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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Accuracy metrics for judging time scale algorithms

Time scales have been constructed in different ways to meet the many demands placed upon them for time accuracy, frequency accuracy, long-term stability, and robustness. Usually, no single time scale is optimum for all purposes. In the context of the impending availability of high-accuracy intermittently-operated cesium fountains, we reconsider the question of evaluating the accuracy of time scales which use an algorithm to span interruptions of the primary standard. We consider a broad class of calibration algorithms that can be evaluated and compared quantitatively for their accuracy in the presence of frequency drift and a full noise model (a mixture of white PM, flicker PM, white FM, flicker FM, and random walk FM noise). We present the analytic techniques for computing the standard uncertainty for the full noise model and this class of calibration algorithms. The simplest algorithm is evaluated to find the average-frequency uncertainty arising from the noise of the cesium fountain's local oscillator and from the noise of a hydrogen maser transfer-standard. This algorithm and known noise sources are shown to permit interlaboratory frequency transfer with a standard uncertainty of less than 10(exp -15) for periods of 30-100 days.

Douglas, R. J.↗
DOE OSTI2022

Getting lost tracking the carbon footprint of hydropower

In the transition to low-carbon electricity, well-quantified estimates of carbon dynamics are needed to ensure that emissions reduction targets are achieved. Here we review the state of the science on carbon accounting for hydropower reservoirs and identify limitations and future solutions. Nearly all research on reservoir greenhouse-gas (GHG) emissions has focused on individual reservoirs in isolation without considering their position in a freshwater network draining organic matter from upstream watersheds or the coordinated operation of reservoir cascades. Second, carbon inventories have extrapolated from a small, non-probabilistic sample of highly variable measurements of GHG emissions to unsampled reservoirs. A stronger statistical foundation is needed to estimate a global inventory and its uncertainty. Third, attribution to hydropower is based on ranks assigned to reservoir purpose. Instead, the physical influence of hydropower on carbon dynamics could be directly measured. Fourth, current carbon-accounting practices neglect time. A time-varying approach would quantify variation in emissions for electricity portfolios from changes in the fuel mix at different times and account for ancillary services, i.e., the ability to support the grid when variable renewables are not available without using natural gas. Reservoirs also sequester a significant portion of inflowing carbon in sediments and slow the carbon cycle by delaying the return of carbon to the atmosphere for decades to centuries. Together, these refinements would help to illuminate pathways toward meeting energy demand with the longest-possible delay in returning carbon to the atmosphere and without adding ancient sources to the pool of carbon cycling through aquatic ecosystems.

13 HYDRO ENERGY↗
DOE OSTI2021

All-Atom Simulation of 3D Hot Spot Formation in Shocked TATB Explosive

TATB is an insensitive high explosive (IHE) critical to the stockpile that is challenging to model at the continuum scale. Advanced detonation models in the Cheetah high explosive chemistry code require validation though subscale simulations. High explosive initiation is determined by micron-scale physics of hot spots formed a shock-collapsed pores. Pore sizes between 100 nm and 1 μm are believed to be the most important for determining the shock sensitivity of TATB. This range of pore sizes is difficult to access at the atomic scale through allatom molecular dynamics (MD) simulations, even with Sierra-class computers. Quasi-2D simulations are widely used and allow much larger pore sizes (up to 400 nm) to be studied, but the applicability of 2D simulations to the actual 3D pore response is not understood. Resolving these uncertainties through “full physics” MD modeling is key for generalizing, parameterizing, and validating the kinds of continuum models used to inform design, safety, and performance. This work was a continuation of FY20 efforts pushing simulations to full 3D with the largest-ever all-atom simulations of an explosive. These were the first all-atom full-3D simulations of large hot spots thought to govern explosive detonation and required over a billion atoms. Simulations were performed using LAMMPS, an open SNL science code. MD explosive models present unique challenges, even for established codes such as LAMMPS. Their model forms are more complex than typical models for metals, while simulating high temperature-pressure conditions is demanding and increases computational cost. Scaling problems in GPU-enabled MD algorithms initially limited simulations to <100 million atoms but were resolved through collaboration with SNL. An overall 24x speedup was obtained relative to CPU machines. Specialized analysis of these simulations required a bottom-up refactoring and algorithm parallelization of in-house codes and application of computer vision algorithms to extract meaningful information.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Process design and techno-economic analysis for bio-based graphite and liquid hydrocarbons production from lignocellulosic biomass

