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

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At least 739 records · Page 41

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NASA NTRS2006

YAM- A Framework for Rapid Software Development

YAM is a software development framework with tools for facilitating the rapid development and integration of software in a concurrent software development environment. YAM provides solutions for thorny development challenges associated with software reuse, managing multiple software configurations, the development of software product-lines, multiple platform development and build management. YAM uses release-early, release-often development cycles to allow developers to incrementally integrate their changes into the system on a continual basis. YAM facilitates the creation and merging of branches to support the isolated development of immature software to avoid impacting the stability of the development effort. YAM uses modules and packages to organize and share software across multiple software products. It uses the concepts of link and work modules to reduce sandbox setup times even when the code-base is large. One side-benefit is the enforcement of a strong module-level encapsulation of a module s functionality and interface. This increases design transparency, system stability as well as software reuse. YAM is in use by several mid-size software development teams including ones developing mission-critical software.

simulations↗

NASA NTRS2015

A Survey of the Isentropic Euler Vortex Problem Using High-Order Methods

The flux reconstruction (FR) method offers a simple, efficient, and easy to implement method, and it has been shown to equate to a differential approach to discontinuous Galerkin (DG) methods. The FR method is also accurate to an arbitrary order and the isentropic Euler vortex problem is used here to empirically verify this claim. This problem is widely used in computational fluid dynamics (CFD) to verify the accuracy of a given numerical method due to its simplicity and known exact solution at any given time. While verifying our FR solver, multiple obstacles emerged that prevented us from achieving the expected order of accuracy over short and long amounts of simulation time. It was found that these complications stemmed from a few overlooked details in the original problem definition combined with the FR and DG methods achieving high-accuracy with minimal dissipation. This paper is intended to consolidate the many versions of the vortex problem found in literature and to highlight some of the consequences if these overlooked details remain neglected.

High-Order↗

NASA NTRSDate not supplied

Empirical Hydrometeor Type Identification from GMI Brightness Temperature Measurements

Brightness temperatures (TB) from GMI and related sensors contain information about the types and amounts of precipitation particles in a column. Approaches like GPROF or machine learning are good for providing precipitation estimates and other quantified information, but it can be difficult to understand why / how a given set of TBs traces to a given retrieval solution. Outlier TBs that are not well-represented in a training sample can lead to dubious solutions. Our goal is to use the scattering signatures at multiple frequencies to determine whether hail is present, or otherwise graupel, or otherwise snow, or otherwise liquid rain without a substantial precipitation ice component. (A hierarchical approach is employed, so a prediction of hail can include the presence of all other categories, but a prediction of snow implies a lack of graupel or hail, and a prediction of liquid rain implies a lack of any detectable precipitation ice.)

Daniel J. Cecil↗

NASA NTRSDate not supplied

Hydrometeor Identification from GMI Radiometer, Trained Using Polarimetric Radar

Daniel J. Cecil↗

DOE OSTI2021

R3IT, Rapid Rural Resilient Interconnection Toolkit, Final Technical Report

Utilities nationwide are beginning to experience multiple solar interconnection requests per day. This request volume will soon overwhelm utility engineers, especially at smaller utilities which serve the majority of the landmass of the United States. In this project, we developed open-source software tool that automates the interconnection approval process and removes 80% of the time required to approve an interconnection and hence allow more solar to be quickly and safely integrated with the grid. This software is accompanied by a guidebook detailing interconnection best practices and utility lessons learned. Competing solar interconnection software is extremely expensive at ten times the cost of typical distribution engineering tools, it’s closed source which makes it hard to integrate into utility workflows, and it doesn’t address the hardest power flow modeling challenges of interconnection screening. Our solution was built built with our utility partners who are already experiencing multiple interconnection requests per day and integrates best practices from our past work with DOE and the cooperative community on solar integration. The resulting free and open-source solution has been disseminated through our media channels and conferences that engage over 1,900 utilities nationwide.

