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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Comprehensive defect evaluation of advanced nuclear fuels using high-resolution acoustic signals and optimized sensor separation

Graphite pebble composite structures based on TRistructural-ISOtropic (TRISO) particles are being developed as core nuclear fuels in advanced power reactors, promising safe operation at increased temperatures. Ensuring the structural integrity of these nuclear fuels requires comprehensive and accurate non-destructive evaluation (NDE) techniques to characterize defects and damage in the pebbles. However, traditional acoustic evaluation methods face limitations in defect characterization due to the highly attenuative, and geometrically and compositionally complex nature of these structures. This study proposes an improved acoustic NDE technique for accurate detection and classification of anticipated relevant defects and damage in graphite pebbles using high-resolution acoustic signals and optimized transmit-receive sensor networks. The proposed approach utilizes a triangular three-sensor network as the base unit, comprising three transmit-receive sensors. The sensor separation distance, as well as acoustic excitation center frequency, pulse-width, and bandwidth are optimized to enhance spatial resolution and improve signal-to-noise ratio, enabling effective characterization of the smallest size and widest range of defects in pebbles. Furthermore, the use of the triangular sensor configuration instead of a more conventional transmit-receive sensor pair expands the inspection region from a one-dimensional linear path to a two-dimensional area, increasing spatial coverage. To mitigate challenges associated with processing of complex acoustic signals arising from high-frequency, high-bandwidth excitation in these structures, a machine-learning-based signal processing algorithm is integrated with the sensor network. In the machine-learning-based algorithm, multi-domain features are extracted from the acoustic signals to capture intricate signal characteristics, significantly improving defect identification and classification compared to traditional approaches. The proposed acoustic NDE technique offers considerable promise for practical and reliable defect/damage diagnostics of advanced nuclear pebble fuels.

42 ENGINEERING↗
NASA NTRS1990

Integrated analysis and applications

An overview is presented of current research activities which, in a broad context, are focused on the development and verification of integrated structural analysis and optimal design capabilities for advanced aerospace propulsion and power systems. The overview encompasses a variety of subject areas including: (1) composite materials; (2) advanced structural analysis; (3) constitutive modeling; (4) computational simulation; (5) probabilistic analysis; and (6) multidisciplinary optimization. Typical results are presented which illustrate the benefit and utility of the emerging technologies as applied to propulsion and power system structures.

Hopkins, Dale A.↗
DOE OSTI2021

Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules

Advances in machine learning provide the ability to leverage data from expensive simulations of high-energy-density experiments to significantly cut down on computational time and costs associated with the search for optimal target designs. This study presents an application of cutting-edge Bayesian optimization methods to the one-dimensional (1D) design optimization of double shell graded layer targets for inertial confinement fusion experiments. This investigation attempts to reduce hydrodynamic instabilities while retaining high yields for future NIF experiments. Machine learning methods can use predictive physics simulations to identify graded layer designs from within the vast design space that demonstrate high predicted performance, including novel designs with high uncertainty in performance that may hold unexpected promise. By applying machine learning tools to the simulation design, we map the trade-off between 1D yield and instability, specifically isolating parameter ranges, which maintain high performance while showing significantly improved Rayleigh–Taylor stability over the point design. Furthermore, the groundwork laid in this study will be a useful design tool for future NIF experiments with graded layer targets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Advancement of the General Aviation Synthesis Program Using Python to Enable Optimization-Based Hybrid-Propulsion Aircraft Design

In support of Electrified Powertrain Flight Demonstrator and Advanced Air Transport Technologies programs at NASA, engineers at NASA Ames and NASA Glenn Research Centers have developed a new tool for coupled engine and airframe optimization and analysis. The new tool combines the engineering-level analysis methods of the FORTRAN General Aviation Synthesis Program (GASP) with the OpenMDAO framework to handle highly coupled problems that legacy tools struggle to optimize. The tool has been verified to match GASP with good agreement on a 737 MAX8 baseline vehicle closure problem, and preliminary efforts have been made to integrate the pyCycle thermodynamic cycle analysis tool for electrified engine optimization in the context of a vehicle optimization problem.

