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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 145 records · Page 8

All sources NASA NTRS DOE OSTI

NASA NTRS2020

Detector Channel Combining Results from a High Photon Efficiency Optical Communications Link Test Bed

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) is developing a low cost, scalable, photon-counting receiver prototype for space-to-ground optical communications links. The receiver is being tested in a test bed that emulates photon-starved space-to-ground optical communication links. The receiver uses an array of single-pixel fiber-coupled superconducting nanowire single-photon detectors. The receiver is designed to receive the high photon efficiency serially concatenated pulse position modulation (SCPPM) waveform specified in the Consultative Committee for Space Data Systems (CCSDS) Optical Communications Coding and Synchronization Blue Book Standard. The optical receiver consists of an array of single-pixel superconducting nanowire detectors, analog phase shifters for channel alignment, digitizers for each detector channel, and digital processing of the received signal. An overview of the test bed and arrayed receiver system is given. Simulation and system characterization results are presented. The data rate increase of using a four-channel arrayed detector system over using one single pixel nanowire detector is characterized. Results indicate that a single-pixel detector is capable of receiving data at a rate of 40 Mbps and a four-channel arrayed detector system is capable of receiving data at a rate of 130 Mbps.

Downey, Jennifer N.↗

NASA NTRS2018

Assessing C2 Communications for UAS Traffic Management

The National Aeronautics and Space Administration's (NASA) Unmanned Aircraft Systems (UAS) Traffic Management (UTM) project works to develop tools and technologies essential for safely enabling civilian low-altitude UAS operations. Currently there is no established infrastructure to enable and safely manage the widespread use of low-altitude airspace and UAS operations, regardless of the type of UAS. The UTM technical challenge will develop comprehensive and validated airspace operations and integration requirements to safely enable large-scale persistent access to visual line of sight and autonomous beyond visual line of sight small UAS in low-altitude airspace. Within the UTM project, a number of communications technologies to support UTM command and control (C2) are under investigation. In particular, commercial networked cellular systems are being tested and assessed for their ability to meet the reliability, scalability, cybersecurity and redundancy required. NASA Glenn Research Center is studying some of the aspects of employing such networks for UTM C2 communications. This includes the development of a test platform for sensing and characterizing the airborne C2 communications environment at various altitudes and in various terrains and topologies, measuring such aspects as received signal strength and interference. System performance aspects such as latency in the link, handover performance, packet error loss rate, drop outs, coverage gaps and other aspects impacting UTM operation will also be assessed. In this paper we explore some of the C2 approaches being proposed and demonstrated in the UTM project, the reliability, availability and other general C2 performance requirements, and approaches to evaluating and analyzing UTM C2 links based on commercial cellular networks.

air traffic control↗

NASA NTRS2018

Assessing C2 Communications

aircraft communications↗

DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗

DOE OSTI2024

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗

DOE OSTI2020

Efficient solvers for hybridized three-field mixed finite element coupled poromechanics

We consider a mixed hybrid finite element formulation for coupled poromechanics. A stabilization strategy based on a macro-element approach is advanced to eliminate the spurious pressure modes appearing in undrained/incompressible conditions. The efficient solution of the stabilized mixed hybrid block system is addressed by developing a class of block triangular preconditioners based on a Schur-complement approximation strategy. Robustness, computational efficiency and scalability of the proposed approach are theoretically discussed and tested using challenging benchmark problems on massively parallel architectures.

42 ENGINEERING↗

DOE OSTI2025

Ensuring High-Purity Liquid Argon for the LBNF FDC: Collaborative Cryogenics Research Between UNICAMP and Fermilab