The worldwide demand for graphite, as the main anode material for Li-ion batteries, is expected to double by 2028 since it supports the use of electricity, including transient renewable sources, for energy storage, sustainable mobility, and automation. However, the dependence on non-renewable and external resources jeopardizes the world supply chain. This study explores the technical and economic performance of transforming lignocellulosic biomass into biographite and fuel-grade hydrocarbons through pyrolysis bio-oil upgrading. According to simulation results, the total power demand for the biorefinery reached 10,784 kWh per tonne of biographite, of which 36 % can be supplied by the heat integration network and power plant. Sensitivity and risk analyses were conducted to evaluate the economics, with process yields identified as the most relevant indicators to the minimum selling price (MSP). The analysis revealed a promising cost-competitive range for biographite MSP against fossil-based graphite (medium quality synthetic graphite Chinese market price ~$\$$4.2/kg). Case D, which includes biofuels as a byproduct, presents the best metrics, reaching a MSP of $\$$3.3/kg of anode-grade biographite with a profit margin of 27 %. While including biofuels in the product slate provides the best economic performance, the uncertainty associated with the big capital investment makes its risk 13 % higher to attain an IRR >20 % than the case in which biographite is produced as a standalone product. Overall, this study demonstrates that integrated biorefineries can produce a cost-competitive bio-based anode material for Li-ion batteries.

09 BIOMASS FUELS↗
DOE OSTI2022

The uncertain role of rising atmospheric CO 2 on global plant transpiration

As CO 2 concentration in the atmosphere rises, there is a need for improved physical understanding of its impact on global plant transpiration. This knowledge gap poses a major hurdle in robustly projecting changes in the global hydrologic cycle. For this reason, here we review the different processes by which atmospheric CO 2 concentration affects plant transpiration, the several uncertainties related to the complex physiological and radiative processes involved, and the knowledge gaps which need to be filled in order to improve predictions of plant transpiration. Additionally, although there is a high degree of certainty that rising CO 2 will impact plant transpiration, the exact nature of this impact remains unclear due to complex interactions between CO 2 and climate, and key aspects of plant morphology and physiology. The interplay between these factors has substantial consequences not only for future climate and global vegetation, but also for water availability needed for sustaining the productivity of terrestrial ecosystems. Future changes in global plant transpiration in response to enhanced CO 2 are expected to be driven by water availability, atmospheric evaporative demand, plant physiological processes, emergent plant disturbances related to increasing temperatures, and the modification of plant physiology and coverage. Considering the universal sensitivity of natural and agricultural systems to terrestrial water availability we argue that reliable future projections of transpiration is an issue of the highest priority, which can only be achieved by integrating monitoring and modeling efforts to improve the representation of CO 2 effects on plant transpiration in the next generation of earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Mycorrhizal Distributions Impact Global Patterns of Carbon and Nutrient Cycling

Most tree species predominantly associate with a single type of mycorrhizal fungi, which can differentially affect plant nutrient acquisition and biogeochemical cycling. Uncertainties in mycorrhizal distributions are non-trivial, and current estimates disagree in up to 50% over 40% of the land area, including tropical forests. Remote sensing capabilities for mycorrhizal detection show promise for refining these estimates further. Here, we address for the first time the impact of mycorrhizal distributions on global carbon and nutrient cycling. Using the state-of-the-art carbon-nitrogen economics within the Community Land Model version 5, we found Net Primary Productivity (NPP) increased throughout the 21st century by 20%; however, as soil nitrogen has progressively become limiting, the costs to NPP for nitrogen acquisition—that is, to mycorrhizae—have increased at a faster rate by 60%. This suggests that nutrient acquisition will increasingly demand a higher portion of assimilated carbon to support the same productivity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Challenges Facing Design and Analysis Tools

