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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Analysis of Eye-Tracking Data During Conditions Conducive to Loss of Airplane State Awareness

In the constant drive to further the safety and efficiency of air travel, the complexity of avionics-related systems and of the procedures for interacting with them appear to be on an ever-increasing trend. While this growing complexity often yields productive results with respect to system capabilities and flight efficiency, it typically places a larger burden on pilots to manage increasing amounts of information and to understand intricate system designs. This can be problematic as too much information and/or ineffective provisions of information can potentially overwhelm and/or confuse pilots, and as a result, increase the likelihood of loss of airplane state awareness (ASA). One way to gain more insight into this issue is through experimentation using more objective measures. This study summarizes an analysis of eye-tracking data obtained during a high-fidelity flight simulation study that included most of the complexities of current flight decks, as well as several planned for the next generation air transportation system. Multiple analyses were performed to understand how the 22 participating airline pilots were observing ASA-related information provided during different stages of flights and in response to specific events within these stages. Also, study findings are compared to data presented in similar previous studies to assess trends or common themes regarding how airline crews apply visual attention in complex flight deck and operational environments.

Dill, Evan T.↗
NASA NTRSDate not supplied

Flutter-Constrained Optimization with the Linearized Frequency-Domain Approach

Due to the high computational cost associated with unsteady aeroelastic analysis, state-of-the-art aeroelastic optimizations based on computational fluid dynamics typically ignore critical constraints like flutter and aeroelastic gust response. The linearized frequency-domain method offers an approach for adding high-fidelity flutter constraints to multidisciplinary optimizations at relatively low cost compared to other unsteady computational fluid dynamics methods. In recent work, sensitivities have been implemented for the linearized frequency-domain method in FUN3D. In this work, the linearized frequency-domain method and associated sensitivities are applied to gradient-based aeroelastic optimization with flutter constraints based on computational fluid dynamics. An overview of the flutter constraint formulation and implementation is provided, and then two optimization problems are presented. The first optimization increases the flutter speed of a pitch and plunge airfoil at transonic conditions using the minimal amount of geometric changes. The second optimization minimizes the mass of the AGARD 445.6 wing subject to a flutter constraint.

Aeroelasticity↗
NASA NTRSDate not supplied

Flutter-Constrained Optimization with the Linearized Frequency-Domain Approach

Due to the high computational cost associated with unsteady aeroelastic analysis, state-of-the-art aeroelastic optimizations based on computational fluid dynamics typically ignore critical constraints like flutter and aeroelastic gust response. The linearized frequency-domain method offers an approach for adding high-fidelity flutter constraints to multidisciplinary optimizations at relatively low cost compared to other unsteady computational fluid dynamics methods. In recent work, sensitivities have been implemented for the linearized frequency-domain method in FUN3D. In this work, the linearized frequency-domain method and associated sensitivities are applied to gradient-based aeroelastic optimization with flutter constraints based on computational fluid dynamics. An overview of the flutter constraint formulation and implementation is provided, and then two optimization problems are presented. The first optimization increases the flutter speed of a pitch and plunge airfoil at transonic conditions using the minimal amount of geometric changes. The second optimization minimizes the mass of the AGARD 445.6 wing subject to a flutter constraint.

Aeroelasticity↗
NASA NTRSDate not supplied

CFD Predictions of Fluctuating Pressure Environments on NASA’s SLS Ascent Unsteady Aerodynamics Wind Tunnel Test