14 SOLAR ENERGY↗

NASA NTRS2001

Learning Sequences of Actions in Collectives of Autonomous Agents

In this paper we focus on the problem of designing a collective of autonomous agents that individually learn sequences of actions such that the resultant sequence of joint actions achieves a predetermined global objective. We are particularly interested in instances of this problem where centralized control is either impossible or impractical. For single agent systems in similar domains, machine learning methods (e.g., reinforcement learners) have been successfully used. However, applying such solutions directly to multi-agent systems often proves problematic, as agents may work at cross-purposes, or have difficulty in evaluating their contribution to achievement of the global objective, or both. Accordingly, the crucial design step in multiagent systems centers on determining the private objectives of each agent so that as the agents strive for those objectives, the system reaches a good global solution. In this work we consider a version of this problem involving multiple autonomous agents in a grid world. We use concepts from collective intelligence to design goals for the agents that are 'aligned' with the global goal, and are 'learnable' in that agents can readily see how their behavior affects their utility. We show that reinforcement learning agents using those goals outperform both 'natural' extensions of single agent algorithms and global reinforcement, learning solutions based on 'team games'.

Turner, Kagan↗

NASA NTRS1992

Computation of unsteady flows over airfoils

Two methods are described for calculating unsteady flows over rapidly pitching airfoils. The first method is based on an interactive scheme in which the inviscid flow is obtained by a panel method. The boundary layer flow is computed by an interactive method that makes use of the Hilbert integral to couple the solutions of the inviscid and viscous flow equations. The second method is based on the solution of the compressible Navier-Stokes equations. The solution of these equations is obtained with an approximately factorized numerical algorithm, and with single block or multiple grids which enable grid embedding to enhance the resolution at isolated flow regions. In addition, the attached flow region can be computed by the numerical solution of compressible boundary layer equations. Unsteady pressure distributions obtained with both methods are compared with available experimental data.

Ekaterinaris, J. A.↗

DOE OSTI2026

Supervisory Control and Data Acquisition for Electrochemical Separation Experimentation

The Python-based program is a laboratory automation tool designed to control and monitor electrochemical systems. The tool was developed for capacitive deionization (CDI) experiments, but it can be used for any system that requires controlled voltage or current segments and multi-parameter monitoring. The program integrates hardware components to run user-defined experimental parameters, providing operational control of a programmable power supply, peristaltic pump, and data acquisition devices. Currently, the program is structured with a workflow that includes an initialization (or pre-run) phase, a main loop, and a post-experiment stabilization (or post-run) phase. The initialization phase prepares and stabilizes the cell, ensuring that the electrodes and solution reach a baseline state before the experiment begins. The main loop consists of multiple voltage segments that repeat, controlling the experiment while recording key parameters such as time, voltage, current, pH, and conductivity. Finally, the post-experiment stabilization phase allows the system to stabilize after the experiment, returning the cell and solution to equilibrium conditions before ending the sequence. The program is designed with four variations, each tailored to different experimental needs. All variations include both the initialization and post-experiment stabilization stages, which run for a set amount of time, voltage, current, and flow rate before and after the main experiment block. The main loop runs for a set number of cycles, as defined by the user input, and each cycle is composed of 2 or 4 segments. The 4 program variations are described as follows: Program 1: The main program includes 2 segments. Each segment is defined to have a set duration, flow rate, voltage, and current. This program measures conductivity, flow rate, voltage, and current. Program 2: The main program expands Program 1 to include 4 segments. Each segment has a specified duration, flow rate, voltage, and current. Like Program 1, it measures conductivity, flow rate, voltage, and current. Program 3: The main program consists of 2 segments, each defined by time, flow rate, voltage, and current. In addition to conductivity, flow rate, voltage, and current, Program 3 collects pH and temperature data through a 4-channel data acquisition device. Program 4: This program independently controls two channels of a multi-channel power supply simultaneously. While conductivity can only be measured for one cell at a time, the dual-channel control makes it possible to operate two cells simultaneously under different voltage/current conditions. The main program includes 2 segments.For each program, all measurements are automatically logged and integrated into a single Excel output file. Data are displayed in numerical format and plotted, both in real time, to track system performance. A key feature of the program is its ability to synchronize all outputs so that every measurement shares a single timestamp, ensuring accurate alignment of voltage, current, pH, conductivity, and pH data.By combining hardware control, real-time monitoring, and unified data collection, this program significantly reduces manual workload and minimizes errors, making it a reliable platform for researchers, engineers, and laboratory technicians conducting CDI experiments, among other electrochemical tests.