The design and analysis of future aerospace systems will strongly rely on advanced engineering analysis tools used in combination with risk mitigation procedures. The implications of such a trend place increased demands on these tools to assess off-nominal conditions, residual strength, damage propagation, and extreme loading conditions in order to understand and quantify these effects as they affect mission success. Advances in computer hardware such as CPU processing speed, memory, secondary storage, and visualization provide significant resources for the engineer to exploit in engineering design. The challenges facing design and analysis tools fall into three primary areas. The first area involves mechanics needs such as constitutive modeling, contact and penetration simulation, crack growth prediction, damage initiation and progression prediction, transient dynamics and deployment simulations, and solution algorithms. The second area involves computational needs such as fast, robust solvers, adaptivity for model and solution strategies, control processes for concurrent, distributed computing for uncertainty assessments, and immersive technology. Traditional finite element codes still require fast direct solvers which when coupled to current CPU power enables new insight as a result of high-fidelity modeling. The third area involves decision making by the analyst. This area involves the integration and interrogation of vast amounts of information - some global in character while local details are critical and often drive the design. The proposed presentation will describe and illustrate these areas using composite structures, energy-absorbing structures, and inflatable space structures. While certain engineering approximations within the finite element model may be adequate for global response prediction, they generally are inadequate in a design setting or when local response prediction is critical. Pitfalls to be avoided and trends for emerging analysis tools will be described.

Knight, Norman F., Jr.↗
NASA NTRS2006

Workshop on the Impacts of Aviation on Climate Change

Projections indicate that demand for aviation transportation will increase by more than two fold over the next few decades. Timely action is needed to understand and quantify the potential climate impacts of aviation emissions particularly given the sustained lapse over the last several years in U.S. research activities in this area. In response to the stated needs, a group of international experts participated in the Workshop on the Impacts of Aviation on Climate Change during June 7-9, 2006 in Boston, MA. The workshop focus was on the impacts of subsonic aircraft emissions in the UT/LS region and on the potential response of the climate system. The goals of the workshop were to assess and document the present state of scientific knowledge, to identify the key underlying uncertainties and gaps, to identify ongoing and further research needed, to explore the development of climate impact metrics, and to help focus the scientific community on the aviation-climate change research needs. The workshop concluded that the major ways that aviation can affect climate, in agreement with the 1999 assessment by the Intergovernmental Panel on Climate Change (IPCC), are the direct climate effects from CO2 and water vapor emissions, the indirect forcing on climate resulting from changes in the distributions and concentrations of ozone and methane as a primary consequence of aircraft nitrogen oxide (NOx) emissions, the direct effects (and indirect effects on clouds) from emitted aerosols and aerosol precursors, and the climate effects associated with contrails and cirrus cloud formation. The workshop was organized in three subgroups: (1) Effects of aircraft emissions on the UT/LS chemical composition, (2) Effects of water and particle emissions on contrails and on cirrus clouds, and (3) Impacts on climate from aircraft emissions and identification of suitable metrics to measure these impacts. The workshop participants acknowledged the need for focused research specifically to address the uncertainties and gaps in our understanding of current and projected impacts of aviation on climate and to develop metrics to better characterize these impacts. This may entail coordination and/or expansion of existing and planned climate research programs, or new activities. Such efforts should include strong and continuing interactions among the science and aviation communities as well as policymakers to develop well-informed decisions.

