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NASA NTRSDate not supplied

The Satellite Flythrough and Reconstruction Softwares at CCMC

The next-generation of model data visualization to be offered at the Community Coordinated Modeling Center (CCMC) will be based on Kamodo, an open source python package. To increase the usefulness of our services, we are also developing new capabilities based on this software, called the satellite flythrough and the reconstruction tools, to be offered both as packages for offline analysis and through an online interface (coming soon). The satellite flythrough software ‘flies’ a satellite trajectory, whether real or imaginary, through either model data hosted at CCMC or on a personal machine. This service greatly simplifies the complexity of users’ access to model data, abstracting away the time-consuming details of model data formats and interpolation. We demonstrate execution times of a few seconds to a few minutes for several example flythroughs of a trajectory stretching over a few days, depending on the parameters chosen. We also demonstrate a reconstruction tool built on top of the satellite flythrough software, for use with mission planning and model-data comparisons. This tool, based on reconstructions provided for the GDC Science and Technology Definition Team, converts multiple, simultaneous satellite flythroughs into two-dimensional reconstructions. The reconstruction tool provides a software capability for satellite constellations to determine how many satellites are needed and in what configuration to resolve the desired features in the model data. Both tools are currently available through GitHub for a selection of CCMC-hosted ITM models. Finally, we present initial results from work in progress and plans for future work, including an expansion of the reconstruction tool to provide 3D reconstruction capabilities and a line-of-sight calculation tool.

software, python↗

DOE OSTI2020

Modeling Market Interactions

This slide deck provides a high-level overview of wholesale electricity market design model development activities occurring at NREL. The motivation for this work, as well as the broader NREL modeling landscape in which it belongs, is first presented. Then the various model formulations and features are described.

agent-based simulation↗

DOE OSTI2023

Nuclear's Role in the U.S. Electricity System: A Multi-Model Inter-Comparison Analysis

A variety of US electric power sector capacity expansion models are used by decision makers and analysts to evaluate competition among generation, transmission, and storage technologies to meeting the demands of the system. CEMs use least-cost optimization to identify optimal portfolios of investments capable of satisfying all specified requirements. While CEMs are a useful tool to inform pathways to meet future needs, projections can differ significantly between tools for apparently similar scenario assumptions. Differences in model structure, scope, and input assumption contribute to this issue. This work compares model response with harmonization between four modeling teams on issues significant to the representation and development of nuclear energy.

capacity expansion model↗

DOE OSTI2023

Insights on Nuclear Energy from Long-Term System Models: Inter-Model Comparison Results

capacity expansion model↗

DOE OSTI2024

Advancing Understanding of Geothermal Representation in the Power Sector to Accelerate Deployment

Driven mostly by decarbonization goals, geothermal interest in the US power sector has grown considerably, gradually evolving from being considered a niche technology, to being recognized as a viable source of clean, baseload, grid-balancing power, and renewable electric power generation. Furthermore, recent technical advances that could greatly accelerate deployment in the near future, and US federal incentives for low-carbon generation technologies, including geothermal, can enable opportunities for integrating geothermal into utilities resource planning portfolios. However, in general, utilities do not have in-house expertise to evaluate geothermal technologies and its potential role in helping decarbonize the grid as well as help them achieve their individual decarbonization goals. To date, geothermal is rarely included in Capacity Expansion Models (CEM) which utilities use in their planning activities and resource/technologies prioritization. To this end, EPRI and NREL are working together in a DOE-GTO funded research project to improve geothermal understanding (opportunities, value, risks) among the power industry to help accelerate geothermal deployment. In the present paper we describe the approach and preliminary findings of this work, focused on two topics: 1) Expand the degree of understanding on the value, opportunity, and risk of geothermal technologies among utilities and related companies/groups, specifically around geothermal for power generation. 2) Improve representation of geothermal power technologies in capacity expansion models (CEM).

capacity expansion models↗

DOE OSTI2022

Effect of modeled time horizon on quantifying the need for long-duration storage

Long-Duration Energy Storage (LDES) has gained interest due to its key role in attaining a decarbonized, low-cost, and stable grid driven by variable renewable electricity (VRE). Currently, there is a wide range of LDES technologies being developed to provide electricity with 8+ hours of consecutive discharge. However, current capacity expansion models used in long-term planning processes rarely consider low cost LDES as a candidate technology. If they do, the storage balancing horizon (SBH) of the model usually only considers non-consecutive 1-day periods that do not capture the potential of LDES to shift energy across multiple days or even seasons. Addressing these limitations in existing models, this work explores the ways in which the optimal energy storage changes when increasing the number of consecutive days in the SBH and how these changes will impact planners who are determining the future roles of energy storage. Our analysis uses SWITCH, an open-source capacity expansion model with a high spatial resolution for the entire Western Electricity Coordinating Council (WECC) in a zero-carbon scenario in 2050. We find that the number of consecutive days in the SBH changes both the total selected power and energy capacity of LDES when storage energy and power capacity overnight costs are $\$$13 USD/kWh (or less) and $113 USD/kW, respectively. We also find that the amount of required energy in storage to drive a future VRE-driven WECC grid ranges from 2.5 TWh to 16.0 TWh depending on the length of the SBH. The optimal storage duration (energy to power ratio) we obtain ranges from 10 h to 620 h among all the scenarios. Furthermore, depending on the storage cost assumption, we observe different charge/discharge patterns when varying the length of the SBH. Given our results, we anticipate that as more LDES technologies become commercially available, it will be critical to increase the length of the SBH to fully capture the benefits of LDES assets in long-term planning processes of high VRE-driven grids.

