Search NASA⌕ Search

SEARCH · Search NASA

Results for “Hybrids”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Enabling Large Focal Plane Arrays Through Mosaic Hybridization

We have demonstrated advances in mosaic hybridization that will enable very large format far-infrared detectors. Specifically we have produced electrical detector models via mosaic hybridization yielding superconducting circuit patbs by hybridizing separately fabricated sub-units onto a single detector unit. The detector model was made on a 100mm diameter wafer while four model readout quadrant chips were made from a separate 100mm wafer. The individually fabric.ted parts were hybridized using a Suss FCI50 flip chip bonder to assemble the detector-readout stack. Once all of the hybridized readouts were in place, a single, large and thick silicon substrate was placed on the stack and attached with permanent epoxy to provide strength and a Coefficient of Thermal Expansion match to the silicon components underneath. Wirebond pads on the readout chips connect circuits to warm readout electronics; and were used to validate the successful superconducting electrical interconnection of the model mosaic-hybrid detector. This demonstration is directly scalable to 150 mm diameter wafers, enabling pixel areas over ten times the area currently available.

Miller, Timothy M.↗
NASA NTRS2016

Hybrid Reality Lab Capabilities - Video 2

Our Hybrid Reality and Advanced Operations Lab is developing incredibly realistic and immersive systems that could be used to provide training, support engineering analysis, and augment data collection for various human performance metrics at NASA. To get a better understanding of what Hybrid Reality is, let's go through the two most commonly known types of immersive realities: Virtual Reality, and Augmented Reality. Virtual Reality creates immersive scenes that are completely made up of digital information. This technology has been used to train astronauts at NASA, used during teleoperation of remote assets (arms, rovers, robots, etc.) and other activities. One challenge with Virtual Reality is that if you are using it for real time-applications (like landing an airplane) then the information used to create the virtual scenes can be old (i.e. visualized long after physical objects moved in the scene) and not accurate enough to land the airplane safely. This is where Augmented Reality comes in. Augmented Reality takes real-time environment information (from a camera, or see through window, and places digitally created information into the scene so that it matches with the video/glass information). Augmented Reality enhances real environment information collected with a live sensor or viewport (e.g. camera, window, etc.) with the information-rich visualization provided by Virtual Reality. Hybrid Reality takes Augmented Reality even further, by creating a higher level of immersion where interactivity can take place. Hybrid Reality takes Virtual Reality objects and a trackable, physical representation of those objects, places them in the same coordinate system, and allows people to interact with both objects' representations (virtual and physical) simultaneously. After a short period of adjustment, the individuals begin to interact with all the objects in the scene as if they were real-life objects. The ability to physically touch and interact with digitally created objects that have the same shape, size, location to their physical object counterpart in virtual reality environment can be a game changer when it comes to training, planning, engineering analysis, science, entertainment, etc. Our Project is developing such capabilities for various types of environments. The video outlined with this abstract is a representation of an ISS Hybrid Reality experience. In the video you can see various Hybrid Reality elements that provide immersion beyond just standard Virtual Reality or Augmented Reality.

Delgado, Francisco J.↗
NASA NTRS2017

Dawn Spacecraft Performance at Ceres: Results of Hybrid Control for Ceres Mapping

Dawn is a low-thrust interplanetary spacecraft currently orbiting the dwarf planet Ceres, to better understand the early creation of the solar system. Launched in September 2007, Dawn arrived at Vesta in July 2011. After a 16-month successful science campaign at Vesta, Dawn departed for Ceres, arriving in early 2015. The Dawn spacecraft uses both reaction wheel assemblies (RWA) and a reaction control system (RCS) to provide 3-axis attitude control for the spacecraft. Reaction wheels were designed to be the primary system for attitude control, however two wheels have shown high friction anomalies and have been removed from service. The project has implemented a hybrid control algorithm using two reaction wheels and RCS thrusters. This hybrid control capability enabled Dawn to achieve very high science return, while significantly conserving remaining hydrazine propellant. With only two remaining healthy RWA’s, hybrid control became part of the baseline plan for Ceres science operations. The Dawn team developed specific operational approaches in which sequences were developed with careful consideration of science versus resource trades. Commanding and sequence planning also incorporated contingency planning, in the event that another reaction wheel may fail. Despite the differences in operational approach between Vesta and Ceres, both campaigns achieved very rich scientific data return. This paper highlights Dawn’s recent flight experience with hybrid attitude control during Ceres orbit operations. The discussion includes the approaches utilized by the Dawn team to address unique operational challenges presented by the hybrid approach, and reviews spacecraft performance under hybrid control in low orbit at Ceres. Additionally, methods used to optimize hydrazine use and thereby extend the science campaign will be presented. Finally, a preliminary assessment of an orbit transfer with two reaction wheels, during extended mission operations, is discussed.