Ensuring High-Purity Liquid Argon for the LBNF FDC: Collaborative Cryogenics Research Between UNICAMP and Fermilab R. Doubnik1, M. Adamowski1, F. de M. Blaszczyk1, A. Hahn1, D. Montanari1, R. Mrowca1, Z. West1, C. Adriano2, T. P.M. Alegre2, D. Correia2, A. A. B. Machado2, E. Segreto2, R. G. Gonçalves3, E. M. Assaf6, J. M. Assaf3, D. Cardoso3, M. B. Fontes4, H. Da Motta4, D. Noriler5, P. J. G. Pagliuso2. 1 Fermi National Accelerator Laboratory, PO Box 500, Batavia IL 60510, United States. 2 “Gleb Wataghin” Institute of Physics, UNICAMP, Campinas-SP, 13083-859, Brazil. 3 CPqMAE - Research Center on Advanced Materials and Energy, Federal University of São Carlos (UFSCar), São Carlos, 13565-905, Brazil. 4 The Brazilian Center for Research in Physics (CBPF), Rio de Janeiro-RJ, 22290-180, Brazil. 5 School of Chemical Engineering, UNICAMP, Campinas-SP, 13083-859, Brazil. 6 São Carlos Institute of Chemistry, University of São Paulo, São Carlos-SP, 13566-590, Brazil. Email: rdoubnik@fnal.gov Abstract. The Long-Baseline Neutrino Facility (LBNF) located at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, hosts the Deep Underground Neutrino Experiment (DUNE). This experiment employs cryostats containing nearly 70,000 metric tons of high-purity liquid argon (LAr). Ensuring LAr purity is critical for achieving the required electron lifetime, which directly impacts the experiment’s signal-to-noise ratio. The Horizontal Drift (HD) detector demands an electron lifetime exceeding 3 ms within its 3.5 m drift, equivalent to less than 100 parts-per-trillion (ppt) Oxygen contamination, while the Vertical Drift (VD) detector requires over 6 ms electron lifetime within its 6.0 m drift, corresponding to less than 50 ppt Oxygen contamination. To mitigate Nitrogen (N2) quenching of scintillation light, N2 contamination must remain below 1 ppm, as higher levels can result in up to a 20 % loss of light. The Brazil State University of Campinas (UNICAMP) significantly contributes to LBNF FDC through the development of argon purification and regeneration systems for HD and VD cryostats. UNICAMP designed and constructed the Purification Liquid Argon Cryostat (PuLArC), a small-scale test facility holding approximately 90 liters of LAr. Tests using PuLArC demonstrated that 1.2 kg of Li-FAU zeolite could reduce N₂ contamination from 20-50 ppm to 0.1-1.0 ppm within 1-2 hours. Tests at Fermilab’s Iceberg cryostat (2,596 liters) confirmed scalability, with 3 kg of Li-FAU reducing N₂ contamination from ~5 ppm to <1 ppm over 96 hours cycles without active circulation. This presentation will detail the research methods, test setups, and results, showcasing the potential of Li-FAU as an alternative to Molecular Sieve 4A for large-scale LAr systems. This advancement enhances DUNE's precision and demonstrates the impact of international collaboration on cryogenic research.

Doubnik, Roza [Fermilab]↗

NASA NTRS2013

Scale Model Thruster Acoustic Measurement Results

Subscale rocket acoustic data is used to predict acoustic environments for full scale rockets. Over the last several years acoustic data has been collected during horizontal tests of solid rocket motors. Space Launch System (SLS) Scale Model Acoustic Test (SMAT) was designed to evaluate the acoustics of the SLS vehicle including the liquid engines and solid rocket boosters. SMAT is comprised of liquid thrusters scalable to the Space Shuttle Main engines (SSME) and Rocket Assisted Take Off (RATO) motors scalable to the 5-segment Reusable Solid Rocket Motor (RSTMV). Horizontal testing of the liquid thrusters provided an opportunity to collect acoustic data from liquid thrusters to characterize the acoustic environments. Acoustic data was collected during the horizontal firings of a single thruster and a 4-thruster (Quad) configuration. Presentation scope. Discuss the results of the single and 4-thruster acoustic measurements. Compare the measured acoustic levels of the liquid thrusters to the Solid Rocket Test Motor V - Nozzle 2 (SRTMV-N2).

