SEARCH · Search NASA

Results for “Synergistic dynamic testing”

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.

SourceResearch connectionFrom yearThrough year

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

At least 19 records

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Emergence of Complex Modes in Lightly Damped Structural Systems

In this short presentation format, we will provide a set of useful equations, relationships, and physical interpretations direct from the damped two-mode interaction problem. We will demonstrate that the results from the two-mode interaction problem are in direct agreement with observed test data. Practical methods for estimating the parameters driving the relations direct from modal test data are provided. We finalize with two examples demonstrating weak and strong interactions.

spaceflight hardware↗

NASA NTRS2000

X-38 Experimental Aerothermodynamics

The X-38 program seeks to demonstrate an autonomously returned orbital test flight vehicle to support the development of an operational Crew Return Vehicle for the International Space Station. The test flight, anticipated in 2002, is intended to demonstrate the entire mission profile of returning Space Station crew members safely back to earth in the event of medical or mechanical emergency. Integral to the formulation of the X-38 flight data book and the design of the thermal protection system, the aerothermodynamic environment is being defined through a synergistic combination of ground based testing and computational fluid dynamics. This report provides an overview of the hypersonic aerothermodynamic wind tunnel program conducted at the NASA Langley Research Center in support of the X-38 development. Global and discrete surface heat transfer force and moment, surface streamline patterns, and shock shapes were measured on scaled models of the proposed X-38 configuration in different test gases at Mach 6, 10 and 20. The test parametrics include angle of attack from 0 to 50 degs, unit Reynolds numbers from 0.3 x 10 (exp 6) to 16 x 10 (exp 6)/ ft, rudder deflections of 0, 2, and 5 deg. and body flap deflections from 0 to 30 deg. Results from hypersonic aerodynamic screening studies that were conducted as the configuration evolved to the present shape at, presented. Heavy gas simulation tests have indicated that the primary real gas effects on X-38 aerodynamics at trim conditions are expected to favorably influence flap effectiveness. Comparisons of the experimental heating and force and moment data to prediction and the current aerodynamic data book are highlighted. The effects of discrete roughness elements on boundary layer transition were investigated at Mach 6 and the development of a transition correlation for the X-38 vehicle is described. Extrapolation of ground based heating measurements to flight radiation equilibrium wall temperatures at Mach 6 and 10 were made and generally compared to within 50 deg F of flight prediction.

Horvath, Thomas J.↗

DOE OSTI2026

4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer’s Diagnosis

Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics. Recent studies demonstrate enhanced diagnostic sensitivity for Alzheimer’s disease (AD) through synergistic integration of neuroimaging data (e.g., sMRI, fMRI) with tabular data (e.g., behavioral and cognitive tests). However, the intrinsic heterogeneity across modalities (e.g., 4D spatiotemporal fMRI dynamics vs. 3D anatomical sMRI structure) presents critical challenges for discriminative feature fusion, often leading to information loss or biased fusion. To bridge this gap, we propose M2M-AlignNet: a multimodal co-attention network with latent alignment for early AD diagnosis using sMRI and fMRI. At the core of our approach is a multi-patch-to-multi-patch (M2M) contrastive loss function that quantifies and reduces representational discrepancies via weighted patch correspondence, explicitly aligning fMRI components across brain regions with their sMRI structural substrates without one-to-one constraints. Additionally, we propose a latent-as-query co-attention module to autonomously discover fusion patterns, circumventing modality prioritization biases while minimizing feature redundancy. We conduct extensive experiments to confirm the effectiveness of our method and highlight the correspondence between fMRI and sMRI as AD biomarkers.

Wei, Yuxiang [Georgia Institute of Technology]↗

NASA NTRS1989

An integrated CFD/experimental analysis of aerodynamic forces and moments

Aerodynamic analysis using computational fluid dynamics (CFD) is most fruitful when it is combined with a thorough program of wind tunnel testing. The understanding of aerodynamic phenomena is enhanced by the synergistic use of both analysis methods. A technique is described for an integrated approach to determining the forces and moments acting on a wind tunnel model by using a combination of experimentally measured pressures and CFD predictions. The CFD code used was FLO57 (an Euler solver) and the wind tunnel model was a heavily instrumented delta wing with 62.5 deg of leading-edge sweep. A thorough comparison of the CFD results and the experimental data is presented for surface pressure distributions and longitudinal forces and moments. The experimental pressures were also integrated over the surface of the model and the resulting forces and moments are compared to the CFD and wind tunnel results. The accurate determination of various drag increments via the combined use of the CFD and experimental pressures is presented in detail.

