Results for “hybrid analysis and modeling” — Page 6

DOE OSTI2021

Hybrid Analytics Solution to Improve Coal Power Plant Operations

This project focused on developing advanced methods for thermal performance monitoring of a coal-fueled power plant. The specific goal was to develop and demonstrate a new thermal performance monitoring approach using a hybrid model that integrates a physics-based heat balance model with a machine learning-based pattern recognition model. The hybrid model enables increased accuracy and scope of the thermal analysis and an improved ability to monitor and detect changes in plant operation. This new approach takes full advantage of the individual model capabilities and creates an important new set of capabilities not previously possible using the two types of models separately. Using the heat balance model, a rich set of derived parameters (virtual sensors) are calculated from the measured plant operating data at each time point. The combined measured and derived data values are used by machine learning algorithms to create pattern recognition models over the range of normal unit operation. To create the monitoring models, historical data from normal operation of the plant is first processed by the heat balance model to compute the derived parameter data. The result is a greatly expanded set of normal operating data that can be used as input to create the pattern recognition model. Once the models are calibrated for normal operation, the hybrid model is suitable for use in continuous online monitoring. During online monitoring, new plant operating data is processed first by the heat balance model and then by the pattern recognition model. Results from the pattern recognition model quantify the deviation of each measured or derived parameter from its expected value in normal operation. The hybrid models can detect abnormal changes in plant operating data with very high accuracy and sensitivity. When abnormal behavior is detected, alerts are generated automatically for evaluation by the plant monitoring staff. The new hybrid solution product was developed and verified in the performance of the project. The hybrid solution was tested first in a simulation environment that mimicked the plant data systems and infrastructure used by U.S. power generating plants and utilities. The hybrid solution was then deployed for real-time, online monitoring of an operating coal-fueled power plant at a field test site. Field testing demonstrated that all hybrid solution development objectives were accomplished. The project work was based on combining the capabilities of two existing software products to create the new hybrid solution product. One of these was the existing MapEx® heat balance product and the other was the existing SureSense® advanced pattern recognition product. Each of these separate products was assessed to be at a Technology Readiness Level (TRL) of 9 at the start of the effort. The hybrid solution product was assessed to be at a TRL of 2 at the start of the project based on early feasibility work by the project team. At completion of the field testing performed in the project, the hybrid solution product was assessed to be at a TRL of 7. The project team expects that the hybrid solution product will be deployed commercially and will achieve a TRL of 9 within one year after completion of the project.

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS1986

Progress in the development of parabolized Navier-Stokes (PNS) methodology for analyzing propulsive jet mixing problems

A brief review of 2D PNS methodology is first presented which describes the specialized features of supersonic shock-capturing and subsonic pressure-split models required for the analysis of aircraft, rocket and scramjet jet mixing problems. These features include techniques for dealing with various types of embedded and interfacing subsonic regions, the inclusion of finite-rate chemistry and the direct-coupling with potential flow solutions. Preliminary 3D extensions of this PNS methodology geared to supersonic and subsonic rectangular free jet mixing problems are also reviewed. New 3D PNS work will be described which includes the development of a hybrid supersonic/subsonic free jet mixing model, and, a supersonic model geared to the analysis of turbulent mixing and combustion processes occurring in scramjet combustor/nozzle flowfields.

Dash, S. M.↗

DOE OSTI2025

Direct numerical simulations for hybrid rocket boundary layers: Performance modeling and scaling

This paper presents a comprehensive performance and scaling analysis of direct numerical simulations for reacting boundary layers, focusing on slab burner configurations. Using a PETSc-based finite volume CFD framework, the study evaluates the scalability and computational cost of flow, chemistry, and radiation evaluations across 2D and 3D simulations. Polymethyl methacrylate (PMMA) is the fuel with pure O 2 as the oxidizer, modeled using a detailed chemical kinetics mechanism with 113 species and 660 reactions. A ray-tracing-based radiation solver, designed for distributed memory applications, is implemented to model radiation heat transfer. Parallel scalability is analyzed for the coupled flow, chemistry, and radiation heat transfer processes. Weak and strong scaling studies are conducted on up to 15,000 computational ranks, revealing robust performance when flow cells exceed 200 per rank. Chemistry evaluations dominate the computational cost in large 3D simulations, accounting for approximately 40% of the total runtime, while flow processes contribute around 35%, and radiation solver contributions remain below 10% due to reduced evaluation frequencies. GPU accelerated chemistry evaluation, implemented with Zero-RK, demonstrates significant promise, achieving up to a 4x speedup for workloads exceeding 30,000 cells per GPU. However, diminishing returns are observed for smaller workloads due to CPU-GPU communication overhead. This study identifies key challenges, including memory bottlenecks and the effects of domain partitioning on flow scalability, while highlighting the potential of GPU-accelerated chemistry to reduce computational costs. In conclusion, these findings provide realizable run configurations for 2D, 3D, and GPU-accelerated cases, offering insights for optimizing reactive flow solvers.

