Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “decision analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

How structural differences influence cross-model consistency: An electric sector case study

Multiple models are often employed to describe a range of possible outcomes for one or more scenarios, yielding insights into causal relationships and their uncertainties. Electric sector capacity expansion scenarios are a common topic of such efforts due to the economic influence of the electric sector, but model results typically span a broad solution space despite efforts to harmonize input assumptions, making decision implications difficult to discern. This study investigates the relationship between input harmonization and cross-model scenario consistency under disparate electric sector scenarios. We compare cross-model consistency between two state-of-the-art electric sector capacity expansion models (GCAM-USA and ReEDS) for six electric sector scenarios comprising alternate assumptions about fossil fuel resource availability, technology innovation, and long-term economy-wide transitions under four harmonization configurations varying model representations of electricity demand, fuel prices, renewable resources, and capacity retirements. These comparisons reveal that cross-model consistency can vary across scenarios under a given harmonization configuration, suggesting that harmonization efforts must often be scenario-specific if comparable cross-model consistency is desired. Model structural differences can hinder consistency, and the impact of these differences can depend on the scenario. Ultimately, thorough harmonization can reveal insights into cross-model consistency, which can be used to tighten uncertainty bounds and improve the decision-making implications of multi-model activities.

Cohen, Stuart↗
DOE OSTI2021

DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology

Abstract The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual and time-consuming, and require specific expertise. Deep learning has the potential to revolutionize image-based screens by greatly improving their scope, speed, and efficiency. Here, we used transfer learning and a convolutional neural network (CNN) to analyze over 47,000 confocal microscopy images from Arabidopsis wild-type and mutant plants with abnormal division of one of three essential energy organelles: chloroplasts, mitochondria, or peroxisomes. We have built a deep-learning framework, DeepLearnMOR (Deep Learning of the Morphology of Organelles), which can rapidly classify image categories and identify abnormalities in organelle morphology with over 97% accuracy. Feature visualization analysis identified important features used by the CNN to predict morphological abnormalities, and visual clues helped to better understand the decision-making process, thereby validating the reliability and interpretability of the neural network. This framework establishes a foundation for future larger-scale research with broader scopes and greater data set diversity and heterogeneity.

Plant Sciences↗
NASA NTRS2008

Entry, Descent, and Landing Operations Analysis for the Mars Phoenix Lander

The Mars Phoenix lander was launched August 4, 2007 and remained in cruise for ten months before landing in the northern plains of Mars in May 2008. The one-month Entry, Descent, and Landing (EDL) operations phase prior to entry consisted of daily analyses, meetings, and decisions necessary to determine if trajectory correction maneuvers and environmental parameter updates to the spacecraft were required. An overview of the Phoenix EDL trajectory simulation and analysis that was performed during the EDL approach and operations phase is described in detail. The evolution of the Monte Carlo statistics and footprint ellipse during the final approach phase is also provided. The EDL operations effort accurately delivered the Phoenix lander to the desired landing region on May 25, 2008.

Prince, Jill L.↗
DOE OSTI2026

Forensic characterization of surrogate nuclear explosion debris: radiochemical and spectroscopic strategies for method validation

Surrogate nuclear explosion debris (SNED) has emerged as a critical platform for advancing post-detonation nuclear forensic analysis in the absence of readily accessible historic materials. SNED enables controlled investigation and validation of analytical methodologies used to interrogate the chemical, isotopic, radiological, and microstructural signatures preserved in nuclear explosion debris. This review presents an integrated assessment of destructive and non-destructive analytical techniques commonly employed within decision-driven nuclear forensic workflows. Each technique is discussed individually while highlighting how it contributes to different stages of post-detonation analysis. Core methods – including gamma and alpha spectrometry, ICP-MS, TIMS, SIMS, SEM-EDS, XRF, LIBS, vibrational spectroscopy, and X-ray absorption spectroscopy – are critically evaluated with respect to forensic maturity, information content, and matrix limitations. Emphasis is placed on the role of SNED in benchmarking multi-modal workflows and identifying gaps in reproducing heterogeneity, fractionation, and radiation-driven evolution relevant to forensic attribution.

