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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Storyline Visualization: A Compelling Way to Understand Patterns over Time and Space

Storyline visualization is a compelling way to understand patterns over time and space. Much effort has been spent developing efficient and aesthetically pleasing layout optimization algorithms. But what if those algorithms are optimizing the wrong things? To answer this question, we conducted a design study with different storyline layout algorithms. We found that users with our new design principles for storyline visualization outperform existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Towards an improved understanding of the influence of subpixel vegetation structure on pixel-level spectra: a simulation approach

The planned NASA Hyperspectral Infrared Imager (HyspIRI) mission, equipped with an imaging spectrometer that has the capability of monitoring ecosystems globally, will provide an unprecedented opportunity to address scientific challenges related to ecosystem function and change. However, uncertainty remains around the impact of subpixel vegetation structure, in combination with the point spread function, on pixel-level imaging spectroscopy data. We estimated structural parameters, e.g., leaf area index (LAI), canopy cover, and tree location, from HyspIRI spectral data, with the goal of assessing how subpixel variation in these parameters impact pixel-level imaging spectroscopy data. The fine-scale variability of real vegetation structure makes this a challenging endeavor. Therefore, we utilized a simulation-based approach to counter the time-consuming and often destructive sampling needs of vegetation structural analysis and to simultaneously generate synthetic HyspIRI data pre-launch. Three virtual scenes were constructed, corresponding to the actual vegetation structure in the National Ecological Observatory Network’s (NEON) Pacific Southwest Domain (Fresno, CA). These included an oak savanna, a dense coniferous forest, and a conifer-manzanita-mixed forest. Simulated spectroscopy data for these scenes were then generated using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Simulations first were used to verify the physical model, virtual scene geometrical information, and simulation parameters. This was followed by simulations of HyspIRI data, where within-pixel structural variability was introduced, e.g., by iteratively changing per-pixel canopy cover and tree placement, tree clustering, leaf area index (LAI), etc., between simulation runs for the virtual scenes. Finally, narrow-band vegetation indices (VIs) were extracted from the data in an attempt to describe the variability of the subpixel structural parameters; this was done in order to assess VI robustness to changes in structural “levels”, as well as placement of trees/canopies within the instrument’s instantaneous field-of-view (IFOV). Our ultimate goal is not only to better understand how such subpixel variability influence imaging spectroscopy outputs, but also to better estimate vegetation structural parameters using spectra. We constructed regression models for LAI (R2 = 0.92) and canopy cover (R2 = 0.97) with narrow-band VIs via this simulation approach. Our models ultimately are intended to improve the HyspIRI mission’s ability to monitor global vegetation structure.

Van Aardt, Jan↗
DOE OSTI2017

Stereo-Optic High Definition Imaging: A New Technology to Understand Bird and Bat Avoidance of Wind Turbines