Valentino, Lauren [Argonne National Laboratory (AN↗

DOE OSTI2022

The Essential Role of Integrated Nuclear-Renewable Energy Systems in Achieving Economy-wide Net Zero Solutions

Background/Objectives. The Biden administration has committed to full decarbonization of the U.S. electricity grid by 2035 and economy-wide net zero emissions by 2050. These aggressive goals demand immediate action if we are to be successful, and they require us to think more holistically about our clean energy options. Focused laboratory initiatives, such as the INL Integrated Energy Systems (IES) Initiative, and multiple programs within the Department of Energy are working together to address these holistic solutions. Approach/Activities. Traditionally, electricity generation and management and meeting energy demands for industry and transportation are considered independently. As we seek to achieve net zero, we need to reassess our energy demands. When we consider overall energy use, only one-third is in the form of electricity. Additional energy demands are in the form of heat or steam for industrial processes, as well as transportation. Emissions across these sectors are much harder to abate, and electrification may not be the best option. Reducing environmental emissions at an affordable cost, while maintaining grid reliability and resilience, will require us to use all of the clean energy resources that we have available. That means coordinating the use of nuclear, renewables, and fossil fuels with carbon capture to meet growing demands for electricity, industrial applications, and mobility. The primary focus of IES research is to assess the technical and economic potential of various IES solutions to enhance the flexibility and utilization of nuclear energy generators working alongside renewable generators to meet an array of energy demands—thereby maximizing the utilization of clean energy resources across all energy sectors. Various energy applications and product streams beyond electricity are being evaluated, ranging from generation of potable water to production of hydrogen, fertilizers, synthetic fuels, and various chemicals. Results/Lessons Learned. This presentation will highlight the wide array of RD&D being conducted to develop and deploy nuclear-based IES that will be key to achieving our net zero goals. By working with key collaborators in the nuclear industry, analytical studies are now becoming a reality in multiple demonstration projects.

08 HYDROGEN↗

DOE OSTI2025

SolarSTARTS: Solar-Assisted State-Aware and ResilienT infrastructure System

This final technical report provides a description and results of the design, development, testing, and validation of the Automated Resilience Management System (ARMS) solution, which integrates different systems to collect, store and process relevant information from the power distribution system to automatically manage the operation of multiple energy assets to enhance the resilience of the power grid against high-impact physical incidents (e.g., hurricanes) and cyber threats. The developed solution comprises a suite of novel AI-based algorithms designed to first detect, locate, and classify anomalies by collecting and analyzing data from multiple monitoring and control devices

14 SOLAR ENERGY↗

DOE OSTI2020

Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation

This study addresses the problem of calibrating utility-maximizing nested logit activity-based travel demand model-systems. After estimation, it is common practice to use aggregate measurements to calibrate the estimated model-system’s parameters prior to their application in transportation planning, policy making, and operations. However, calibration of activity-based model-systems has received much less attention. Existing calibration approaches are myopic heuristics in the sense that they do not consider the fundamental inter-dependencies among choice-models and do not have a systematic way to adjust model parameters. Also, other purely simulation-based approaches do not perform well in large-scale applications. In this study, we focus on utility-maximizing nested logit activity-based model-systems and calibrating aggregate statistics such as activity shares, mode shares, time-dependent & mode-specific OD flows, and time-dependent & mode-specific sensor counts. We formulate the calibration problem as a simulation-based optimization problem and propose a stochastic gradient-based solution procedure to solve it. The solution procedure relies on microsimulation to calculate expectations of the aggregate statistics of interest to the calibration problem. Additionally, we derive approximate analytical expressions for the gradient of the objective function —that are evaluated through microsimulation on mini-batches of the population. The proposed solution procedure is sensitive to the fundamental structure of the activity-based model-system and is non-myopic in considering the dependencies across its model components. The formulated optimization problem is non-convex, highly nonlinear, and potentially has multiple-minima. Lastly, we show —through a real-world application— that the proposed solution procedure outperforms other state-of-the-art purely simulation-based optimization approaches in terms of computational efficiency, stability, and convergence. We also compare various gradient-based solution algorithms to determine the best algorithm to update the parameters. This work has the potential to facilitate wider and easier application of activity-based model-systems.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2000

Robotic Precursor Missions for Mars Habitats

Infrastructure support for robotic colonies, manned Mars habitat, and/or robotic exploration of planetary surfaces will need to rely on the field deployment of multiple robust robots. This support includes such tasks as the deployment and servicing of power systems and ISRU generators, construction of beaconed roadways, and the site preparation and deployment of manned habitat modules. The current level of autonomy of planetary rovers such as Sojourner will need to be greatly enhanced for these types of operations. In addition, single robotic platforms will not be capable of complicated construction scenarios. Precursor robotic missions to Mars that involve teams of multiple cooperating robots to accomplish some of these tasks is a cost effective solution to the possible long timeline necessary for the deployment of a manned habitat. Ongoing work at JPL under the Mars Outpost Program in the area of robot colonies is investigating many of the technology developments necessary for such an ambitious undertaking. Some of the issues that are being addressed include behavior-based control systems for multiple cooperating robots (CAMPOUT), development of autonomous robotic systems for the rescue/repair of trapped or disabled robots, and the design and development of robotic platforms for construction tasks such as material transport and surface clearing.

