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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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NASA NTRS2006

Structure Detection of Nonlinear Aeroelastic Systems with Application to Aeroelastic Flight Test Data

This viewgraph presentation reviews the applicability of NARMAX structure detection to aeroelastic systems. In conclusion, the simulation results demonstrate bootstrap approach for structure computation of aircraft structural stiffness provided a high rate of true model selection: 1. T-test and stepwise regression methods had difficulty providing accurate results 2. Work contributes to understanding of the use of structure detection for modelling and identification of aerospace systems. 3. Limitation of model complexity that can be studied with these structure computation techniques 4. Result of the large number of candidate terms, for a given model order, and the data length required to guarantee convergence 5. Another approach to structure computation problem uses a least absolute shrinkage and selection operator (LASSO)

Kukreja, Sunil L.↗

DOE OSTI2026

Higher-order LaSDI: Reduced order modeling with multiple time derivatives

Solving complex partial differential equations (PDEs) is essential across scientific disciplines but often requires numerical models that can be prohibitively expensive in time-sensitive applications. Reduced-order models (ROMs) address this challenge by exploiting low-dimensional structure to create fast approximations. The Latent Space Dynamics Identification (LaSDI) framework has demonstrated success in learning ROMs for parameterized PDE families, but remains limited to first-order systems. Here, in this paper, we propose Higher-Order LaSDI (HLaSDI), which extends the LaSDI framework to PDEs with arbitrary order of time derivatives. This generalization significantly expands the applicability of LaSDI-based methods to systems previously outside their scope, including hyperbolic PDEs. We demonstrate HLaSDI’s accuracy and efficiency on several linear and nonlinear benchmark problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

MASK4 Test Campaign for Sandia WaveBot Device

This data and report details the findings from a wave tank test focused on production of useful work of a wave energy converter (WEC) device. The experimental system and test were specifically designed to validate models for power transmission throughout the WEC system. Additionally, the validity of co-design informed changes to the power take-off (PTO) were assessed and shown to provide the expected improvements in system performance. These data describe the "MASK4" wave tank test of the Sandia WaveBot device. The WaveBot device has been tested a number of times in different permutations at the US Navy's Maneuvering and Sea Keeping (MASK) basin. Each test in this series is referred to as MASK1, MASK2, etc. The WaveBot device was first tested in one degree of freedom (heave) in 2016. This MASK1 test focused primarily on system identification and modeling. After MASK1, major modifications were performed to improve the overall real-time control and measurement system, improve the heave drive train, and add surge and pitch degrees of freedom. The second set of testing, which was broken up in to two stages: MASK2A and MASK2B, focused on bench testing and closed-loop control performance as well as nonlinear modeling. MASK3 then focused on multi-input, multi-output modeling and control for maximization of electrical power. The attached report presents the results from MASK4, which focuses on detailed modeling of the power conversion chain and validation co-design principles by way of the introduction of a magnetic spring. The test log, report, and data from the MASK4 test of the WaveBot augmented with a tunable magnetic spring. Processing codes can be found at the Github link below.

16 TIDAL AND WAVE POWER↗

NASA NTRS1990

Modification of the background flow by roll vortices

Use of observed wind profiles, such as those obtained from ascent or descent aircraft soundings, for the identification of the expected roll modes is hindered by the fact that these modes are able to modify the wind profiles. When such modified wind profiles are utilized to estimate the critical values of the dynamic and thermodynamic forcing rates, large errors in the preferred orientation angles and aspect ratios of the rolls may result. Nonlinear analysis of a 14 coefficient spectral model of roll circulations shows that the primary modification of the background wind is the addition of a linear component. When the linear profile having the correct amount of shear is subtracted from the observed cross-roll winds, then the pre-roll wind profile can be estimated. A preliminary test of this hypothesis is given for a case in which cloud streets were observed during FIRE.

Shirer, Hampton N.↗

NASA NTRS2022

Adaptive Force Control for Small Celestial Body Sampling

An adaptive force control algorithm for small celestial body sampling for a variety of surface properties is developed. The control algorithm consists of an adaptive controller combined with feedback linearization. When a spacecraft makes contact with the surface, it must maintain a desired contact force in order to capture a sample. The properties of the surface are unknown or uncertain before contact with the surface is made. The adaptive controller performs system identification online to create an input-output model of the feedback linearized system. From the input-output model a block observable canonical form is realized and the control input is determined by model predictive control (MPC) to maintain a desired contact force in spite of the unknown surface properties. The approach is applied to a variety of surface properties with linear and nonlinear contact models.

