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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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NASA NTRS2018

Global Data Assembly Center (GDAC) report to the GHRSST Science Team

In 2017-2018 the Global Data Assembly Center (GDAC) at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) provided ingest, archive, distribution and user services for GHRSST operational data streams with improved and evolved tools, services, and tutorials and interfaced with the user community to address technical inquiries. The GDAC provided access to new GHRSST datasets as well retired a significant number of deprecated GHRSST datasets from its discovery services. The following sections summarize and document the specific achievements of the GDAC to the GHRSST community.

Finch, Chris↗

NASA NTRS2018

Global Data Assembly Center (GDAC) Report to the GHRSST Science Team

Finch, Chris↗

NASA NTRS2022

Applicability of Digital Flight to the Operations of Self-Piloted Unmanned Aircraft Systems in the National Airspace System

Unmanned Aircraft Systems (UAS) hold great promise for a new era of specialized missions, including personal air transportation, cargo flight operations, aerial surveys, inspections, firefighting and more. The anticipated market growth is significant. To unlock its scalability and incumbent benefits requires a human to oversee multiple flights simultaneously, focusing on multi-vehicle mission management and relinquishing to autonomous systems their active role in controlling the aircrafts’ flight paths. Key to the realization of these scalability benefits is minimally-encumbered access to the National Airspace System (NAS), which poses some unique challenges for self-piloted UAS aircraft operations. These include the requirement for compatibility with existing airspace structures and operations including Visual Flight Rules (VFR) and Instrument Flight Rules (IFR), neither of which were developed to accommodate the unique needs and capabilities of UAS. This paper explores the applicability of Digital Flight to the operations of self-piloted UAS. As proposed by NASA, Digital Flight is a flight operations capability, enabled by a set of cooperative procedures and digital technologies, in which flight operators ensure flight-path safety through automated separation and flight path management in lieu of visual procedures and Air Traffic Control separation services. Flights operating under potentially-forthcoming rules of Digital Flight employ advanced automation technologies, information sharing, connectivity to operational data, and cooperative behaviors through distributed decision-making to maintain safety and achieve mission objectives. Designed for integration with VFR and IFR operations in shared NAS airspace, potentially as a third set of flight rules, Digital Flight may provide the mechanism for UAS operators – and all aircraft operators – to scale and diversify their operations beyond what is achievable under current regulations.

DFR↗

NASA NTRS2020

Global Data Assembly Center (gdac) Report to the GHRSST Science Team

In 2019-2020 the Global Data Assembly Center (GDAC) at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) provided ingest, archive, distribution and user services for GHRSST operational data streams with improved and evolved tools, services, and tutorials, and interfaced with the user community to address technical inquiries. Several new GHRSST datasets including reprocessed MODIS Aqua/Terra L2P with improved cloud screening were made available. The PO.DAAC participated in the evolving GHRSST data management re architecture and development activities. GHRSST Science Team member Edward Armstrong assumed the role of co-leader of the CEOS SST Virtual Constellation. The following sections summarize and document the specific achievements of the GDAC to the GHRSST community.

Finch, Chris↗

DOE OSTI2026

Scalable GPS Data Logging To Support Advanced Fleet Analysis

This highlight details the key takeaways from a project that utilized NLR's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline. National Laboratory of the Rockies researchers developed and demonstrated low-cost, open-source Arduino data loggers with 3D-printed cases that are compatible with global navigational systems and built with components available ubiquitously worldwide, enabling cost-effective collection and analysis of fleet operational data. Validated on an overseas transit bus fleet, NLR analysis showed that, with sufficient charging opportunities, 90% of observed duty cycles could be accomplished by electric buses with no modifications to operations.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Maximizing energy efficiency in wastewater treatment plants: A data-driven approach for waste heat recovery and an economic analysis using Organic Rankine Cycle and thermal energy storage

Maximizing energy efficiency through waste heat recovery (WHR) processes is crucial for sustainable and ecofriendly operations across multiple industries, notably in wastewater treatment plants (WWTPs). This work proposes a comprehensive approach for assessing the WHR feasibility in WWTPs, structured in two main objectives. Firstly, an Artificial Neural Network (ANN) model is developed to accurately predict WHR based on operational data, including biogas temperature, biogas pressure, daily production in kWh, and WHR values in kWh th . The second objective focuses on economically evaluating the WHR feasibility based on the estimated WHR values obtained by the ANN model, and then realistically assessing the economic feasibility of integrating the Organic Rankine Cycle (ORC) and Seasonal Thermal Energy Storage (STES) systems. With an application to the As-Samra WWTP located in Jordan, the developed ANN model demonstrates promising results in the validation phase, with a root mean square error (RMSE) of 2206 kWh/day, a mean absolute error (MAE) of 1674 kWh/day, and an R-squared (R 2 ) value of 68%. On the other hand, the economic analysis reveals that an optimal ORC system of 412.14 kW e capacity yields a Net Present Value (NPV) of 2.09 million US dollars, a Levelized Cost of Energy (LCOE) of 0.0749 USD/kWh, a Payback Period (PBP) of 4.8 years, and annual revenues of 428 kUSD. This work also investigates the techno-economic feasibility of integrating ORC-STES. Results indicate that the LCOE and PBP are highly affected by the ORC's capital cost, and integrating STES increases the LCOE to 0.0824 USD/kWh, rendering its integration with ORC infeasible. In conclusion, this study aims to advance the understanding and application of WHR in WWTPs, paving the way for more efficient and sustainable practices in the field.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