Huntsberger, Terry↗

DOE OSTI2025

Synergistic learning with multi-task DeepONet for efficient PDE problem solving

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs). However, implementing MTL in this context is complex, as it requires task-specific modifications to accommodate various scenarios representing different physical processes. To this end, we present a multi-task deep operator network (MT-DeepONet) to learn solutions across various functional forms of source terms in a PDE and multiple geometries in a single concurrent training session. We introduce modifications in the branch network of the vanilla DeepONet to account for various functional forms of a parameterized coefficient in a PDE. Additionally, we handle parameterized geometries by introducing a binary mask in the branch network and incorporating it into the loss term to improve convergence and generalization to new geometry tasks. Our approach is demonstrated on three benchmark problems: (1) learning different functional forms of the source term in the Fisher equation; (2) learning multiple geometries in a 2D Darcy Flow problem and showcasing better transfer learning capabilities to new geometries; and (3) learning 3D parameterized geometries for a heat transfer problem and demonstrate the ability to predict on new but similar geometries. Finally, our MT-DeepONet framework offers a novel approach to solving PDE problems in engineering and science under a unified umbrella based on synergistic learning that reduces the overall training cost for neural operators.

42 ENGINEERING↗

NASA NTRS2011

Combined Weekly Coordinate Solutions from SLR and DORIS

In International Terrestrial Reference Frame (ITRF) 2005 and ITRF2008, the approach for the construction of solutions by the IERS has been for individual analysis centers of each technique to process geodetic tracking data, and for each technique to develop a solution (or contribution) that is integrated into the final ITRF solution by careful combination of the technique solutions. The connections between the geodetic networks are realized by the application of local ties. In an alternate approach, we may assure processing homogeneity by creating normal equations for different techniques with the same orbit determination software, using identically derived algorithms. Another derivative of this approach is to realize the ties between the techniques using satellites tracked with multiple techniques; in effect tieing the networks together using satellite dynamics. In this solution, we develop a time series and a set of cumulative solutions from Satellite Laser Ranging (SLR) & Doppler Orbitography and Radio-positioning Integrated by Satellite (DORIS) based on homogeneous processing with the NASA GEODYN precise orbit determination suite of programs, where we jointly combine weekly the SLR data to Lageos1, Lageos2, Starlette, and Stella with the DORIS data from SPOT2-SPOT5, as well as satellites that utilize both techniques (TOPEX/Poseidon, Envisat, Jason-2). We discuss the modeling that is applied including upgrades implemented since the submission of the GSC ITRF2008 contributions for IDS. Firstly, we compare the SLR-only solutions comprising four geodetic satellites with the standard approach of utilizing only Lageos1 & Lageos2. Secondly, we evaluate the impact on the DORIS coordinates of the joint analysis with the SLR data.

Lemoine, F.↗

NASA NTRSDate not supplied

Connecting Federal Agencies to Satellite Earth Observations: NASA’s Satellite Needs Working Group Assessment Process

The Satellite Needs Working Group (SNWG), part of the U.S. Group on Earth Observations (USGEO), surveys agencies across the U.S. Government to identify the satellite Earth observations each agency needs to fulfill its high-priority objectives and responsibilities. Around 20 civilian agencies participate in the SNWG survey every two years. After receiving the surveys, the National Aeronautics and Space Administration (NASA) conducts an in-depth evaluation of each agency's needs in collaboration with fellow satellite Earth data providers, the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS). This assessment process, which takes place over an eight-month period, is divided into distinct phases. NASA first assembles an assessment team with the necessary subject matter expertise to evaluate each submitted need. An in-depth interview with each submitting agency then follows, featuring discussion of current and upcoming satellite missions as well as potential new activities that NASA, NOAA, and/or USGS could undertake to meet the agency's needs. The assessment teams then further evaluate these potential activities or solutions to identify how many agencies would benefit and estimate how much their level of satisfaction would increase. Solutions expected to have broad-reaching and significant agency benefits are proposed by NASA for funding. SNWG agencies receive an assessment report for each submitted need, in which the tri-agency assessment teams provide a detailed evaluation of the agency need, information on relevant satellite missions, and links to specific datasets or training resources. The SNWG Management Office at NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT) contributes to NASA’s SNWG assessment in a variety of capacities, including statistical analysis of the survey responses to identify trends and similar needs across multiple agencies. The Management Office also provides analytics for the new proposed solutions, demonstrating and quantifying their potential value. For activities that receive funding, the Management Office manages the implementation process and works to maximize the benefit to SNWG agencies via a Stakeholder Engagement Program.