Melton, John E.↗

DOE OSTI2023

Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structure–function coupling

At the biosphere–atmosphere interface, nonlinear interdependencies among components of an ecohydrological complex system can be inferred using multivariate high frequency time series observations. Information flow among these interacting variables allows us to represent the causal dependencies in the form of a directed acyclic graph (DAG). Here, we use high frequency multivariate data at 10 Hz from an eddy covariance instrument located at 25 m above agricultural land in the Midwestern US to quantify the evolutionary dynamics of this complex system using a sequence of DAGs by examining the structural dependency of information flow and the associated functional response. We investigate whether functional differences correspond to structural differences or if there are no functional variations despite the structural differences. We base our analysis on the hypothesis that causal dependencies are instigated through information flow, and the resulting interactions sustain the dynamics and its functionality. To test our hypothesis, we build upon causal structure analysis in the companion paper to characterize the information flow in similarly clustered DAGs from 3-min non-overlapping contiguous windows in the observational data. We characterize functionality as the nature of interactions as discerned through redundant, unique, and synergistic components of information flow. Through this analysis, we find that in turbulence at the biosphere–atmosphere interface, the variables that control the dynamic character of the atmosphere as well as the thermodynamics are driven by non-local conditions, while the scalar transport associated with CO and H 2 O is mainly driven by short-term local conditions.

58 GEOSCIENCES↗

DOE OSTI2021

Synergistic data analytics for electromechanical oscillation in electric power systems

Accurate real-time estimation of the four electromechanical oscillation properties, i.e., dominant oscillation modes, mode shapes, participation factors, and coherent groups, is of great importance to assess and mitigate potential electromechanical oscillations in interconnected power systems. While eigenvalue analysis can realize such estimation, it requires precise linearized dynamic models and accurate parameters, which are highly difficult to obtain in practice. Data fusion-based modal estimation methods can extract the properties of electromechanical oscillations from measurement data without the power system model and parameters, but most of the time only one or two property assessments can be accomplished each time. To overcome this challenge, this paper presents a synergistic data analytics solution to characterize the dynamic behaviors of electromechanical oscillations from real-time measurement data. Here, the proposed method uses optimized variable projection, and it is capable of estimating all four electromechanical oscillation behavior properties from measured responses. Case studies are performed using the simulated measurement data of a 16-generator 68-bus test system and the field measurements collected by the PMUs deployed in the Yunnan Power Grid. The results demonstrate that the proposed synergistic data analytics solution can achieve satisfactory performance in capturing the properties of electromechanical oscillations from measurement data and exhibit strong robustness against measurement noise when compared with existing measurement-based methods.

42 ENGINEERING↗

DOE OSTI2022

Vertebrate and invertebrate competition for carrion in human‐impacted environments depends on abiotic factors

Abstract Human altered landscapes have caused declines in the diversity of wildlife where behaviorally plastic species (i.e., mesocarnivores and invasive species) tend to monopolize these areas and consume predictable and readily accessible food resources, such as human food waste and carrion. Increased consumption of carrion by vertebrates and invasive invertebrate species can alter population dynamics of native necrophagous insects relying on these resources. We tested the hypothesis that vertebrate scavengers and invasive species reduce blow fly (1) ability to use carrion and (2) reproduction in human‐impacted environments in central Texas, USA, with season, habitat (field and wooded landscapes), and carrion type (species of carrion and coat color) acting synergistically. Vertebrate scavengers in this habitat, of which 75% of the documented species were mesocarnivores and obligate scavengers, consumed 100% of carrion during the winter and 62% during summer despite having low species richness (2–5 species). Of the remaining carcasses available for arthropod activity during summer, the invasive red imported fire ant, Solenopsis invicta (Hymenoptera: Formicidae), monopolized 34%, and blow flies (e.g., Lucilia eximia and Chrysomya rufifacies [Diptera: Calliphoridae]) were only able to colonize 25%. Approximately 90% of carrion that was utilized by blow flies was co‐colonized by fire ants, and subsequent production of adult blow flies experienced up to a ninefold reduction in production compared with carcasses that were not scavenged by vertebrates or fire ants. Our results demonstrate oviposition resources used by blow flies in environments altered by human activity are reduced significantly by vertebrate scavengers and an invasive ant species. Future research should determine whether competitive interactions between vertebrate and invasive ant competitors for access to carrion resources have population‐level impacts to blow flies in human‐mediated ecosystems, or whether blow flies are able to shift to other resources to maintain sustainable populations and continue providing ecosystem services, such as pollination.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Weather aging effects on modified asphalt with rubber-polyethylene composites