There is a critical need to develop monitoring tools to track aerofauna (birds and bats) in three dimensions around wind turbines. New monitoring systems will reduce permitting uncertainty by increasing the understanding of how birds and bats are interacting with wind turbines, which will improve the accuracy of impact predictions. Biodiversity Research Institute (BRI), The University of Maine Orono School of Computing and Information Science (UMaine SCIS), HiDef Aerial Surveying Limited (HiDef), and SunEdison, Inc. (formerly First Wind) responded to this need by using stereo-optic cameras with near-infrared (nIR) technology to investigate new methods for documenting aerofauna behavior around wind turbines. The stereo-optic camera system used two synchronized high-definition video cameras with fisheye lenses and processing software that detected moving objects, which could be identified in post-processing. The stereo- optic imaging system offered the ability to extract 3-D position information from pairs of images captured from different viewpoints. Fisheye lenses allowed for a greater field of view, but required more complex image rectification to contend with fisheye distortion. The ability to obtain 3-D positions provided crucial data on the trajectory (speed and direction) of a target, which, when the technology is fully developed, will provide data on how animals are responding to and interacting with wind turbines. This project was focused on testing the performance of the camera system, improving video review processing time, advancing the 3-D tracking technology, and moving the system from Technology Readiness Level 4 to 5. To achieve these objectives, we determined the size and distance at which aerofauna (particularly eagles) could be detected and identified, created efficient data management systems, improved the video post-processing viewer, and attempted refinement of 3-D modeling with respect to fisheye lenses. The 29-megapixel camera system successfully captured 16,173 five-minute video segments in the field. During nighttime field trials using nIR, we found that bat-sized objects could not be detected more than 60 m from the camera system. This led to a decision to focus research efforts exclusively on daytime monitoring and to redirect resources towards improving the video post- processing viewer. We redesigned the bird event post-processing viewer, which substantially decreased the review time necessary to detect and identify flying objects. During daytime field trials, we determine that eagles could be detected up to 500 m away using the fisheye wide-angle lenses, and eagle-sized targets could be identified to species within 350 m of the camera system. We used distance sampling survey methods to describe the probability of detecting and identifying eagles and other aerofauna as a function of distance from the system. The previously developed 3-D algorithm for object isolation and tracking was tested, but the image rectification (flattening) required to obtain accurate distance measurements with fish-eye lenses was determined to be insufficient for distant eagles. We used MATLAB and OpenCV to improve fisheye lens rectification towards the center of the image, but accurate measurements towards the image corners could not be achieved. We believe that changing the fisheye lens to rectilinear lens would greatly improve position estimation, but doing so would result in a decrease in viewing angle and depth of field. Finally, we generated simplified shape profiles of birds to look for similarities between unknown animals and known species. With further development, this method could provide a mechanism for filtering large numbers of shapes to reduce data storage and processing. These advancements further refined the camera system and brought this new technology closer to market. Once commercialized, the stereo-optic camera system technology could be used to: a) research how different species interact with wind turbines in order to refine collision risk models and inform mitigation solutions; and b) monitor aerofauna interactions with terrestrial and offshore wind farms replacing costly human observers and allowing for long-term monitoring in the offshore environment. The camera system will provide developers and regulators with data on the risk that wind turbines present to aerofauna, which will reduce uncertainty in the environmental permitting process.

Adams, Evan↗
DOE OSTI2018

Understanding Composition, Formation, and Aging of Organic Aerosols in Wildfire Emissions via Combined Mountain Top and Airborne Measurements

Wildfires are an important source of global aerosols that have pronounced impacts on regional air quality and global climate. A major component of wildfire emissions is organic, including primary organic aerosols (POA) and large amounts of gases that can be oxidized to form secondary organic aerosols (SOA). The environmental impacts of wildfire emissions are strongly correlated with the chemical, optical, and microphysical properties of biomass-burning organic aerosols (BBOA), which are dependent, in a complex manner, on combustion and atmospheric aging processes. Here we discuss results from recent field and laboratory studies aimed at understanding the chemical composition, formation, and evolution of BBOA in wildfire emissions. Comprehensive analysis of a large number of fresh and aged (up to days of aging) fire plumes originating in the Pacific Northwest region of the United States reveals that the enhancement of BBOA mass concentrations relative to the enhancement of the sum of CO and CO2 in wildfire smokes is primarily driven by combustion efficiency, whereas the composition and properties of the BBOA are strongly influenced by atmospheric oxidation and other aging processes. These results shed light on organic aerosol formation and transformation in biomass burning emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Understanding Infrastructure Dependency During Natural Hazards Events for Actionable Disaster Responses

One major focus of the Sendai Framework for Disaster Risk Reduction 2015-2030 is to “Build Back Better” in recovery, rehabilitation and reconstruction to achieve disaster risk reduction and resilient communities. While forecasting and decision-making tools help predict and understand future impacts of natural hazards, given the interdependency of infrastructures, predictive approaches fail to identify long-term impacts of an extreme event on infrastructures and recovery time. As seen following hurricane Maria’s impacts in Puerto Rico, the failure of power infrastructure not only isolated the island from the outside world, but also contributed to the lack of supply of clean and filtered water, and sewage treatment. While there have been several decision support tools that look at the vulnerability and resilience of different critical infrastructure systems as a single system and/or coupled systems, there still exists a gap in quantifying system vulnerabilities due to cascading impacts. In this paper, we present a conceptual framework for quantifying dynamic resilience of interdependent infrastructure systems. The proposed approach should provide the knowledge needed for stakeholders and decision-makers to prioritize their efforts during recovery to build resiliency.