Katrina Virts↗

NASA NTRS2016

The Core Flight System (cFS) Community: Providing Low Cost Solutions for Small Spacecraft

In February 2015 the NASA Goddard Space Flight Center (GSFC) completed the open source release of the entire Core Flight Software (cFS) suite. After the open source release a multi-NASA center Configuration Control Board (CCB) was established that has managed multiple cFS product releases. The cFS was developed and is being maintained in compliance with the NASA Class B software development process requirements and the open source release includes all Class B artifacts. The cFS is currently running on three operational science spacecraft and is being used on multiple spacecraft and instrument development efforts. While the cFS itself is a viable flight software (FSW) solution, we have discovered that the cFS community is a continuous source of innovation and growth that provides products and tools that serve the entire FSW lifecycle and future mission needs. This paper summarizes the current state of the cFS community, the key FSW technologies being pursued, the development/verification tools and opportunities for the small satellite community to become engaged. The cFS is a proven high quality and cost-effective solution for small satellites with constrained budgets.

System↗

DOE OSTI2023

SALSA_python (SALSython) v1

SALSA_python (Semi-Analytical Leakage Solutions for Aquifers) is a software that computed semi-analytical solutions for hydraulic head and brine leakage in multilayered aquifer–aquitard systems with geologic pressure forcing. It can simulate brine leakage into aquifers in a multi-aquifer-aquitard system with multiple injection and leaky wells. This situation is encountered in underground CO2 storage wherein brine leakage from pressurized reservoirs into aquifers is of concern. SALSA_python calls the original SALSA[1] subroutines in python by using the salsa2.so library. This enables the incorporation and coupling of SALSA computations into existing python-based codes and tools. SALSA_python is available for Linux and Mac operating systems. [1] Cihan, A., Oldenburg, C. M., & Birkholzer, J. T. (2022). Leakage from coexisting geologic forcing and injection-induced pressurization: A semi-analytical solution for multilayered aquifers with multiple wells. Water Resources Research, 58, e2022WR032343.

Bhuvankar, Pramod↗

NASA NTRS2005

Application of Parallel Adjoint-Based Error Estimation and Anisotropic Grid Adaptation for Three-Dimensional Aerospace Configurations

This paper demonstrates the extension of error estimation and adaptation methods to parallel computations enabling larger, more realistic aerospace applications and the quantification of discretization errors for complex 3-D solutions. Results were shown for an inviscid sonic-boom prediction about a double-cone configuration and a wing/body segmented leading edge (SLE) configuration where the output function of the adjoint was pressure integrated over a part of the cylinder in the near field. After multiple cycles of error estimation and surface/field adaptation, a significant improvement in the inviscid solution for the sonic boom signature of the double cone was observed. Although the double-cone adaptation was initiated from a very coarse mesh, the near-field pressure signature from the final adapted mesh compared very well with the wind-tunnel data which illustrates that the adjoint-based error estimation and adaptation process requires no a priori refinement of the mesh. Similarly, the near-field pressure signature for the SLE wing/body sonic boom configuration showed a significant improvement from the initial coarse mesh to the final adapted mesh in comparison with the wind tunnel results. Error estimation and field adaptation results were also presented for the viscous transonic drag prediction of the DLR-F6 wing/body configuration, and results were compared to a series of globally refined meshes. Two of these globally refined meshes were used as a starting point for the error estimation and field-adaptation process where the output function for the adjoint was the total drag. The field-adapted results showed an improvement in the prediction of the drag in comparison with the finest globally refined mesh and a reduction in the estimate of the remaining drag error. The adjoint-based adaptation parameter showed a need for increased resolution in the surface of the wing/body as well as a need for wake resolution downstream of the fuselage and wing trailing edge in order to achieve the requested drag tolerance. Although further adaptation was required to meet the requested tolerance, no further cycles were computed in order to avoid large discrepancies between the surface mesh spacing and the refined field spacing.

Lee-Rausch, E. M.↗