X-ray spectroscopic methods↗
NASA NTRS2022

Scientist-Stakeholder Relationships Drive Carbon Data Product Transfer Effectiveness within NASA Program

Carbon cycle science is at the heart of research on global climate change and its long-term impacts, as it examines the exchange of carbon between the atmosphere, oceans, land, and the impact of fossil fuel emissions on this cycling. Given the urgency of the climate challenge, NASA’s Carbon Monitoring System (CMS) requires all funded investigators to identify and work with stakeholder organizations at project inception to accelerate the transfer of the products developed by funded research into decision making systems. In this study, we contribute to the literature through the implementation of a quantitative analysis of 908 unique survey responses from funded investigators to explore the maturity of the scientist-stakeholder engagement. The paper employs multiple correspondence analysis to provide evidence to support policy options to increase stakeholder integration into research programs. Despite limitations of the dataset used, we demonstrated that multiple funding rounds, long-standing relationships between the stakeholder and scientist, and the scientific productivity of the PI, including the ability to produce datasets and research papers on these datasets, all contribute to carbon products moving from research to operational use. The maturity of relationships between scientists and stakeholders was shown to result improved stakeholder engagement. The use of carbon products should be identified in every stage of the program, and that capacity building is needed to support both existing and newly identified stakeholders better understand and use CMS products. As Federal, State, and local policy on climate adaptation and mitigation matures, the need for information on carbon will expand. Building of stakeholder-scientist relationships in CMS results in an effective generation and use of datasets to support this need and prototype ways that improved information needed for decision making can be created.

Monitoring↗
NASA NTRS2022

Scientist-Stakeholder Relationships Drive Carbon Data Product Transfer Effectiveness within NASA Program

Carbon cycle science is at the heart of research on global climate change and its long-term impacts, as it examines the exchange of carbon between the atmosphere, oceans, land, and the impact of fossil fuel emissions on this cycling. Given the urgency of the climate challenge, NASA's Carbon Monitoring System (CMS) requires all funded investigators to identify and work with stakeholder organizations at project inception to accelerate the transfer of the products developed by funded research into decision making systems. In this study, we contribute to the literature through the implementation of a quantitative analysis of 908 unique survey responses from funded investigators to explore the maturity of the scientist-stakeholder engagement. The paper employs multiple correspondence analysis to provide evidence to support policy options to increase stakeholder integration into research programs. Despite limitations of the dataset used, we demonstrated that multiple funding rounds, long-standing relationships between the stakeholder and scientist, and the scientific productivity of the Principal Investigator, including the ability to produce datasets and research papers on these datasets, all contribute to carbon products moving from research to operational use. The maturity of relationships between scientists and stakeholders was shown to result improved stakeholder engagement. The use of carbon products should be identified in every stage of the program, and that capacity building is needed to support both existing and newly identified stakeholders better understand and use CMS products. As federal, state, and local policy on climate adaptation and mitigation matures, the need for information on carbon will expand. Building of stakeholder-scientist relationships in CMS results in an effective generation and use of datasets to support this need and prototype ways that improved information needed for decision making can be created.

Molly E Brown↗
NASA NTRS2016

Initial Analysis of and Predictive Model Development for Weather Reroute Advisory Use

In response to severe weather conditions, traffic management coordinators specify reroutes to route air traffic around affected regions of airspace. Providing analysis and recommendations of available reroute options would assist the traffic management coordinators in making more efficient rerouting decisions. These recommendations can be developed by examining historical data to determine which previous reroute options were used in similar weather and traffic conditions. Essentially, using previous information to inform future decisions. This paper describes the initial steps and methodology used towards this goal. A method to extract relevant features from the large volume of weather data to quantify the convective weather scenario during a particular time range is presented. Similar routes are clustered. A description of the algorithm to identify which cluster of reroute advisories were actually followed by pilots is described. Models built for fifteen of the top twenty most frequently used reroute clusters correctly predict the use of the cluster for over 60 of the test examples. Results are preliminary but indicate that the methodology is worth pursuing with modifications based on insight gained from this analysis.

machine learning↗
DOE OSTI2024

Transportation and System Analysis Collaborations in Support of a Federal Consolidated Interim Storage Facility