Kenneth R. Lyons↗
DOE OSTI2021

Using occupant interaction with advanced lighting systems to understand opportunities for energy optimization: Control data from a hospital NICU

For this study, lighting system control data from five patient rooms in a neonatal intensive care unit (NICU) over a 25-week monitoring period were analyzed to better understand how the NICU staff and families interacted with an automatic tunable lighting system (variable spectrum and intensity) with manual override options. The data were analyzed to determine the amount of time spent in available lighting modes, estimate energy consumption, and to observe any patterns in how the lighting system was being used by occupants. Further analysis revealed opportunities for optimizing the system for occupant needs and energy savings. The patient room occupants were clearly engaged in using the lighting system throughout the 24-hour day to meet needs and preferences. Giving occupants control over the lighting system did not result in a considerable increase in energy use. Implementing a time-out would minimize the extended periods of time when the lights were left on without need at the highest brightness level, further decreasing energy use. This analysis reveals the potential of lighting control data to contribute to energy savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Advanced Modeling and Process-Materials Co-Optimization Strategies for Swing Adsorption Based Gas Separations

This project devised a computational framework for simultaneously co-optimizing pressure swing adsorption process designs along with the sorbent materials (specifically, metal-organic frameworks) to be employed in the associated packed bed columns. The materials optimization aspect involved search over a design space that can describe the material’s molecular structure, while the process optimization aspect considered various process degrees of freedom for steps arising in various cycle configurations. This framework was demonstrated on the separation of nitrogen and carbon dioxide, which arises ubiquitously in a multitude of post-combustion carbon capture and “blue” hydrogen production applications. Our results led to metal-organic framework molecular descriptor choices that are predicted to outperform standard structures used in practice, providing guidance for future metal-organic framework synthesis efforts.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2007

Performance Optimization of Storable Bipropellant Engines to Fully Exploit Advanced Material Technologies

This paper summarizes the work performed to dale on the NASA Cycle 3A Advanced Chemical Propulsion Technology Program. The primary goals of the program are to design, fabricate, and test high performance bipropellant engines using iridium/rhenium chamber technology to obtain 335 seconds specific impulse with nitrogen tetroxide/hydrazine propellants and 330 seconds specific impulse with nitrogen tetroxide/monomethylhydrazine propellants. Aerojet has successfully completed the Base Period of this program, wherein (1) mission and system studies have been performed to verify system performance benefits and to determine engine physical and operating parameters, (2) preliminary chamber and nozzle designs have been completed and a chamber supplier has been downselected, (3) high temperature, high pressure off-nominal hot fire testing of an existing state-of-the-art high performance bipropellant engine has been completed, and (4) thermal and performance data from the engine test have been correlated with new thermal models to enable design of the new engine injector and injector/chamber interface. In the next phase of the program, Aerojet will complete design, fabrication, and test of the nitrogen tetroxide/hydrazine engine to demonstrate 335 seconds specific impulse, and also investigate improved technologies for iridium/rhenium chamber fabrication. Achievement of the NRA goals will significantly benefit NASA interplanetary missions and other government and commercial opportunities by enabling reduced launch weight and/or increased payload. At the conclusion of the program, the objective is to have an engine ready for final design and qualification for a specific science mission or commercial application. The program also constitutes a stepping stone to future, development, such as higher pressure pump-fed in-space storable engines.

Miller, Scott↗
DOE OSTI2023

Scale-up Testing of Advanced Polaris Membrane in CO2 Capture Technology

This final technical report describes work conducted by Membrane Technology and Research, Inc. (MTR) for the U.S. Department of Energy (DOE), National Energy Technology Lab (NETL) on the scale-up and testing of advanced Polaris™ membrane CO2 capture technology at the Technology Centre Mongstad (TCM) under award number DE-FE0031591. The work was performed from August 1, 2018 through January 31, 2023. The overall goal of this project was to design, build and operate an advanced Polaris membrane CO2 capture system at TCM. MTR was assisted in this project by Trimeric Corporation (Trimeric), an engineering design services company, the Carbon Capture Simulation for Industry Impact (CCSI2), a partnership among national laboratories, industry, and academic institutions, and the Technology Centre Mongstad (TCM), who provided the host site for the slipstream field test. This report details the work conducted to scale-up MTR’s second-generation (Gen-2) Polaris membrane and advanced planar membrane modules to a final form factor optimized for commercial use; validate their performance in an engineering-scale field test at TCM; and to show the potential of the MTR process to meet DOE CO2 capture targets from large source point emitters. Work for this project included membrane optimization and scale-up, advanced planar module design and fabrication, design and fabrication of an engineering-scale field test membrane skid, operation of the field test skid processing Residue Fluid Catalytic Cracker (RFCC) industrial flue gas at TCM, and a detailed techno-economic analysis (TEA) of the MTR membrane post-combustion process for CO2 capture. This project validated recent membrane technology advancements at the engineering-scale, moves the MTR advanced post-combustion capture technology to TRL-6, and mitigates risk in future Large Pilot or Demonstration scale-up activities.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2017