Smith, Brett A.↗
NASA NTRS2018

Model-Invariant Hybrid RANS-LES Computations on Unstructured Meshes

Hybrid RANS-LES computations combine the bene ts of RANS and LES so that LES is used in regions where the accuracy of RANS deteriorates. The numerous hybrid approaches are limited by the speci cation of the LES-RANS interface, which can cause nonphysical results such as log-layer mismatch and low shear stress. The hybrid RANS-LES approach based on the concept of model invariance, mitigates these problems, enabling seamless blending of the RANS and LES regions while forming the basis for interpreting the results in the interface region. This hybrid formulation was implemented in the NASA FUN3D unstructured code and computations for ow in a channel at Reynolds number of 3300 (based on the channel half width h and the bulk in ow velocity u(infinity) were carried out. An isotropic stochastic turbulence generator was implemented to generate in ow turbulence. The present approach was able to eliminate the log-layer mismatch and predict the shear stress fairly well. Thus, the model-invariant hybrid formulation coupled with the isotropic turbulence in ow generation serves as a physically meaningful way of performing hybrid RANS-LES computations.

Sreekanth, R.↗
NASA NTRS2021

Hybrid phenology matching model for robust crop phenological retrieval

Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands, assessing gross and net primary production, and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models. It relaxes the geometrical scaling assumption and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations in terms of time and space are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). Compared to the benchmark phenology matching model, the hybrid model is more robust to the decreasing levels of phenological reference calibrations, and is particularly advantageous in retrieving crop early phenological stages (e.g., planted and emerged stages) when the phenological reference information is limited. This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds 3 considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.

Phenology↗
NASA NTRSDate not supplied

Design Exploration of a Mild Hybrid Electrified Aircraft Propulsion Concept

NASA has performed in-depth research and analysis into hybrid-electric aircraft concepts featuring high levels of electrification. Many of these concepts were found to be infeasible, whereas feasible designs were significantly heavier and more costly compared to conventional concepts. This study presents the development and analysis of an alternative mild hybrid concept that uses relatively low levels of stored electric energy applied strategically throughout the mission. The team researched several mild hybrid technologies, developed integrated aircraft models, and then evaluated the effects of these technologies on vehicle level performance metrics. Technologies explored in this study include the Turbine Electrified Energy Management system, electric taxi, electric climb assist, and replacing the auxiliary power unit with on-board electric energy storage. Key enabling technologies were energy storage devices that prioritized high power output over energy capacity and integrating electric motors with the shafts of the gas turbine engines. Significant performance improvements were observed when applying multiple technologies separately and in combination, with the most promising configuration incorporating all of them at once. Results for the mild hybrid configuration indicate an 8.2% reduction in block fuel, a 7.7% reduction in equivalent CO2 emissions, and a 3.5% increase in gross takeoff weight compared to a conventional baseline. Results of a cost analysis suggest the mild hybrid slightly reduces direct operating cost plus interest, which demonstrates that aircraft emissions can be significantly reduced without the cost increases associated with highly electrified concepts.

Turbine Electrified Energy Managment↗
DOE OSTI2025

A deep learning and finite element approach for exploration of inverse structure–property designs of lightweight hybrid composites

Hybrid composites have important applications, such as high-performance and lightweight materials in aerospace and automotive industries. Hybrid composites utilize the synergy of diverse fillers to achieve desired material properties, but usually have more complicated microstructures. While topology optimization can optimize a particular property, designing hybrid composites for customized mechanical performances, e.g. full-range stress–strain curve, remains challenging. Here, a computational framework that integrated finite element analysis (FEA) and artificial intelligence (AI) methods of Conditional Generative Adversarial Networks (cGAN) deep learning and transfer learning was developed to establish inverse structure–property relationships and design tailor-made hybrid composites. Based on FEA-generated datasets of hybrid fiber-particle–matrix microstructures and their corresponding full-range stress–strain curves, a cGAN architecture was trained to generate tailored microstructures and establish structure–property relationships. Similarity in microstructural features and well-matched stress–strain curves based on the AI-generated composites were achieved. In conclusion, transfer learning was used to expand the pre-trained model for designing different materials systems.

