Search NASA⌕ Search

SEARCH · Search NASA

Results for “Machining dynamics”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Modeling of Fillets in Thin-Walled Structures for Dynamic Analysis

Relatively new developments in manufacturing methods have made it possible to produce machined parts with a wall thickness of less than 0.010". While the parts are being machined. Fillets of relatively large radius to thickness ratios are created by the milling tool. This paper discusses an accurate new technique for finite-element modeling of fillets for dynamic and stiffness analysis using efficient plate elements rather than more computationally intensive, high density solid meshes, A simple filleted cantilever beam of 0.040" thickness, 1.6" length, 1" depth, and 0.250" radius in the corner was modeled using both solid elements and several different plate element geometries. The finite element results were then compared with static and modal testing of a machined sample. The highest-scoring plate element technique uses a bridge of elements that is constructed through the tangent point of the Fillet radius such that the thickness of the element matches tile volume of the fillet. This model produces errors of less than 4.6 percent for static loading and less than 7.4 percent for modal analysis. This simple plate element technique will prove critical for efficient, timely, and accurate dynamic analysis of complex thin-walled structures.

Seugling, Richard M.↗
DOE OSTI2026

Multireference Methods for Chemistry and Materials Science: Automated Active Spaces, Efficient Dynamic Correlation, and Extended Systems

While multiconfigurational approaches have long been relegated to expert practitioners working on a case-by-case basis, recent developments have increasingly made these methods more routine and applicable to broader sets of systems. This article outlines the state-of-the-art in multiconfigurational approaches, with an emphasis on moving from delicate hand-selected pathways through configuration space toward more robust and efficient approaches to treating a host of challenging chemical systems accurately. First, we overview recent work in automated active-space selection, which has enabled increasingly large-scale applications of multireference methods to modeling vertical excitations and reactivity. Second, we highlight the increasingly efficient methods for recovering correlation energy beyond the active space, as headlined by extensions of pair-density functional theory and its role in accurate and efficient treatment of excited-state dynamics and its utilization to train machine-learned potentials. Finally, we highlight recent efforts to treat extended systems that until recently have lied beyond the traditional limits of active-space methods, giving center stage to product-form wave functions of the localized active space family of methods that allow for the computation of multiconfigurational band structures. These recent advancements point to a broader use of multireference approaches for high-impact chemical and materials science applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Direct Ab Initio Simulation of the Synthesis of BaZrO 3 and the Microstructure Impacts on Proton Transport

Controlling and predicting the processing-structure-performance relationship in functional materials is a grand challenge in materials science, with important implications for a wide range of emerging applications; a high fidelity understanding of the performance impact of microstructures formed under synthesis conditions is required to develop advanced materials, such as solid-state fuel cells and electrolyzers. Using the ceramic BaZrO 3 as a case study, we directly simulate the synthesis and investigate how proton transport is dictated by microstructures. We develop a framework that couples density functional theory (DFT), machine-learning interatomic potential (MLIP) driven molecular dynamics, and grand canonical Monte Carlo to perform large-scale, microstructure-resolved, atomistic simulations of proton transport in experimentally representative polycrystalline structures. Our fully ab initio approach, using a MLIP as a proxy for DFT, allows us to quantify the competition between two distinct diffusion mechanisms: one associated with grain-boundary regions and another within grains. When the impacts of grain boundaries are taken into account, proton transport exhibits substantial deviation from the bulk oxide limit. This addresses long-standing discrepancies between theory and experiments. Our integrated approach provides atomistic insight into microstructure-dependent proton pathways in BaZrO 3 and establishes a general protocol for predicting processing-structure-performance relationships.

organic↗
DOE OSTI2026

2D nanoconfinement distorts the solvation structure of hydroxide but not of hydronium

Understanding ion-specific behavior in nanoconfined water is essential for controlling charge transport and selectivity in two-dimensional membranes. Motivated by recent experiments revealing anomalous dielectric and transport behavior of water confined between hBN sheets, we use machine-learning-accelerated first-principles molecular dynamics to investigate the interfacial propensities of hydronium and hydroxide ions under similar confinement. We find that hydronium remains interfacial across all confinement regimes, whereas hydroxide shifts toward the interior as the environment becomes more bulk-like. This contrasting behavior reflects the combined influence of hydrogen bonding, interfacial water layering, and the polarization of hBN, which collectively stabilize hydronium at the surface while making hydroxide slightly more favorable within the structured interior. These findings expose an asymmetry in ion-surface coupling and establish a microscopic origin for hydronium’s enhanced interfacial affinity. The results provide mechanistic insight into ion partitioning in two-dimensional channels and highlight the collective structuring of confined water as a key determinant of interfacial ion thermodynamics.