CFD Scalability↗

DOE OSTI2026

Tandem Photovoltaics Core Program Final Technical Report

The Tandem Photovoltaics Core Program was a multi-year initiative aimed at advancing hybrid tandem solar cell technologies to enhance solar module efficiency beyond the limits of single junction devices. This project focused on the development, testing, and scaling of prototype photovoltaic devices, with the goal of achieving commercial relevance and driving industry adoption. The work was divided into three tasks: 1) Comparative Analysis of Tandem Technologies: This task focused on quantifying energy yield under real-world conditions and assessing economic viability of tandems relative to silicon-based modules. The project's modeling framework incorporated performance data, cost of materials, and manufacturing process impacts to optimize tandem designs 2) Tandem Integration and Prototyping: In this task, we developed innovative tandem designs by combining metal halide perovskite (MHP) top cells and silicon (Si) bottom cells. The project focuses on both mechanical integration and direct deposition techniques to enable compatibility with commercially relevant Si technologies, such as passivated contact or PERC cells. 3) Scale-up and Reliability: This task addressed the challenges of large-area fabrication by developing scalable deposition methods and robust interconnection schemes for tandems. The project looked at different accelerated testing such as thermal cycling, damp heat exposure, and potential induced degradation, to ensure long-term stability of devices in field conditions. Tandem solar cells can greatly increase module efficiency beyond conventional single junction (SJ) devices, which are approaching their theoretical limit. There are many ways to fabricate a tandem cell or module in terms of materials used, configuration, and terminal connection. This SETO core project focused critical factors in enabling tandems to enter the market, including hardware integration, technoeconomic analysis (TEA), and energy yield analysis. We focused on MHP/Si hybrid tandem solar cells and modules as a model system for their versatility in module design comparisons, providing valuable insights for other tandem options. While champion cells with areas <1cm2 are regularly demonstrated by groups around the world, it is significantly more challenging to translate these advances into modules, and fewer institutions and companies are working at the module level. This project addressed questions about module fabrication, testing, and reliability that are hard to answer without actually fabricating prototypes. We also performed analysis and road-mapping activities to understand the potential for a wider variety of tandems, including all-perovskite tandems fabricated in collaboration with the Perovskite PV core program. Detailed technical results from this project are described for each task in Section 7.

14 SOLAR ENERGY↗

DOE OSTI2024

Unraveling the mechanism of tip-enhanced molecular energy transfer

Electronic Energy Transfer (EET) between chromophores is fundamental in many natural light-harvesting complexes, serving as a critical step for solar energy funneling in photosynthetic plants and bacteria. The complicated role of the environment in mediating this process in natural architectures has been addressed by recent scanning tunneling microscope experiments involving EET between two molecules supported on a solid substrate. These measurements demonstrated that EET in such conditions has peculiar features, such as a steep dependence on the donor-acceptor distance, reminiscent of a short-range mechanism more than of a Förster-like process. By using state of the art hybrid ab initio/electromagnetic modeling, here we provide a comprehensive theoretical analysis of tip-enhanced EET. In particular, we show that this process can be understood as a complex interplay of electromagnetic-based molecular plasmonic processes, whose result may effectively mimic short range effects. Therefore, the established identification of an exponential decay with Dexter-like effects does not hold for tip-enhanced EET, and accurate electromagnetic modeling is needed to identify the EET mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2016

Innovative Approach for Developing Spacecraft Interior Acoustic Requirement Allocation