Weather aging may cause more severe degradation on asphalt than thermal-oxidative aging due to the synergistic effect among oxygen, heat, ultraviolet (UV) radiation, and moisture. This study aimed to investigate weather aging effects and anti-aging mechanisms on asphalt modified with rubber-polyethylene (PE) composites. The modified binder blends and asphalt mixtures were subjected to outdoor weather aging. Dynamic shear rheometer (DSR), Fourier transform infrared spectroscopy (FTIR), Gel permeation chromatography (GPC), and optical microscopy tests were adopted to characterize the rheological properties, aging resistance, polymer degradation, and anti-aging mechanisms of modified binder blends, respectively. Mixture performance tests including Asphalt Pavement Analyzer (APA) and Ideal-CT tests were used to evaluate rutting and cracking resistance of asphalt mixtures. Results showed that weather aging can cause more severe aging to asphalt binders than pressure aging vessel (PAV) due to the severe degradation of PE particles. Rubber-PE composites alloyed with an extruder proved to stabilize PE particles in asphalt and significantly improve the aging resistance of modified binder blends. Furthermore, the enhanced aging resistance is attributed to the dispersion of PE particles and carbon black released by soluble rubbers.

Aging resistance↗

DOE OSTI2020

Styrene-Based Elastomer Composites with Functionalized Graphene Oxide and Silica Nanofiber Fillers: Mechanical and Thermal Conductivity Properties

The mechanical and thermal conductivity properties of two composite elastomers were studied. Styrene–butadiene rubber (SBR) filled with functionalized graphene oxide (GO) and silica nanofibers, and styrene–butadiene–styrene (SBS) block copolymers filled with graphene oxide. For the SBR composites, GO fillers with two different surface functionalities were synthesized (cysteamine and dodecylamine) and dispersed in the SBR using mechanical and liquid mixing techniques. The hydrophilic cysteamine-based GO fillers were dispersed in the SBR by mechanical mixing, whereas the hydrophobic dodecylamine-based GO fillers were dispersed in the SBR by liquid mixing. Silica nanofibers (SnFs) were fabricated by electrospinning a sol–gel precursor solution. The surface chemistry of the functionalized fillers was studied in detail. The properties of the composites and the synergistic improvements between the GO and SnFs are presented. For the SBS composites, GO fillers were dispersed in the SBS elastomer at several weight percent loadings using liquid mixing. Characterization of the filler material and the composite elastomers was performed using x-ray photoelectron spectroscopy, x-ray diffraction, transmission electron microscopy, scanning electron microscopy, thermogravimetric analysis, dynamic mechanical analysis, tensile testing, nanoindentation, thermal conductivity and abrasion testing.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Finch: Toxicity Dose Response Curve Prediction of Chemical Compounds and Mixtures

A paradigm shift in chemical risk assessment is emphasizing mixture testing over single compound analysis, eliminating animal testing, and adopting advanced modeling approaches to understand mixture activity profiles. However, existing computational models largely focus on single chemicals, with few effective solutions for modeling complex mixtures that account for synergistic or antagonistic effects and multiple Modes of Action (MoA). Conventional methods like concentration addition (CA) and independent action (IA) are insufficient for this task as they are designed for simplistic interactions and struggle to account for the dynamic and multifaceted nature of chemical mixtures, such as overlapping MoA and non-linear interactions. Finch offers a novel approach utilizing deep learning (DL) embeddings and multi-task quantitative structure-activity relationship (QSAR) models to improve chemical exposure prediction. By leveraging molecular descriptors, physiochemical properties, and large language model (LLM) embeddings from SMILES inputs, Finch preserves critical information in a latent space thereby enhancing predictive accuracy. The multi-task learning aspect of Finch is highly advantageous, as it simultaneously optimizes multiple loss functions, leveraging all available data across tasks to develop generalized representations that effectively capture complex ingredient interactions within mixtures.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2017