Kenny, R. Jeremy↗

NASA NTRS2016

UTM Safely Enabling UAS Operations in Low-Altitude Airspace

Conduct research, development and testing to identify airspace operations requirements to enable large-scale visual and beyond visual line of sight UAS operations in the low-altitude airspace. Use build-a-little-test-a-little strategy remote areas to urban areas Low density: No traffic management required but understanding of airspace constraints. Cooperative traffic management: Understanding of airspace constraints and other operations. Manned and unmanned traffic management: Scalable and heterogeneous operations. UTM construct consistent with FAAs risk-based strategy. UTM research platform is used for simulations and tests. UTM offers path towards scalability.

UTM low-altitude↗

NASA NTRS2017

Unmanned Aircraft Systems Traffic Management (UTM) Safely Enabling UAS Operations in Low-Altitude Airspace

Low-altitude UTM↗

DOE OSTI2020

Scaled-up Neuromorphic Array Communications Controller (SNACC) for Large-scale Neural Networks

Neuromorphic computing is one promising post-Moore’s law era technology, which takes inspiration from biological brains to perform computing tasks. The human brain contains billions of neurons with trillions of synapses and as neuromorphic hardware systems scale to larger and larger sizes, the communication system used to transfer information between neuromorphic elements and traditional computers must scale to keep up. In prior work, we describe the use of a separate neuromorphic array communications controller to support low-latency, high-throughput communication between our neuromorphic systems and a traditional computer. In this work, the neuromorphic array communications controller is used to support the scaling of a neuromorphic development system which uses multiple neuromorphic processors arranged in a two-dimensional array. The neuromorphic array communications controller, along with scalable local connections, is used to create a scalable neuromorphic platform to enable the development and testing of large neuromorphic network arrays.

Young, Aaron↗

DOE OSTI2023

Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II

Deep neural network (DNN) potentials are an emerging tool for simulation of dynamical atomistic systems, with the promise of quantum mechanical accuracy at speedups of 10000$\times$. As with other DNN methods, hyperparameters used during training can make a substantial difference in model accuracy, and optimal settings vary with dataset. To enable rapid tuning of hyperparameters for DNN potential training, we developed a scalable multiobjective optimization evolutionary algorithm for supercomputers and tested it on the Summit system at the Oak Ridge Leadership Computing Facility (OLCF). The multiobjective approach is required due to the coupling of two learned values defining the potential: the energy and force. Using a large-scale implementation of the NSGA-II algorithm adapted for training DNN potentials, we discovered several optimal multiobjective combinations, including best choices of activation functions, learning rate scaling scheme, and pairing of the two radial cutoffs used in the three dimensional descriptor function.

Coletti, Mark↗

DOE OSTI2025

Tensorized Interior Radiative Heat Transfer for a Scalable and Calibrated Building Energy Simulator

Building energy simulation is a critical tool for developing and testing advanced control strategies, such as Reinforcement Learning (RL), to provide demand flexibility and affordable energy costs. The recently introduced Smart Buildings Control Suite (sbsim) provides a lightweight, scalable, and data-calibrated simulation environment based on a 2D finite-difference model. However, the initial model primarily focused on conductive and convective heat transfer, neglecting the significant impact of long-wave radiative heat exchange between interior surfaces. This paper presents a significant extension to the sbsim framework by incorporating a physically-grounded model for interior radiative heat transfer. Our primary contribution is the development and integration of a fully tensorized radiative heat transfer module, which preserves the computational efficiency and scalability of the original simulator. This was achieved by developing a pipeline for view factor calculation, including an algorithm to identify directly seeing surfaces within complex floor plans, and formulating the net radiation equations for efficient execution on modern hardware accelerators. We validate the numerical accuracy of our tensorized implementation by comparing its results against a traditional iterative approach, demonstrating identical outcomes. This enhancement increases the physical fidelity of sbsim, enabling more accurate training of RL agents for building energy optimization.

Ham, Sang woo↗

NASA NTRS1995

Parallel volume ray-casting for unstructured-grid data on distributed-memory architectures

As computing technology continues to advance, computational modeling of scientific and engineering problems produces data of increasing complexity: large in size and unstructured in shape. Volume visualization of such data is a challenging problem. This paper proposes a distributed parallel solution that makes ray-casting volume rendering of unstructured-grid data practical. Both the data and the rendering process are distributed among processors. At each processor, ray-casting of local data is performed independent of the other processors. The global image composing processes, which require inter-processor communication, are overlapped with the local ray-casting processes to achieve maximum parallel efficiency. This algorithm differs from previous ones in four ways: it is completely distributed, less view-dependent, reasonably scalable, and flexible. Without using dynamic load balancing, test results on the Intel Paragon using from two to 128 processors show, on average, about 60% parallel efficiency.