This paper details work that is being conducted on predicting fluctuating pressure environments on NASA launch vehicles using computational fluid dynamics (CFD). The accurate characterization of these aeroacoustics environments is necessary in the analysis of vehicle structural health and aerodynamic performance and in the vehicle design process. High-fidelity simulations of the unsteady flow over NASA’s SLS Ascent Unsteady Aerodynamics wind tunnel Test (AUAT) using the hybrid Reynolds-averaged Navier-Stokes (RANS)-large eddy simulations (LES) methodologies in Mississippi States’ Loci/CHEM solver are presented. The two available methods, Nelson-Nichols and dynamic hybrid RANS-LES (DHRL), are compared head-to-head on identical grids at two Mach numbers. At the high-subsonic Mach number, locally supersonic flow expanding over a shoulder induces a separation-reattachment system that is predicted poorly by the Nelson-Nichols method due to an under-prediction in unsteady content. The DHRL solution predicts the spatial extent and frequency domain response of this system well but under-predicts the peak noise levels due to a delay in the RANS-to-LES transition. At the low-supersonic Mach number, compression corner dynamics dominate the unsteady flow. While the Nelson Nichols solution captures this phenomenon well, another solution on a refined grid indicates that some model stress depletion is occurring. The DHRL solution again performs well at this Mach number and matches the wind tunnel data fairly closely, but perhaps most impressively, is fairly agnostic to increasing grid resolution. The findings in this paper are expected to be generally applicable and will be used to guide the prediction of aeroacoustics environments of other NASA launch vehicles.

CFD↗
NASA NTRS2012

Linked-List-Based Multibody Dynamics (MBDyn) Engine

This new release of MBDyn is a software engine that calculates the dynamics states of kinematic, rigid, or flexible multibody systems. An MBDyn multibody system may consist of multiple groups of articulated chains, trees, or closed-loop topologies. Transient topologies are handled through conservation of energy and momentum. The solution for rigid-body systems is exact, and several configurable levels of nonlinear term fidelity are available for flexible dynamics systems. The algorithms have been optimized for efficiency and can be used for both non-real-time (NRT) and real-time (RT) simulations. Interfaces are currently compatible with NASA's Trick Simulation Environment. This new release represents a significant advance in capability and ease of use. The two most significant new additions are an application programming interface (API) that clarifies and simplifies use of MBDyn, and a link-list infrastructure that allows a single MBDyn instance to propagate an arbitrary number of interacting groups of multibody top ologies. MBDyn calculates state and state derivative vectors for integration using an external integration routine. A Trickcompatible interface is provided for initialization, data logging, integration, and input/output.

MacLean, John↗
DOE OSTI2024

50 MW Segmented Ultralight Morphing Rotors for Wind Energy

A multi-institutional team designed a 50 Megawatt (MW) rated wind turbine featuring downwind aeroelastic morphing to reduce blade loads and allow an ultralight segmented rotor (mass reduction of about 25% compared to a conventional upwind rotor). The team used a control co-design approach with state-of-the art simulations including for the rotor and tower design using non-linear fluid-structure interactions and control algorithms, and the team also designed, built, and field-tested an aeroelastically-scaled downwind rotor to demonstrate this novel technology and validate the design tool fidelity. In a follow-on phase, these results were used along with an updated Levelized Cost Of Energy (LCOE) methodology to co-design a more detailed set of 25 MW rated turbine designs (including individual pitch control) based on minimum LCOE with highly flexible blades for an Atlantic Ocean offshore fixed-bottom design targeted towards market technology evaluation. A set of upwind and downwind designs at 25 MW rated scales were found to provide the best LCOE, with strong improvements over all previous offshore reference turbines (NREL 5MW, DTU 10 MW and IEA 15 MW). The optimized design would represent the world’s largest offshore turbine design, which combines several state-of-the-art structural, aerodynamic, and control technologies into a new and optimized system concept.

17 WIND ENERGY↗
DOE OSTI2021

Exploring Whether Subsurface Fluid Production Can Minimize Triggered Seismicity in Geothermal Fields