organic↗
DOE OSTI2025

Breaking the curse of dimensionality: Solving configurational integrals for crystalline solids by tensor networks

Accurately evaluating configurational integrals for dense solids remains a central and difficult challenge in the statistical mechanics of condensed systems. Here, we present a tensor network approach that reformulates the high-dimensional configurational integral for identical-particle crystals into a sequence of computationally efficient summations. We represent the integrand as a high-dimensional tensor and apply tensor-train (TT) decomposition together with a custom TT-cross interpolation. This approach circumvents the need to explicitly construct the full tensor. We introduce tailored rank-1 and rank-2 schemes optimized for sharply peaked Boltzmann probability densities, typical for identical-particle crystals. When applied to the calculation of internal energy and pressure-temperature curves for crystalline Cu and Ar at high (GPa) pressures, as well as the alpha-to-beta phase transition diagram of Sn, our method accurately reproduces molecular dynamics simulation results using tight-binding, machine learning, hierarchical interacting particle–neural network, and modified embedded atom method potentials,all within seconds of computation time.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Nonlinear Ensemble Filtering with Diffusion Models: Application to the Surface Quasigeostrophic Dynamics

The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here, we explore another promising solution in which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential data assimilation. Unlike standard machine learning methods, the proposed ensemble score filter (EnSF) is completely training free and can efficiently generate a set of analysis ensemble members. Here, in this study, we apply the EnSF to a surface quasigeostrophic model and compare its performance against the popular local ensemble transform Kalman filter (LETKF), which makes Gaussian assumptions in the analysis step. Numerical tests demonstrate that EnSF maintains stable performance in the absence of localization and for a variety of experimental settings. We find that while LETKF maintains optimal performance in the case of linear observations of the entire state and a perfect model, EnSF shows improvements over LETKF when nonlinear observations are assimilated and the system is subject to unexpected model errors. A spectral decomposition of the analysis results in this nonlinear observation regime shows that the largest improvements over LETKF occur at large scales (small wavenumbers), where LETKF lacks sufficient ensemble spread. Overall, this initial application of EnSF to a geophysical model of intermediate complexity motivates further development of the algorithm for more realistic problems.

Artificial intelligence↗
DOE OSTI2024

Gold-Thiolate Nanocluster Dynamics Dataset and Supplementary Files

Supplementary information accompanying the article “Gold-Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential” including the training/testing dataset, potential files, and simulation results.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Space human factors publications: 1980-1990

A 10 year cummulative bibliography of publications resulting from research supported by the NASA Space Human Factors Program of the Life Science Division is provided. The goal of this program is to understand the basic mechanisms underlying behavioral adaptation to space and to develop and validate system design requirements, protocols, and countermeasures to ensure the psychological well-being, safety, and productivity of crewmembers. Subjects encompassed by this bibliography include selection and training, group dynamics, psychophysiological interactions, habitability issues, human-machine interactions, psychological support measures, and anthropometric data. Principal Investigators whose research tasks resulted in publication are identified by asterisk.

Dickson, Katherine J.↗
NASA NTRS1991

Human factors issues in the use of artificial intelligence in air traffic control. October 1990 Workshop

The objective of the workshop was to explore the role of human factors in facilitating the introduction of artificial intelligence (AI) to advanced air traffic control (ATC) automation concepts. AI is an umbrella term which is continually expanding to cover a variety of techniques where machines are performing actions taken based upon dynamic, external stimuli. AI methods can be implemented using more traditional programming languages such as LISP or PROLOG, or they can be implemented using state-of-the-art techniques such as object-oriented programming, neural nets (hardware or software), and knowledge based expert systems. As this technology advances and as increasingly powerful computing platforms become available, the use of AI to enhance ATC systems can be realized. Substantial efforts along these lines are already being undertaken at the FAA Technical Center, NASA Ames Research Center, academic institutions, industry, and elsewhere. Although it is clear that the technology is ripe for bringing computer automation to ATC systems, the proper scope and role of automation are not at all apparent. The major concern is how to combine human controllers with computer technology. A wide spectrum of options exists, ranging from using automation only to provide extra tools to augment decision making by human controllers to turning over moment-by-moment control to automated systems and using humans as supervisors and system managers. Across this spectrum, it is now obvious that the difficulties that occur when tying human and automated systems together must be resolved so that automation can be introduced safely and effectively. The focus of the workshop was to further explore the role of injecting AI into ATC systems and to identify the human factors that need to be considered for successful application of the technology to present and future ATC systems.

