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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Design of the Next Generation Aircraft Noise Prediction Program: ANOPP2

The requirements, constraints, and design of NASA's next generation Aircraft NOise Prediction Program (ANOPP2) are introduced. Similar to its predecessor (ANOPP), ANOPP2 provides the U.S. Government with an independent aircraft system noise prediction capability that can be used as a stand-alone program or within larger trade studies that include performance, emissions, and fuel burn. The ANOPP2 framework is designed to facilitate the combination of acoustic approaches of varying fidelity for the analysis of noise from conventional and unconventional aircraft. ANOPP2 integrates noise prediction and propagation methods, including those found in ANOPP, into a unified system that is compatible for use within general aircraft analysis software. The design of the system is described in terms of its functionality and capability to perform predictions accounting for distributed sources, installation effects, and propagation through a non-uniform atmosphere including refraction and the influence of terrain. The philosophy of mixed fidelity noise prediction through the use of nested Ffowcs Williams and Hawkings surfaces is presented and specific issues associated with its implementation are identified. Demonstrations for a conventional twin-aisle and an unconventional hybrid wing body aircraft configuration are presented to show the feasibility and capabilities of the system. Isolated model-scale jet noise predictions are also presented using high-fidelity and reduced order models, further demonstrating ANOPP2's ability to provide predictions for model-scale test configurations.

Lopes, Leonard V., Dr.↗
NASA NTRS2012

Computational Aeroelastic Analysis of the Semi-Span Super-Sonic Transport (S4T) Wind-Tunnel Model

A summary of the computational aeroelastic analysis for the Semi-Span Super-Sonic Transport (S4T) wind-tunnel model is presented. A broad range of analysis techniques, including linear, nonlinear and Reduced Order Models (ROMs) were employed in support of a series of aeroelastic (AE) and aeroservoelastic (ASE) wind-tunnel tests conducted in the Transonic Dynamics Tunnel (TDT) at NASA Langley Research Center. This research was performed in support of the ASE element in the Supersonics Program, part of NASA's Fundamental Aeronautics Program. The analysis concentrated on open-loop flutter predictions, which were in good agreement with experimental results. This paper is one in a series that comprise a special S4T technical session, which summarizes the S4T project.

Sanetrik, Mark D.↗
NASA NTRS2017

Genetic Algorithm-Guided, Adaptive Model Order Reduction of Flexible Aircrafts

This paper presents a methodology for automated model order reduction (MOR) of flexible aircrafts to construct linear parameter-varying (LPV) reduced order models (ROM) for aeroservoelasticity (ASE) analysis and control synthesis in broad flight parameter space. The novelty includes utilization of genetic algorithms (GAs) to automatically determine the states for reduction while minimizing the trial-and-error process and heuristics requirement to perform MOR; balanced truncation for unstable systems to achieve locally optimal realization of the full model; congruence transformation for "weak" fulfillment of state consistency across the entire flight parameter space; and ROM interpolation based on adaptive grid refinement to generate a globally functional LPV ASE ROM. The methodology is applied to the X-56A MUTT model currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that X-56A ROM with less than one-seventh the number of states relative to the original model is able to accurately predict system response among all input-output channels for pitch, roll, and ASE control at various flight conditions. The GA-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The adaptive refinement allows selective addition of the grid points in the parameter space where flight dynamics varies dramatically to enhance interpolation accuracy without over-burdening controller synthesis and onboard memory efforts downstream. The present MOR framework can be used by control engineers for robust ASE controller synthesis and novel vehicle design.

Numerical Analysi↗
NASA NTRS2018

Assessment of the Mars Helicopter Thermal Design Sensitivities Using the Veritrek Software

The Mars Helicopter will be a technology demonstration conducted during the Mars 2020 mission. The primary mission objective is to achieve several 90-second flights and capture visible light images via forward and nadir mounted cameras. These flights could possibly provide reconnaissance data for sampling site selection for other Mars surface missions. The helicopter is powered by a solar array, which stores energy in secondary batteries for flight operations, imaging, communications, and survival heating. The helicopter thermal design is driven by minimizing survival heater energy while maintaining compliance with allowable flight temperatures in a variable thermal environment. Due to the small size of the helicopter and its complex geometries, along with the fact that it operates with very low power and small margins, additional care had to be paid while planning thermal tests and designing the thermal system. A Thermal Desktop® model has been developed to predict the thermal system’s performance. A reduced-order model (ROM) created with the Veritrek software has been utilized to explore the sensitivities of the thermal system’s drivers, such as electronics dissipations, gas gaps, heat transfer coefficients, etc., as well as to assess and verify the final thermal design. This paper presents the performance of the Veritrek software products and the details of the ROM creation process. The results produced by Veritrek were utilized to study the effect of the major thermal design drivers and Mars environment on the Mars Helicopter in as little as 10 days, an effort that would have taken over 4 months using traditional thermal analysis techniques.