Quadrelli, Marco M↗

NASA NTRS2000

Wormhole Formation in RSRM Nozzle Joint Backfill

The RSRM nozzle uses a barrier of RTV rubber upstream of the nozzle O-ring seals. Post flight inspection of the RSRM nozzle continues to reveal occurrence of "wormholes" into the RTV backfill. The term "wormholes", sometimes called "gas paths", indicates a gas flow path not caused by pre-existing voids, but by a little-understood internal failure mode of the material during motor operation. Fundamental understanding of the mechanics of the RSRM nozzle joints during motor operation, nonlinear viscoelastic characterization of the RTV backfill material, identification of the conditions that predispose the RTV to form wormholes, and screening of candidate replacement materials is being pursued by a joint effort between Thiokol Propulsion, NASA, and the Army Propulsion & Structures Directorate at Redstone Arsenal. The performance of the RTV backfill in the joint is controlled by the joint environment. Joint movement, which applies a tension and shear load on the material, coupled with the introduction of high pressure gas in combination create an environment that exceeds the capability of the material to withstand the wormhole effect. Little data exists to evaluate why the material fails under the modeled joint conditions, so an effort to characterize and evaluate the material under these conditions was undertaken. Viscoelastic property data from characterization testing will anchor structural analysis models. Data over a range of temperatures, environmental pressures, and strain rates was used to develop a nonlinear viscoelastic model to predict material performance, develop criteria for replacement materials, and quantify material properties influencing wormhole growth. Three joint simulation analogs were developed to analyze and validate joint thermal barrier (backfill) material performance. Two exploratory tests focus on detection of wormhole failure under specific motor operating conditions. A "validation" test system provides data to "validate" computer models and predictions. Finally, two candidate replacement materials are being screened and "validated" using the developed test systems.

Stevens, J.↗

DOE OSTI2023

Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems

We consider a high-dimensional nonlinear computational model of a dynamical system, parameterized by a vector-valued control parameter, in the presence of uncertainties represented by an uncontrolled parameter modeled by a vector-valued random variable, and possibly with stochastic excitation. The objective is to construct a statistical surrogate model where the input is any deterministic value of the control parameter, and the output is a vector-valued observation of the computational model, which is a random vector whose probability measure is updated using a target dataset. To construct this statistical surrogate model, the stochastic response of the computational model must be built, which is a vector-valued time-discretized stochastic process in high dimension, depending on the control parameter. It is assumed that the computational cost of a single evaluation of the deterministic model is high. For the probabilistic updating, we consider a subset of the components of the observation of the computational model, defined as the “identification observation” of the computational model, for which a small target dataset is available. Therefore, the target dataset is associated with partial observability, corresponding to an incomplete data case. Given a prior probability model of the random control and uncontrolled parameters, a training dataset is constructed, consisting of realizations of the random triplet composed of the stochastic response, the random identification observation, and the random control parameter. Since the computational cost of a single evaluation of the deterministic model is assumed to be large, the training dataset is also of small size. The main challenges in this problem are the high dimensionality, partial observability leading to incomplete data in the target dataset for the identification observation of the computational model (which is not sufficient to identify the computational stochastic responses), and the availability of a small training dataset. To address these challenges, we propose a methodology based on statistical methods for constructing necessary reduced representations, direct probabilistic learning under constraints using probabilistic learning on manifolds (PLoM) constrained by the target dataset, and the use of a weak formulation of the Fourier transform of probability measures. Statistical conditioning is also employed to explore the learned dataset. The constructed predictive statistical surrogate model can be implemented in the context of online computation. Here, we apply this approach to a problem of nonlinear stochastic dynamics in high dimensions within the framework of deformable solids mechanics.