Fluid injection and production related to energy recovery and other industrial operations alter the pressure and stress state of subsurface reservoirs which can lead to induced seismicity. Here, the primary goal is to investigate the hypothesis that the modulation of stress state in subsurface reservoirs through fluid injection/production operations can reduce the likelihood of inducing seismicity. Validation of this mitigation strategy will provide an active operational method to control induced seismicity in subsurface reservoir exploitations such as geothermal energy recovery and carbon storage. In this project, we analyze the relationship between extensive fluid production and the paucity of aftershock activity at the Coso Geothermal plant (CGP) following the 2019 Ridgecrest earthquake sequence, where high rates of aftershock triggering were expected. We developed a high-fidelity multiphase coupled thermo-hydro-mechanical (THM) model to simulate fluid injection/production activities at the CGP between 1986 and 2020. THM results of surface subsidence due to high rates of fluid production agree well with field observations from global positioning system (GPS) and interferometric synthetic aperture radar (InSAR). Subsequently, the variations of pore pressure, temperature and stress state were used to drive numerical earthquake simulations of the aftershock response to the 2019 Ridgecrest earthquakes. The earthquake simulations show that seismic quiescence may occur following the Ridgecrest event, depending on the initial stress state at the time of the mainshock and at the initiation of geothermal energy production. Seismic quiescence occurs in 20% of the cases we explored, where rates are decreased by 50% of the background rate in the two years following the mainshock. In circumstances where aftershock rates increase near the CGP following Ridgecrest, the average rate change is a factor of two larger than background rates from simulations with no operations. These findings indicate that unlike many other operations that bring faults closer to failure, operations at the CGP are acting to stabilize faults such that triggering in the current stress state is minimal.

58 GEOSCIENCES↗
DOE OSTI2022

Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands

Abstract. Growth in adoption of distributed wind turbines for energy generation is significantly impacted by challenges associated with siting and accurate estimation of the wind resource. Small turbines, at hub heights of 40 m or less, are greatly impacted by terrestrial obstacles such as built structures and vegetation that can cause complex wake effects. While some progress in high-fidelity complex fluid dynamics (CFD) models has increased the potential accuracy for modelling the impacts of obstacles on turbulent wind flow, these models are too computationally expensive for practical siting and resource assessment applications. To understand the efficacy of available models in situ, this study evaluates classic and commonly used methods alongside new state-of-the-art lower-order models derived from CFD simulations and machine learning approaches. This evaluation is conducted using a subset of an extensive original dataset of measurements from more than 300 operational wind turbines in the northern Netherlands. The results show that data-driven methods (e.g. machine learning and statistical modelling) are most effective at predicting production at real sites with an average error in annual energy production of 2.5 %. When sufficient data may not be available de novo to support these data-driven approaches, models derived from high-fidelity simulations show promise and reliably outperform classic methods. On average these models have 6.3 %–11.5 % error compared with 26 % for classic methods and 27 % baseline error for reanalysis data without obstacle correction. While more performant on average, these methods are also sensitive to the quality of obstacle descriptions and reanalysis inputs.

17 WIND ENERGY↗
DOE OSTI2026

FunDiff: diffusion models over function spaces for physics-informed generative modeling

Recent advances in generative modeling-particularly diffusion models and flow matching-have been widely used for synthesizing discrete data such as images and videos. However, adapting these models to physical applications remains challenging, as the quantities of interest are continuous functions governed by complex physical laws. To address this, we introduce FunDiff, an efficient and robust framework for generative modeling in function spaces. FunDiff combines a latent diffusion process with a function autoencoder architecture to handle input functions with varying discretizations, generates continuous functions that can be evaluated at arbitrary locations, and seamlessly incorporate physical priors. These priors are enforced through architectural constraints or physics-informed loss functions, ensuring that generated samples satisfy fundamental physical laws. We theoretically establish minimax optimality guarantees for density estimation in function spaces, demonstrating that diffusion-based estimators achieve optimal convergence rates under suitable regularity conditions. We further demonstrate the practical effectiveness of FunDiff across diverse applications in fluid dynamics and solid mechanics. Empirical results indicate that our method can generate physically consistent samples with high fidelity to the target distribution, and exhibit robustness to noisy and low-resolution data.