Hengeveld, Derek W.↗
NASA NTRS1982

Applications of system identification methods to the prediction of helicopter stability, control and handling characteristics

A set of results on rotorcraft system identification is described. Flight measurements collected on an experimental Puma helicopter are reviewed and some notable characteristics highlighted. Following a brief review of previous work in rotorcraft system identification, the results of state estimation and model structure estimation processes applied to the Puma data are presented. The results, which were obtained using NASA developed software, are compared with theoretical predictions of roll, yaw and pitching moment derivatives for a 6 degree of freedom model structure. Anomalies are reported. The theoretical methods used are described. A framework for reduced order modelling is outlined.

Padfield, G. D.↗
NASA NTRS1990

Optimal model reduction and frequency-weighted extension

In this paper the quadratically optimal model reduction problem for single-input, single-output systems is considered. The reduced order model is determined by minimizing the integral of the magnitude-squared of the transfer function error. It is shown that the numerator coefficients of the optimal approximant satisfy a weighted least squares problem and, on this basis, a two-step iterative algorithm is developed combining a least squares solver with a gradient minimizer. The existence of globally optimal stable solutions to the optimization problem is established, and convergence of the algorithm to stationary values of the cost function is proved. The formulation is extended to handle the frequency-weighted optimal model reduction problem. Three examples demonstrate the optimization algorithm.

Spanos, J. T.↗
NASA NTRS1992

A new algorithm for L2 optimal model reduction

In this paper the quadratically optimal model reduction problem for single-input, single-output systems is considered. The reduced order model is determined by minimizing the integral of the magnitude-squared of the transfer function error. It is shown that the numerator coefficients of the optimal approximant satisfy a weighted least squares problem and, on this basis, a two-step iterative algorithm is developed combining a least squares solver with a gradient minimizer. Convergence of the proposed algorithm to stationary values of the quadratic cost function is proved. The formulation is extended to handle the frequency-weighted optimal model reduction problem. Three examples demonstrate the optimization algorithm.

Spanos, J. T.↗
NASA NTRSDate not supplied

Powder Bed Fusion Laser Beam Metals Additive Manufacturing: Process Monitoring Approaches for Qualification and Certification

The use of in-situ process monitoring is of interest to lower the cost of inspection for the qualification of powder bed fusion laser beam metal (PBF-LB/M) additively manufactured (AM) parts. Precise monitoring of the PBF-LB/M AM build process constitutes a multi-scale and multi-discipline task. There are several significant challenges to the in-situ approach: the synchronization of sensor signals to process steps; the physical interpretation and classification of sensor signals; managing very large datasets; and comparing the inputs with the observed monitoring signals. At NASA Langley Research Center, a configurable architecture additive testbed has been developed to monitor the build process with synchronized sensors. The philosophy and method adopted for the synchronization of the cameras with laser power and position throughout a complex PBF-LB/M AM build will be described. The synchronized in-situ monitoring signals are compared with ex-situ nondestructive inspection, x-ray computed tomography (XCT). Such comparisons permit a better understanding of how the sequential process actions of LPBF-AM can affect build quality. The multi-scale and complex process of printing additively manufactured (AM) parts can have unexpected, but predictable, build conditions that result in material microstructure variability. This presentation will describe an additive manufacturing model-based process metric (AM-PM) computational method that is a fully parallel reduced order modeling approach developed to evaluate the evolution of AM processes. This method couples the known sequence of the AM process with a physically informed nearest neighbors’ calculation to map the conditions of a part-scale build. The result is a map of the build that is derived directly from build files or in-situ process monitoring sensors. The methodology of the approach will be described and mapped to the porosity observed from XCT for a complex PBF-LB/M build. Such comparative results develop understanding of how the sequential process actions can affect the PBF-LB/M AM build quality and microstructure variability.