Hockaday, Stephen↗
NASA NTRS1997

Efficient Load Balancing and Data Remapping for Adaptive Grid Calculations

Mesh adaption is a powerful tool for efficient unstructured- grid computations but causes load imbalance among processors on a parallel machine. We present a novel method to dynamically balance the processor workloads with a global view. This paper presents, for the first time, the implementation and integration of all major components within our dynamic load balancing strategy for adaptive grid calculations. Mesh adaption, repartitioning, processor assignment, and remapping are critical components of the framework that must be accomplished rapidly and efficiently so as not to cause a significant overhead to the numerical simulation. Previous results indicated that mesh repartitioning and data remapping are potential bottlenecks for performing large-scale scientific calculations. We resolve these issues and demonstrate that our framework remains viable on a large number of processors.

Oliker, Leonid↗
NASA NTRS1998

Parallel Load Balancing for Adaptive Unstructured Meshes

Mesh adaption is a powerful tool for efficient unstructured-grid computations but causes load imbalance among processors on a parallel machine. We describe a novel method to dynamically balance the processor workloads with a global view. Mesh question, repartitioning, processor assignment, and remapping are critical components of the framework that must be accomplished rapidly and efficiently so as not to cause a significant overhead to the numerical simulation. A data redistribution model will also be presented that predicts the remapping cost. This model is required to determine whether the gain from a balanced workload distribution offsets the cost of data movement. Results presented will demonstrate that this is an effective dynamic load balancing strategy which remains viable on a large number of processors.

Biswas, Rupak↗
NASA NTRS1993

CMOS Active Pixel Image Sensor

A new CMOS active pixel image sensor is reported. The sensor uses a 2.0mue double-poly, double-metal foundry CMOS process and is realized as a 28 x 28 array of 40 mue x 40 mue pixels. The Sensor features TTL compatible voltages, low noise and large dynamic range, and will be useful in machine vision and smart sensor applications.

CMOS↗
NASA NTRSDate not supplied

Real-time Hardware-in-the-Loop Evaluation of a Partially Turboelectric Propulsion Control Design

In support of aviation fuel burn and emission reduction goals, NASA is pursuing high-payoff research investments that promise to transform aviation. This includes investments in Electrified Aircraft Propulsion (EAP). Multiple technology challenges must be addressed to unlock the full potential of EAP. This includes addressing challenges related to propulsion controls, which will be vital for ensuring efficient coordinated operation of EAP subsystems. This paper presents results from real-time hardware-in-the-loop testing of a control design for a single aisle partially-turboelectric aircraft propulsion concept conducted at the NASA Electric Aircraft Testbed (NEAT) facility. The control system under test is designed for a propulsion concept consisting of two wing-mounted turbofan engines that produce thrust and generate electrical power to drive a boundary layer ingesting tailfan propulsor via an electrical motor. An integrated control strategy is applied to ensure coordinated operation of the turbofan and tailfan subsystems during steady-state and transient operation throughout the flight envelope. The NEAT test of this integrated control design consists of a partially hardware-in-the-loop, partially simulated configuration. A subscale representation of the electrical system design is implemented in hardware and mechanically coupled to electric machines that emulate turbomachinery and propulsor shaft dynamics. The hardware configuration is then operated under the control of a real-time computer application that runs a simulation of the propulsion system and the developed control logic. The NEAT facility test campaign includes a series of experiments that subject the control design to throttle transients conducted throughout the flight envelope and full-flight mission profiles. Testing under simulated performance degradation is also conducted to evaluate control design robustness. This includes constant and abrupt changes in degradation levels. Results from the hardware-in-the-loop test are presented and shown to be in good agreement with pre-test simulation predictions demonstrating the efficacy of the integrated control design approach.

