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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Impact of Future Low-Emissions Combustor Technology on Acoustic Scaling Laws

The overall goal of a recent research project at the Raytheon Technologies Research Center, under National Aeronautics and Space Administration sponsorship, was to develop a first-ofits-kind database of detailed unsteady measurements characterizing noise sources of far-term advanced low-emissions aero-combustors. The program addressed the need for fundamental combustion-noise experiments which, in the near term, enable improvements to reduced-order models for use in system level noise assessments at the preliminary design stage for advanced air transport vehicles. In addition, optical measurement techniques were refined and validated for usage at the higher pressures and temperatures relevant to future combustor designs. In the long term, results from this program can be utilized to validate high-fidelity prediction methods suited for detailed multi-disciplinary acoustics/emissions combustor design. This paper concentrates on one aspect of the overall project, namely an examination of the legacy scaling laws for broadband combustor noise.

aeroacoustics, turbomachinery noise, combustor noi↗
NASA NTRSDate not supplied

Impact of Future Low-Emissions Combustor Technology on Acoustic Scaling Laws

The work is a summary and further analysis of unlimited-rights acoustic data acquired under NRA Contract NNC16CA39C (September 2016 - November2019) with United Technologies (now Raytheon Technologies) Research Center, East Hartford, Connecticut. NASA/CR-20205011099/VOL1, https://ntrs.nasa.gov/citations/20205011099. The overall goal of the Contract was to develop a first-of-its- kind database of detailed unsteady measurements characterizing noise sources of far-term advanced low-emissions aero-combustors. The program addressed the need for fundamental combustion-noise experiments which, in the near term, enable improvements to reduced-order models for use in system level noise assessments at the preliminary design stage for advanced air transport vehicles. In addition, optical measurement techniques were refined and validated for usage at the higher pressures and temperatures relevant to future combustor designs. In the long term, results from this program can be utilized to validate high-fidelity prediction methods suited for detailed multi-disciplinary acoustics/emissions combustor design. This presentation concentrates on one aspect of the overall project, namely an examination of the legacy scaling laws for broadband combustor noise.

aeroacoustics, turbomachinery noise, combustor noi↗
NASA NTRSDate not supplied

Supervisory On-line Optimal Control of an Electric Power Microgrid Design for Lunar Habitation

The following article describes an optimal control algorithm for the operation and study of an electric microgrid designed to power a lunar habitat. A photovoltaic (PV) generator powers the habitat and the presence of predictable lunar eclipses necessitates a system to prioritize and control loads within the microgrid. The algorithm consists of a reduced order model (ROM) that describes the microgrid, a discretization of the equations that result from the ROM, and an optimization formulation that controls the microgrid’s behavior. In order to validate this approach, the paper presents results from simulation based on lunar eclipse information and a schedule of intended loads.

Joseph Young↗
NASA NTRSDate not supplied

Flutter Prediction Report in Support of the High Angle Working Group at the Third Aeroelastic Prediction Workshop

This paper presents a summary of the computational flutter results associated with the AIAA third Aeroelastic Prediction Workshop (AePW-3) High Angle Working Group (HAWG). The computational results are compared against the experimental data collected during the Pitch and Plunge Apparatus (PAPA) Benchmark Supercritical Wing (BSCW) test campaign conducted in the Transonic Dynamics Tunnel (TDT) at NASA Langley Research Center thirty years ago. During that test several flutter points were identified at transonic conditions. One of these points, specifically near Mach 0.8 and 5° angle of attack, became a focal point of the computational challenge with in HAWG. Various fidelity time-domain, Reduced Order Model (ROM) and Linearized Frequency Domain (LFD) methods were used by seven participating teams. While there are encouraging trends in the computational results, the range of the predicted flutter dynamic pressure is still quite large due to the stall flutter mechanism. A description of each participating team’s software and methods is included.