Affordable public transit services are crucial for communities since they enable residents to access employment, education, and other services. Unfortunately, transit services that provide wide coverage tend to suffer from relatively low utilization, which results in high fuel usage per passenger per mile, leading to high operating costs and environmental impact. Electric vehicles (EVs) can reduce energy costs and environmental impact, but most public transit agencies have to employ them in combination with conventional, internal-combustion engine vehicles due to the high upfront costs of EVs. To make the best use of such a mixed fleet of vehicles, transit agencies need to optimize route assignments and charging schedules, which presents a challenging problem for large transit networks. We introduce a novel problem formulation to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging, while serving an existing fixed-route transit schedule. We present an integer program for optimal assignment and scheduling, and we propose polynomial-time heuristic and meta-heuristic algorithms for larger networks. We evaluate our algorithms on the public transit service of Chattanooga, TN using operational data collected from transit vehicles. Our results show that the proposed algorithms are scalable and can reduce energy use and, hence, environmental impact and operational costs. For Chattanooga, the proposed algorithms can save $145,635 in energy costs and 576.7 metric tons of CO 2 emission annually.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1993

Precise navigation for the Earth Observing System (EOS)-AM1 spacecraft using the TDRSS Onboard Navigation System (TONS)

As the baseline navigation system for the Earth Observing System (EOS)-AM1 spacecraft, the Tracking and Data Relay Satellite System (TDRSS) Onboard Navigation System (TONS) is required to provide precise position and velocity information for imaging instrument calibration and routine operations. This paper presents the results of real-time navigation performance evaluations with respect to TONS-based orbit and frequency determination to satisfy this requirement. Both covariance and simulation analysis of EOS-AM1 navigation accuracy and analysis using operational data from Landsat-4 are presented. Local (half orbit) and global (multiple orbits) tracking are considered using a way-forward link services. Improvements in navigation accuracies by using enhanced gravity models beyond the Goddard Earth Model (GEM)-T3 are also discussed. Key objectives of the analysis are to evaluate nominal performance and potential sensitivities and to address algorithm improvements such as TDRS ephemeris biasing, ionosphere model, and gravity process noise models slated for implementation. Results indicate that TONS can be configured to meet the proposed instrument navigation requirements of 20 meters, 3-sigma.

Folta, David C.↗

NASA NTRS2001

DAO's Next Generation Physical-Space/Finite-Volume Data Assimilation System: Formulation and Initial Evaluation

The Physical-space/Finite-volume Data Assimilation System (fvDAS) is the next generation global atmospheric data assimilation system in development at the Data Assimilation Office at NASA's Goddard Space Flight Center. It is based on a new finite-volume general circulation model jointly developed by NASA and NCAR, and on the Physical-Space Statistical Analysis System (PSAS) developed at the DAO. In this talk we will describe the general system formulation, the adaptive quality control and general aspects of the error covariance modeling. The NASA-NCAR GCM is a completely new model which replaces the CEOs GCM used in the previous GEOS-1/2/3 Data Assimilation systems. A particular configuration of adaptive Statistical Quality Control and the Physical-space Statistical Analysis System (PSAS) are currently implemented in DAO's operational Data Assimilation System. However, the unique finite-volume formulation of the NASA-NCAR GCM, combined with the generality of the observation-space formulation of PSAS, provides for a very simple and accurate model-analysis interface. The system assimilates a variety of conventional and satellite observations. In particular, TOVS Level 1B radiances are assimilated using a 1-D variational scheme, both in clear sky and cloudy conditions. Computationally, the fvDAS runs approximately 10 times faster than the operational GEOS-Terra system. We will show that the next-generation fvDAS has much improved observation-minus-6hr forecast (O-F) statistics, as well as 5-day forecast skills. Top of the atmosphere radiation fields are in closer agreement with CERES measurements, with realistic precipitation and moisture fields. We will also show that the finite-volume formulation of the fvDAS produce assimilated fields which are more suitable for driving constituent transport models.

daSilva, A.↗

NASA NTRS2006

Validating the Airspace Concept Evaluation System for Different Weather Days

This paper extends the process for validating the Airspace Concept Evaluation System using real-world historical flight operational data. System inputs such as flight plans and airport en-route capacities, are generated and processed to create a realistic reproduction of a single day's operations within the National Airspace System. System outputs such as airport throughput, delays, and en-route sector loads are then compared to real world operational metrics and delay statistics for the reproduced day. The process is repeated for 4 historical days with high and low traffic volume and delay attributed to weather. These 4 days are simulated using default en-route capacities and variable en-route capacities used to emulate weather. The validation results show that default enroute capacity simulations are closer to real-world data for low weather days than high weather days. The use of reduced variable enroute capacities adds a large delay bias to ACES but delay trends between weather days are better represented.

