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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Carbon Nanotube Nanoelectrode Array as an Electronic Chip for Ultrasensitive Label-free DNA Detection

A reliable nanoelectrode array based on vertically aligned multi-walled carbon nanotubes (MWNTs) embedded in SiO2 is used for ultrasensitive DNA detection. Characteristic nanoelectrode behavior is observed using low-density MWNT arrays for measuring both bulk and surface immobilized redox species such as K4Fe(CN)6 and ferrocene derivatives. The open-end of MWNTs are found to present similar properties as graphite edge-plane electrodes with wide potential window, flexible chemical functionalities, and good biocompatibility. BRCA1 related oligonucleotide probes with 18 bp are selectively functionalized at the open ends of the nanotube array and specifically hybridized with oligonucleotide targets incorporated with a polyG tag. The guanine groups are employed as the signal moieties in the electrochemical measurements. R(bpy)(sup 2+, sub 3) mediator is used to further amplify the guanine oxidation signal. The hybridization of sub-attomoles of DNA targets is detected electrochemically by combining the MWNT nanoelectrode array with the R(bpy)(sup 2+, sub 3) amplification mechanism. This technique was employed for direct electrochemical detection of label-free PCR amplicon from a healthy donor through specific hybridization with the BRCA1 probe. The detection limit is estimated to be less than 1000 DNA molecules since abundant guanine bases in the PCR amplicon provides a large signal. This system provides a general platform for rapid molecular diagnostics in applications requiring ultrahigh sensitivity, high-degree of miniaturization, and simple sample preparation, and low-cost operation.

Li, Jun↗
DOE OSTI2020

A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys

We present a deep machine learning (ML)–based technique for accurately determining σ g and Ω m from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos suite of N -body simulations, which comprises 40 cosmological volume simulations spanning a range of cosmological parameter values, and we account for uncertainties in galaxy formation scenarios through the use of generalized halo occupation distributions (HODs). We explore a trio of ML models: a 3D convolutional neural network (CNN), a power spectrum–based fully connected network, and a hybrid approach that merges the two to combine physically motivated summary statistics with flexible CNNs. We describe best practices for training a deep model on a suite of matched-phase simulations, and we test our model on a completely independent sample that uses previously unseen initial conditions, cosmological parameters, and HOD parameters. Despite the fact that the mock observations are quite small (~0.07 h -3 Gpc 3 ) and the training data span a large parameter space (six cosmological and six HOD parameters), the CNN and hybrid CNN can constrain estimates of σ g and Ω m to ~3% and ~4%, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A lumped particle direct simulation Monte-Carlo method combined with the collisional-radiative model for simulations of non-equilibrium laser-induced plasma plumes

Collisional plasma plumes induced by laser irradiation of material targets exhibit large variations in local density as well as ionization and excitation states, making purely hydrodynamic or kinetic simulations inaccurate or infeasible. To address this challenge and capture non-equilibrium effects in laser-induced plasma plumes at arbitrary degrees of ionization, we develop a hybrid computational approach that combines the kinetic direct simulation Monte Carlo (DSMC) method with a collisional-radiative model (CRM). This ℓDSMC-CRM approach utilizes a lumped particle method to represent minor fractions of excited ions in particle-based simulations and a special coarse-graining technique for atomic spectra and photoionization rates, ensuring numerical convergence at reduced computational cost. The hybrid approach is applied to simulate spatially homogeneous relaxation as well as one- and two-dimensional expansions of plasma plumes induced by irradiation of a copper target by a nanosecond laser pulse in a vacuum or background gas. The comparison with an equilibrium model, where local Saha-Boltzmann equilibrium is enforced, shows that the non-equilibrium effects play a dominant role. The equilibrium model can fail to predict the flow structure and strongly underestimate the degree of absorption of laser radiation by the plume. The ℓDSMC-CRM approach is validated against experimental data demonstrating reasonable agreement with the experimental electron density and temperature, while the equilibrium model is found to dramatically underestimate electron density and temperature. The flexibility of the ℓDSMC-CRM approach allows for its seamless integration into existing DSMC frameworks, making it a valuable tool for high-fidelity plasma modeling in laser-material interactions, laser-based manufacturing, and beyond.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