Hybrid composites↗
DOE OSTI2025

Resonance ultrasound prediction of residual stress within a hybrid layer for additively manufactured samples

Hybrid additive manufacturing (AM) involves secondary processes or energy sources to alter specified locations within the build volume. Each hybrid step can refine the grain size, increase dislocation density, or modify residual stresses. Typically, the changes in mechanical properties are not confined within a single layer but have a compounding effect on preceding layers. Existing methods of measuring AM residual stress are limited in terms of their sensitivity, or they are destructive measurements. We propose using resonant ultrasound spectroscopy (RUS) to measure the residual stress in hybrid-AM components noninvasively, based on changes to the resonances, compared to a stress-free component. In this paper, we use finite element models to simulate residual stress in hybrid-AM components and to examine the sensitivity of RUS measurements in terms of frequency shifts and mode shapes with respect to single hybrid layers. Then, the RUS results are used to predict stress for a layer at a known location with unknown stress. Here, the approach highlights the capabilities of RUS to address an AM characterization challenge.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Deployment of Traditional and Hybrid Machine Learning for Critical Heat Flux Prediction in the CTF Thermal-Hydraulics Code

Critical heat flux (CHF) marks the transition from nucleate to film boiling, where heat transfer to the working fluid can rapidly deteriorate. Accurate CHF prediction is essential for efficiency, safety, and preventing equipment damage, particularly in nuclear reactors. Although widely used, empirical correlations frequently exhibit discrepancies when compared to experimental data, limiting their reliability in diverse operational conditions. Traditional machine learning (ML) approaches have demonstrated potential for CHF prediction but often suffer from limited interpretability, data scarcity, and insufficient knowledge of physical principles. Hybrid model approaches, which combine data-driven ML with base models, mitigate these concerns by incorporating prior knowledge of the domain. This study integrates an externally trained purely data-driven ML model and two hybrid models (using the Biasi and Bowring CHF correlations) within the CTF subchannel code via a custom Fortran framework. Performance was evaluated using two validation cases: a subset of the Nuclear Regulatory Commission (NRC) CHF database and the Bennett dryout experiments. In both cases, the hybrid models demonstrated significantly lower error metrics compared to conventional empirical correlations, with the best models often reducing relative error by about 5 percentage points. The pure ML model achieved comparable accuracy, outperforming the hybrid Biasi model in the NRC test case (3.3% versus 5.5% relative error) but exhibiting slightly higher error against the hybrid Bowring model in the Bennett test case (7.7% versus 6.1%). Trend analysis of error parity indicated that ML-based models reduced the tendency for CHF overprediction, improving overall accuracy. These results demonstrate that ML-based CHF models can be effectively integrated into subchannel codes and could potentially increase performance compared to conventional methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Automated Hybrid Variance Reduction on Advanced Architectures in the Shift Monte Carlo Code

Monte Carlo transport methods are the most accurate schemes for solving problems with complex energy and spatial features, but they come with a high computational cost. Although hybrid methods have enabled the use of Monte Carlo transport for a large class of problems, they still require significant computing resources. Modern multicore CPUs with large numbers of compute cores and graphical processing units (GPUs) provide opportunities to optimize the memory and run-time costs of hybrid Monte Carlo methods. This paper documents the development and analysis of three Monte Carlo transport algorithms that support hybrid transport using the consistent adjoint-driven importance sampling (CADIS) and forward-weighted CADIS methods in the Shift Monte Carlo code: history-based transport using static and dynamic threading on multicore CPUs and event-based transport enabling weight window tracking on GPUs. The results are shown for two challenging hybrid problems on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility. The results show that all three methods yield good performance and enable solutions of difficult fixed-source transport problems in less than 2 min on 20 nodes of Frontier. Dynamic threading was observed to give up to 20% better scaling behavior than static threading. Moreover, the AMD Instinct 250X GPU was found to give 9 to 11 times greater throughput per graphics compute die than the best CPU performance. In conclusion, additional opportunities for optimization of hybrid transport on GPUs are discussed.

Denovo↗
DOE OSTI2025

Characterization of Gramicidin A in Triblock and Diblock Polymersomes and Hybrid Vesicles via Continuous Wave Electron Paramagnetic Resonance Spectroscopy