42 ENGINEERING↗

DOE OSTI2021

Plans for Expanded Data Testing at ORNL [Slides]

VALID expansion is on-going and should provide coverage over a wider range of systems in the future. Adding additional application types beyond critical experiments can also be used in expanded data testing. Two other test suites have been developed at ORNL for other purposes, but never waste a good test suite. These suites are more likely to be relevant to testing differences in data as compared to assessing accuracy of evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Modeling the Water Systems of the Western US to Support Climate‐Resilient Electricity System Planning

Abstract Electricity and water systems in the Western US (WUS) are closely connected, with hydropower comprising 20% of total annual WUS generation, and electricity related to water comprising about 7% of total WUS electricity use. Because of these interdependencies, the threat of climate change to WUS resources will likely have compounding electricity impacts on the Western Interconnect grid. This study describes a WUS‐wide water system model with a particular emphasis on estimating climate impacts on hydropower generation and water‐related electricity use, which can be linked with a grid expansion model to support climate‐resilient electricity planning. The water system model combines climatically‐driven physical hydrology and management of both water supply and demand allocation, and is applied to an ensemble of 15 climate scenarios out to 2050. Model results show decreasing streamflow in key basins of the WUS under most scenarios. Annual water‐related electricity use increases up to 4%, and by up to 6% during the summer months, driven by growing agricultural demands met increasingly through a shift toward energy‐intensive groundwater to replace declining surface water. Total annual hydropower generation changes by +5% to −20% by mid‐century but declines in most scenarios, with decreases in summer generation by up to nearly −30%. Water‐related electricity use increases tend to coincide with hydropower generation declines, annually and seasonally, demonstrating the importance of concurrently evaluating the climate signal on both water‐for‐energy and energy‐for‐water to inform planning for grid reliability and decarbonization goals.

13 HYDRO ENERGY↗

DOE OSTI2020

2020 Standard Scenarios Report: A U.S. Electricity Sector Outlook

This report summarizes the results of 47 forward-looking 'standard scenarios' of the U.S. power sector simulated by the National Renewable Energy Laboratory (NREL) using the Regional Energy Deployment System (ReEDS) and Distributed Generation (dGen) capacity expansion models. The annual Standard Scenarios, which are now in their sixth year, have been designed to capture a range of possible power system futures considering a variety of factors that impact power sector evolution.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Regional Energy Deployment System (ReEDS) Model Documentation: Version 2019

The Regional Energy Deployment System (ReEDS) model is a capacity expansion and dispatch model that is primarily used for the contiguous U.S. electric power sector. The model relies on system-wide least cost optimization to estimate the type and location of future generation and transmission capacity. This document describes details of how the model is formulated, how it functions, and many of the key inputs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Regional Energy Deployment System (ReEDS) Model Documentation (Version 2020)

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2007

International Space Station USOS Waste and Hygiene Compartment Development

The International Space Station (ISS) currently provides human waste collection and hygiene facilities in the Russian Segment Service Module (SM) which supports a three person crew. Additional hardware is planned for the United States Operational Segment (USOS) to support expansion of the crew to six person capability. The additional hardware will be integrated in an ISS standard equipment rack structure that was planned to be installed in the Node 3 element; however, the ISS Program Office recently directed implementation of the rack, or Waste and Hygiene Compartment (WHC), into the U.S. Laboratory element to provide early operational capability. In this configuration, preserved urine from the WHC waste collection system can be processed by the Urine Processor Assembly (UPA) in either the U.S. Lab or Node 3 to recover water for crew consumption or oxygen production. The human waste collection hardware is derived from the Service Module system and is provided by RSC-Energia. This paper describes the concepts, design, and integration of the WHC waste collection hardware into the USOS including integration with U.S. Lab and Node 3 systems.

Link, Dwight E., Jr.↗

DOE OSTI2021

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement. Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1989

A mandate for space education

Issues related to public education in preparation for a manned Mars program are discussed. Consideration is given to the near- and long-term goals of the space program, the benefits of human expansion in space, and long-range planning for fundamental problem areas in space education. Important concerns for space educators are outlined.

