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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Deep-Learning-Based Koopman Modeling for Online Control Synthesis of Nonlinear Power System Transient Dynamics

Power system stability and control have become more challenging due to the increasing uncertainty associated with renewable generation. Here, the performance of conventional control is highly driven by the physics-based offline-developed dynamic models that can deviate from the actual system characteristics under different operating conditions and/or configurations. Data-driven approaches based on online measurements can be a better solution to addressing these issues by capturing real-time operation conditions. This article describes a novel fully data-driven probabilistic framework to derive a linear representation of postcontingency grid dynamics and online prescribe control based on the derived model to enhance transient stability. The complex nonlinear power system dynamics is approximated by a linear model by using multiple neural network modules that infer distributions of the observations and introducing a Koopman layer to sample possible Koopman linear models from the inferred distributions. The trained model features linearity that can be easily incorporated into the existing linear control design paradigm and ease the controller design process. The effectiveness of Koopman-based control designs is validated through comparative case studies, which demonstrate increased prediction accuracy and control performance when applied to a power system with heterogeneous generator dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Nonlinear Virtual Inertia Control of WTGs for Enhancing Primary Frequency Response and Suppressing Drivetrain Torsional Oscillations

Virtual inertia controllers (VICs) for wind turbine generators (WTGs) have been recently developed to compensate the reduction of inertia in power systems. However, VICs can induce drivetrain torsional oscillations of WTGs. This paper addresses this issue and develops a novel nonlinear VIC based on objective holographic feedback theory and the definition of a completely controllable system of Brunovsky type. Simulation results under various scenarios demonstrate that the proposed technique outperforms existing VICs in terms of enhancement of system frequency nadir, suppression of WTG drivetrain torsional oscillations, fast and smooth recovery of WTG rotor speed to the original maximum power point (MPP) before the disturbance as well as preventing secondary frequency dip caused by traditional VIC. The proposed technique is also able to adaptively coordinate multiple WTGs to enhance the frequency support and the dynamic performance of each WTG.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Propagating Parameter Uncertainty in Power System Nonlinear Dynamic Simulations Using a Koopman Operator-Based Surrogate Model

In this work, we propose a Koopman operator-based surrogate model for propagating parameter uncertainties in power system nonlinear dynamic simulations. First, we augment a priori known state-space model by reformulating parameters deemed uncertain as pseudo-state variables. Then, we apply the Koopman operator theory to the resulting state-space model and obtain a linear dynamical system model. This transformation allows us to analyze the evolution of the system dynamics through its Koopman eigenfunctions, eigenvalues, and modes. Of particular importance for this letter, the obtained linear dynamical system is a surrogate that enables the evaluation of parameter uncertainties by simply perturbing the initial conditions of the Koopman eigenfunctions associated with the pseudo-state variables. Simulations carried out on the New England test system reveal the excellent performance of the proposed method in terms of accuracy and computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Applications of Lifted Nonlinear Cuts to Convex Relaxations of the AC Power Flow Equations

Here, we demonstrate that valid inequalities, or lifted nonlinear cuts (LNC), can be projected to tighten the Second Order Cone (SOC), Convex DistFlow (CDF), and Network Flow (NF) relaxations of the AC Optimal Power Flow (AC-OPF) problem. We conduct experiments on 38 cases from the PGLib-OPF library, showing that the LNC strengthen the SOC and CDF relaxations in 100% of the test cases, with average and maximum differences in the optimality gaps of 6.2% and 17.5% respectively. The NF relaxation is strengthened in 46.2% of test cases, with average and maximum differences in the optimality gaps of 1.3% and 17.3% respectively. We also study the trade-off between relaxation quality and solve time, demonstrating that the strengthened CDF relaxation outperforms the strengthened SOC formulation in terms of runtime and number of iterations needed, while the strengthened NF formulation is the most scalable with the lowest relaxation quality improvement due to these LNC.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Direct Nonlinear Approximation for Security Region Boundary of Integrated Energy Systems: A Polynomial Chaos Expansion Solution

The strong interdependence of electricity, gas, and heating systems can facilitate fault propagation within integrated energy systems (IESs), posing significant challenges to secure operation. This paper proposes a polynomial chaos expansion (PCE)-based approximation method to accurately characterize the IES security region boundary (IES–SRB). By integrating the Karush-Kuhn-Tucker conditions with PCE theory, the IES-SRB approximation problem is reformulated as a set of nonlinear equations concerning the approximation coefficients. Using the Galerkin projection method, these equations are further transformed into a system of projection equations that govern the polynomial approximation coefficients in the IES-SRB approximation. To reduce computational complexity while maintaining high approximation accuracy, a piecewise polynomial approximation method is proposed. Numerical studies on the E39-G20-H6 and E118-G96-H52 IES test systems demonstrate that the proposed method can accurately and effectively construct IES security regions.