Wuebbles, Don↗
NASA NTRS2013

The Effect of Agricultural Growing Season Change on Market Prices in Africa

Local agricultural production is a key element of food security in many agricultural countries in Africa. Climate change and variability is likely to adversely affect these countries, particularly as they affect the ability of smallholder farmers to raise enough food to feed themselves. Seasonality influences farmers' decisions about when to sow and harvest, and ultimately the success or failure of their crops. At a 2009 conference in the United Kingdom hosted by the Institute of Development Studies, Jennings and Magrath (2009) described farmer reports from East Asia, South Asia, Southern Africa, East Africa and Latin America. Farmers indicate significant changes in the timing of rainy seasons and the pattern of rains within seasons, including: More erratic rainfall, coming at unexpected times in and out of season; Extreme storms and unusually intense rainfall are punctuated by longer dry spells within the rainy season; Increasing uncertainty as to the start of rainy seasons in many areas; Short or transitional second rainy seasons are becoming stronger than normal or are disappearing altogether. These farmer perceptions of change are striking in that they are geographically widespread and are remarkably consistent across diverse regions (Jennings and Magrath, 2009). The impact of these changes on farmers with small plots and few resources is large. Farming is becoming riskier because of heat stress, lack of water, pests and diseases that interact with ongoing pressures on natural resources. Lack of predictability in the start and length of the growing season affects the ability of farmers to invest in appropriate fertilizer levels or improved, high yielding varieties. These changes occur at the same time as the demand for food is rising and is projected to continue to rise for the next fifty years (IAASTD, 2008). Long-term data records derived from satellite remote sensing can be used to verify these reports, providing necessary analysis and documentation required to plan effective adaptation strategies. Remote sensing data can also provide some understanding of the spatial extent of these changes and whether they are likely to continue. Given the agricultural nature of most economies on the African continent, agricultural production continues to be a critical determinant of both food security and economic growth (Funk and Brown, 2009). Crop phenological parameters, such as the start and end of the growing season, the total length of the growing season, and the rate of greening and senescence are important for planning crop management, crop diversification, and intensification. The World Food Summit of 1996 defined food security as: "when all people at all times have access to sufficient, safe, nutritious food to maintain a healthy and active life". Food security roughly depends on three factors: 1) availability of food; 2) access to food and 3) appropriate use of food, as well as adequate water and sanitation. The first factor is dependent on growing conditions and weather and climate. In a previous paper we have investigated this factor by evaluating the effect of large scale climate oscillation on land surface phenology (Brown et al., 2010). We found that all areas in Africa are significantly affected by at least one type of large scale climate oscillations and concluded that these somewhat predictable oscillations could perhaps be used to forecast agricultural production. In addition, we have evaluated changes in agricultural land surface phenology over time (Brown et al., 2012). We found that land surface phenology models, which link large-scale vegetation indices with accumulated humidity, could successfully predict agricultural productivity in several countries around the world. In this chapter we are interested in the effect of variability in peak timing of the growing season, or phenology, on the second factor of food security, food access. In this chapter we want to determine if there is a link between market prices and land surface phenology and to determine which markets are vulnerable to land surface phenology changes and variability and which market prices are not correlated.

Prices↗
DOE OSTI2020

Sensitivity and Uncertainty Quantification of Transition Scenario Simulations

This report documents the first collective attempt at developing and applying capabilities to quantify uncertainties, assess parametric sensitivities, and optimize multiple parameters and metrics in fuel cycle simulations generated by the SA&I Campaign. To do this, external codes that were designed to perform sensitivity analysis and uncertainty quantification (SA&UQ) needed to be coupled to the SA&I Campaign’s nuclear fuel cycle simulators (NFCS). In FY20, two approaches were pursued: 1) coupling Cyclus to an ORNL-internal code called MOT (Metaheuristic Optimization Tool) and 2) coupling DYMOND to the opensource SA&UQ tool kit Dakota. The primary objective of having these NFCS/SA&UQ coupled capabilities is to better inform DOE-NE and other stakeholders on the results generated from the NFCS. For a given set of fuel cycle strategies, policies, and technology assumptions that make up a fuel cycle scenario, these NFCS have traditionally been used by the SA&I Campaign to provide quantitative answers in terms of year-by-year mass flows, infrastructure requirements, costs, etc. With these newly developed coupled capabilities, the SA&I Campaign can now efficiently simulate hundreds or thousands of these scenarios, sample large ranges of parameters and assumptions, and use the unique features of the SA&UQ tools to process the data. This enables providing answers with known and propagated uncertainties, determining the sensitivity of important metrics to different parameters and assumptions, quantifying how much fuel cycle and technology parameters impact each other, and producing optimized fuel cycle strategies for single and multiple variables. To demonstrate these new capabilities, the Cyclus/MOT was used to model several scenarios ranging from simple fleet retirements to transitions to advanced reactors. Specifically, for a transition scenario from LWRs to SFRs and advanced LWRs, uncertainty quantification, sensitivity analysis, and optimization studies were applied to cases involving single and multiple parameter (input) and single and multiple metric (output) variations. In addition, a similar transition scenario was modeled to demonstrate how to optimize the reprocessing capacity parameter to minimize two performance metrics while taking into account uncertainties from two other parameters. Lastly, a depletion module based on SCALE/ORIGEN was added in Cyclus to simulate the third scenario that was designed to quantify the impact of the modeling assumption that all LWR used nuclear fuel have the same burnup. The newly developed DYMOND/Dakota capability was also applied to a transition scenario from the existing fleet to small modular reactors and fast reactors. This particular scenario involves not only explicit isotopic depletion via ORIGEN-2, but also includes multirecycling and utilizing the criticality search feature to determine the fresh fuel composition of recycled fuel, a feature unique to the DYMOND NFCS. A large database of simulations were run with 4 main parameters that were sampled: start date of reprocessing, reprocessing capacity, energy demand growth rate, and advanced reactor share of the fleet. The 4 main metrics were uranium consumption, enrichment requirements, waste generation, and levelized cost of electricity using data from the Cost Basis Report. The demonstrated SA&UQ results include those that inform on how to choose parameters to avoid “failed” scenarios, Sobol’ indices that inform on the importance of various parameters individually and synergistically, and Analysis of Variance (ANOVA) studies that decompose parameter ranges into groups and informs on whether variations are statistically significant.