Predictive Modeling of Fast-Curing Thermosets in Nozzle-Based Extrusion

This work presents an approach to modeling the dynamic spreading and curing behavior of thermosets in nozzle-based extrusions. Thermosets cover a wide range of materials, some of which permit low-temperature processing with subsequent high-temperature and high-strength working properties. Extruding thermosets may overcome the limited working temperatures and strengths of conventional thermoplastic materials used in additive manufacturing. This project aims to produce technology for the fabrication of thermoset-based structures leveraging advances made in nozzle-based extrusion, such as fused deposition modeling (FDM), material jetting, and direct writing. Understanding the synergistic interactions between spreading and fast curing of extruded thermosetting materials will provide essential insights for applications that require accurate dimensional controls, such as additive manufacturing [1], [2] and centrifugal coating/forming [3]. Two types of thermally curing thermosets -- one being a soft silicone (Ecoflex 0050) and the other being a toughened epoxy (G/Flex) -- served as the test materials in this work to obtain models for cure kinetics and viscosity. The developed models align with extensive measurements made with differential scanning calorimetry (DSC) and rheology. DSC monitors the change in the heat of reaction, which reflects the rate and degree of cure at different crosslinking stages. Rheology measures the change in complex viscosity, shear moduli, yield stress, and other properties dictated by chemical composition. By combining DSC and rheological measurements, it is possible to establish a set of models profiling the cure kinetics and chemorheology without prior knowledge of chemical composition, which is usually necessary for sophisticated mechanistic modeling. In this work, we conducted both isothermal and dynamic measurements with both DSC and rheology. With the developed models, numerical simulations yielded predictions of diameter and height of droplets, along with width and height of extruded lines cured at varied temperatures. Experimental results carried out on a goniometric platform and a nozzle-based 3D printer showed agreement with the numerical simulations. Finally, this presentation will show how the models are adaptable to the planning of tool paths and designs in additive manufacturing.

Xie, Jingjin↗

DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗

NASA NTRS1972

Evaluation of spacecraft boom deployment dynamics by combined analysis and testing.

Discussion of the deployment of multiple hinged booms from the IMP-I satellite, which is a good example of the use of the combined analysis and testing approach for preflight system reliability evaluation. The procedures used for test and evaluation of the booms are described, and problems encountered and results achieved are considered. Analytical methods, using a digital computer program, and functional test operation with the best possible environmental simulation, were both applied to the problem and comparisons between analytical and test results were made to evaluate their validity and to reveal the effects of errors and imperfections. There is a synergistic advantage in the combined approach in that mutual comparison gives better evaluation of both analysis and test results than independent study of either. An appendix presents some results of a later and as yet incomplete test program involving the IMP-H satellite.

Lang, W. E.↗

NASA NTRS2004

Overview of Transonic to Hypersonic Stage Separation Tool Development for Multi-Stage-to-Orbit Concepts

An overview of research efforts at NASA in support of the stage separation and ascent aerothermodynamics research program is presented. The objective of this work is to develop a synergistic suite of experimental, computational, and engineering tools and methods to apply to vehicle separation across the transonic to hypersonic speed regimes. Proximity testing of a generic bimese wing-body configuration is on-going in the transonic (Mach numbers 0.6, 1.05, and 1.1), supersonic (Mach numbers 2.3, 3.0, and 4.5) and hypersonic (Mach numbers 6 and 10) speed regimes in four wind tunnel facilities at the NASA Langley Research Center. An overset grid, Navier-Stokes flow solver has been enhanced and demonstrated on a matrix of proximity cases and on a dynamic separation simulation of the bimese configuration. Steady-state predictions with this solver were in excellent agreement with wind tunnel data at Mach 3 as were predictions via a Cartesian-grid Euler solver. Experimental and computational data have been used to evaluate multi-body enhancements to the widely-used Aerodynamic Preliminary Analysis System, an engineering methodology, and to develop a new software package, SepSim, for the simulation and visualization of vehicle motions in a stage separation scenario. Web-based software will be used for archiving information generated from this research program into a database accessible to the user community. Thus, a framework has been established to study stage separation problems using coordinated experimental, computational, and engineering tools.