Electrified Aircraft Propulsion↗
NASA NTRSDate not supplied

Real-time Hardware-in-the-Loop Evaluation of a Partially Turboelectric Propulsion Control Design

In support of aviation fuel burn and emission reduction goals, NASA is pursuing high-payoff research investments that promise to transform aviation. This includes investments in Electrified Aircraft Propulsion (EAP). Multiple technology challenges must be addressed to unlock the full potential of EAP. This includes addressing challenges related to propulsion controls, which will be vital for ensuring efficient coordinated operation of EAP subsystems. This paper presents results from real-time hardware-in-the-loop testing of a control design for a single aisle partially-turboelectric aircraft propulsion concept conducted at the NASA Electric Aircraft Testbed (NEAT) facility. The control system under test is designed for a propulsion concept consisting of two wing-mounted turbofan engines that produce thrust and generate electrical power to drive a boundary layer ingesting tailfan propulsor via an electrical motor. An integrated control strategy is applied to ensure coordinated operation of the turbofan and tailfan subsystems during steady-state and transient operation throughout the flight envelope. The NEAT test of this integrated control design consists of a partially hardware-in-the-loop, partially simulated configuration. A subscale representation of the electrical system design is implemented in hardware and mechanically coupled to electric machines that emulate turbomachinery and propulsor shaft dynamics. The hardware configuration is then operated under the control of a real-time computer application that runs a simulation of the propulsion system and the developed control logic. The NEAT facility test campaign includes a series of experiments that subject the control design to throttle transients conducted throughout the flight envelope and full-flight mission profiles. Testing under simulated performance degradation is also conducted to evaluate control design robustness. This includes constant and abrupt changes in degradation levels. Results from the hardware-in-the-loop test are presented and shown to be in good agreement with pre-test simulation predictions demonstrating the efficacy of the integrated control design approach.

Electrified Aircraft Propulsion↗
NASA NTRSDate not supplied

CLAIRE: Enabling Heterogeneous Communication Network Optimization for Robust and Resilient Operations

In this paper, we present the capabilities of the CLAIRE System which provides resilient communications for NASA in presence of interference and congestion for a heterogeneous multi-vendor network. CLAIRE increases mission science data return to improve resource efficiencies and ensures resilience in the unpredictable space environment for NASA missions and communication networks. CLAIRE provides technology / waveform agnostic cognitive control plane that is instantiated at the Application Layer (APP) so that it can ride on NASA’s HDTN bundle protocol or any other protocol stack that is used by the network. The cognitive control plane is instantiated using Heartbeats (HTBTs). CLAIRE is assisted by Wideband UHF-Ka Band RF Sensing that leverages advances in the Direct Digital Transceiver (DDTRX) technology. The Wideband RF Sensing is driven by statistical signal processing and machine learning algorithms. Interference is mitigated using Dynamic Spectrum Access (DSA). Finally, CLAIRE addresses congestion using spectrum aware packet forwarding algorithm. CLAIRE provides an extensible protocol that allows passing of RF spectrum situational awareness, cross-layer sensing, delay tolerant networking and dynamic spectrum access information that can help with network optimization. Cross-Layer Sensing (CLS) and CLAIRE Decision Engine (CDE) enable spectrum and delay aware packet forwarding and Dynamic Spectrum Access during cases of severe interference.

cognitive communications↗
NASA NTRS2023

Real-Time Hardware-in-the-Loop Evaluation of A Partially Turboelectric Propulsion Control Design

In support of aviation fuel burn and emission reduction goals, NASA is pursuing high-payoff research investments that promise to transform aviation. This includes investments in Electrified Aircraft Propulsion (EAP). Multiple technology challenges must be addressed to unlock the full potential of EAP. This includes addressing challenges related to propulsion controls, which will be vital for ensuring efficient coordinated operation of EAP subsystems. This paper presents results from real-time hardware-in-theloop (HIL) testing of a control design for a single aisle partially-turboelectric aircraft propulsion concept conducted at the NASA Electric Aircraft Testbed (NEAT) facility. The control system under test is designed for a propulsion concept consisting of two wing-mounted turbofan engines that produce thrust and generate electrical power to drive a boundary layer ingesting tailfan propulsor via an electrical motor. An integrated control strategy is applied to ensure coordinated operation of the turbofan and tailfan subsystems during steady-state and transient operation throughout the flight envelope. The NEAT test of this integrated control design consists of a partially HIL, partially simulated configuration. A subscale representation of the electrical system design is implemented in hardware and mechanically coupled to electric machines that emulate turbomachinery and propulsor shaft dynamics. The hardware configuration is then operated under the control of a real-time computer application that runs a simulation of the propulsion system and the developed control logic. The NEAT facility test campaign includes a series of experiments that subject the control design to throttle transients conducted throughout the flight envelope and full-flight mission profiles. Testing under simulated performance degradation is also conducted to evaluate control design robustness. This includes constant and abrupt changes in degradation levels. Results from the HIL test are presented and shown to be in good agreement with pretest simulation predictions demonstrating the efficacy of the integrated control design approach.

Electrified Aircraft Propulsion↗
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗
NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