Feng, B.↗
NASA NTRS2019

Collaborative Seamless Manager for Airspace Resources and Traffic

In today's NAS, airlines conduct pre-departure flight planning with limited information about traffic congestion. Then, as events evolve (pre-departure, on the surface, or inflight) and are impacted by congestion, limited options for changing flight plans are offered to the airlines, and, in some cases, flight plans are changed by air traffic managers on behalf of the airlines. For the airlines, this produces flight plan uncertainty and possible disruption to their business plans. The Collaborative Seamless Manager of Airspace Resources and Traffic (CSMART) is a tool being developed for the 2045 Next Gen System that will enhance the airlines' ability to flight plan, both pre-departure and during flight. CSMART achieves this, using the UTM architecture approach, by connecting via the internet airlines with other airlines and airlines with FAA agents. Flight plans, including digital, discrete planned trajectories with tolerances, are passed through the connection. Planned trajectories are used to create probabilistic predictions of when and where congestion will occur. Pre-departure, airline agents will use the predictions to collaboratively and seamlessly create flight plans that avoid congestion or go through it, depending on business objectives. In cases, where demand exceeds capacity and priorities need to be set, airline agents will have the ability to negotiate with each other to set them. Moreover, if flights are impacted by congestion during flight, CSMART will allow airlines more real-time decision making options for changing flight plans. This seminar presents the general CSMART concept and tool and research needed to develop it.

Windhorst, Robert D.↗
NASA NTRS1997

Probabilistic Analysis of Ground-Holding Strategies

The Ground-Holding Policy Problem (GHPP) has become a matter of great interest in recent years because of the high cost incurred by aircraft suffering from delays. Ground-holding keeps a flight on the ground at the departure airport if it is known it will be unable to land at the arrival airport. The GBPP is determining how many flights should be held on the ground before take-off and for how long, in order to minimize the cost of delays. When the uncertainty associated with airport landing capacity is considered, the GHPP becomes complicated. A decision support system that incorporates this uncertainty, solves the GHPP quickly, and gives good results would be of great help to air traffic management. The purpose of this thesis is to modify and analyze a probabilistic ground-holding algorithm by applying it to two common cases of capacity reduction. A graphical user interface was developed and sensitivity analysis was done on the algorithm, in order to see how it may be implemented in practice. The sensitivity analysis showed the algorithm was very sensitive to the number of probabilistic capacity scenarios used and to the cost ratio of air delay to ground delay. The algorithm was not particularly sensitive to the number of periods that the time horizon was divided into. In terms of cost savings, a ground-holding policy was the most beneficial when demand greatly exceeded airport capacity. When compared to other air traffic flow strategies, the ground-holding algorithm performed the best and was the most consistent under various situations. The algorithm can solve large problems quickly and efficiently on a personal computer.