Murphy, Kelly J.↗

DOE OSTI2021

A comprehensive review of nonlinear oscillators in hydrokinetic energy harnessing using flow-induced vibrations

A comprehensive review of hydrokinetic energy converters based on alternating lift technology (ALT) is provided. Emphasis is on nonlinear oscillators based on Flow Induced Vibration (FIV) or Oscillation (FIO). Due to strong coupling in Fluid-Structure Interaction (FSI), and in order to maximize the hydrokinetic harnessed energy, design of nonlinear oscillators and analysis by model tests or computational fluid dynamics dominates this area. Research confirmed that the nonlinear oscillator can harvest energy from a stochastic excitation modeled by a generic wide spectrum, and overcome the most severe oscillator limitations: specifically, the need for continuous frequency tuning due to the narrow bandwidth response, and low efficiency outside the narrow bandwidth oscillator response. This review covers the following aspects of nonlinear oscillators in ALT converters: (1) Geometric changes in oscillator cross-section; e.g., circular, square, rectangular, or trilateral shapes. (2) Passive turbulence control of FIV/FIO. (3) Position based nonlinear stiffness. (4) Multi-cylinder synergistic FIV/FIO. (5) Mechanically linked oscillators. (6) Velocity-based, nonlinear, adaptive harnessing damping.

42 ENGINEERING↗

NASA NTRS1996

(abstract) Using TOPEX/Poseidon Sea Level Observations to Test the Sensitivity of an Ocean Model to Wind Forcing

It has been demonstrated that current-generation global ocean general circulation models (OGCM) are able to simulate large-scale sea level variations fairly well. In this study, a GFDL/MOM-based OGCM was used to investigate its sensitivity to different wind forcing. Simulations of global sea level using wind forcing from the ERS-1 Scatterometer and the NMC operational analysis were compared to the observations made by the TOPEX/Poseidon (T/P) radar altimeter for a two-year period. The result of the study has demonstrated the sensitivity of the OGCM to the quality of wind forcing, as well as the synergistic use of two spaceborne sensors in advancing the study of wind-driven ocean dynamics.

forcing radar altimeter global sea level↗

DOE OSTI2024

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Molecular design is a critical aspect of various scientific and industrial fields, where the properties of molecules hold significant importance. In this study, a 3-fold methodology design is presented that leverages the power of generative artificial intelligence (AI), predictive modeling, and reinforcement learning to create tailored molecules with desired properties. This model synergistically combines deep learning techniques with Self-Referencing Embedded Strings (SELFIES) molecular representation to build a generative model that generates valid molecules and a graphical neural network model that accurately forecasts molecular properties. The Variational Autoencoder (VAE) coupled with reinforcement learning helps refine molecule generation based on targeted attributes. Data from an experimental study involving surfactants were used to test the framework. A validation of the structural integrity of the molecules generated was conducted, and Tanimoto similarities were used to quantify the similarity and diversity between the original and generated molecular structures. Also, saliency maps for the generated surfactants were produced to identify the features explaining the property values. Lastly, molecular dynamics simulations were used to validate the stability of the generated molecules. The results showed that the proposed framework can effectively produce valid molecules within the set property threshold value.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Applying the Velocity Gradient Technique in NGC 1333: Comparison with Dust Polarization Observations

Magnetic fields (B-fields) are ubiquitous in the interstellar medium (ISM), and they play an essential role in the formation of molecular clouds and subsequent star formation. However, B-fields in interstellar environments remain challenging to measure, and their properties typically need to be inferred from dust polarization observations over multiple physical scales. In this work, we seek to use a recently proposed approach called the velocity gradient technique (VGT) to study B-fields in star-forming regions and compare the results with dust polarization observations in different wavelengths. The VGT is based on the anisotropic properties of eddies in magnetized turbulence to derive B-field properties in the ISM. We investigate that this technique is synergistic with dust polarimetry when applied to a turbulent diffused medium for the purpose of measuring its magnetization. Specifically, we use the VGT on molecular line data toward the NGC 1333 star-forming region ( 12 CO, 13 CO, C 18 O, and N 2 H + ), and we compare the derived B-field properties with those inferred from 214 and 850 μm dust polarization observations of the region using Stratospheric Observatory for Infrared Astronomy/High-Resolution Airborne Wide-band Camera Plus and James Clerk Maxwell Telescope/POL-2, respectively. We estimate both the inclination angle and the 3D Alfvénic Mach number M A from the molecular line gradients. Crucially, testing this technique on gravitationally bound, dynamic, and turbulent regions, and comparing the results with those obtained from polarization observations at different wavelengths, such as the plane-of-sky field orientation, is an important test on the applicability of the VGT in various density regimes of the ISM. We in general do not find a close correlation between the velocity gradient inferred orientations and the dust inferred magnetic field orientations.

79 ASTRONOMY AND ASTROPHYSICS↗