Engineering↗

DOE OSTI2016

Time dependent mechanical modeling for polymers based on network theory

Despite of a lot of attempts during recent years, complex mechanical behaviour of polymers remains incompletely modelled, making industrial design of structures under complex, cyclic and hard loadings not totally reliable. The non linear and dissipative viscoelastic, viscoplastic behaviour of those materials impose to take into account non linear and combined effects of mechanical and thermal phenomena. In this view, a visco-hyperelastic, viscoplastic model, based on network description of the material has recently been developed and designed in a complete thermodynamic frame in order to take into account those main thermo-mechanical couplings. Also, a way to account for coupled effects of strain-rate and temperature was suggested. First experimental validations conducted in the 1D limit on amorphous rubbery like PMMA in isothermal conditions led to pretty goods results. In this paper a more complete formalism is presented and validated in the case of a semi crystalline polymer, a PA66 and a PET (either amorphous or semi crystalline) are used. Protocol for identification of constitutive parameters is described. It is concluded that this new approach should be the route to accurately model thermo-mechanical behaviour of polymers using a reduced number of parameters of some physical meaning.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2015

Stress measurement in thick plates using nonlinear ultrasonics

In this paper the interaction between nonlinear ultrasonic characteristics and stress state of complex loaded thick steel plates using fundamental theory of nonlinear ultrasonics is investigated in order to measure the stress state at a given cross section. The measurement concept is based on phased array placement of ultrasonic transmitter-receiver to scan three angles of a given cross section using Rayleigh waves. The change in the ultrasonic data in thick steel plates is influenced by normal and shear stresses; therefore, three measurements are needed to solve the equations simultaneously. Different thickness plates are studied in order to understand the interaction of Rayleigh wave penetration depth and shear stress. The purpose is that as the thickness becomes smaller, the shear stress becomes negligible at the angled measurement. For thicker cross section, shear stress becomes influential if the depth of penetration of Rayleigh wave is greater than the half of the thickness. The influences of plate thickness and ultrasonic frequency on the identification of stress tensor are numerically studied in 3D structural geometry and Murnaghan material model. The experimental component of this study includes uniaxial loading of the plate while measuring ultrasonic wave at three directions (perpendicular, parallel and angled to the loading direction). Instead of rotating transmitter-receiver pair for each test, a device capable of measuring the three angles is designed.

42 ENGINEERING↗

DOE OSTI2024

KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. Finally, this versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2007

On Problems Associated with Modeling Wing-Tail Configurations from Wind Tunnel Data

This paper considers factors that contribute to poor identification of unsteady aerodynamics from wind tunnel data for an airliner configuration. One approach to modeling a wing-tail configuration is considered and applied to both steady and large-amplitude forced pitch oscillation wind tunnel data taken over a wide range of angles of attack but a limited range of amplitude and frequencies. The identified models fit the measured data well but in some cases with inaccurate parameters. Only limited conclusions can be drawn from analysis of the current data set until further experiments can be performed to resolve the identification issues. The analysis of measured and simulated data provides some insights and guidance on how an effective experiment may be designed for wing-tail configurations with nonlinear unsteady aerodynamics.

Murphy, Patrick C.↗

NASA NTRS1996

An efficient approach to ARMA modeling of biological systems with multiple inputs and delays

This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.

Non-NASA Center↗

DOE OSTI2022

On Projection of Safe Operation for Grid-Following Inverters - Grid Parameter Estimation

This work investigates the projection of safe operation for grid-following inverters using a reference model. This work applies recursive-least square and model reference adaptive estimation techniques to estimate the unknown grid parameters. The estimated grid parameters are used in a dynamic reference model to project the safe operation of the inverter. This work also demonstrates that the controller nonlinearity and parameter variations can cause unsafe operations, such as unexpected instability at low power levels, namely hidden mode of instability. This instability issue in the nominal operating range may occur for given control parameters in a weak-grid condition. The dynamic reference model for PQ-controlled inverters is applied to analyze this instability issue using the estimated grid parameters. Both adaptive identification techniques can estimate the unknown parameters accurately, and the dynamic reference model can safeguard inverters from an unsafe operation, i.e., hidden instability, by examining the incoming new power setpoints before engaging them to the local controller. The findings are experimentally verified using a small-scale two-level 208 V, 5 kVA inverter feeding a 12 kW NHR 9410 power grid emulator.

