SEARCH · Engineering Papers

Results for “Hybrid model”

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 325 records · Page 18

All sources NASA NTRS DOE OSTI

NASA NTRS1995

One-Dimensional Hybrid Satellite Track Model for the Dynamics Explorer 2 (DE 2) Satellite

A one-dimensional hybrid satellite track model has been developed to calculate the high-latitude thermospheric/ionospheric structure below the satellite altitude using Dynamics Explorer 2 (DE 2) satellite measurements and theory. This model is based on Emery et al. satellite track code but also includes elements of Roble et al. global mean thermosphere/ionosphere model. A number of parameterizations and data handling techniques are used to input satellite data from several DE 2 instruments into this model. Profiles of neutral atmospheric densities are determined from the MSIS-90 model and measured neutral temperatures. Measured electron precipitation spectra are used in an auroral model to calculate particle impact ionization rates below the satellite. These rates are combined with a solar ionization rate profile and used to solve the O(+) diffusion equation, with the measured electron density as an upper boundary condition. The calculated O(+) density distribution, as well as the ionization profiles, are then used in a photochemical equilibrium model to calculate the electron and molecular ion densities. The electron temperature is also calculated by solving the electron energy equation with an upper boundary condition determined by the DE 2 measurement. The model enables calculations of altitude profiles of conductivity and Joule beating rate along and below the satellite track. In a first application of the new model, a study is made of thermospheric and ionospheric structure below the DE 2 satellite for a single orbit which occurred on October 25, 1981. The field-aligned Poynting flux, which is independently obtained for this orbit, is compared with the model predictions of the height-integrated energy conversion rate. Good quantitative agreement between these two estimates has been reached. In addition, measurements taken at the incoherent scatter radar site at Chatanika (65.1 deg N, 147.4 deg W) during a DE 2 overflight are compared with the model calculations. A good agreement was found in lower thermospheric conductivities and Joule heating rate.

Deng, Wei↗

DOE OSTI2020

A One‐Dimensional Model for Turbulent Mixing in the Benthic Biolayer of Stream and Coastal Sediments

Abstract In this paper, we develop and validate a rigorous modeling framework, based on Duhamel's Theorem, for the unsteady one‐dimensional vertical transport of a solute across a flat sediment‐water interface (SWI) and through the benthic biolayer of a turbulent stream. The modeling framework is novel in capturing the two‐way coupling between evolving solute concentrations above and below the SWI and in allowing for a depth‐varying diffusivity. Three diffusivity profiles within the sediment (constant, exponentially decaying, and a hybrid model) are evaluated against an extensive set of previously published laboratory measurements of turbulent mass transfer across the SWI. The exponential diffusivity profile best represents experimental observations and its reference diffusivity scales with the permeability Reynolds number, a dimensionless measure of turbulence at the SWI. The depth over which turbulence‐enhanced diffusivity decays is of the order of centimeters and comparable to the thickness of the benthic biolayer. Thus, turbulent mixing across the SWI may serve as a universal transport mechanism, supplying the nutrient and energy fluxes needed to sustain microbial growth, and nutrient processing, in the benthic biolayer of stream and coastal sediments.

Grant, Stanley B.↗

DOE OSTI2021

In-situ effect in cross-ply laminates under various loading conditions analyzed with hybrid macro/micro-scale computational models

In this article, multi-scale finite element analyses based on three-dimensional (3D) hybrid macro/micro-scale computational models subjected to various loading conditions are carried out to examine the in-situ effect imposed by the neighboring plies on the failure initiation and propagation of cross-ply laminates. A detailed comparative study on crack suppression mechanisms due to the effect of embedded laminar thickness and adjacent ply orientation is presented. Furthermore, we compare the results of in-situ transverse failure strain and strength between the computational models and analytical predictions. Good agreements are generally observed, indicating the constructed computational models are highly accurate to quantify the in-situ effect. Subsequently, empirical formulas for calculating the in-situ strengths as a function of embedded ply thickness and different ply angle between embedded and adjacent plies are developed, during which several material parameters are obtained using a reverse fitting method. Finally, a new set of failure criteria for σ 22 -τ 12 , σ 22 -τ 23 , and σ 11 -τ 12 accounting for the in-situ strengths are proposed to predict laminated composites failure under multi-axial stress states. This study demonstrates an effective and efficient computational technique towards the accurate prediction of the failure behaviors and strengths of cross-ply laminates by including the in-situ effects.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Geospatial modeling of near subsurface temperatures of the contiguous United States for assessment of materials degradation

Abstract Understanding subsurface temperature variations is crucial for assessing material degradation in underground structures. This study maps subsurface temperatures across the contiguous United States for depths from 50 to 3500 m, comparing linear interpolation, gradient boosting (LightGBM), neural networks, and a novel hybrid approach combining linear interpolation with LightGBM. Results reveal heterogeneous temperature patterns both horizontally and vertically. The hybrid model performed best achieving a root mean square error of 2.61 °C at shallow depths (50–350 m). Model performance generally decreased with depth, highlighting challenges in deep temperature prediction. State-level analyses emphasized the importance of considering local geological factors. This study provides valuable insights for designing efficient underground facilities and infrastructure, underscoring the need for depth-specific and region-specific modeling approaches in subsurface temperature assessment.

