpg5 NLR immunity receptor interact to regulate stem rust resistance in barley

Functionally antagonistic integrated domains of the Rpg5 NLR immunity receptor interact to regulate stem rust resistance in barley

Primary author: Shyam Solanki
Co-author(s): Gazala Ameen; Deepika Arora; Pawel Borowicz; Robert Brueggeman
Faculty sponsor: Robert S Bruggeman

Primary college/unit: Agricultural, Human and Natural Resource Sciences
Campus: Pullman

Abstract:

Immunity is important for plants to protect themselves from pathogens. Immunity activation relies on perception of pathogen molecules or by changes they induce to colonize the host tissues. The molecules/proteins pathogens manipulate are susceptibility targets encoded by vulnerable regions of the genome that are under selective pressure by the pathogen. A new paradigm of plant immune receptor evolution suggests plant genome reorganization directed by the pressure exerted by pathogens leading to gene fusion of these susceptibility targets with NLR plant immunity receptors resulting in integrated domains (IDs) that act as pathogen ‘bait-proteins’. These NLR-ID baits allow plants to monitor pathogen induced changes in the host. We identified a barley NLR immune receptor Rpg5 containing a serine threonine kinase (STPK) ID that confers resistance against Puccinia graminis f. sp. tritici (Pgt), the stem rust pathogen. The Rpg5-STPK-ID progenitor, PRK1, an Arabidopsis stomatal kinase AtAPK1b ortholog was hypothesized as important for stomata opening during respiration. Confocal microscopy showed Pgt host entry through stomata in the dark expelling the current dogma that a light period is required for stomata opening and pathogen entry, suggesting stomatal manipulation by Pgt possibly targeting PRK1 to enter the host during the night when the stomates are closed. We hypothesize that the pathogen manipulates PRK1 to open the stomata and enter in the host at night when Pgt spores adapted to germinate, thus forcing the host to evolve the Rpg5-STPK NLR-ID which recognizes the pathogen’s attempt to manipulate PRK1 leading to the activation of plant defense responses.

Elucidating mechanisms that cause potato glycoalkaloids to spike

Elucidating mechanisms that cause potato glycoalkaloids to spike

Primary author: Moe Hnin Si
Co-author(s): Sen Lin; Roy Navarre

Primary college/unit: Agricultural, Human and Natural Resource Sciences
Campus: WSU-IAREC, Prosser, WA

Abstract:

Developing new potatoes with increased amounts of phytonutrients and low amounts of neurotoxic glycoalkaloids (GLKs) benefits for producers and consumers of potato. Light-induced accumulation of GLKs and concurrent greening tubers is a major problem in rejecting greening tubers from markets, which some have estimated can cause up to 15% -17% of the crop to be culled. Metabolite levels are genetically determined, but several factors such as environmental cues or tuber color can affect their final content. Transcriptomic and metabolomic approaches were applied to monitor levels of GLKs, chlorophyll, and carotenoids, and 32 target genes (biosynthetic genes and/or regulators) in potatoes exposed to light. Levels of GLKs were markedly spiked in eight white and two color flesh genotypes among the 20 studied (10 white and 10 color), exceeding the accepted limit of 20mg/100g FW. Other genotypes had less spiking of GLKs. Metabolic analysis across different genotypes showed color potato with higher amounts of carotenoids had less GLK spiking and revealed a possible mechanism of metabolite sharing by trading of isoprenoid intermediates between the cytosol and the plastid. Only four genotypes among those tested showed a positive correlation between greening and GLKs levels, implying that potatoes showing greening do not necessarily have higher GLKs content. We assessed the relative importance of transcriptional control at GLK regulatory points by assessing gene-gene, gene-metabolite and metabolite-metabolite correlations. These findings provide insights into mechanisms that control levels of GLKs and suggest potato breeding programs may benefit from evaluating spiking potential of breeding lines.

Textures in Uranium-10wt% Molybdenum Alloy Nuclear Fuels

Textures in Uranium-10wt% Molybdenum Alloy Nuclear Fuels

Primary author: Benjamin Schuessler
Faculty sponsor: David P. Field

Primary college/unit: Voiland College of Engineering and Architecture
Campus: Pullman

Abstract:

Uranium – 10wt% Molybdenum (U10Mo) is currently being considered as a next generation nuclear fuel for advanced research reactors. Its usage of low-enriched uranium (LEU) is preferable over the high-enriched (HEU) counterpart as it facilitates the demand to reduce the overall stockpile of HEU materials. However, manufacturing of the U10Mo fuels can be difficult. Varying processing conditions can alter the material in ways that can be detrimental to the overall fuel performance. Studying the effect of manufacturing processes on the microstructure-properties and -performance of the U10Mo is critical to the reliable usage of the fuel for future reactor research. This study focuses on how rolling and annealing of the U10Mo fuel plates affect how the orientations of the crystals inside the material are arranged, otherwise known as crystallographic texture. Mechanical properties can depend on the texture of the material, and by knowing the texture, one can extrapolate how the material will behave under various loading and operating conditions. U10Mo plates were rolled down to various thicknesses and annealed, then characterized using electron backscatter diffraction (EBSD) to gather crystal orientation information. After rolling, the U10Mo exhibit typical rolling textures seen in body-centered cubic metals and after annealing, the U10Mo showed a “randomized” texture. These textures tell a story of how the mechanical properties of the U10Mo evolve throughout the manufacturing process and provide valuable insight into how to adjust the manufacturing procedures to maximize the microstructure-properties and -performance of the fuel.

CropSyst regional parameterization and calibration over Columbia River Basin

CropSyst regional parameterization and calibration over Columbia River Basin

Primary author: Fabio Scarpare
Co-author(s): Claudio Stockle; Roger Nelson; Kirti Rajagopalan; Mingliang Liu; Jennifer Adam
Faculty sponsor: Jennifer Adam

Primary college/unit: Voiland College of Engineering and Architecture
Campus: Pullman

Abstract:

Crop water demand is key for policy and resource decision-making questions, including the processing of new irrigation water rights, examining water availability for both out-of-stream and instream uses. Conventional model calibration methods, which concentrate on a model’s performance at plot scale, cannot be used for large-scale regional simulation. Therefore, this study aims to describe a low-data approach used for developing detailed crop parameterization data required for regional level application. CropSyst was parameterized and calibrated based on its sensitivity analysis for the main agricultural irrigated lands in the Columbia River Basin; for most of Oregon, eastern Washington, southern Idaho, and western Montana States. Twenty-five crop types among cereal, forage, fruits and vegetables were selected by using the USDA Crop Data Layer 2018 in each sub-region. Thirty-six years of daily meteorological variables were used to drive the simulations. The calibration was performed by first adjusting the growing season (defined as planting to maturity). Next, the phenological development stages between planting and maturity (end of vegetative growth, flowering, beginning of yield formation, senescence and full senescence if reached) with the green canopy cover development were adjusted. Yield calibration was the last step performed, which was based on model`s sensitivity analysis. Scientific papers and irrigation field trials performed by several Research Extension Centers with less than ten years old developed in the same region were used as main sources for model evaluation. The simulation results were satisfactory and similar to those observed in the literature data, which enable its use across the Pacific Northwest.