Sheel, Minakshi↗
DOE OSTI2023

Highly Resolved Projections of Passenger Electric Vehicle Charging Loads for the Contiguous United States: Results From and Methods Behind Bottom-Up Simulations of County-Specific Household Electric Vehicle Charging Load (Hourly 8760) Profiles Projected Through 2050 for Differentiated Household and Vehicle Types

This report documents enhancements made to the TEMPO (Transportation Energy & Mobility Pathway Options TM ) model to project spatially, demographically, and temporally resolved national-scale EV charging load profiles and describes three scenarios and corresponding datasets created for the NREL demand-side grid (dsgrid) project in support of bulk power systems modeling. In brief, TEMPO was enhanced to disaggregate national and annual energy demand projections into household and county-level projections of passenger electric vehicle (EV) hourly charging load profiles (8760 profiles), accounting for consumer, travel, and temperature variations that impact EV energy demand. In alignment with NREL's forward-looking grid modeling, three scenarios for EV adoption covering 2020-2050 were created-- Annual Energy Outlook (AEO) Reference Case, Electrification Futures Study (EFS) High Electrification, and All EV Sales by 2035 --and associated datasets have been included in the dsgrid platform for public use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Computational Math Problems for a Clean Energy Future

Cutting edge computational mathematics are ubiquitous in renewable energy research. Problems in resilient and reliable electric grid operations, infrastructure planning, wind farm yaw control, and more demand sophisticated and scalable computational tools that enable the transition of renewable energy technologies from proof of concept to deployment into our energy system. The mission of the Computational Science Center at NREL is to lead the lab's efforts to solve energy challenges using high-performance computing (HPC), computational science, applied mathematics, scientific data management, visualization, and informatics. In this poster, we provide a short overview of three areas of computational mathematics research at NREL: wind power scenario generation for stochastic grid operations and infrastructure planning, improved rational function approximations for electromagnetic transients codes, and wind farm yaw control using a combination of the Alternating Direction Method of Multipliers (ADMM) and reinforcement learning (RL). Increasing penetrations of renewable energy into power grids motivate the investigation of new approaches to characterizing uncertainty for five-minute economic dispatch problems. Similarly, as the penetration of distributed energy resources on power grids increases, it becomes important to revisit our methods of modelling transient phenomena, i.e. electromagnetic transients programs. Finally, the combination of ADMM and RL for wind farm yaw control presented here can potentially increase the efficiency of the deployed distributed controllers by orders of magnitude.

ADMM↗
NASA NTRS2011

Advancing Aeronautics: A Decision Framework for Selecting Research Agendas

Publicly funded research has long played a role in the development of aeronautics, ranging from foundational research on airfoils to development of the air-traffic control system. Yet more than a century after the research and development of successful controlled, sustained, heavier-than-air flight vehicles, there are questions over the future of aeronautics research. The field of aeronautics is relatively mature, technological developments within it have become more evolutionary, and funding decisions are sometimes motivated by the continued pursuit of these evolutionary research tracks rather than by larger factors. These developments raise questions over whether public funding of aeronautics research continues to be appropriate or necessary and at what levels. Tightened federal budgets and increasing calls to address other public demands make these questions sharper still. To help it address the questions of appropriate directions for publicly funded aeronautics research, the National Aeronautics and Space Administration's (NASA's) Aeronautics Research Mission Directorate (ARMD) asked the RAND Corporation to assess the elements required to develop a strategic view of aeronautics research opportunities; identify candidate aeronautic grand challenges, paradigms, and concepts; outline a framework for evaluating them; and exercise the framework as an example of how to use it. Accordingly, this research seeks to address these questions: What aeronautics research should be supported by the U.S. government? What compelling and desirable benefits drive government-supported research? How should the government--especially NASA--make decisions about which research to support? Advancing aeronautics involves broad policy and decisionmaking challenges. Decisions involve tradeoffs among competing perspectives, uncertainties, and informed judgment.