Laser Powder Bed Fusion↗
NASA NTRS1993

Model reduction results for flexible space structures

This paper describes the novel subsystem balancing technique for obtaining reduced-order models of flexible structures, and investigates its properties fully. This method can be regarded as a combination of the best features of modal truncation (efficiency) and internal balancing (accuracy); it is particularly well suited to the typical practical case of structures which possess clusters of close modes. Numerical results are then presented demonstrating the results obtained by applying subsystem balancing to the Air Force Phillips Laboratory ASTREX testbed, the Jet Propulsion Laboratory antenna facility, and the NASA Marshall Space Flight Center ACES structure.

Williams, Trevor↗
DOE OSTI2025

First‐Order Empirical Interpolation Method for Real‐Time Solution of Parametric Time‐Dependent Nonlinear PDEs

ABSTRACT We present a model reduction approach for the real‐time solution of time‐dependent nonlinear partial differential equations (PDEs) with parametric dependencies. A major challenge in constructing efficient and accurate reduced‐order models for nonlinear PDEs is the efficient treatment of nonlinear terms. We address this by unifying the implementation of hyperreduction methods to deal with nonlinear terms. Furthermore, we introduce a first‐order empirical interpolation method (EIM) to provide an efficient approximation of the nonlinear terms in time‐dependent PDEs. We demonstrate the effectiveness of our approach on the Allen–Cahn equation, which models phase separation, and the Buckley–Leverett equation, which describes two‐phase fluid flow in porous media. Numerical results highlight the accuracy, efficiency, and stability of the proposed method compared with both the Galerkin–Newton approach and hyper‐reduced models using the standard EIM.

Nguyen, Ngoc Cuong [Center for Computational Engin↗
DOE OSTI2025

Surrogate models for linear response

Linear response theory is a well-established method in physics and chemistry for exploring excitations of many-body systems. In particular, the quasiparticle random-phase approximation (QRPA) provides a powerful microscopic framework by building excitations on top of the mean-field vacuum; however, its high computational cost limits model calibration and uncertainty quantification studies. Here, we present two complementary QRPA surrogate models and apply them to study response functions of finite nuclei. One is a reduced-order model that exploits the underlying QRPA structure, while the other utilizes the recently developed parametric matrix model algorithm to construct a map between the system’s Hamiltonian and observables. Our benchmark applications, the calculation of the electric dipole polarizability of 180 Yb and the 𝛽-decay half-life of 80 Ni, show that both emulators can achieve 0.1%–1% accuracy while offering a 6–7 orders of magnitude speedup compared to state-of-the-art QRPA solvers. These results demonstrate that the developed QRPA emulators are well positioned to enable Bayesian calibration and large-scale studies of computationally expensive physics models describing the properties of many-body systems.

Beta decay↗
NASA NTRS1992

ROM/RMF control of the flexible, articulated-truss space crane

The large size and extreme flexibility of an articulated space crane entail great difficulties in precision positioning. Control strategies for the flexible multibody system are here developed using a Timoshenko-beam finite-element model (FEM) which captures the system's low frequency characteristics. From the FEM, a reduced-order model (ROM) controller is designed which rotates the fixed shape crane about the base alpha joint to actuate positioning control. Eigenvalue analysis of the closed-loop system shows the destabilizing effect of controller-structure interaction (CSI). Making use of a parallel bank of residual mode filters (RMFs) compensated for CSI, restoring system stability and performance. The linear control law is simulated in closed-loop with the beam model. The ROM-based control law with RMF compensation is an effective, low-order controller.

Reisenauer, Brian T.↗
DOE OSTI2025

Acceleration of Power System Dynamic Simulations Using a Deep Equilibrium Layer and Neural ODE Surrogate

The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. Here, in this paper, we propose a data-driven surrogate model based on implicit machine learningspecifically deep equilibrium layers and neural ordinary differential equationsto learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.

Neural ordinary differential equations↗
NASA NTRS2021

Weather Intelligent Navigation Data and Models for Aviation Planning (WINDMAP)

WINDMAP addresses the emerging needs in the aviation community of providing real-time weather forecasting to improve the safety of low altitude aircraft operations. This is accomplished through the integration of real-time observations from autonomous systems, such as drones and urban air taxis, with numerical weather prediction models and flight management and safety systems. To solve this problem, several technical challenges have been identified. These include (1) developing autonomous UAS capable of conducting observations accurately and reliably; (2) determining the number and frequency of required observations and the sensitivity of these observations in data sparse regions of the lower atmosphere;(3) assimilating dense observational data into models in real-time with sufficient resolution and accuracy; (4) developing novel physics-based reduced order models capable of incorporating diverse data sets; and (5)integrating real-time forecasting into UTM and DAA (detect-and-avoid) architectures for path planning and navigation. The goal of this proposed effort is to demonstrate the value of using small UAS to collect measurements of the dynamic and thermodynamic properties of the lower atmosphere at scales that match or exceed the spatio-temporal resolution of today’s best numerical weather prediction models