Pawel Chwalowski↗
NASA NTRS2024

Development of a Data Fusion Methodology for Lineload Aerodynamic Databases for a Launch Vehicle during Liftoff and Transition

The need for databases for the distributed loading on launch vehicles during the early portion of flight necessitates the use of expensive computational flows in regimes where wake effects dominate. While also being expensive, this is a regime that computational tools tend to historically have problems simulating accurately. To help tackle this problem, a method of data fusion to combine computational results to wind tunnel derived force and moment data is developed. Using this method, significant reduction in computational costs and increases in confidence of the final product is possible and has been used to generate several databases for the Space Launch System (SLS) at NASA. While the full details of database generation are not part of this work, the crucial method at its core is developed here. Two SLS geometries are used throughout the work to demonstrate the techniques. These are two of the larger geometries and represent both planned crewed missions to the Moon as well as potential cargo missions to deep space. The method uses principal component analysis (PCA) to generate a reduced ordered model (ROM) to help fill in the full parameter space. Other similar techniques are explored, but were not found to have a significant result on the predictions of the ROM. Because the full number of components are kept to generate the model, this lack of difference is expected. This method is then extended to ensure that predicted surfaces match trusted force and moment data derived from wind tunnel testing. This extension is done by setting up a constrained optimization problem in order to minimize the deviation from the surface resolved computational data while still integrating to the desired values. When generating the constrained optimization problem, a weighting factor to balance these competing needs is introduced. The work compares previously introduced weighting terms from similar work to the proposed terms and shows that the previously used terms do not have as desirable behavior in this flow regime. This method is then expanded by developing a technique to incorporate uncertainty quantification into the developed data fusion methodology. This expansion takes a two pronged approach. One examines transferring the uncertainties in the force and moment database and characterizes how those adjustments change the predicted lineloads. The second looks at model form error and looks how rebuilding the model using slightly different data changes the predictions. These two terms are then combined in order to create an uncertainty model that takes both effects into account. The limitations of the proposed methods is then discussed as well as possible techniques to address these shortcomings.

Launch Vehicles↗
NASA NTRS2024

Influence of Ductility on the Performance of Lunar Habitat Structures Under Recurrent Disturbances

This research examines how ductility affects the durability of lunar surface structures against recurring disturbances like moonquakes, micrometeorite impacts, and thermal cycles over an extended period. The structural performance at various levels of ductility was determined by adjusting material parameters and the thickness of a reference multilayered dome structure. Moonquake and micrometeorite impact-induced lateral displacements were estimated using a reduced-order model under a control-oriented dynamic computational modeling framework. The study considered the degradation of the metallic dome’s strength properties over time due to thermal cycles. Fragility curves were generated by assessing the likelihood of reaching three predefined damage levels as a result of multiple hazards. Additionally, a discounted cash flow analysis was conducted to incorporate a financial aspect into the performance comparison. The findings revealed that structures with sufficient ductility capacity have a lower probability of sustaining severe damage or collapsing within a shorter time frame. Hence, having ductile structures in lunar environments is advantageous as it allows the postponement of maintenance and repair actions, thereby conserving scarce resources for more urgent tasks. Moreover, the financial analysis demonstrated that lunar habitats with higher ductile capacities result in larger net present values, offering a higher return on the initial investment.

Arsalan Majlesi↗
NASA NTRSDate not supplied

Practical Considerations using Weighted-Acceleration Compensation Techniques for Dynamic Force Measurements in Wind Tunnels

Most wind tunnel force measurement systems are designed and calibrated for high-accuracy static measurements, but may have limited ability to sense dynamic forces and moments. As a means to improve the measurement bandwidth of dynamic forces, researchers have proposed using a weighted-acceleration technique, which uses accelerometer data to compensate and correct for system dynamics. The sum of weighted-accelerations method is attractive as the acceleration data applies a correction to the traditional static measurements. The focus of this paper is on practical aspects of implementing a weighted-acceleration technique in an experimental arrangement. A formulation of the weighted-acceleration methodology is validated on a reduced-order model and demonstrated on a tabletop experiment that serves as a proxy to a wind tunnel force measurement system. As part of this system, a two component normal force and pitching moment balance was designed and fabricated. A static and dynamic calibration of the system was performed, and the weighted-acceleration technique was used to reconstruct impact forces acting on the system. Practical considerations of the experimental design that improve the success of the methodology are discussed.