Wang, Sifan [Yale University, New Haven, CT (Unite↗
DOE OSTI2025

Advancing the STS Neutron Moderator Design with an Automated Optimization Workflow and Unstructured Mesh Modeling

With the Second Target Station approaching its final design phase, a detailed neutronics evaluation of its critical components is necessary. Optimizing the dimensions of the two cold-source moderators that are at the heart of this facility presents a multi-objective optimization problem for which an accurate geometric description is crucial. We have applied a fully automated optimization workflow in which a detailed unstructured mesh geometry is automatically generated with Attila4MC, starting from a parametrized CREO geometry followed by preprocessing with SpaceClaim. With this geometry, a MCNP run is performed to calculate the brightness metrics, which are subsequently provided to the optimization algorithm in DAKOTA that provides new parameters and drives the optimization loop until convergence. In this paper, we show the results of the analysis that are used for the final design of the cylindrical and tube moderator. The optimization simulations provide a refinement to and confirmation of the conclusions of the previous design iteration. Additional to the optimization, a sensitivity study is performed to study the effect of minor geometry changes, which is important for the final engineering design. In conclusion, with these studies, we demonstrate that the automated workflow and high-fidelity unstructured mesh modeling are efficient tools for a thorough design evaluation.

DAKOTA↗
DOE OSTI2025

Indications for Freeze-Out of Charge Fluctuations in the Quark-Gluon Plasma at the LHC

The D-measure of net-charge fluctuations quantifies the variance of net charge in strongly interacting matter. It was introduced over 20 years ago as a potential signal of quark-gluon plasma (QGP) in heavy-ion collisions, where it is expected to be suppressed due to the fractional electric charges of quarks. Measurements have been performed at RHIC and LHC, but the conclusion has been elusive in the absence of quantitative calculations for both scenarios. We address this issue by employing a recently developed formalism of density correlations and incorporate resonance decays, local charge conservation, and experimental kinematic cuts. We find that the hadron gas scenario is in fair agreement with the ALICE data for $\sqrt{{𝑠}_{\textrm{NN}}}$ = 2.76 TeV Pb–Pb collisions only when a very short rapidity range of local charge conservation is enforced, while the QGP scenario is in excellent agreement with experimental data and largely insensitive to the range of local charge conservation. A Bayesian analysis of the data utilizing different priors yields moderate evidence for the freeze-out of charge fluctuations in the QGP phase relative to hadron gas. The upcoming high-fidelity measurements from LHC Run 2 will serve as a precision test of the two scenarios.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Coarse Graining Discrete Element Method Information in Particle-in-Cell Length Scales Using a Machine Learning Approach

This report details the development of a machine learning (ML)-driven framework to coarse-grain inter-particle collision dynamics from high-fidelity Discrete Element Method (DEM) simulations to Particle-in-Cell (PIC) scales for gas-solid systems. Traditional PIC models, while computationally efficient, rely on empirical granular stress formulations that fail to capture the full complexity of collision physics, particularly the heterogeneity in particle dynamics. This study adopts a bottom-up approach, integrating insights from DEM simulations to improve the physical fidelity and interpretability of PIC-scale models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Optimizing Simulation Fidelity in Direct-Drive Inertial Confinement Fusion with Cassio

Recently, the National Ignition Facility (NIF) demonstrated that inertial confinement fusion (ICF) is capable to achieve thermonuclear (TN) ignition in the laboratory, making it a crucial method on the path to replicate the Sun’s power production mechanism on Earth. However, the physics governing the high-energy density environments is very complex and remains a challenge to fully understand and model. For example, dopants in the TN fuel are important diagnostic tools to extract the thermodynamic conditions of the plasma. However, if their concentration is chosen too high, they can significantly degrade the performance of an ICF capsule. In this study, we use the Los Alamos National Laboratory radiation-hydrodynamics code Cassio to model ICF implosions of capsules which contain deuterium fuel with high-Z dopants like Krypton and Argon from the high-Z campaign conducted 15 years ago. We focus on how chosen computational and physics parameters influence the implosion outcomes. By systematically changing the resolution of the computational mesh and the photon energies as well as modifying settings for the laser drive and TN fuel pre-heat effects, we assess the impact on experimentally measured performance metrics like neutron production from TN burn and x-ray emission during the implosion. Our results will help to improve the fidelity of simulations and guide future numerical studies and experimental designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Atoms-to-devices models for post-irradiation annealing kinetics in III-V semiconductors