Fatigue Life Analysis of Tapered Hybrid Composite Flexbeams

Nonlinear-tapered flexbeam laminates from a full-size composite helicopter rotor hub flexbeam were tested under combined constant axial tension and cyclic bending loads. The two different graphite/glass hybrid configurations tested under cyclic loading failed by delamination in the tapered region. A 2-D finite element model was developed which closely approximated the flexbeam geometry, boundary conditions, and loading. The analysis results from two geometrically nonlinear finite element codes, ANSYS and ABAQUS, are presented and compared. Strain energy release rates (G) obtained from the above codes using the virtual crack closure technique (VCCT) at a resin crack location in the flexbeams are presented for both hybrid material types. These results compare well with each other and suggest that the initial delamination growth from the resin crack toward the thick region of the flexbeam is strongly mode II. The peak calculated G values were used with material characterization data to calculate fatigue life curves and compared with test data. A curve relating maximum surface strain to number of loading cycles at delamination onset compared reasonably well with the test results.

Murri, Gretchen B.↗
DOE OSTI2021

Scalable Programming Workflows for Validation of Quantum Computers

Hybrid quantum-classical workflows have become standard methods for executing variational algorithms and other quantum simulation techniques, which are key applications for noisy intermediate scale quantum (NISQ) computers. Validating these simulations is an important task which helps gauge the progress of quantum computer development, and classical simulation can serve as a tool to this end. Both exact and more scalable approximate methods with quantifiable error bounds can be used in validation tasks where the applicable metrics include the distance from a calculable ground truth, the quality of an error model fit to data, etc. Here we present a library extension that includes methods for validation of quantum simulations based on scalable hybrid workflows executable on high performance computers. We provide examples that use approximate methods based on tensor networks and stabilizer simulators to bound the error of quantum simulations on NISQ hardware.

Nguyen, Thien↗
NASA NTRS1980

Development of LaRC 160/NR150B2 polyimide graphite hybrid composites

A method for co-curing NR150B2 and LaRC 160 prepregs into hybrid composites was developed. The processing characteristics and the properties of the hybrid composites were compared with those of laminates fabricated from the individual component prepregs. Resin forms were selected and optimized and a new NR150 formulation was investigated. The new formulation greatly facilitated the processing and the performance of this system. Quality control techniques were evaluated and developed, high quality laminates were fabricated from both individual resin systems, and hybrid laminates were successfully co-cured. Optimum hybrid forms were investigated and several novel approaches were explored. An optimum hybrid system was developed that utilizes a LaRC curing schedule but shows no degradation of mechanical properties after aging 500 hr in air at 260 C.

Maximovich, M. G.↗
DOE OSTI2020

Analysis of the performance of a hybrid CPU/GPU 1D2D coupled model for real flood cases

Coupled 1D2D models emerged as an efficient solution for a two-dimensional (2D) representation of the floodplain combined with a fast one-dimensional (1D) schematization of the main channel. At the same time, high-performance computing (HPC) has appeared as an efficient tool for model acceleration. In this work, a previously validated 1D2D Central Processing Unit (CPU) model is combined with an HPC technique for fast and accurate flood simulation. Due to the speed of 1D schemes, a hybrid CPU/GPU model that runs the 1D main channel on CPU and accelerates the 2D floodplain with a Graphics Processing Unit (GPU) is presented. Since the data transfer between sub-domains and devices (CPU/GPU) may be the main potential drawback of this architecture, the test cases are selected to carry out a careful time analysis. Here, the results reveal the speed-up dependency on the 2D mesh, the event to be solved and the 1D discretization of the main channel. Additionally, special attention must be paid to the time step size computation shared between sub-models. In spite of the use of a hybrid CPU/GPU implementation, high speed-ups are accomplished in some cases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Vision-based localization for cooperative robot-CNC hybrid manufacturing

Wire and arc additive manufacturing (WAAM) has shown promise in recent years for producing large-scale parts with higher deposition rates than other additive processes. WAAM is often combined with subtractive machining to form a hybrid manufacturing process. This hybrid process can be realized by retrofitting computer numerical control (CNC) machines with deposition heads, adding spindles and deposition heads to robots, or developing part localization methods to transfer parts from an additive cell to a CNC machine. Here, a novel, robot-CNC hybrid configuration is introduced where a maneuverable robot is placed in front of a CNC machine to deposit material within the machine envelop. Furthermore, this method removes the need for part localization and the extensive machine modifications required for retrofitting; however, the problem of robot localization is also added. In this work, the effects of error in vision-based, contactless robot localization on machining parameters in a robot-machine hybrid process were studied. Performance was characterized on an implementation of this system using classical computer vision techniques. In addition, machining simulations were conducted to evaluate the effects of image-induced error on chip thickness, material removal rate, and machining allowance. Initial tests show that computer vision could adequately locate a robot for the hybrid WAAM process within .5 mm.