Dynamic force measurement↗
NASA NTRSDate not supplied

Practical Considerations using Weighted-Acceleration Compensation Techniques for Dynamic Force Measurements in Wind Tunnels

Most wind tunnel force measurement systems are designed and calibrated for high-accuracy static measurements, but may have limited ability to sense dynamic forces and moments. As a means to improve the measurement bandwidth of dynamic forces, researchers have proposed using a weighted-acceleration technique, which uses accelerometer data to compensate and correct for system dynamics. The sum of weighted-accelerations method is attractive as the acceleration data applies a correction to the traditional static measurements. The focus of this paper is on practical aspects of implementing a weighted-acceleration technique in an experimental arrangement. A formulation of the weighted-acceleration methodology is validated on a reduced-order model and demonstrated on a tabletop experiment that serves as a proxy to a wind tunnel force measurement system. As part of this system, a two component normal force and pitching moment balance was designed and fabricated. A static and dynamic calibration of the system was performed, and the weighted-acceleration technique was used to reconstruct impact forces acting on the system. Practical considerations of the experimental design that improve the success of the methodology are discussed.

Dynamic force measurement↗
DOE OSTI2020

Nonlinear power flow control for networked AC/DC microgrids

A method for designing feedforward and feedback controllers for integration of stochastic sources and loads into a nonlinear networked AC/DC microgrid system is provided. A reduced order model for general networked AC/DC microgrid systems is suitable for HSSPFC control design. A simple feedforward steady state solution is utilized for the feedforward controls block. Feedback control laws are provided for the energy storage systems. A HSSPFC controller design is implemented that incorporates energy storage systems that provides static and dynamic stability conditions for both the DC random stochastic input side and the AC random stochastic load side. Transient performance was investigated for the feedforward/feedback control case. Numerical simulations were performed and provided power and energy storage profile requirements for the networked AC/DC microgrid system overall performance. The HSSPFC design can be implemented in the Matlab/Simulink environment that is compatible with real time simulation/controllers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Optimization Methodology of Pebble Bed HTGR Start-Up and Running-in Strategy

In recent years interest in advanced reactor technologies has increased significantly. However, the methods used for analysis of traditional nuclear reactors are insufficient to consider all the different and varied advanced reactor designs without further development. One promising advanced reactor design is the pebble bed reactor (PBR). PBRs possess unique operational and fuel cycle features that require the development of specific analysis methodologies to adequately design and analyze the systems. There is a need in PBR research for a capability to analyze and optimize the process of transitioning from the start-up reactor core to the equilibrium core (known as the “running-in” of the reactor). The start-up of a PBR and the transition to the equilibrium core is a complex, multi-physics challenge that has not yet been well researched and has many opportunities for design, analysis, and optimization of the process. In this research, a methodology is defined to consider the potential strategies in PBR start-up and run-in to the equilibrium core. Multiple candidate software are considered, and their pros and cons are discussed for the PBR-specific application in the methodology. A preliminary software selection for the physics engine is made, and initial verification of key modules is performed. Software to support optimization of the reactor run-in strategies through reduced order modeling (ROM) and machine learning are considered. Challenges for a full implementation of the methodology are discussed. Additional code selections and verification of models relevant to the application are needed before full demonstration of the methodology can be achieved and an optimal strategy determined.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Development of Multiresolution Capabilities for the Holistic Energy Resource Optimization Network (HERON) tool A progress update