Von Puttkamer, Jesco↗

NASA NTRS1990

An assessment of the government liquid hydrogen requirements for the 1995-2005 time frame

The results of government study of long range liquid hydrogen (LH2) requirements for the time period of 1995 through the year 2005 are presented. To assure an adequate supply of LH2 is available in support of various programs, it is imperative a long range projection of LH2 requirements be developed and maintained. This information is vital in planning for necessary procurement actions and assuring adequate industry lead time to acquiring the necessary production and distribution capabilities. The Advanced Launch System and High-Altitude Long-Endurance programs may represent the predominant government needs for LH2 in the long range. The assembled data clearly indicates a need for KSC (Kennedy Space Center) constant program/project surveillance. Also clear is the need for KSC to monitor industry's plans for LH2 plant production and distribution expansion.

Bain, Addison↗

NASA NTRS2007

SERVIR: From Space to Village. A Regional Monitoring and Visualization System For Environmental Management Using Satellite Applications For Sustainable Development

NASA is committed to providing technological support and expertise to regional and national organizations for earth science monitoring and analysis. This commitment is exemplified by NASA's long-term relationship with Central America. The focus of these efforts has primarily been to measure the impact of human development on the environment and to provide data for the management of human settlement and expansion in the region. Now, NASA is planning to extend and expand this capability to other regions of the world including Africa and the Caribbean. NASA began using satellite imagery over twenty-five years ago to locate important Maya archeological sites in Mesoamerica and to quantify the affect of deforestation on those sites. Continuing that mission, NASA has partnered with the U.S. Agency for International Development (USAID), the World Bank, the Water Center for the Humid Tropics of Latin America and the Caribbean (CATHALAC) and the Central American Commission for Environment and Development (CCAD) to develop SERVIR (Sistema Regional de Visualizacion y Monitoreo), for the Mesoamerican Biological Corridor. SERVIR has become one of the most important aspects of NASA's geospatial efforts in Central America by establishing a common access portal for information that affects the lives, livelihood and future of everyone in the region. SERVIR, most commonly referred to as a regional visualization and monitoring system, is a scientific and technological platform that integrates satellite and other geospatial data sets to generate tools for improved decision-making capabilities. It has a collection of data and models that are easily accessible to earth science managers, first responders, NGO's (Non-Government Organizations) and a host of others. SERVIR is currently used to monitor and forecast ecological changes as well as provide information for decision support during severe events such as forest fires, red tides,and tropical storms. Additionally, SERVIR addresses the nine societal benefit areas of the Global Earth Observation System (GEOSS): disasters, ecosystems, biodiversity, weather, water, climate, health, agriculture and energy.

Sever, Tom↗

NASA NTRS2016

Stereo Vision-Based Obstacle Avoidance for Micro Air Vehicles Using an Egocylindrical Image Space Represntation

Micro air vehicles which operate autonomously at low altitude in cluttered environments require a method for on-board obstacle avoidance for safe operation. Prior approaches can be divided between purely reactive approaches, mapping low-level visual features directly to headings to maneuver the vehicle around the obstacle, and deliberative methods that use on-board 3-D sensors to create a 3-D, voxel-based world model, which is then used to generate collision free 3-D trajectories. In this paper, we use forward-looking stereo vision with a large horizontal and vertical field of view and project range from stereo into a novel robot-centered, cylindrical, inverse range map we call an egocylinder. With this implementation we reduce the complexity of our world representation from a 3D map to a 2.5D image space representation, which supports very efficient motion planning and collision-checking. Configuration space expansion is done very efficiently on the egocylinder as an image processing function. Deploying a fast reactive motion planner directly on the configuration space expanded egocylinder image, we demonstrate the effectiveness of this new approach experimentally in an indoor environment.

Micro air vehicles↗

NASA NTRSDate not supplied

Implementing Geometric Surface Imperfections into Sandwich Composite Cylinder Finite Element Method Models

The buckling responses of certain cylindrical shell structures are extremely sensitive to geometric surface imperfections. The NASA Engineering and Safety Center (NESC) Shell Buckling Knockdown Factor Project (SBKF) is conducting research to develop analysis-based buckling design recommendations. Experiments are used to verify the analysis-based factors, but the sensitivity of the test articles to geometric imperfections requires implementing as-manufactured imperfections into high-fidelity finite element method models. Data collection methods such as structured light scanning are used for all geometric surface data used in this work. Common preprocessing and visualization steps used in SBKF are discussed, and steps on how surface scans are prepared for implementation into a finite element model is described. The Python Tool for Implementing Geometric Imperfections in Reduced Structures (Py_TIGIRS), written specifically for the use with SBKF, is briefly described and uses eight functions to extract, modify, and write geometric imperfections into Abaqus input files. Results of the pre-processing methods and results from Py_TIGIRS are provided and compared for Composite Test Article (CTA) 8.2B. Excellent agreement between the visualized scan data and the FEM-extracted geometry is demonstrated. A brief example of why geometric surface imperfections are significant in nonlinear numerical analyses for thin cylinders in axial compression is provided as motivation to use tools such as Py_TIGIRS. Future development of Py_TIGIRS including expansion to structures of arbitrary geometry is planned.

Sandwich structures↗