The U.S. Department of Energy’s Integrated Waste Management (IWM) program under the Office of Nuclear Energy is planning for the future transportation, storage, and eventual disposal of spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from nuclear power plant and waste custodian sites across the United States. To better enable informed decision-making regarding the back end of the nuclear fuel cycle, the IWM program has been sponsoring the development and application of system analysis tools capable of analyzing various options for managing SNF and HLW. With these tools, integrated waste management system (IWMS) architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analyses and assessments typically use these modeling and simulation tools to investigate implications of changes in various assumptions and parameters such as acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, start and stop dates of facilities, etc. The Next Generation System Analysis Model (NGSAM) is an agent-based simulation toolkit that is used for a system-level simulation and analysis focused on SNF management in the United States. NGSAM’s primary purpose is to provide a system analyst with capabilities to model the IWMS and gain insights into SNF and HLW management alternatives including the impact of system choices, associated cost estimates, and development of integrated yet flexible approaches. An analyst using NGSAM can define several factors like the number of storage facilities, capacity at each facility, transportation schedules, shipment rates, and other conditions. IWM is also developing the Stakeholder Tool for Assessing Radioactive Transportation (START). START is a web-based geospatial tool developed to provide the visualization and initial evaluation of transportation options associated with future SNF and HLW shipment planning and operations. This includes characterizing safety, economic, and environmental conditions at and in proximity to shipment origins as well as along prospective transportation routes. Information from the START tool can be presented/shared as maps, graphics, geospatial files, and tabular form to enable easy data representation and export functionality. The system analysis, NGSAM, and START teams have been working closely for several years before formalizing this collaboration. START provides the SNF transportation routing information for use in NGSAM. System analysts use the NGSAM tool to generate results (schedule, costs, infrastructure acquisitions, shipment rates, etc.). Subject-matter experts and system analysts work together to inform how NGSAM should model the waste management system. Specifically, this paper discusses the collaborations between the system analysis, NGSAM, and START teams and their accomplishments over the past year. Some examples of these efforts include calibrating and adding data to the START output files to meet NGSAM needs, gaining a better understanding of START data used in NGSAM as well as how updates in START data could result in an improved NGSAM analysis. Detailed examples of various tasks that have been performed by the team will be discussed in the full paper. Continued efforts in this direction are expected in the coming years.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Analysis of Supply Chains and Advanced Manufacturing of Small Hydropower Systems

Clean energy manufacturing is a sector of increasing importance both in the United States and worldwide. In the United States, it has been presented as an engine for job creation in a sector of the U.S. economy that was hit hard by the 2008-2009 recession. The Clean Energy Manufacturing Analysis Center (CEMAC), sponsored by the U.S. Department of Energy (DOE), conducts credible, objective, industry-relevant, recurring, and consistent analyses of clean energy technologies. In this project, the National Renewable Energy Laboratory (NREL) and the Oak Ridge National Laboratory (ORNL) collaborated in a hydropower manufacturing and supply chain analysis primarily for U.S. small hydropower (SHP) market to provide insights on supply chain constraints, manufacturing cost, location factors, and opportunities for hydropower using advanced manufacturing technologies. The goal of the project is to improve understanding of the manufacturing opportunities in the U.S. hydropower supply chain, specific competitive advantages, and factors for manufacturing location decisions for SHP. These insights could be used to inform investment and research strategies, policy, and other decisions that could promote economic growth and strengthen U.S. manufacturing capabilities. The project scope included a design for manufacturing and assembly (DFMA) costs analysis and a levelized cost of electricity (LCOE) sensitivity analysis of modular, representative SHP turbines. A representative SHP system was chosen to be made up of two 5 megawatt (MW) turbines, as these Kaplan style turbines could be useable for many non-powered dams (NPDs) across the U.S. The sensitivity analysis used the Integrated Design and Assessment (IDEA) tool, which has helped highlight the impact of the turbine equipment cost on the potential capital expenditure (CAPEX) and LCOE of the installed, representative system. In addition, the project team analyzed international tradeflows (e.g., export and import of hydropower turbines and the parts used in hydropower systems). As part of this project, hydropower stakeholder interviews were undertaken to help identify insights related to hydropower manufacturing opportunities for U.S. players in SHP, the country-specific manufacturing advantages in the United States and threats, and key factors behind manufacturing location decisions made by the SHP industry. Key opportunities for the US SHP market and industry include: Foreign export markets such as Chile, China, Columbia, India and Japan, have an estimated 71,795 MW of technical potential that could be serviced with SHP installations. Utilization of existing manufacturing capacity and expertise, along with economies of scale benefits, could result in increased global competitiveness through lower investment requirements and component costs, which could lead to increased U.S. strength in the export of SHP systems and components. Technology advances such as modular turbine designs, standardized units for conduit systems, precast systems, and improved powertrain technologies could help reduce costs, particularly for manufacturing a small number of units. The DOE is also exploring modular hydropower designs, which would integrate standard independently-produced components, validated to meet multiple specifications. Using modules in product design simplifies manufacturing activities such as inspection, testing, assembly, and purchasing, may increase economies of scale to lower total system cost. There are significant opportunities for the use of composites and Additive Manufacturing (AM) for hydropower components, when design changes are made to hydropower systems. By taking advantage of the corrosion resistance, light-weighting and structural benefits, composites and AM could help significantly alter hydropower structures and installations.