Hybrid manufacturing↗
NASA NTRS1990

High temperature acoustic and hybrid microwave/acoustic levitators for materials processing

The physical acoustics group at the Jet Propulsion Laboratory developed a single mode acoustic levitator technique for advanced containerless materials processing. The technique was successfully demonstrated in ground based studies to temperatures of about 1000 C in a uniform temperature furnace environment and to temperatures of about 1500 C using laser beams to locally heat the sample. Researchers are evaluating microwaves as a more efficient means than lasers for locally heating a positioned sample. Recent tests of a prototype single mode hybrid microwave/acoustic levitator successfully demonstrated the feasibility of using microwave power as a heating source. The potential advantages of combining acoustic positioning forces and microwave heating for containerless processing investigations are presented in outline form.

Barmatz, Martin↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
DOE OSTI2020

A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings

In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD. We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization. We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Reliable statistics-based detection and investigation of anomalies in a SMART valve system

Reliable anomaly detection and diagnosis are critical for the safe operation of complex engineered systems. This study presents a unified framework that integrates statistical, model-based, and data-driven techniques for anomaly detection and investigation, demonstrated on SMART valve systems in hybrid energy applications. Four detection methods—mean deviation, seasonal extreme studentized deviate, ARIMA forecasting, and matrix profiling—were implemented and compared. Matrix profiling was particularly effective in revealing subtle deviations and hidden relationships among variables. Anomaly investigation was performed by analyzing variable-level and grouped signal profiles, with system topology incorporated to distinguish primary faults from propagated effects. Grouping signals by type enhanced interpretability, enabling accurate localization of anomalies across multi-dimensional datasets. Experimental results confirmed the framework's capability to consistently detect and isolate anomalies while providing actionable insights into system interdependencies. The proposed methodology offers a robust, interpretable, and scalable solution for condition monitoring, with potential applications in safety-critical domains such as nuclear energy, aerospace, and process industries.

ARIMA models↗
DOE OSTI2022

Vulnerability and resilience of urban energy ecosystems to extreme climate events: A systematic review and perspectives

We reviewed the present studies on the vulnerability and resilience of the energy ecosystem (most parts of the energy ecosystem), considering extreme climate events. This study revealed that the increased interactions formed during the transformation of the energy landscape into an ecosystem could notably increase the vulnerability of the energy infrastructure. Such complex ecosystem cannot be assessed using the present state of the art models used by the energy system modelers. Therefore, this study introduces a novel analogy known as the COVID analogy to understand the propagation of disruption within and beyond the energy ecosystem and organized the present state of the art based on the COVID analogy. The analogy helps to categorize the vulnerability of the energy infrastructure into three stages. The study revealed that although there are many publications covering the vulnerability and resilience of the energy infrastructure, considering extreme climate events, the majority are focused on the direct impact of extreme climate on the energy ecosystem. In addition, most of the studies do not consider the impact of future climate variations during this assessment. The propagation of disruptions was assessed mainly for wildfires and hurricanes. Further, there is a clear research gap in considering vulnerability assessment for interconnected energy infrastructure. Here, the transformation of energy systems into a complex ecosystem notably increases the complexity, making it difficult to assess vulnerability and resilience. A shift from a centralized to decentralized modeling architecture could be beneficial when considering the complexities brought by that transformation. Hybrid models consisting of both physical and data-driven machine learning techniques could also be beneficial in this context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Temperature-mediated molecular ladder self-assembly employing Diels–Alder cycloaddition