Wu, Chenghao [Northeast Electric Power University]↗
DOE OSTI2025

Canopy Structure Exhibits Linear and Nonlinear Links to Biome‐Level Maximum Light Use Efficiency

Maximum light use efficiency (ε max ) represents a plant's capacity to convert light into carbon during photosynthesis. Although prior studies have explored ε max variations between sunlit and shaded leaves or its temporal ties to canopy structure, the spatial relationship between biome-level ε max (ε biome ) and biome structure remains poorly understood. We analysed data from 320 eddy covariance sites (~855 site-years) with satellite-derived near-infrared reflectance of vegetation (NIRv) and leaf area index (LAI). We introduced NIRvN (NIRv/LAI) to isolate architectural effects from leaf quantity. Site-level ε max was calculated and aggregated by biome to derive ε biome . Results show ε biome rises nonlinearly with NIRv and LAI, saturating at high LAI, with crops and tropical evergreen forests deviating from this trend. Conversely, ε biome decreases linearly with increasing NIRvN, indicating that biomes with greater NIR scattering efficiency exhibit lower ε biome . These results enhance understanding of structural influences on carbon uptake across global biomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Experimental Performance of a Nonlinear Control Strategy to Regulate Temperature of a High-Temperature Solar Reactor

Abstract Despite the significant potential of solar thermochemical process technology for storing solar energy as solid-state solar fuel, several challenges have made its industrial application difficult. It is important to note that solar energy has a transient nature that causes instability and reduces process efficiency. Therefore, it is crucial to implement a robust control system to regulate the process temperature and tackle the shortage of incoming solar energy during cloudy weather. In our previous works, different model-based control strategies were developed namely a proportional integral derivative controller (PID) with gain scheduling and adaptive model predictive control (MPC). These methods were tested numerically to regulate the temperature inside a high-temperature tubular solar reactor. In this work, the proposed control strategies were experimentally tested under various operation conditions. The controllers were challenged to track different setpoints (500 °C, 1000 °C, and 1450 °C) with different amounts of gas/particle flowrates. Additionally, the flow controller was tested to regulate the reactor temperature under a cloudy weather scenario. The ultimate goal was to produce 5 kg of reduced solar fuel magnesium manganese oxide (MgMn2O4) successfully, and the controllers were able to track the required process temperature and reject disturbances despite the system's strong nonlinearity. The experimental results showed a maximum error in the temperature setpoint of less than 0.5% (6 °C), and the MPC controller demonstrated superior performance in reducing the control effort and rejecting disturbances.

Energy & Fuels↗
DOE OSTI2024

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

Complex structural systems deployed for aerospace, civil, or mechanical applications must operate reliably under varying operational conditions. Structural health monitoring (SHM) systems help ensure the reliability of these systems by providing continuous monitoring of the state of the structure. SHM relies on synthesizing measured data with a predictive model to make informed decisions about structural states. However, these models—which may be thought of as a form of a digital twin—need to be updated continuously as structural changes (e.g., due to damage) arise. We propose an uncertainty-aware machine learning model that enforces distance preservation of the original input state space and then encodes a distance-aware mechanism via a Gaussian process (GP) kernel. The proposed approach leverages the spectral-normalized neural GP algorithm to combine the flexibility of neural networks with the advantages of GP, subjected to structure-preserving constraints, to produce an uncertainty-aware model. This model is used to detect domain shift due to structural changes that cannot be observed directly because they may be spatially isolated (e.g., inside a joint or localized damage). This work leverages detection theory to detect domain shift systematically given statistical features of the prediction variance produced by the model. The proposed approach is demonstrated on a nonlinear structure being subjected to damage conditions. In conclusion, it is shown that the proposed approach is able to rely on distances of the transformed input state space to predict increased variance in shifted domains while being robust to normative changes.