13 HYDRO ENERGY↗
DOE OSTI2015

Design Concepts for an Outage Control Center Information Dashboard

The nuclear industry, and the business world in general, is facing a rapidly increasing amount of data to be dealt with on a daily basis. In the last two decades, the steady improvement of data storage devices and means to create and collect data along the way influenced the manner in which we deal with information. Most data is still stored without filtering and refinement for later use. Many functions at a nuclear power plant generate vast amounts of data, with scheduled and unscheduled outages being a prime example of a source of some of the most complex data sets at the plant. To make matters worse, modern information and communications technology is making it possible to collect and store data faster than our ability to use it for making decisions. However, in most applications, especially outages, raw data has no value in itself; instead, managers, engineers and other specialists want to extract the information contained in it. The complexity and sheer volume of data could lead to information overload, resulting in getting lost in data that may be irrelevant to the task at hand, processed in an inappropriate way, or presented in an ineffective way. To prevent information overload, many data sources are ignored so production opportunities are lost because utilities lack the ability to deal with the enormous data volumes properly. Decision-makers are often confronted with large amounts of disparate, conflicting and dynamic information, which are available from multiple heterogeneous sources. Information and communication technologies alone will not solve this problem. Utilities need effective methods to exploit and use the hidden opportunities and knowledge residing in unexplored data resources. Superior performance before, during and after outages depends upon the right information being available at the right time to the right people. Acquisition of raw data is the easy part; instead, it is the ability to use advanced analytical, data processing and data visualization methods to turn the data into reliable information and comprehensible, actionable information. Techniques like data mining, filtering and analysis only work reliably for well-defined and well-understood problems. The path from data to decision is more complex. The ability to communicate knowledge during outages and emergent issues is crucial. This paper presents an approach to turn the unused data into an opportunity: applying principles from semiotics, human factors and visual analytics to transform the traditional way of processing outage data into media that will improve the collective situation awareness, knowledge, decisions, actions and overall performance of the entire outage team, and also support the reliability, quality and overall effectiveness of maintenance work. The application of the proposed visualization methods will become the medium of a semi-automated analytical process where humans and machines cooperate using their respective, distinct capabilities for the most effective results.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