The Orion Multi-Purpose Crew Vehicle (MPCV) is an American spacecraft for carrying four astronauts during deep space missions. This paper describes an innovative application of Power Injection Method (PIM) for allocating Orion cabin continuous noise Sound Pressure Level (SPL) limits to the sound power level (PWL) limits of major noise sources in the Environmental Control and Life Support System (ECLSS) during all mission phases. PIM is simulated using both Statistical Energy Analysis (SEA) and Hybrid Statistical Energy Analysis-Finite Element (SEA-FE) models of the Orion MPCV to obtain the transfer matrix from the PWL of the noise sources to the acoustic energies of the receivers, i.e., the cavities associated with the cabin habitable volume. The goal of the allocation strategy is to control the total energy of cabin habitable volume for maintaining the required SPL limits. Simulations are used to demonstrate that applying the allocated PWLs to the noise sources in the models indeed reproduces the SPL limits in the habitable volume. The effects of Noise Control Treatment (NCT) on allocated noise source PWLs are investigated. The measurement of source PWLs of involved fan and pump development units are also discussed as it is related to some case-specific details of the allocation strategy discussed here.

Chu, S. Reynold↗

NASA NTRS2016

Innovative Approach for Developing Spacecraft Interior Acoustic Requirement Allocation

The Orion Multi-Purpose Crew Vehicle (MPCV) is an American spacecraft for carrying four astronauts during deep space missions. This paper describes an innovative application of Power Injection Method (PIM) for allocating Orion cabin continuous noise Sound Pressure Level (SPL) limits to the sound power level (PWL) limits of major noise sources in the Environmental Control and Life Support System (ECLSS) during all mission phases. PIM is simulated using both Statistical Energy Analysis (SEA) and Hybrid Statistical Energy Analysis-Finite Element (SEA-FE) models of the Orion MPCV to obtain the transfer matrix from the PWL of the noise sources to the acoustic energies of the receivers, i.e., the cavities associated with the cabin habitable volume. The goal of the allocation strategy is to control the total energy of cabin habitable volume for maintaining the required SPL limits. Simulations are used to demonstrate that applying the allocated PWLs to the noise sources in the models indeed reproduces the SPL limits in the habitable volume. The effects of Noise Control Treatment (NCT) on allocated noise source PWLs are investigated. The measurement of source PWLs of involved fan and pump development units are also discussed as it is related to some case-specific details of the allocation strategy discussed here.

Chu, S. Reynold↗

NASA NTRS2021

Revolutionary Vertical Lift Technology (RVLT) Side-By-Side Hybrid Concept Vehicle Powertrain Dynamic Model

The Side-by-Side (SBS) Hybrid is one of several Revolutionary Vertical Lift Technology (RVLT) concept aircraft identified by NASA to investigate Urban Air Mobility (UAM) requirements. This paper presents a dynamic model of the SBS Hybrid powertrain built using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) and the Electrical Modeling and Thermal Analysis Toolbox (EMTAT). The model consists of the rotors, electrical power system, and turboshaft engines connected through freewheeling clutches, gearboxes, and multiple shafts. This research effort models the complex behavior of the powertrain, including the operation of the freewheeling clutches and electrical power system at the simulation time scale of the shaft dynamics. Several simulations highlight the key features present in the model and demonstrate its operation.

Powertrain↗

NASA NTRS2021

Revolutionary Vertical Lift Technology (RVLT) Side-By-Side Hybrid Concept Vehicle Powertrain Dynamic Model

The Side-by-Side (SBS) Hybrid is one of several Revolutionary Vertical Lift Technology (RVLT) concept aircraft identified by NASA to investigate Urban Air Mobility (UAM) requirements. This paper presents a dynamic model of the SBS Hybrid powertrain built using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) and the Electrical Modeling and Thermal Analysis Toolbox (EMTAT). The model consists of the rotors, electrical power system, and turboshaft engines connected through freewheeling clutches, gearboxes, and multiple shafts. This research effort models the complex behavior of the powertrain, including the operation of the freewheeling clutches and electrical power system at the simulation time scale of the shaft dynamics. Several simulations highlight the key features present in the model and demonstrate its operation.

powertrain↗

DOE OSTI2025

Physics-based hybrid machine learning for critical heat flux prediction with uncertainty quantification