Omitaomu, Femi↗
DOE OSTI2017

A Dual-Lattice Discrete Element Model to Understand Hydraulic Fracturing in a Naturally Fractured System

Interaction between hydraulic fractures (HF) and natural fractures (NF) may lead to complex fracture networks as a consequence of branching and merging of the natural and hydraulically-induced/reopened fractures. Conventional simulation methods, with assumptions of homogeneous and continuous media, are not able to capture realistic fracture patterns in the presence of pre-existing natural fractures (open or healed). These natural discontinuities alter the local stress field and impact the fracturing fluid pressure distribution when hydraulically-driven fractures approach natural discontinuities. Modeling the propagation of one or more propagating hydraulic fractures in naturally fractured reservoirs should carefully consider the complex mechanical and hydraulic interactions between hydraulically-driven and natural fractures. A rigorous hydraulic fracturing model based on a coupled discrete element method (DEM) and conjugate network flow model is described in this work. The coupled DEM-network flow model conveniently accounts for both continuous poroelasticity and discrete natural fractures simultaneously and provides more realistic fracture growth patterns in low-permeability rocks. This dual lattice DEM-network flow model can capture both tension-induced rock failure and shear-induced natural discontinuity sliding and dilation. Simulation results revealed that reactivation of natural fractures becomes more difficult when the following conditions are present: larger magnitude of stress difference, higher intercepting angle between the natural fracture and the propagating hydraulic fracture (higher implies more orthogonal to the natural fracture), larger natural fracture cohesion and angle of internal friction and/or lower natural fracture permeability - leading to continued growth of the hydraulic fracture. The term stress difference was colloquially used in the preceding sentence. For clarity, we will define this as ΔS = S H,max – S h,min ). The quantitative results of numerous simulations reinforced the intuitive understanding of the underlying phenomena. Other simulations reinforced insights from laboratory experimentation and field measurements that lower injection rates and treating fluid viscosities facilitate preferential fluid intake into natural fractures. Finally, this simulation capability for predicting the nature of complex fracture networks potentially enables optimizing treatment design and completion strategy in naturally fractured reservoirs.

42 ENGINEERING↗
DOE OSTI2019

Utilizing M3D-C1 to understand triggering of ELMs in DIII-D pellet pacing experiments

M3D-C1, a code for solving the linear and non-linear extended-MHD equations in toroidal geometry, is used to model pellet ELM triggering in DIII-D ITER-like plasmas. Understanding of the physical mechanisms of ELM triggering and improved modeling are required for condent extrapolation to ITER and beyond. M3D-C1 results run in linear 2D mode show that the localized perturbation is due to the pellet destabilizing peeling-ballooning modes. Calculations of linear peeling-ballooning stability as a function of pellet size suggest that a 2D pellet density ring underestimates the eects of the pellet.

Diem, Stephanie↗
DOE OSTI2019

Argonne broadens understanding of America’s transportation and mobility systems

Mobility in America is changing fast. To understand where it’s headed, Argonne caught up with Michael Berube, the acting deputy assistant secretary for transportation in the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy. While at the laboratory, Berube took time to highlight the ways in which technology and consumer demand are shaping transportation, and the insights national laboratories like Argonne can provide about the future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Toward Greater Understanding of Upstream and Downstream Manufacturing Processes for Automotive of Li-Ion Batteries

This work focuses on cost associated with extraction and purification of raw materials used in Li-ion batteries such as Co, Ni, Mn and graphite. This work also attempts to expand cost analysis to cover costs associated with cells and modules packaging. Value chain analysis will be used to facilitate understanding of costs in the manufacturing processes of Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2019

Developing Publicly Available LCA Guidance, Data, and Tools for Environmental Understanding of Emerging CO2 Utilization Research - LCAXIX

This work was presented at the American Center for Life Cycle Assessment 2019 Conference (LCAXIX) in Tucson, AZ on September 25, 2019. Capturing carbon dioxide (CO2) emissions from power and industrial sources and using that CO2 to make useful products is an emerging area of research that will benefit from a consistent and unbiassed framework like life cycle assessment/analysis (LCA) to understand the environmental impacts and net life cycle GHG reductions compared to the current state of the alternative in the marketplace. In the interest of supporting the creation of useful and consistent LCAs of CO2 utilization projects, the DOE National Energy Technology Laboratory has developed a CO2 utilization LCA toolkit. The primary audience for the toolkit is the group of principal investigators that have been awarded a grant through a DOE Carbon Use and Reuse funding opportunity announcement. However, we believe that there is potential wider application beyond those grant recipients.