COVID-19 Data Curation Effort: An Initial Analysis of the Data

During the COVID-19 pandemic of 2020, major case reporting outlets quickly coalesced around two or three primary vendors. Johns Hopkins University and The New York Times were among the more prominent, and all were of great value to the nation, particularly during the uncertain early stages of the pandemic. They primarily focused on three major attributes: number of new cases, deaths, and recovery. Recognizing that many states were reporting very detailed data sets (e.g., hospital beds) at a county level or finer, the ORNL Pandemic Modeling team embarked on a major data curation effort from March to June 2020 for the purpose of capturing this wealth of detailed data. The challenge of curating this data was daunting. The number of attributes reported by the states grew on almost on a weekly basis. States were routinely shifting their web tool strategies away from easily parsable HTML-based formatting to new Tableau and ArcGIS content. This growth in the sheer number of attributes, combined with the unpredictable shifts in data format, meant an aggressive and agile combination of automated scripting and manual scraping was required to capture new daily streams. Further, the team had to scale up staff and widen its approach for capture and storage. As a result, the team collected more than 11 million data points. Following the close of this data collection effort on June 30 th , 2020, the team embarked on a major effort to appraise what had been collected, including an inventory list, spatial completeness, temporal completeness, scale and geographic characteristics, and a determination. A report on this matter was submitted on September 15 th , 2020, titled “DOE COVID-19 Data Curation Effort: Overview of Data Collection Coverage”. Over 2000 unique attributes had been netted over a wide range of spatial scales, including state, county, zip codes, health regions, and census blocks. Over 11 million individual data points were collected across these attributes, and spatial coverage (in total) included all 50 states and multiple territories. What became apparent in the process is that in the absence of any data standards, many states reported a wide variety of unique attributes that were not always compatible with attributes reported in other states. As time continued, states began adding new attributes and offering finer grain detail in some older attributes. This meant that not all data streams existed for the entire time period; in fact, the number tended to increase dramatically towards the end. Often, states would begin an attribute series and then stop altogether. These highly variable and uncertain conditions illuminated the need for harmonization approaches that would reconcile and conflate changing attribute names and detail over time. For example, grouping racial data reported as either Black or African American, depending on the state, into a single harmonized attribute. These choices would make a within-state analysis possible during the time period and lead to potential between-state analytics later on. This was almost entirely a manual decision process, requiring some subjective decision-making at times, to prevent a fragmented, short-lived collection of time series fragments that would offer few insights into trends, patterns, and correlates. This report imports harmonized data for state and county into the World Spatio-Temporal Analytics and Mapping Project (WSTAMP). WSTAMP is a major space-time analysis and visualization tool developed at ORNL for the National Geospatial-Intelligence Agency specifically for this kind of exploratory analysis. WSTAMP offers a rich analytical and graphical environment consisting of a wide range of analytics. These include time series plots, statistical summaries, data mining techniques, trend and pattern detection, and hypothesis generation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Quantitative Risk Management - Risk Informed Decision Making on High-Risk Waste Remediation Projects - 19022

For every $1 billion invested in projects, $97 million is wasted due to poor project performance. That means 9.7% of money spent on projects is wasted due to poor risk and uncertainty management. High-risk projects, similar to those in waste remediation, have the most to lose. A solid quantitative risk management program is one of the keys to increasing the likelihood that a project will succeed. A quantitative risk program solves two major problems that challenge the effective management of risk and uncertainty: 1. Risks arise at all levels of an organization; however, not all organizational levels have an effective way to identify and resolve them. This can lead to risks that, even if identified, are not reported. 2. Decision-makers are often forced to operate solely on intuition when implementing mitigation strategies, not fully understanding the effects that certain decisions have on the program's cost and schedule. A quantitative risk program uses a risk register and builds it into the project/program integrated schedule to assess the impact of these risks, typically at the 80% confidence contingency level for specific milestones and/or project completion. Implementing a quantitative risk program drives a forward-thinking, proactive mentality to problem solving into all levels of the organization. A healthy risk program for regular reporting and communication ensures that all members of an organization are thinking about risks and using their communication channels to resolve them. It gives a method of communication and incentive for all levels in the organization and helps to ensure that risks will be reported and resolved. Pacific Northwest National Laboratory (PNNL) has developed tools and methods that provide a level of realism for analyzing different scenarios and estimating contingency that does not exist in the standard industry risk tool kit. These tools and methods provide decision-makers with actionable, quantitatively based information to guide their decision making. The first tool is a probabilistic branching risk model for mitigation strategies. Current quantitative risk analysis tools in the industry simulate two cases: fully mitigated and un-mitigated. In the fully mitigated case, the user assumes that every planned mitigation activity is successful. In the un-mitigated case, the user assumes that none are successful. The truth lies between these two extremes. The PNNL team has developed a probabilistic risk model for mitigation scenarios that includes a probability that a mitigation activity will succeed and a probability that a mitigation action will be funded. These probabilities are elicited from subject matter experts and ensure that the contingency calculations at the specified confidence are approaching reality. The second tool developed at PNNL is the Risk Box, which is a tool that integrates with different scenarios in the risk register. For example, say the user is faced with two different and mutually exclusive mitigation actions to reduce a risk. The Risk Box methodology performs the contingency calculations and then displays them on a time-phased graph that shows how each scenario reduces the risk contingency needed on the project/program. This graph then provides quantitative information to show decision-makers which mitigation action provides the greatest reduction (cost and schedule) in risk contingency for the program. Implementing these and other tools within a structured risk program increases the likelihood that a project will come in on schedule and budget, ensures that all personnel are proactively thinking about problems and resolutions, creates communication channels for all members to submit and resolve potential problems, produces a prioritized list of risk rankings to focus leadership on issues that may cause the biggest problems, and implements processes that drive updates to the risk program as the risk profile changes. PNNL has been developing these methods and using them on projects for over 20 years. They have been successfully applied on waste management projects, high-tech research and development projects, and construction projects, and they continue to be used on a number of U.S. Department of Energy projects today. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2004