Science & Technology - Other Topics↗

NASA NTRS1998

A Coupled Plasma Dynamics and Gas Flow Model for Semiconductor Processing

A continuum modeling approach by self-consistently coupling plasma dynamics and gas flow will be presented for the analysis of high density plasma reactors. Experimental data shows that gas flow distribution affects the etch rate uniformity even at low pressures (6-20 mTorr) and flow rates (20-70 sccm). This study will investigate the effects of gas flow and gas energy on bulk plasma densities and temperatures using a continuum model. The model solves multidimensional equations of mass balance for neutrals and ions, gas momentum, separate energy equations for electrons and neutrals and Maxwell's equations for power coupling. A test case of N2 plasma in a 300mm TCP etch reactor, for which hybrid model and Langmuir probe data are available, is chosen for this analysis. Our preliminary results show that modeling gas flow and energy improves the predictions of electron density and its spatial variation in the reactor when compared with the experimental data. The aim of this study is to identify the operating conditions for the TCP reactor when a self-consistent modeling of gas flow is important.

Bose, Deepak↗

DOE OSTI2025

Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.

Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U↗

DOE OSTI2024

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Advanced Modeling of Beam Physics and Performance Optimization for Nuclear Physics Colliders

High energy colliders provide a critical tool in nuclear physics study by probing the fundamental structure and dynamics of matter. To maximize the potential of scientific discovery in nuclear physics study, it is important to optimize the parameters of these colliders to attain the best performance. The performance of a collider is typically measured by its integrated luminosity of colliding beams since the probability of a new event is proportional to the integrated luminosity. However, the achievable luminosity is limited by the electromagnetic interactions (beam-beam effects) of two colliding beams at higher energy, and the interplay between the space-charge effects and the beam-beam effects at lower energy. To achieve the best performance of a collider means to attain the highest luminosity of the collider with optimized collider parameters. Optimizing the collider’s machine parameters is both computationally and experimentally expensive. A fast and robust computational framework including beam-beam and space-charge effects will be critical to attaining the best performance of the collider. In this project, we will study the beam dynamics challenges, specifically the interplay of the space-charge and the beam-beam effects, and the machine tuning models for maximizing the performance of RHIC experiments. We will develop an advanced modeling framework based on first-principles physical simulations, lattice models and the state-of-the-art machine learning methods and apply this framework to performance improvement of the RHIC in operation. We will build data manipulation packages to connect the simulation data and the experimental data with the framework, develop a self-consistent hybrid model of space-charge and beam-beam effects, study underlying physics mechanisms, build surrogate models using the labeled data, integrate the models into the advanced modeling framework, and apply the framework to RHIC luminosity (STAR and sPHENIX) optimization. The success of this project would substantially improve the performance of existing and future colliders and increase the opportunity for scientific discovery.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Influences of δB contribution and parallel inertial term of energetic particles on MHD-kinetic hybrid simulations: a case study of the 1/1 internal kink mode

The magnetohydrodynamic-kinetic (MHD-kinetic) hybrid model (Park et al 1992 Phys. Fluids B 4 2033–7) has been widely applied in studying energetic particles (EPs) problems in fusion plasmas for past decades. The pressure-coupling scheme or the current-coupling scheme is adopted in this model. However, two noteworthy issues arise in the model application: firstly, the coupled term introduced in the pressure-coupling scheme, (∇•P h ) ⟂ , is often simplified by ∇•P h , which is equivalent to neglecting the parallel inertial term of EPs; secondly, besides the $δf$ contribution caused by changing in the EP distribution function, the magnetic field perturbation (the $δB$ contribution) generated during development of the instabilities should also be considered, but it is often ignored in existing hybrid simulations. In this paper, we derive the analytical formulations under these two coupling schemes and then numerically study the representative case of the linear stability of the $m/n$ = $1/1$ internal kink mode (IKM) (Fu et al 2006 Phys. Plasmas 13 052517) by using the CLT-K code. Further, it is found that the approximated models can still yield reasonable results when EPs are isotopically distributed. But it fails completely in cases with anisotropic EP distributions. In addition, we further investigate the influence of EP's orbit width on the stability of IKM and verify the equivalence between pressure-coupling scheme and the current-coupling scheme.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗

DOE OSTI2021

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.