Cooney, Gregory↗
DOE OSTI2018

Understanding Occupancy Patterns in a Commercial Space

Heating, ventilation, and air conditioning (HVAC) is a prime source of energy consumption in the US. In 2006, approximately 35% of energy in the US was used for HVAC [1]. Usually building operators use a static schedule for controlling HVAC systems without having a deeper understanding of how many people use the building at different times of the day. In addition, HVAC systems operate by assuming maximum occupancy in each room, which leads to a significant energy waste, e.g., an HVAC system providing ventilation for 30 people when there are only 10 people in a room. Considering how many people use different rooms at different times of the day is very crucial for achieving building energy efficiency and providing occupant comfort. There have been several attempts to collect long term occupancy patterns from office buildings, e.g., Mitsubishi’s Electronic Research Lab (MERL) dataset [14] and Colorado School of Mines (CSMBB) dataset. However, PIR motion detectors are used in these projects for sensing occupancy and hence these datasets only reflect whether a room is occupied or not without revealing the actual person count. Similar datasets are also collected from households. Only a few datasets from commercial spaces capture actual person count, and even in these cases, the data collection period was very limited, e.g., in [3] only 5 days of occupancy data is collected as ground truth. We are the first to collect long term (9 months) occupancy data (people count) from an 11,000 square foot commercial office (second floor of Bosch Research and Technology Center Pittsburgh). In this paper, we outline some statistical results from our empirical study and explore their implications for saving energy.

42 ENGINEERING↗
DOE OSTI2019

Plant Water Profiler: A Tool for Understanding Water Use, Cost and Savings Potential for Manufacturing Plants

In the 1746 Poor Richard’s Almanack, Benjamin Franklin published “When the well’s dry, we know the worth of water” (Franklin, 1746); industrial water users have begun to see the truth in these words as they examine the potential and reality of water scarcity becoming a significant business risk. Through the authors’ interactions with manufacturers via the U.S. Department of Energy’s Better Plants program and Strategic Analysis projects, they realized that manufacturing facilities often do not fully understand the risks to the water supply and the consequential risks to their business. The authors’ also noticed that many facilities do not know how their water is used (baselining), how they compare with their peers (benchmarking), the actual cost impacts of water use (true cost of water), and potential areas for water savings. This results in a lack of motivation and ability to identify and implement effective water-efficiency measures.The Plant Water Profiler Tool (PWP) was created to help manufacturing plant management address these issues by identifying and accounting for water use in manufacturing operations, helping determine the true cost of water, and providing some high-level estimates for peer comparison. The PWP uses a water mass balance and common engineering equations to estimate water use by each of the facility’s systems (e.g., differentiating process water use from cooling and restroom water use), even when no submetering data is available. This new tool also helps users identify possible areas for water savings and associated water and energy cost savings, making it a potentially useful resource for corporations conducting water assessments. Additionally, governments and private organizations can use the methodology behind this tool to design a survey to help build a database of industrial water use. This paper and corresponding presentation provide an overview of current industrial water use practices and tools, as well as the value of determining the true cost of water. It also briefly describes the methodology behind the tool, then focuses on its functionality through an industrial case study and lessons learned from implementing the case study.

Armstrong, Kristina O.↗
DOE OSTI2020

From Atom to Engine: Understanding Fundamental Effects of Structure on Combustion Using Tandem Experiment and Computation

The ability to tie structural features to a fuel candidate’s ignition properties provides a path for rational design of advantaged fuels that facilitate higher-efficiency, lower-emitting combustion in engines. The time evolution of a fuel’s radical population directly correlates with global combustion parameters, and because the generated radicals depend on the initial structure and reaction pathways of the fuel molecule, it follows that the molecular structure of a fuel has a direct impact on this cascade, and therefore salient ignition-tied fuel properties. This work aims to understand the impact of structural features in the context of global combustion properties for direct application to emerging engine technologies. Specifically, we highlight the ability to identify potential fuel blendstocks for specific engine strategies by examining and elucidating the radical cascade tied to ignition. A representative subset of alcohols possessing a variety of structural features (e.g. branching, chain length) was selected for study using a robust combination of experimental and computational methods. The approach begins with identifying short-lived radicals and likely abstraction sites upon pyrolysis in a microreactor coupled with photoionization mass spectrometry for direct detection of product species. In tandem, electronic structure calculations were performed to calculate the relevant potential energy surfaces for fuel pyrolysis as well as the subsequent O2 addition-isomerization reactions, which govern ignition behavior. Furthermore, the theoretical energetic and ro-vibrational data are used to calculate rate constants for direct ignition modeling to explore how structural effects impact NTC behavior. The microreactor data combined with electronic structure theory calculations provide a well-informed picture of the reactions relevant to ignition for each fuel in this study, as well as the most likely radicals generated under engine conditions. This thorough fundamental picture of fuel decomposition and ignition chemistry is used to correlate the observed ignition behavior to molecular structural effects.