Linear Power-Flow Models in Multiphase Distribution Networks: Preprint

This paper considers multiphase unbalanced distribution systems and develops approximate power-flow models where bus-voltages, line-currents, and powers at the point of common coupling are linearly related to the nodal net power injections. The linearization approach is grounded on a fixed-point interpretation of the AC power-flow equations, and it is applicable to distribution systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. The proposed linear models can facilitate the development of computationally-affordable optimization and control applications -- from advanced distribution management systems settings to online and distributed optimization routines. Performance of the proposed models is evaluated on different test feeders.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2019

Refining Lucy Mission Delta-V During Spacecraft Design Using Trajectory Optimization Within High-Fidelity Monte Carlo Maneuver Analysis

Recent advances linking medium-fidelity trajectory optimization and high-fidelity trajectory propagation/maneuver design software with Monte Carlo maneuver analysis and parallel processing enabled realistic statistical delta-V estimation well before launch. Completing this high-confidence, refined statistical maneuver analysis early enabled release of excess delta-V margin for increased dry mass margin for the Lucy Jupiter Trojan flyby mission. By 3.3 years before launch, 16 of 34 TCMs had 1000 re-optimized trajectory design samples, yielding tens of m/s lower 99%-probability delta-V versus targeting maneuvers to one optimal trajectory. One year later, 1000 re-optimized samples of all deterministic maneuvers and subsequent flybys further lowered estimated delta-V.

Trojan↗
DOE OSTI2016

Exploration of magnetic perturbation effects on advanced divertor configurations in NSTX-U

The control of divertor heat loads—both steady state and transient—remains a key challenge for the successful operation of ITER and FNSF. Magnetic perturbations provide a promising technique to control ELMs (Edge Localized Modes) (transients), but understanding their detailed impact is difficult due to their symmetry breaking nature. One approach for reducing steady state heat loads is so called “advanced divertors” which aim at optimizing the magnetic field configuration: the snowflake and the (super-)X-divertor. It is likely that both concepts—magnetic perturbations and advanced divertors—will have to work together, and we explore their interaction based on the NSTX-U setup. An overview of different divertor configurations under the impact of magnetic perturbations is presented, and the resulting impact on plasma edge transport is investigated with the EMC3-EIRENE code. Variations in size of the magnetic footprint of the perturbed separatrix are found, which are related to the level of flux expansion on the divertor target. Non-axisymmetric peaking of the heat flux related to the perturbed separatrix is found at the outer strike point, but only in locations where flux expansion is not too large.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Advancement of the General Aviation Synthesis Program Using Python to Enable Optimization-Based Hybrid-Propulsion Aircraft Design

In support of the Electrified Powertrain Flight Demonstrator and Advanced Air Transport Technologies projects at NASA, a new tool has been developed at NASA’s Ames and Glenn Research Centers to enable coupled engine and airframe optimization and analysis. The new tool combines the engineering-level analysis methods and empirical models of the FORTRAN General Aviation Synthesis Program (GASP) with the Python-based OpenMDAO framework to provide a modular framework for efficient gradient-based optimization with the aim of incorporating new subsystem models for unconventional configurations. The tool has been verified against GASP analyses of several aircraft models and mission formulations. Preliminary efforts have been made to integrate pyCycle, a thermodynamic cycle analysis tool, to enable simultaneous optimization of hybrid propulsion system and vehicle parameters while taking full mission performance and constraints into account. This will improve current capabilities to assess impacts of electrified powertrain technologies on future aircraft designs.

Kenneth R. Lyons↗
NASA NTRSDate not supplied

Advancement of the General Aviation Synthesis Program Using Python to Enable Optimization-Based Hybrid-Propulsion Aircraft Design

In support of the Electrified Powertrain Flight Demonstrator and Advanced Air Transport Technologies projects at NASA, a new tool has been developed at NASA's Ames and Glenn Research Centers to enable coupled engine and airframe optimization and analysis. The new tool combines the engineering-level analysis methods and empirical models of the FORTRAN General Aviation Synthesis Program (GASP) with the Python-based OpenMDAO framework to provide a modular framework for efficient gradient-based optimization with the aim of incorporating new subsystem models for unconventional configurations. The tool has been verified against GASP analyses of several aircraft models and mission formulations. Preliminary efforts have been made to integrate pyCycle, a thermodynamic cycle analysis tool, to enable simultaneous optimization of hybrid propulsion system and vehicle parameters while taking full mission performance and constraints into account. This will improve current capabilities to assess impacts of electrified powertrain technologies on future aircraft designs.

Kenneth R Lyons↗