INL researchers work on technoeconomic analyses for integrated energy systems (IES) using the Framework for Optimization of ResourCes and Economics (FORCE). Within FORCE, researchers use the Holistic Energy Resource Optimization Network (HERON) tool to conduct optimization of grid portfolios under uncertain market conditions. These optimizations determine optimal capacities for all IES components and strategies for resource dispatch which maximize some economic metric (e.g., net present value). Resource dispatch occurs on finer timescales (typically hours) and thus are asked to respond to a given time series (e.g. hourly load demand profiles for a grid, or pre-determined electricity prices). Volatile and complex bidding dynamics as well as poorly forecasted weather events within deregulated markets add uncertainty to the time series; FORCE can address this uncertainty by training a reduced order model on historical time series and generate unique synthetic time series which represent individual scenarios or realizations of the market. The IES configuration can be simulated under these different sampled realizations and a stochastic optimization is conducted which optimizes the expected value of the desired economic metric. The training of a synthetic time series generator is limited by the chosen time resolution; dynamics can occur on different time scales. Seasonal demand trends can dominate faster dynamical events (such as power outages from certain sectors or severe weather events) which might not get captured correctly by the trained model. In this report, we investigate different ways of addressing the training and generation of time series on multiple time scales using three main algorithms: wavelet decomposition, dynamic mode decomposition, and generative adversarial networks for time series. We demonstrate a time series analysis that yields information on not just the frequency space but also temporal space: where a fast Fourier transform can provide what frequencies dominate, the new algorithms can provide when the frequencies dominate as well. These analyses can help improve IES optimization by allowing researchers to couple simulations at different timescales when it is most needed - seasonal, day-ahead, and real time optimization - with greater computational efficiency. Future work will include implementation of a subset of the proposed algorithms into the FORCE toolset and application of these analyses into multiple timescale optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Design and Flow Considerations of Additively Manufactured, Internal Cooling Geometries for Small Industrial Gas Turbines

Additive manufacturing is now a mainstream technology and can be utilized to rapidly develop and test turbine airfoil cooling networks. This paper reports on an ongoing effort to integrate advanced internal cooling architectures in a realistic blade profile for test in a high-speed cascade. Airfoil cooling schemes were developed using reduced order modeling and computer aided design. However, the additive manufacturing impacts on cooling channel flow performance were unknown. Test articles consisting of typical cooling features and networks were derived from the designs and flow proved to identify additive manufacturing impacts on performance and develop guidelines to mitigate these impacts.

additive manufacturing↗
DOE OSTI2026

A Physics-Based Digital Twin for Wave Elevation and Seabed Moment Estimation of Offshore Monopiles: Preprint

In this work, we present a proof of concept of a physics-based digital twin for a monopile structure (with overhead inertia) subjected to wave loading. The digital twin is formulated using reduced-order models derived from first principles and combined with a Kalman filter for state estimation. The proposed framework estimates the monopile top motion, the wave elevation, and the section forces and moments along the pile using primarily acceleration measurements at the monopile top. Key innovations include the use of a hydrodynamic shape function to represent distributed wave loading in a compact and computationally efficient manner, and the introduction of a shaping filter to augment the state-space with wave kinematics. Synthetic measurement data are generated using OpenFAST and used as a reference to assess the performance of the digital twin. Results demonstrate that the wave elevation can be accurately reconstructed without direct sea-state measurements as long as the wave regime is inertia-dominated. Under the ideal tested conditions, the total hydrodynamic force and sea-bed bending moment are estimated with relative errors on the order of 1% and correlation coefficients exceeding 96%. Future work will evaluate the estimator's performance under operational uncertainties and more complex loading conditions.

17 WIND ENERGY↗
DOE OSTI2023

GPLaSDI: Gaussian Process Latent Space Dynamics Identification

GPLaSDI is a software that introduces GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the reduced order model (ROM) predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. We demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error.