Understanding atomic displacement damage and propagating this understanding into validated defect-aware radiation damage models is a critical step toward achieving predictive, atoms-to-systems assessment of radiation vulnerabilities of microelectronics. These models are essential for evaluating the performance and reliability of nuclear stockpile components or space-based electronics (satellites). The goal of this project was to bridge a fundamental science gap between defect spectroscopies and atomistic modeling of radiation-induced defects on the one hand, and simulations of device response on the other hand, to develop predictive defect-aware damage models for high-fidelity device simulations of radiation-damaged III-V semiconductors.

36 MATERIALS SCIENCE↗
NASA NTRS2013

Compact Full-Field Ion Detector System for CubeSat Science Beyond LEO

NASA Glenn Research Center (GRC) is applying its expertise and facilities in harsh environment instrumentation to develop a Compact Full-Field Ion Detector System (CFIDS). The CFIDS is designed to be an extremely compact, low cost instrument, capable of being flown on a wide variety of deep space platforms, to provide comprehensive (composition, velocity, and direction) in situ measurements of heavy ions in space plasma environments with higher fidelity, than previously available.

Extraterrestrial Radiation↗
DOE OSTI2022

Anderson acceleration stability in NDA-accelerated k-eigenvalue problems

Anderson acceleration (AA) has been used to improve the stability and convergence rate of multiphysics iterative methods for reactor analysis. Most applications studied assume a tightly converged solution for the different physics problems, and AA is usually applied to state variables like temperature, density, and heat generation rate. In this paper, we study the theoretical performance of AA in NDA-accelerated k-eigenvalue problems. The problems and algorithms studied are simplified from the coupled iteration scheme adopted by MPACT and many other high-fidelity whole-core reactor codes. Compared to previous analyses of AA for these iteration schemes, we study the case with a partially converged neutronics solution and possibly partially converged nonlinear diffusion acceleration (NDA)/coarse mesh finite difference (CMFD) solutions. We observe that the performance of the iteration scheme with AA is very sensitive to the initial guess and is affected by the partially converged CMFD solutions. When the NDA solution is fully converged, using AA cannot achieve the optimal convergence rate in large-sized problems. Conversely, if the NDA solution is partially converged, the iteration scheme with AA can diverge or converge extremely slowly. It is found that the loss of robustness for AA is due to the fact that it is applied to the iterative subspace of state variables rather than the fundamental unknowns of the governing equations. To improve the robustness, the scalar flux should also be considered in the implementation of AA. After considering the residuals of flux, we observe that the stability is regardless of the partial convergence of NDA solutions. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Performance characterization and modeling of an oscillating surge wave energy converter

Abstract Testing wave energy converters in the ocean could be expensive and complex, which necessitates the use of numerical modeling. However, accurately modeling the response of wave energy converters with high-fidelity simulations can be computationally intensive in the design stage where different configurations must be considered. Reduced-order models based on simplified equations of motion can be very useful in the design, optimization, or control of wave energy converters. Given the complex dynamics of wave energy converters, accurate representation, and evaluation of relative contributions by different forces are required. This effort is concerned with a performance characterization of the hydrodynamic response of an oscillating surge wave energy converter that is based on a reduced-order model. A state-space model is used to represent the radiation damping term. Morison’s representation of unsteady forces is used to account for the nonlinear damping. Wave tank tests are performed to validate simulations. A free response simulation is used to determine the coefficients of the state-space model. Torque-forced simulations are used to identify the coefficients of the nonlinear damping term for different amplitudes and wave frequencies. The impact of varying these coefficients on the response is investigated. An assessment of the capability of the model in predicting the hydrodynamic response under irregular forcing is performed. The results show that the maximum error is 3% when compared with high-fidelity simulations. It is determined that the nonlinear damping is proportional to the torque amplitude and its effects are more pronounced as the amplitude of the flap oscillations increases.

16 TIDAL AND WAVE POWER↗