AERONAUTICS↗
NASA NTRS2020

In-Time UAV Flight-Trajectory Estimation and Tracking Using Bayesian Filters

Rapid increase of UAV operation in the next decade in areas of on-demand delivery, medical transportation services, law enforcement, traffic surveillance and several others pose potential risks to the low altitude airspace above densely populated areas. Safety assessment of airspace demands the need for a novel UAV traffic management (UTM) framework for regulation and tracking of the vehicles. Particularly for low-altitude UAV operations, quality of GPS measurements feeding into the UAV is often compromised by loss of communication link caused by presence of trees or tall buildings in proximity to the UAV flight path. Inaccurate GPS locations may yield to unreliable monitoring and inaccurate prognosis of remaining battery life and other safety metrics which rely on future expected trajectory of the UAV. This work therefore proposes a generalized monitoring and prediction methodology for autonomous UAVs using in-time GPS measurements. Firstly, a typical 4D smooth trajectory generation technique from a series of waypoint locations with associated expected times-of-arrival based on B-spline curves is presented. Initial uncertainty in the vehicle's expected cruise velocity is quantified to compute confidence intervals along the entire flight trajectory using error interval propagation approach. Further, the generated planned trajectory is considered as the prior knowledge which is updated during its flight with incoming GPS measurements in order to estimate its current location and corresponding kinematic profiles. Estimation of position is denoted in dicrete state-space representation such that position at a future time step is derived from position and velocity at current time step and expected velocity at the future time step. A linear Bayesian filtering algorithm is employed to efficiently refine position estimation from noisy GPS measurements and update the confidence intervals. Further, a dynamic re-planning strategy is implemented to incorporate unexpected detour or delay scenarios. Finally, critical challenges related to uncertainty quantification in trajectory prognosis for autonomous vehicles are identified, and potential solutions are discussed at the end of the paper. The entire monitoring framework is demonstrated on real UAV flight experiments conducted at the NASA Langley Research Center.

Banerjee, Portia↗
NASA NTRS2021

Highland Lakes Water Resources: Using NASA Earth Observations to Improve Detection Systems for Harmful Algal Events in the Highland Lakes in Central Texas

Beginning in 2019, harmful algal events in Austin, Texas, caused canine deaths in the Lady Bird Lake and Lake Travis reservoirs. These reservoirs are part of the larger Highland Lakes chain, managed by the Lower Colorado River Authority (LCRA) and the City of Austin Department of Watershed Protection (CoA DWP), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable algal event conditions in central Texas, the LCRA and CoA DWP partnered with NASA DEVELOP to improve algal event early-warning systems through the application of remote sensing and machine learning. An Earth observation-based algal monitoring system will assist the responsible agencies in predicting algal conditions and communicating hazards to the public. The NASA DEVELOP team utilized Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data to produce products including chlorophyll-a concentrations, cyanobacteria detections, turbidity, and water surface temperature. Chlorophyll-a concentrations were retrieved with a pre-trained machine learning model (mixture density network) and spectral indices, while the other products were derived from spectral indices. In situ field data were used to validate and quantify uncertainties for each product. The validations show strong correlations for chlorophyll-a and water surface temperature. Time series analyses of chlorophyll-a concentrations show peaks in the severe drought years (2015 and 2016). This project's resulting products enable monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain and will ultimately support water management, decision making, and risk communication.

Kaitlynn Hietpas↗
DOE OSTI2021

Supply analysis of preferential market incentive for energy crops

This analysis explores the valuation of feedstock quality attributes of switchgrass and miscanthus – two energy crops poised for future expansion – and compares the relative economic availability of these two crops under two scenarios: (i) uniform price assumptions (i.e., no incentive for quality), and (ii) a scenario of a price premium based on convertibility (i.e., an incentive for quality). Given data on cellulose content, hemicellulose content, and their relative convertibility, miscanthus is expected to be 11% more efficient at conversion to biofuels than switchgrass under the biochemical conversion route. Based on this scenario of improved conversion efficiency and associated profit, we simulate an 11% price premium for miscanthus over other feedstocks in a base-case scenario. By adding this price premium, supplies of miscanthus increase over the base case by about 4 million (44%), 94 million (64%), and 166 million (94%) tons in year 0, 10, and 20 after simulated contracts for production are initiated respectively. These results emphasize that custom simulations are needed to quantify feedstock availability if supplies are intended to reflect grower response to industry demands for feedstock quality specifications. Farmers can grow ‘peas or carrots’, and price signals from biorefineries will influence what energy crops they produce. Recognizing that the energy crop mix is tractable according to quality characteristics is relevant both for near-term and long-term biofuels research and development. Here, we recommend accounting for market preferences for quality attributes when estimating potential future supplies of energy crops.

09 BIOMASS FUELS↗