Zelinski, Shannon↗

NASA NTRS2014

Integrated Science Investigation of the Sun (ISIS): Design of the Energetic Particle Investigation

The Integrated Science Investigation of the Sun (ISIS) is a complete science investigation on the Solar Probe Plus (SPP) mission, which flies to within nine solar radii of the Sun's surface. ISIS comprises a two-instrument suite to measure energetic particles over a very broad energy range, as well as coordinated management, science operations, data processing, and scientific analysis. Together, ISIS observations allow us to explore the mechanisms of energetic particles dynamics, including their: (1) Origins-defining the seed populations and physical conditions necessary for energetic particle acceleration; (2) Acceleration-determining the roles of shocks, reconnection, waves, and turbulence in accelerating energetic particles; and (3) Transport-revealing how energetic particles propagate from the corona out into the heliosphere. The two ISIS Energetic Particle Instruments measure lower (EPI-Lo) and higher (EPI-Hi) energy particles. EPI-Lo measures ions and ion composition from approx. 20 keV/nucleon-15 MeV total energy and electrons from approx.25-1000 keV. EPI-Hi measures ions from approx. 1-200 MeV/nucleon and electrons from approx. 0.5-6 MeV. EPI-Lo comprises 80 tiny apertures with fields-of-view (FOVs) that sample over nearly a complete hemisphere, while EPI-Hi combines three telescopes that together provide five large-FOV apertures. ISIS observes continuously inside of 0.25 AU with a high data collection rate and burst data (EPI-Lo) coordinated with the rest of the SPP payload; outside of 0.25 AU, ISIS runs in low-rate science mode whenever feasible to capture as complete a record as possible of the solar energetic particle environment and provide calibration and continuity for measurements closer in to the Sun. The ISIS Science Operations Center plans and executes commanding, receives and analyzes all ISIS data, and coordinates science observations and analyses with the rest of the SPP science investigations. Together, ISIS' unique observations on SPP will enable the discovery, untangling, and understanding of the important physical processes that govern energetic particles in the innermost regions of our heliosphere, for the first time. This paper summarizes the ISIS investigation at the time of the SPP mission Preliminary Design Review in January 2014.

Coronal Mass Ejections↗

DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗

DOE OSTI2022

Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. Here, to demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

NREL - GE 1.5MW SLE Wind Turbine / Reviewed Data

This dataset is intended to be a public resource for anyone conducting wind turbine noise research. It contains noise spectra and equivalent sound pressure levels at 11 measurement stations. The dataset also contains wind turbine operational data and meteorological data from a met mast directly upstream of the wind turbine.

17 WIND ENERGY↗

NASA NTRS1953

Preliminary Transient Performance Data on the J73 Turbojet Engine. II - Altitude, 35,000 Feet

A program was undertaken to determine the J73 turbojet engine compressor stall and surge characteristics and combustor blow-out limits encountered during transient engine operation. Data were obtained in the form of oscillograph traces showing the time history of several engine performance parameters with changes in engine fuel flow. The data presented in this report are for step changes in fuel flow at an altitude of 35,000 feet, at flight Mach numbers of 0.3, 0.8, and 1.2, and at several engine-inlet temperatures,

Lubick, Robert J.↗

DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗

NASA NTRS1953

Preliminary Transient Performance Data on the J73 Turbojet Engine: Altitude, 45,000 Feet - 3

A program was undertaken to determine the J73 turbojet engine compressor stall and surge characteristics and combustor blow-out limits enc ountered during transient engine operation. Data were obtained in the form of oscillograph traces showing the time history of several engi ne parameters with changes in engine fuel flow. The data presented in this report are for step and ramp changes in fuel flow at an altitude of 45,000 feet and flight Mach numbers of 0 and 0.8.

McAulay, John E.↗

DOE OSTI2022

Predicting the recovery and grade of a rougher flotation circuit from liberation data

A flotation model has been validated against the survey data obtained from a copper rougher flotation circuit with a circulating load. Here, the model was developed using the hydrophobic force as a kinetic parameter, which made it possible to use the contact angles of the free and composite particles as input parameters so that the model can predict both recoveries and grades. The model also recognizes the role of bubble coarsening in determining the froth phase recoveries and grades. The model predictions are in good agreement with the survey data. The size-by-size recovery curve as predicted from the model shows an optimum particle size range that is consistent with most of the operating data reported in the literature. The model suggests that the upper particle size limit may be determined by the instability of the froth phase rather than the turbulence in the pulp phase of a flotation cell.

36 MATERIALS SCIENCE↗