advanced biofuels↗
DOE OSTI2018

Understanding Climate Change Effects on California Watersheds

California relies on the Sierra Nevada snowpack for a significant portion of its water needs, yet scientists understand very little about how future changes in snowpack volume and timing will influence surface water and groundwater. Berkeley Lab researcher Erica Woodburn is working on an advanced hydrologic model to study how climate change might affect California watersheds in the future.

58 GEOSCIENCES↗
DOE OSTI2020

Understanding the Impact of Memory Access Patterns in Intel Processors

Because of increasing complexity in the memory hierarchy, predicting the performance of a given application in a given processor is becoming more difficult. The problem is worsened by the fact that the hardware needed to deal with more complex memory traffic also affects energy consumption. Moreover, in a heterogeneous system with shared main memory, the memory traffic between the last level cache (LLC) and the memory creates contention between other processors and accelerator devices. For these reasons, it is important to investigate and understand the impact of different memory access patterns on the memory system. This study investigates the interplay between Intel processors' memory hierarchy and different memory access patterns in applications. The authors explore sequential streaming and strided memory access patterns with the objective of predicting LLC-dynamic random access memory (DRAM) traffic for a given application in given Intel architectures. Moreover, the impact of prefetching is also investigated in this study. Experiments with different Intel micro-architectures uncover mechanisms to predict LLC-DRAM traffic that can yield up to 99% accuracy for sequential streaming access patterns and up to 95% accuracy for strided access patterns.

Alaul haque monil, Mohammad↗
DOE OSTI2021

Proteomic approaches for advancing the understanding and application of oleaginous bacteria for bioconversion of lignin to lipid

Biological conversion of current cellulosic biorefinery lignin waste is important for complete valorization of all biomass carbons by biofuels and biochemical production. However, the deconstruction of lignin's complex polymeric framework into intermediates amenable to assimilation and subsequent lipid production for biofuels has proven challenging. Recent systems biology advances have shown promise in facilitating the bioconversion of lignin to lipids. Proteomics-guided synthetic biology approaches have proven to be effective tools for defining and designing new pathways. These pathways may help achieve optimal carbon conversion efficiency of biomass. Nevertheless, substantial challenges remain for the economic conversion of lignin to fuel. Herein, we review salient problems in the bacterial conversion of lignin to lipids with an emphasis on lignin depolymerization activities, in vivo aromatic degradation pathways, and recent advances in lipid production by oleaginous bacteria. Several mass spectrometry-based proteomics technologies and their applications to oleaginous bacteria are highlighted in this review. Together, these studies will help establish a system-level understanding of lignin bioconversion and drive efforts to build a platform for the maximal conversion of lignocellulosic biomass to economical next generation biorefinery.

Li, Xiaolu↗
DOE OSTI2021

Linking Transient Voltage to Spatially-Resolved Luminescence Imaging to Understand Reliability of Perovskite Photovoltaics: Preprint

In this work, we present a methodology to separate effects of perovskite device metastability from irreversible degradation, using stress/rest cycling under constant current bias while collecting a series of electroluminescence images and continuously monitoring voltage. We develop a simulation model and procedures for image processing to better understand the effects of ion parameters on the transient nature of voltage and evolving electroluminescence images.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Robotic Understanding of Spatial Relationships Using Neural-Logic Learning

Understanding spatial relations of objects is critical in many robotic applications such as grasping, manipulation, and obstacle avoidance. Humans can simply reason object's spatial relations from a glimpse of a scene based on prior knowledge of spatial constraints. The proposed method enables a robot to comprehend spatial relationships among objects from RGB-D data. This paper proposed a neural-logic learning framework to learn and reason spatial relations from raw data by following logic rules on spatial constraints. The neural-logic network consists of three blocks: grounding block, spatial logic block, and inference block. The grounding block extracts high-level features from the raw sensory data. The spatial logic blocks can predicate fundamental spatial relations by training a neural network with spatial constraints. The inference block can infer complex spatial relations based on the predicated fundamental spatial relations. Simulations and robotic experiments evaluated the performance of the proposed method.

Wang, Dali↗