Dynamic covalent self-assembly processes often exhibit poor capacities for error-correction owing to the relatively low connectivity rearrangement rates of dynamic covalent interactions and the common use of reaction conditions where the equilibrium state remains fixed. Here, we report a dynamic covalent self-assembly technique employing temperature, a conventional, externally-applied stimulus, to mediate the hybridization of peptoid oligomers bearing maleimide- and furan-based pendant groups to afford molecular ladders incorporating Diels–Alder adduct-based rungs. By raising or lowing the reaction temperature, this system enables the equilibrium state to be readily varied without altering reagent concentrations. Both triethylamine and the Lewis acidic scandium triflate were examined as candidate reaction catalysts; however, only scandium triflate increased the rate of single strand conversion. Additionally, as the Diels–Alder cycloaddition reaction does not liberate a small molecule, a registry-dependent mass change was effected by employing a base-catalyzed thiol-Michael addition reaction between any un-reacted maleimide pendant groups and a low molecular weight thiol to enable the number of Diels–Alder adduct rungs to be readily determined by mass spectrometry. Finally, by employing a slow temperature ramp from high to low temperature, approximating the thermal cycle employed for nucleic acid hybridization, sequence-selective hybridization between model, tetra-functional precursor strands was demonstrated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Stratification of polymer mixtures in drying droplets: Hydrodynamics and diffusion

In this work, we study the evaporation-induced stratification of a mixture of short and long polymer chains in a drying droplet using molecular simulations. We systematically investigate the effects of hydrodynamic interactions (HI) on this process by comparing hybrid simulations accounting for HI between polymers through the multiparticle collision dynamics technique with free-draining Langevin dynamics simulations neglecting the same. We find that the dried supraparticle morphologies are homogeneous when HI are included but are stratified in core–shell structures (with the short polymers forming the shell) when HI are neglected. The simulation methodology unambiguously attributes this difference to the treatment of the solvent in the two models. We rationalize the presence (or absence) of stratification by measuring phenomenological multicomponent diffusion coefficients for the polymer mixtures. The diffusion coefficients show the importance of not only solvent backflow but also HI between polymers in controlling the dried supraparticle morphology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Generation of thermofield double states and critical ground states with a quantum computer

Significance Our experiment prepares two types of nontrivial quantum states on a trapped ion quantum computer: the thermofield double state of the transverse-field Ising model at arbitrary temperature and the quantum critical state of the zero-temperature model. We use techniques motivated by the quantum approximate optimization algorithm, and we implement a hybrid quantum–classical optimization loop to prepare the quantum critical state. Our results pave the way for exploring strongly correlated models at finite temperature and teleportation protocols inspired by black hole physics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures

Abstract Large biomolecular structures are being determined experimentally on a daily basis using established techniques such as crystallography and electron microscopy. In addition, emerging integrative or hybrid methods (I/HM) are producing structural models of huge macromolecular machines and assemblies, sometimes containing 100s of millions of non-hydrogen atoms. The performance requirements for visualization and analysis tools delivering these data are increasing rapidly. Significant progress in developing online, web-native three-dimensional (3D) visualization tools was previously accomplished with the introduction of the LiteMol suite and NGL Viewers. Thereafter, Mol* development was jointly initiated by PDBe and RCSB PDB to combine and build on the strengths of LiteMol (developed by PDBe) and NGL (developed by RCSB PDB). The web-native Mol* Viewer enables 3D visualization and streaming of macromolecular coordinate and experimental data, together with capabilities for displaying structure quality, functional, or biological context annotations. High-performance graphics and data management allows users to simultaneously visualise up to hundreds of (superimposed) protein structures, stream molecular dynamics simulation trajectories, render cell-level models, or display huge I/HM structures. It is the primary 3D structure viewer used by PDBe and RCSB PDB. It can be easily integrated into third-party services. Mol* Viewer is open source and freely available at https://molstar.org/.

Sehnal, David↗
DOE OSTI2023

Optimal coupling of Ho W 10 molecular magnets to superconducting circuits near spin clock transitions

A central goal in quantum technologies is to maximize GT 2 , where G stands for the coupling of a qubit to control and readout signals and T 2 is the qubit’s coherence time. Furthermore, this is challenging, as increasing G (e.g., by coupling the qubit more strongly to external stimuli) often leads to deleterious effects on T 2 . Here, we study the coupling of pure and magnetically diluted crystals of Ho W 10 magnetic clusters to microwave superconducting coplanar waveguides. Absorption lines give a broadband picture of the magnetic energy level scheme and, in particular, confirm the existence of level anticrossings at equidistant magnetic fields determined by the combination of crystal field and hyperfine interactions. Such “spin clock transitions” are known to shield the electronic spins against magnetic field fluctuations. The analysis of the microwave transmission shows that the spin-photon coupling also becomes maximum at these transitions. The results show that engineering spin-clock states of molecular systems offers a promising strategy to combine sizable spin-photon interactions with a sufficient isolation from unwanted magnetic noise sources.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