Multi-Sensor Distributive On-Line Processing, Visualization, and Analysis Infrastructure for an Agricultural Information System at the NASA Goddard Earth Sciences DAAC

The Goddard Space Flight Center Earth Sciences Data and Information Services Center (GES DISC) Distributed Active Center (DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM On-line Visualization and Analysis System precipitation and other satellite data products and services. AIS outputs will be ,integrated into existing operational decision support system for global crop monitoring, such as that of the U.N. World Food Program. The ability to use the raw data stored in the GES DAAC archives is highly dependent on having a detailed understanding of the data's internal structure and physical implementation. To gain this understanding is a time-consuming process and not a productive investment of the user's time. This is an especially difficult challenge when users need to deal with multi-sensor data that usually are of different structures and resolutions. The AIS has taken a major step towards meeting this challenge by incorporating an underlying infrastructure, called the GES-DISC Interactive Online Visualization and Analysis Infrastructure or "Giovanni," that integrates various components to support web interfaces that ,allow users to perform interactive analysis on-line without downloading any data. Several instances of the Giovanni-based interface have been or are being created to serve users of TRMM precipitation, MODIS aerosol, and SeaWiFS ocean color data, as well as agricultural applications users. Giovanni-based interfaces are simple to use but powerful. The user selects geophysical ,parameters, area of interest, and time period; and the system generates an output ,on screen in a matter of seconds.

Teng, William↗
DOE OSTI2024

Yet Another Discriminant Analysis (YADA): A Probabilistic Model for Machine Learning Applications

This paper presents a probabilistic model for various machine learning (ML) applications. While deep learning (DL) has produced state-of-the-art results in many domains, DL models are complex and over-parameterized, which leads to high uncertainty about what the model has learned, as well as its decision process. Further, DL models are not probabilistic, making reasoning about their output challenging. In contrast, the proposed model, referred to as Yet Another Discriminate Analysis(YADA), is less complex than other methods, is based on a mathematically rigorous foundation, and can be utilized for a wide variety of ML tasks including classification, explainability, and uncertainty quantification. YADA is thus competitive in most cases with many state-of-the-art DL models. Ideally, a probabilistic model would represent the full joint probability distribution of its features, but doing so is often computationally expensive and intractable. Hence, many probabilistic models assume that the features are either normally distributed, mutually independent, or both, which can severely limit their performance. YADA is an intermediate model that (1) captures the marginal distributions of each variable and the pairwise correlations between variables and (2) explicitly maps features to the space of multivariate Gaussian variables. Numerous mathematical properties of the YADA model can be derived, thereby improving the theoretic underpinnings of ML. Validation of the model can be statistically verified on new or held-out data using native properties of YADA. However, there are some engineering and practical challenges that we enumerate to make YADA more useful.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Transactive System: Part II: Analysis of Two Pilot Transactive Systems using Foundational Theory and Metrics