Studying membrane proteins in a native environment is crucial to understanding their structural and/or functional studies. Often, widely accepted mimetic systems have limitations that prevent the study of some membrane proteins. Micelles, bicelles, and liposomes are common biomimetic systems but have problems with membrane compatibility, limited lipid composition, and heterogeneity. To overcome these limitations, polymersomes and hybrid vesicles have become popular alternatives. Polymersomes form from amphiphilic triblock or diblock copolymers and are considered more robust than liposomes. Hybrid vesicles are a combination of lipids and block copolymers that form vesicles composed of a mixture of the two. These hybrid vesicles are appealing because they have the native lipid environment of bilayers but also the stability and customizability of polymersomes. Gramicidin A was incorporated into these polymersomes and characterized using continuous wave electron paramagnetic resonance (CW-EPR) and transmission electron microscopy (TEM). EPR spectroscopy is a powerful biophysical technique used to study the structure and dynamic properties of membrane proteins in their native environment. Spectroscopic studies of gramicidin A have been limited to liposomes; in this study, the membrane peptide is studied in both polymersomes and hybrid vesicles using CW-EPR spectroscopy. Lineshape analysis of spin-labeled gramicidin A revealed linewidth broadening, suggesting that the thicker polymersome membranes restrict the motion of the spin label more when compared to liposome membranes. Statement of Significance: Understanding membrane proteins’ structures and functions is critical in the study of many diseases. In order to study them in a native environment, membrane mimetics must be developed that can be suitable for obtaining superior biophysical data quality to characterize structural dynamics while maintaining their native functions and structures. Many currently widely accepted methods have limitations, such as a loss of native structure and function, heterogeneous vesicle formation, restricted lipid types for the vesicle formation for many proteins, and experimental artifacts, which leaves rooms for the development of new biomembrane mimetics. The triblock and diblock polymersomes and hybrid versicles utilized in this study may overcome these limitations and provide the stability and customizability of polymersomes, keeping the biocompatibility and functionality of liposomes for EPR studies of membrane proteins.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2016

Energy Conversion and Storage Requirements for Hybrid Electric Aircraft

Among various options for reducing greenhouse gases in future large commercial aircraft, hybrid electric option holds significant promise. In the hybrid electric aircraft concept, gas turbine engine is used in combination with an energy storage system to drive the fan that propels the aircraft, with gas turbine engine being used for certain segments of the flight cycle and energy storage system being used for other segments. The paper will provide an overview of various energy conversion and storage options for hybrid electric aircraft. Such options may include fuel cells, batteries, super capacitors, multifunctional structures with energy storage capability, thermoelectric, thermionic or a combination of any of these options. The energy conversion and storage requirements for hybrid electric aircraft will be presented. The role of materials in energy conversion and storage systems for hybrid electric aircraft will be discussed.

Energy Conversion↗
NASA NTRS2017

Spacecraft/Rover Hybrids for the Exploration of Small Solar System Bodies: Final Report: NASA NIAC - Phase II Study

This study investigated a mission architecture that allows the systematic and affordable in-situ exploration of small solar system bodies, such as asteroids, comets, and Martian moons (Figure 1). The architecture relies on the novel concept of spacecraft/rover hybrids,which are surface mobility platforms capable of achieving large surface coverage (by attitude controlled hops, akin to spacecraft flight), fine mobility (by tumbling), and coarse instrument pointing (by changing orientation relative to the ground) in the low-gravity environments(micro-g to milli-g) of small bodies. The actuation of the hybrids relies on spinning three internal flywheels. Using a combination of torques, the three flywheel motors can produce a reaction torque in any orientation without additional moving parts. This mobility concept allows all subsystems to be packaged in one sealed enclosure and enables the platforms to be minimalistic. The hybrids would be deployed from a mother spacecraft, which would act as a communication relay to Earth and would aid the in-situ assets with tasks such as localization and navigation (Figure 1). The hybrids are expected to be more capable and affordable than wheeled or legged rovers, due to their multiple modes of mobility (both hopping and tumbling), and have simpler environmental sealing and thermal management (since all components are sealed in one enclosure, assuming non-deployable science instruments). In summary, this NIAC Phase II study has significantly increased the TRL (Technology Readiness Level) of the mobility and autonomy subsystems of spacecraft/rover hybrids, and characterized system engineering aspects in the context of a reference mission to Phobos. Future studies should focus on improving the robustness of the autonomy module and further refine system engineering aspects, in view of opportunities for technology infusion.

mobility platforms↗
NASA NTRS2020

Ignition and Burn in a Hybrid Nuclear Fuel for a Pulsed Rocket Engine

This work explores the parameter space of a cylindrical hybrid nuclear fuel in support of a z-pinch driven pulsed fission-fusion (PuFF) engine. 0D power balance and 1D burn wave calculations have been performed to explore the parameter space of hybrid cylindrical nuclear fuels. The boundary of minimal initial conditions needed to reach breakeven are found within the context of the model. The effects of initial conditions upon the yield at the end of burn wave expansion are also determined. The model is used to examine the minimum initial energies predicted to result in yields of a few MJ. The goal of this work is to guide fuel design, inform future models and experiments as well as look for the most efficient parameter space for ignition in a z-pinch driven hybrid nuclear reaction. The impact of initial parameters upon ignition, burn, and gain are discussed. It is found that a hybrid cylindrical target may breakeven with initial energy near the axis of approximately 4 to 6 MJ in lithium deuteride/uranium 235 fuel. It is also found that magnetic field can lower the threshold further of which the magnitude changes across the parameter space. The dual fusion/fission reactions are found to boost each other leading to lower initial driving energies needed to reach breakeven in the hybrid cylindrical nuclear fuel.