42 ENGINEERING↗

NASA NTRS1994

Illumination in diverse codimensions

This paper derives a model of diffuse and specular illumination in arbitrarily large dimensions, based on a few characteristics of material and light in three-space. It then describes how to adjust for the anomaly of excess brightness in large codimensions. If a surface is grooved or furry, it can be illuminated with a hybrid model that incorporates both the one dimensional geometry (the grooves or fur) and the two dimensional geometry (the surface).

Banks, David C.↗

NASA NTRS2004

Online model-based diagnosis to support autonomous operation of an advanced life support system

This article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed.

Non-NASA Center↗

NASA NTRS2007

Unofficial Road Building in the Brazilian Amazon: Dilemmas and Models for Road Governance

Unofficial roads form dense networks in landscapes, generating a litany of negative ecological outcomes, but unofficial roads in frontier areas are also instrumental in local livelihoods and community development. This trade-off poses dilemmas for the governance of unofficial roads. Unofficial road building in frontier areas of the Brazilian Amazon illustrates the challenges of 'road governance.' Both state-based and community based governance models exhibit important liabilities for governing unofficial roads. Whereas state-based governance has experienced difficulties in adapting to specific local contexts and interacting effectively with local interest groups, community-based governance has a mixed record owing to social inequalities and conflicts among local interest groups. A state-community hybrid model may offer more effective governance of unofficial road building by combining the oversight capacity of the state with locally grounded community management via participatory decision-making.

Perz, Stephen G.↗

NASA NTRS1972

A model following variable stability system for the NASA ARC X-14B.

A description of the basic design concept, hardware design, and flight evaluation of a Variable Stability System (VSS) installed on the NASA ARC X-14B is presented. The NASA ARC X-14B is a twin-engine, single-seated VTOL aircraft. The VSS is unique in that it employs a general purpose airborne digital computer as an integral part of the hybrid model following flight control system. The system design, analysis and testing phases are discussed in the paper from the application of optimal control techniques in the preliminary design of the system, through the flight demonstration of the VSS hardware.

Gallagher, J. T.↗

NASA NTRS2003

Performance Analysis of a Hybrid Overset Multi-Block Application on Multiple Architectures

This paper presents a detailed performance analysis of a multi-block overset grid compu- tational fluid dynamics app!ication on multiple state-of-the-art computer architectures. The application is implemented using a hybrid MPI+OpenMP programming paradigm that exploits both coarse and fine-grain parallelism; the former via MPI message passing and the latter via OpenMP directives. The hybrid model also extends the applicability of multi-block programs to large clusters of SNIP nodes by overcoming the restriction that the number of processors be less than the number of grid blocks. A key kernel of the application, namely the LU-SGS linear solver, had to be modified to enhance the performance of the hybrid approach on the target machines. Investigations were conducted on cacheless Cray SX6 vector processors, cache-based IBM Power3 and Power4 architectures, and single system image SGI Origin3000 platforms. Overall results for complex vortex dynamics simulations demonstrate that the SX6 achieves the highest performance and outperforms the RISC-based architectures; however, the best scaling performance was achieved on the Power3.

Djomehri, M. Jahed↗

NASA NTRS2023

Hybrid CFD Engineering Model of Plume Induced Erosion and Crater Formation During Descent of Lunar Landers

With rapidly increased worldwide interest in landing on the moon, the issue of Plume Surface Interactions (PSI) is gaining attention. Hazards posed by lander plume induced dust and debris, as well as landing site deformation can be mitigated when better understood through predictive simulations. As simulation enabling computational power continues to increase, hybrid Computational Fluid Dynamics (CFD)/Engineering models provide the immediate ability to conduct parametric/trade studies driving design decisions for landers. Reduced order erosion engineering models apply correlations of the surface erosion rate to the plume induced surface forces with the correlations anchored to flight observations from Apollo LM landings. The MSFC propulsion fluid dynamics branch has developed such a hybrid model for predicting the plume induced viscous erosive regression of the Lunar surface beneath a landing vehicle. The (Descent Interpolated Gas Granular Erosion Model) DIGGEM was originally implemented as a post processing tool to calculate induced erosion rates through vehicle descent using CFD solutions of the vehicle plume at several fixed altitudes. This capability has since been advanced in the Loci/Chem-DIGGEM model to allow transient, moving vehicle, fully coupled viscous erosion modeling of vehicle descent PSI. This model has recently been used to make preflight predictions of the erosive regression of the ground beneath a Commercial Lunar Payload Services (CLPS) vehicle in support of measurements to be made by the Stereo Cameras for Lunar Plume Surface Studies (SCALPSS) instrument.

Plume Surface Interaction↗

NASA NTRS2023

Hybrid CFD Engineering Model of Plume Induced Erosion and Crater Formation During Descent of Lunar Landers

Plume Surface Interaction↗