This document is the second of a two-part report. Part 1 reviewed several demonstrations of transactive control and compared them in terms of their payoff functions, control decisions, information privacy, and mathematical solution concepts. It was suggested in Part 1 that these four listed components should be adopted for meaningful comparison and design of future transactive systems. Part 2 proposes qualitative and quantitative metrics that will be needed to compare alternative transactive systems. It then uses the analysis and design principles from Part 1 while conducting more in-depth analysis of two transactive demonstrations: the American Electric Power (AEP) gridSMART Demonstration, which used a double –auction market mechanism, and a consensus method like that used in the Pacific Northwest Smart Grid Demonstration. Ultimately, metrics must be devised and used to meaningfully compare alternative transactive systems. One significant contribution of this report is an observation that the decision function used for thermostat control in the AEP gridSMART Demonstration has superior performance if its decision function is recast to more accurately reflect the power that will be used under for thermostatic control under alternative market outcomes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2010

Analysis of DFW Perimeter Taxiway Operations

This study examines operations of the perimeter taxiway system at Dallas/Fort Worth International Airport (DFW) to characterize and understand the impact of the perimeter taxiway system and to provide operational decision makers with guidance on use of this new airport resource. DFW s perimeter taxiway entered service in December 2008 and is representative of perimeter or end-around taxiways currently in use at several other airports worldwide. This perimeter taxiway analysis is a collaborative effort between NASA and various DFW stakeholders including the FAA, air carriers and the airport operator. The initial investigation has focused on quantifying perimeter taxiway usage and assessing effects on taxi times at both the local and global levels. Local-level results show taxi times via the perimeter taxiway to be about forty-five seconds longer on average, but with significantly less variability. Global-level results show average perimeter taxiway times to be a little more than one minute longer with variability that is comparable to that for other taxi paths.

Engelland, Shawn A.↗
NASA NTRS2018

Socioeconomic Impact Evaluation for Near Real-time Flood Detection in the Lower Mekong River Basin

Flood events pose a severe threat to communities in the Lower Mekong River Basin. The combination of population growth, urbanization, and economic development exacerbate the impacts of these events. Flood damage assessments, critical for understanding the effects of flooding on the local population and informing decision-makers about future risks, are frequently used to quantify the economic losses due to storms. Remote sensing systems provide a valuable tool for monitoring flood conditions and assessing their severity more rapidly than traditional post-event evaluations. The frequency and severity of extreme flood events are projected to increase, further highlighting the need for improved flood monitoring and impact analysis. In this study we integrate a socioeconomic damage assessment model with a near real-time flood remote sensing and decision support tool (NASA's Project Mekong). Direct damages to populations, infrastructure, and land cover are assessed using the 2011 Southeast Asian flood as a case study. Improved land use/land cover and flood depth assessments result in rapid loss estimates throughout the Mekong River Basin. Results suggest that rapid initial estimates of flood impacts can provide valuable information to governments, international agencies, and disaster responders in the wake of extreme flood events.

Mekong Basin↗
DOE OSTI2017

Transactive System: Part II: Analysis of Two Pilot Transactive Systems using Foundational Theory and Metrics

This document is the second of a two-part report. Part 1 reviewed several demonstrations of transactive control and compared them in terms of their payoff functions, control decisions, information privacy, and mathematical solution concepts. It was suggested in Part 1 that these four listed components should be adopted for meaningful comparison and design of future transactive systems. Part 2 proposes qualitative and quantitative metrics that will be needed to compare alternative transactive systems. It then uses the analysis and design principles from Part 1 while conducting more in-depth analysis of two transactive demonstrations: the American Electric Power (AEP) gridSMART Demonstration, which used a double-auction market mechanism, and a consensus method like that used in the Pacific Northwest Smart Grid Demonstration. Ultimately, metrics must be devised and used to meaningfully compare alternative transactive systems. One significant contribution of this report is an observation that the decision function used for thermostat control in the AEP gridSMART Demonstration has superior performance if its decision function is recast to more accurately reflect the power that will be used under for thermostatic control under alternative market outcomes.

24 POWER TRANSMISSION AND DISTRIBUTION↗