Critical heat flux (CHF) is a key quantity in nuclear system modeling due to its impact on heat transfer, safety margins, and reactor performance. This study develops and validates an uncertainty-aware hybrid modeling approach that combines machine learning with physics-based models to predict CHF in cases of dryout. The Biasi and Bowring empirical correlations were paired with three ML uncertainty quantification (UQ) techniques: deep neural network (DNN) ensembles, Bayesian neural networks (BNNs), and deep Gaussian processes (DGPs). A pure ML model without a base model was evaluated for comparison. Model performance was assessed under plentiful (7,350 points) and limited (9 points) training data scenarios using parity, uncertainty distributions, and calibration curves. Results show that the Biasi hybrid DNN ensemble achieved the best overall performance, with a mean absolute relative error of 1.846%, and well-calibrated uncertainty estimates. The BNN-based hybrids showed slightly higher error (2.14%) but superior uncertainty calibration. DGP models underperformed, with over 6% error and poor uncertainty calibration. All hybrid models outperformed pure machine learning configurations, demonstrating resistance against data scarcity. These findings indicate that hybrid modeling significantly improves predictive accuracy, interpretability, and resilience to data scarcity. The integration of uncertainty awareness provides actionable confidence in CHF predictions, which is vital for safety-critical decisions in nuclear applications. This hybrid approach offers a viable pathway for deploying ML models in reactor analysis tools while preserving domain knowledge and physical consistency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2010

Hybrid FEA/SEA Assessment for an Orthogrid Cylindrical Panel Section and Periodic Subsystem Modeling Evaluation

In the lower frequency range, where particular boundary conditions can make a significant difference to panel response characteristics Statistical Energy Analysis (SEA) has never been the analytical tool of choice. In addition to boundary condition effects, SEA is not well suited in frequency bands where no modes or less than a few modes exist. The advent of the Hybrid Module has enabled integration of Finite Element Analysis to expand and enhance the capability for response calculations within VA One into the lower frequency range. Exploration of several additional modeling approaches was completed for the cylindrical orthogrid panel test article that was examined in Reference 1. Comparison of the new analytical response predictions with the measured response data from ground test and the pure SEA results from the reference will be presented. One approach that is considered promising is the periodic subsystem capability. Initially, a detailed FEM of just one region of the test article is defined. After evaluating this small region using symmetric boundary conditions, the FEM may be expanded to determine the properties of the entire system using similar connected regions that map over the entire test article. Another approach is the direct use of a very detailed finite element model of the entire panel, explicitly modeling pocket and rib details of the structure. A third approach is to approximate localized structure geometry details with a smeared property generalization using a PCOMP (NASTRAN card used to define layered composite structures) to define skin layer and ribbed layer for the orthogrid panel. The authors expect to demonstrate that the integrated Hybrid/FEM approach increases confidence in response prediction in the lower frequency range (for example from 20-300 Hz for the test article under consideration). In addition the strength and weakness of each additional approach will be highlighted and compared to those reported with those reported in an earlier paper

Smith, Andrew M.↗

NASA NTRS1995

A hybrid neurocomputing/numerical strategy for nonlinear structural analysis

A hybrid neurocomputing/numerical strategy is presented for geometrically nonlinear analysis of structures. The strategy combines model-free data processing capabilities of computational neural networks with a Pade approximants-based perturbation technique to predict partial information about the nonlinear response of structures. In the hybrid strategy, multilayer feedforward neural networks are used to extend the validity of solutions by using training samples produced by Pade approximations to the Taylor series expansion of the response function. The range of validity of the training samples is taken to be the radius of convergence of Pade approximants and is estimated by setting a tolerance on the diverging approximants. The norm of residual vector of unbalanced forces in a given element is used as a measure to assess the quality of network predictions. To further increase the accuracy and the range of network predictions, additional training data are generated by either applying linear regression to weight matrices or expanding the training data by using predicted coefficients in a Taylor series. The effectiveness of the hybrid strategy is assessed by performing large-deflection analysis of a doubly-curved composite panel with a circular cutout, and postbuckling analyses of stiffened composite panels subjected to an in-plane edge shear load. In all the problems considered, the hybrid strategy is used to predict selective information about the structural response, namely the total strain energy and the maximum displacement components only.