Koushik Datta↗
NASA NTRS2017

Aeroelastic Uncertainty Quantification Studies Using the S4T Wind Tunnel Model

This paper originates from the joint efforts of an aeroelastic study team in the Applied Vehicle Technology Panel from NATO Science and Technology Organization, with the Task Group number AVT-191, titled "Application of Sensitivity Analysis and Uncertainty Quantification to Military Vehicle Design." We present aeroelastic uncertainty quantification studies using the SemiSpan Supersonic Transport wind tunnel model at the NASA Langley Research Center. The aeroelastic study team decided treat both structural and aerodynamic input parameters as uncertain and represent them as samples drawn from statistical distributions, propagating them through aeroelastic analysis frameworks. Uncertainty quantification processes require many function evaluations to asses the impact of variations in numerous parameters on the vehicle characteristics, rapidly increasing the computational time requirement relative to that required to assess a system deterministically. The increased computational time is particularly prohibitive if high-fidelity analyses are employed. As a remedy, the Istanbul Technical University team employed an Euler solver in an aeroelastic analysis framework, and implemented reduced order modeling with Polynomial Chaos Expansion and Proper Orthogonal Decomposition to perform the uncertainty propagation. The NASA team chose to reduce the prohibitive computational time by employing linear solution processes. The NASA team also focused on determining input sample distributions.

Nikbay, Melike↗
NASA NTRS2013

Integrated Model Reduction and Control of Aircraft with Flexible Wings

This paper presents an integrated approach to the modeling and control of aircraft with exible wings. The coupled aircraft rigid body dynamics with a high-order elastic wing model can be represented in a nite dimensional state-space form. Given a set of desired output covariance, a model reduction process is performed by using the weighted Modal Cost Analysis (MCA). A dynamic output feedback controller, which is designed based on the reduced-order model, is developed by utilizing output covariance constraint (OCC) algorithm, and the resulting OCC design weighting matrix is used for the next iteration of the weighted cost analysis. This controller is then validated for full-order evaluation model to ensure that the aircraft's handling qualities are met and the uttering motion of the wings suppressed. An iterative algorithm is developed in CONDUIT environment to realize the integration of model reduction and controller design. The proposed integrated approach is applied to NASA Generic Transport Model (GTM) for demonstration.

AEROELASTIC↗
DOE OSTI2025

Validation of Pronghorn for Natural-Circulation Molten Salt Loops

This paper presents the development and validation of a high-fidelity thermal-hydraulic model of a molten salt natural circulation flow loop, designed for integration within a digital twin framework. The study evaluates the performance of Idaho National Laboratory’s Pronghorn against experimental data from Texas A&M University Molten Salt Flow Loop (MSFL) four Hitec-salt test benchmark data. Natural circulation of high-Prandtl-number fluids exhibits complex, counter-intuitive flow patterns that make pointwise thermocouple readings unreliable. Experimental work at TAMU’s MSFL provides benchmark data, including flow visualization at a test-section and centerline steady-state temperature measurements along the loop. Validation includes four single-phase natural circulation test cases with Hitec salt. Key metrics include flow profile agreement and steady-state temperature accuracy. Pronghorn results for two-dimensional single-phase agree qualitatively with the experimental flow profile. This paper illustrates the importance of Computational Fluid Dynamics (CFD) in elucidating the behavior of high-Prandtl-number thermal-hydraulics, along with how misleading centerline temperature measurements can be. Pronghorn reproduces the axial and radial stratification that makes single thermocouple readings unreliable. Future research will focus on reduced-order modeling techniques to enable rapid simulation suitable for real-time digital twin applications. The validated cases provide a basis for developing reduced-order surrogates aimed at real-time digital-twin applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1989

Model reduction for flexible space structures

This paper presents the conditions under which modal truncation yields a near-optimal reduced-order model for a flexible structure. Next, a robust model reduction technique to cope with the damping uncertainties typical of flexible space structure is developed. Finally, a flexible truss and the COFS-1 structure are used to give realistic applications for the model reduction techniques studied in the paper.

Gawronski, Wodek↗