fission↗
NASA NTRSDate not supplied

An Active Model Split for Hybrid RANS/LES of Compressible Flows

Hybrid RANS/LES models have consistently shown superior accuracy over RANS in predictions of massively separated flows. However, these models have experienced difficulties with modeled stress depletion, smooth-wall separated flow, and shock-separated flows. The Active Model Split (AMS) hybridization was created to address some of the fundamental shortcomings of traditional hybrid RANS/LES models. While previous work considered incompressible flows, this paper demonstrates how the AMS framework can be extended to address compressible flows. AMS-based hybrid models for the turbulent heat flux and turbulent transport terms in the total energy equation are presented. This compressible hybrid RANS/LES framework is demonstrated on a subsonic channel, a supersonic channel, and a transonic shock-boundary layer interaction. Record includes video of presentation, to view download the mp4 video attached run time of 10 mins 18 secs.

hybrid RANS/LES↗
NASA NTRSDate not supplied

Electrical Energy Storage Design Space Exploration for a Hybrid-Electric Six Passenger Quadrotor

Hybrid-electric architectures are a promising means to achieve clean and efficient aircraft propulsion needed for small, short-range electric vertical takeoff and landing (eVTOL) class vehicles. This paper explores the design space for a six-passenger quadrotor hybrid-electric propulsion system and shows that hybrid architectures that are more efficient than engine-only architectures can be built with near-term battery technology (~150 Wh/kg at the pack level). Data obtained shows that hybrid-electric propulsion systems can achieve 28 percent block fuel reduction and 27 percent total energy cost savings over a 120 nautical mile (nmi) mission compared to conventional turboshaft architectures. This work suggests that hybrids are likely the most efficient architectures that can be produced in the near term for this class of vehicle and motivates further development in this area.

Hybrid-electric↗
NASA NTRSDate not supplied

Electrical Energy Storage Design Space Exploration for a Hybrid-Electric Six-Passenger Quadrotor

Hybrid-electric architectures are a promising means to achieve clean and efficient aircraft propulsion needed for small, short-range electric vertical takeoff and landing class vehicles. This paper explores the design space for a six-passenger quadrotor hybrid-electric propulsion system and shows that hybrid architectures that are more efficient than engine-only architectures can be built with near-term battery technology (approximately 150 Wh/kg at the pack level). Data obtained shows that hybrid-electric propulsion systems can achieve 37 percent block fuel reduction and 35 percent total energy cost savings over a 120 nautical mile mission compared to conventional turboshaft architectures. This work suggests that hybrids are likely the most efficient architectures that can be produced in the near term for this class of vehicle and motivates further development in this area.

parallel hybrid electric aircraft↗
DOE OSTI2025

Hybrid Doping Strategy with High‐Entropy Cu/Fe Surface Modification and Zr Bulk Incorporation for Ni‐Rich Cathodes

A hybrid doping strategy combining Zr 4+ bulk doping with high-entropy Cu 2+ /Fe 3+ surface doping is developed to enhance the structural and interfacial stability of Ni-rich layered oxide cathodes. Cu and Fe are selectively introduced at the particle surface via a surface-selective ion-exchange process, forming a ≈15 nm Fe-rich layer while preserving the layered framework. Compared to the pristine cathode, the hybrid sample exhibits significantly improved electrochemical performance in both half-cell and full-cell configurations. In half-cells, the hybrid retains 88.5% and 90.2% after 100 cycles at 1C under 4.6 and 4.5 V, respectively. During high-voltage full-cell cycling, the hybrid cathode maintains over 80% capacity retention, whereas the pristine counterpart retains less than 10% under identical conditions over the same cycling period. XPS, EELS, and DEMS analyses confirm improved oxygen retention, suppressed gas evolution, and stable surface chemistry, while DFT calculations indicate enhanced Me–O bonding in the selected Fe 0.75 Cu 0.25 (Mn 1/16 Co 2/16 Ni 13/16 )O 2 surface composition, which is identified through DFT-calculated mixing energy reaching a minimum at this ratio, indicating the most thermodynamically favorable configuration. In conclusion, these results demonstrate the effectiveness of this hybrid doping strategy in mitigating coupled degradation pathways in Ni-rich cathodes.

15 GEOTHERMAL ENERGY↗