Choi, Youngsoo↗
DOE OSTI2021

Grey-box modeling and application for building energy simulations - A critical review

Grey-box modeling, as one of the three fundamental modeling techniques for building energy models, has many advantages compared with black-box modeling and white-box modeling. Additionally, it has been widely applied to solve problems of building technologies, such as building load estimation, control and optimization, and building-grid integration. However, a thorough review of grey-box modeling is not available. This review study systematically investigated various aspects of grey-box modeling for buildings. First, the fundamental aspects of grey-box modeling are presented, including the theoretical background, modeling of building elements, modeling order, modeling diagram, and order reduction. Second, the detailed modeling approaches are discussed. Third, multiple applications of grey-box modeling are investigated for building energy domain, which are categorized into the following groups: heat dynamics analysis, thermal load estimation, building control and optimization, district/urban scale energy modeling, and building-grid integration. Finally, the available software packages for grey-box modeling are compared. Overall, the challenges of using grey-box modeling can be summarized as follows: (1) the theoretical limitations and assumptions of grey-box modeling are unclear; (2) grey-box model naming convention and structure are confusing; (3) grey-box model creation is vague; (4) suitable applications of grey-box models are unknown; and (5) grey-box models lack unified software solutions for wider adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1993

A consistent model reduction of measured modal parameters for reduced-order active control

The problem of synthesizing reduced-order linear models of vibrating structures for the design of fixed-order dynamic feedback control is investigated. The present technique builds on a recently developed procedure for constructing an objective set of mass and stiffness matrices from measured modal parameters that are akin to the Craig-Bampton synthesized ones obtained from finite element models. The constructed mass and stiffness matrices are determined directly from the identification of experimental data, however, rather than through correlation or reconciliation of a finite element model. A model truncation criterion is then applied to the identified minimum-order mass and stiffness model to satisfy certain observability/controllability requirements for the reduced model. Numerical examples illustrate the effectiveness of the proposed technique for synthesizing reduced-order controllers from system realizations of experimental data. The dynamic performance of the resulting closed-loop models is assessed using the known full-order structural dynamics and compared with existing model reduction techniques.

Alvin, K. F.↗
NASA NTRS1990

Projection and assembly method for multibody component model reduction

The problem addressed is that of obtaining reduced-order component models for use in simulating the dynamics of a multibody system. In certain cases, nonlinear system models may be constructed using linear dynamic models for each component, but allowing large angle motion between components. Without some form of model reduction, system models constructed in this manner may be too large for use in control system design and simulation trades. This paper analyzes one method of component model reduction that allows systems level requirements (e.g., capturing the effect of body 1 reaction wheel noise on body 2 camera pointing) to aid in the selection of the reduced-order component models. Briefly stated, important modes are selected at the system level and projected onto the components, and reduced-order components are then assembled into a reduced-order system model that retains the projected modes.

Bernard, Douglas E.↗
DOE OSTI2024

Bayesian reduced-order deep learning surrogate model for dynamic systems described by partial differential equations

We propose a reduced-order deep-learning surrogate model for dynamic systems described by time-dependent partial differential equations. This method employs space–time Karhunen–Loève expansions (KLEs) of the state variables and space-dependent KLEs of space-varying parameters to identify the reduced (latent) dimensions. Subsequently, a deep neural network (DNN) is used to map the parameter latent space to the state variable latent space. An approximate Bayesian method is developed for uncertainty quantification (UQ) in the proposed KL-DNN surrogate model. The KL-DNN method is tested for the linear advection–diffusion and nonlinear diffusion equations, and the Bayesian approach for UQ is compared with the deep ensembling (DE) approach, commonly used for quantifying uncertainty in DNN models. It was found that the approximate Bayesian method provides a more informative distribution of the PDE solutions in terms of the coverage of the reference PDE solutions (the percentage of nodes where the reference solution is within the confidence interval predicted by the UQ methods) and log predictive probability. The DE method is found to underestimate uncertainty and introduce bias. For the nonlinear diffusion equation, we compare the KL-DNN method with the Fourier Neural Operator (FNO) method and find that KL-DNN is 10% more accurate and needs less training time than the FNO method.

97 MATHEMATICS AND COMPUTING↗