Szewczyk, Z. Peter↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

42 ENGINEERING↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

14 SOLAR ENERGY↗

DOE OSTI2021

Antiferromagnetic character of the quantum phase transition in the Hubbard model on the honeycomb lattice

In this work we provide a unified, comprehensive treatment of all operators that contribute to the antiferromagnetic, ferromagnetic, and charge-density-wave structure factors and order parameters of the hexagonal Hubbard Model. We use the Hybrid Monte Carlo algorithm to perform a systematic, carefully controlled analysis in the temporal Trotter error and of the thermodynamic limit. We expect our findings to improve the consistency of Monte Carlo determinations of critical exponents. We perform a data collapse analysis and determine the critical exponent β = 0.898 (37) for the semimetal-Mott insulator transition in the hexagonal Hubbard Model. Our methods are applicable to a wide range of lattice theories of strongly correlated electrons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2010

Acoustic Noise Prediction of the Amine Swingbed ISS ExPRESS Rack Payload

Acoustics plays a vital role in maintaining the health, safety, and comfort of crew members aboard the International Space Station (ISS). In order to maintain this livable and workable environment, acoustic requirements have been established to ensure that ISS hardware and payload developers account for the acoustic emissions of their equipment and develop acoustic mitigations as necessary. These requirements are verified by an acoustic emissions test of the integrated hardware. The Amine Swingbed ExPRESS (Expedite the PRocessing of ExperimentS to Space) rack payload creates a unique challenge to the developers in that the payload hardware is transported to the ISS in phases, making an acoustic emissions test on the integrated flight hardware impossible. In addition, the payload incorporates a high back pressure fan and a diaphragm vacuum pump, which are recognized as significant and complex noise sources. In order to accurately predict the acoustic emissions of the integrated payload, the individual acoustic noise sources and paths are first characterized. These characterizations are conducted though a series of acoustic emissions tests on the individual payload components. Secondly, the individual acoustic noise sources and paths are incorporated into a virtual model of the integrated hardware. The virtual model is constructed with the use of hybrid method utilizing the Finite Element Acoustic (FEA) and Statistical Energy Analysis (SEA) techniques, which predict the overall acoustic emissions. Finally, the acoustic model is validated though an acoustic characterization test performed on an acoustically similar mock-up of the flight unit. The results of the validated acoustic model are then used to assess the acoustic emissions of the flight unit and define further acoustic mitigation efforts.

Welsh, David↗

NASA NTRS2019

Computational Study of the Side-By-Side Urban Air Taxi Concept

High-fidelity computational fluid dynamics simulations of NASA’s Side-by-side air taxi concept have been carried out. The three-dimensional unsteady Navier-Stokes equations are solved on overset grids using high-order accurate schemes, dual-time stepping, and a hybrid turbulence model. The flow solver has been loosely coupled with a helicopter comprehensive analysis code in order to get the trimmed flight solution. The vehicle simulated is a six-passenger side-by-side intermeshing rotor helicopter with hybrid propulsion for air taxi operations, also known as urban air mobility applications. This concept vehicle is intended to focus and guide NASA research activities in support of aircraft development for emerging aviation markets, in particular vertical take-off and landing air taxi operations.

Ventura Diaz, Patricia↗

NASA NTRS1991

Development of an integrated aeroservoelastic analysis program and correlation with test data

The details and results are presented of the general-purpose finite element STructural Analysis RoutineS (STARS) to perform a complete linear aeroelastic and aeroservoelastic analysis. The earlier version of the STARS computer program enabled effective finite element modeling as well as static, vibration, buckling, and dynamic response of damped and undamped systems, including those with pre-stressed and spinning structures. Additions to the STARS program include aeroelastic modeling for flutter and divergence solutions, and hybrid control system augmentation for aeroservoelastic analysis. Numerical results of the X-29A aircraft pertaining to vibration, flutter-divergence, and open- and closed-loop aeroservoelastic controls analysis are compared to ground vibration, wind-tunnel, and flight-test results. The open- and closed-loop aeroservoelastic control analyses are based on a hybrid formulation representing the interaction of structural, aerodynamic, and flight-control dynamics.

Gupta, K. K.↗