Hossen, Tareq↗

DOE OSTI2026

Physics vs structure: A systematic benchmark of learning strategies for multi-zone building thermal dynamics

Recent advances in physics-informed and data-driven machine learning promise improved thermal models for advanced building control, yet there is limited quantitative evidence on when added physics structure and architectural complexity are beneficial. Here, this work presents a systematic benchmark of five representative system identification methods for modeling multi-zone building thermal dynamics: linear state-space models, multi-layer perceptrons, neural state-space models, neural ordinary differential equations, and physically-consistent neural networks. The methods are evaluated across multiple data regimes and zone coupling strategies. Using a high-fidelity multi-zone commercial building emulator, we examine short-term and long-term prediction accuracy, computational efficiency, and ease of development. Our results reveal critical trade-offs between prediction performance, model complexity, and physical consistency. We demonstrate that decoupled, nonlinear black-box models consistently outperform coupled physics-constrained architectures in both predictive accuracy and out-of-distribution robustness in majority of the test cases for the building type considered in the study. Our findings quantify the cost of complexity in building thermal modeling and provide concrete, actionable, scenario-based guidelines for selecting model classes for control-oriented applications.

Building thermal modeling↗

DOE OSTI2022

Identification of Acoustic Wave Signatures in the Ionosphere From Conventional Surface Explosions Using MF/HF Doppler Sounding

Here, we present an experiment to detect one ton TNT-equivalent chemical explosions using pulsed Doppler radar observations of isodensity layers in the ionospheric E region during two campaigns. The first campaign, conducted on 15 October 2019, produced potential detections of all three shots. The detections closely resemble the temporal and spectral properties predicted using the InfraGA ray tracing and weakly nonlinear waveform propagation model. Here the model predicts that within 6.5–7.25 min of each shot a waveform peaking between 0.9 and 0.4 Hz will impact the ionosphere at 100 km. As the waves pass through this region, they will imprint their signal on an isodensity layer, which is detectable using a Doppler radar operating at the plasma frequency of the isodensity. Within the time windows of each of the three shots in the first campaign, we detect enhanced wave activity peaking near 0.5 Hz. These waves were imprinted on the Doppler signal probing an isodensity layer at 2.785 MHz near 100 km altitude. Despite these detections, the method appears to be unreliable as none of the six shots from the second campaign, conducted on 10 July 2020 were detected. The observations from this campaign were characterized by an increased acoustic noise environment in the microbarom band and persistent scintillation on the radar returns. These effects obscured any detectable signal from these shots and the baseline noise was well above the detection levels of the first campaign.

47 OTHER INSTRUMENTATION↗

DOE OSTI2013

The non-random walk of chiral magnetic charge carriers in artificial spin ice

The flow of magnetic charge carriers (dubbed magnetic monopoles) through frustrated spin ice lattices, governed simply by Coulombic forces, represents a new direction in electromagnetism. Artificial spin ice nanoarrays realise this effect at room temperature, where the magnetic charge is carried by domain walls. Control of domain wall path is one important element of utilizing this new medium. By imaging the transit of domain walls across different connected 2D honeycomb structures we contribute an important aspect which will enable that control to be realized. Although apparently equivalent paths are presented to a domain wall as it approaches a Y-shaped vertex from a bar parallel to the field, we observe a stark non-random path distribution, which we attribute to the chirality of the magnetic charges. These observations are supported by detailed statistical modelling and micromagnetic simulations. The identification of chiral control to magnetic charge path selectivity invites analogy with spintronics.

36 MATERIALS SCIENCE↗

NASA NTRS1985

An adaptive learning control system for large flexible structures

The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

Thau, F. E.↗

DOE OSTI2019

Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation

Abstract Machine learning is receiving more attention in classical engineering fields, and in particular, recurrent neural networks (RNNs) coupled with ensemble regression tools have demonstrated the capability of modeling nonlinear dynamic processes. In Part I of this two‐article series, the Lyapunov‐based model predictive control (LMPC) method using a single RNN model and an ensemble of RNN models, respectively, was rigorously developed for a general class of nonlinear systems. In the present article, computational implementation issues of this new control method ranging from training of the RNN models, ensemble regression of the RNN models, and parallel computation for accelerating the real‐time model calculations are addressed. Furthermore, a chemical reactor example is used to demonstrate the implementation and effectiveness of these machine‐learning tools in LMPC as well as compare them with standard state‐space model identification tools.

Wu, Zhe↗