Ma, Kwan-Liu↗

DOE OSTI2023

Implementation and test of an automated control hunting fault correction algorithm in a fault detection and diagnostics tool

Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Additive manufacturing for electrocaloric terpolymer thin films

Current heating, venting, and air conditioning (HVAC) systems have drawbacks of high energy consumption, large CO 2 emissions, and low efficiency. Electrocaloric (EC) cycles present an eco-friendly alternative by converting thermal energy to electrical energy. Defect-free thin films with uniform thickness are required to achieve optimal EC performance. A scalable thin film fabrication process is essential for integrating EC cycles into HVAC systems. This study introduces EC thin films prepared by electrospray (ES) processing, a manufacturing method that deposits EC polymer layer by layer using high voltage. The resulting films show superior thickness control, smoother surfaces, and improved thermal and electrical properties compared with solution casting. In addition, post-annealing at 120°C enhances the thermal and EC performance, with films achieving a temperature change (ΔT) of 3.6°C at 100 MV/m when tested near room temperature. With the potential for future scalability, the ES method offers a promising approach for fabricating EC thin films.

36 MATERIALS SCIENCE↗

NASA NTRS2005

A Tool for Requirements-Based Programming

Absent a general method for mathematically sound, automated transformation of customer requirements into a formal model of the desired system, developers must resort to either manual application of formal methods or to system testing (either manual or automated). While formal methods have afforded numerous successes, they present serious issues, e.g., costs to gear up to apply them (time, expensive staff), and scalability and reproducibility when standards in the field are not settled. The testing path cannot be walked to the ultimate goal, because exhaustive testing is infeasible for all but trivial systems. So system verification remains problematic. System or requirements validation is similarly problematic. The alternatives available today depend on either having a formal model or pursuing enough testing to enable the customer to be certain that system behavior meets requirements. The testing alternative for non-trivial systems always have some system behaviors unconfirmed and therefore is not the answer. To ensure that a formal model is equivalent to the customer s requirements necessitates that the customer somehow fully understands the formal model, which is not realistic. The predominant view that provably correct system development depends on having a formal model of the system leads to a desire for a mathematically sound method to automate the transformation of customer requirements into a formal model. Such a method, an augmentation of requirements-based programming, will be briefly described in this paper, and a prototype tool to support it will be described. The method and tool enable both requirements validation and system verification for the class of systems whose behavior can be described as scenarios. An application of the tool to a prototype automated ground control system for NASA mission is presented.

Rash, James L.↗

DOE OSTI2022

Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning

This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree–Fock computations. A MOB pairwise decomposition of the correlation part of the dipole moment is applied, and these pair dipole moments could be further regressed as a universal function of MOs. The dipole MOB features consist of the energy MOB features and their responses to electric fields. An interpretable and rotationally equivariant derivative kernel for Gaussian process regression (GPR) is introduced to learn the dipole moment more efficiently. The proposed problem setup, feature design, and ML algorithm are shown to provide highly accurate models for both dipole moments and energies on water and 14 small molecules. To demonstrate the ability of MOB-ML to function as generalized density-matrix functionals for molecular dipole moments and energies of organic molecules, we further apply the proposed MOB-ML approach to train and test the molecules from the QM9 dataset. The application of local scalable GPR with Gaussian mixture model unsupervised clustering GPR scales up MOB-ML to a large-data regime while retaining the prediction accuracy. In addition, compared with the literature results, MOB-ML provides the best test mean absolute errors of 4.21 mD and 0.045 kcal/mol for dipole moment and energy models, respectively, when training on 110 000 QM9 molecules. The excellent transferability of the resulting QM9 models is also illustrated by the accurate predictions for four different series of peptides.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