Algorithms↗
DOE OSTI2022

Nonlinear Model Predictive Control Based on Real-Time Iteration Scheme for Wave Energy Converters Using WEC-Sim

One of several challenges that wave energy technologies face is their inability to generate electricity cost-competitively with other grid-scale energy generation sources. Several studies have identified two approaches to lower the levelised cost of electricity: reduce the cost over the device's lifetime or increase its overall electrical energy production. Several advanced control strategies have been developed to address the latter. However, only a few take into account the overall efficiency of the power take-off (PTO) system, and none of them solve the optimisation problem that arises at each sampling time on real-time. In this paper, a detailed Nonlinear model predictive control (NMPC) approach based on the real-time iteration (RTI) scheme is presented, and the controller performance is evaluated using a time-domain hydrodynamics model (WEC-Sim). The proposed control law incorporates the PTO system's efficiency in a control law to maximise the energy extracted. The study also revealed that RTI-NMPC clearly outperforms a simple resistive controller.

model predictive control↗
DOE OSTI2022

Thin liquid film as an optical nonlinear-nonlocal medium and memory element in integrated optofluidic reservoir computer

Understanding light–matter interaction lies at the core of our ability to harness physical effects and to translate them into new capabilities realized in modern integrated photonics platforms. Here, we present the design and characterization of optofluidic components in an integrated photonics platform and computationally predict a series of physical effects that rely on thermocapillary-driven interaction between waveguide modes and topography changes of optically thin liquid dielectric film. Our results indicate that this coupling introduces substantial self-induced phase change and transmittance change in a single channel waveguide, transmittance through the Bragg grating waveguide, and nonlocal interaction between adjacent waveguides. We then employ the self-induced effects together with the inherent built-in finite relaxation time of the liquid film, to demonstrate that the light-driven deformation can serve as a reservoir computer capable of performing digital and analog tasks, where the gas–liquid interface operates both as a nonlinear actuator and as an optical memory element.

42 ENGINEERING↗
DOE OSTI2026

Enabling robust, stable, and accurate nonlinear optical measurements from squeezed light generated in hot rubidium vapor

The feasibility of nonlinear optical (NLO) imaging and spectroscopy using low intensity quantum states of light including entangled photon and squeezed light sources, such as those driven by two-photon absorption (TPA), has been a topic of ongoing debate. An unambiguous identification of the appreciable quantum advantage in such quantum light applications could enable NLO imaging of biological samples without photodegradation and phototoxicity. Recently, we have constructed a two-mode squeezed light source based on four-wave mixing in 85Rb vapor, which is capable of 7.9 dB of intensity-difference squeezing (IDS), corresponding to 8.7 dB upon electronic noise correction. In this talk, we discuss the stability of our system, including implementation details on achieving and maintaining 8.7 dB of IDS for several hours, and ensuring the two spatially multimode beams are properly overlapped in a sample with minimal optical loss.

Allen, Harry [ORNL] (ORCID:0000000190253914)↗
DOE OSTI2024

ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model

Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here, we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as the target to enable the discovery of protein materials with superior mechanical properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Topology-imprinting in nonlinear metasurfaces

Flat optical components, or metasurfaces, have transformed optical imaging, data storage, information processing, and biomedical applications by providing unprecedented control over light-matter interactions. These nano-engineered structures enable compact, multidimensional manipulation of light’s amplitude, phase, polarization, and wavefront, producing scalar and vector beams with unique properties such as orbital angular momentum and knotted topologies. This flexibility has potential applications in optical communication and imaging, particularly in complex environments such as atmospheric turbulence and undersea scattering. However, designing metasurfaces for shorter wavelengths, such as visible and ultraviolet light, remains challenging due to fabrication limitations and material absorption. Here, we introduce an innovative concept called topology imprinting using judiciously designed all-dielectric nonlinear optical metasurfaces to replicate desired waveforms at fundamental and harmonic frequencies, opening promising avenues for advanced photonic applications.

42 ENGINEERING↗
DOE OSTI2021

A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations

In this work, we propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional neural networks have proven to be highly advantageous for compressing data arising from systems demonstrating a slow-decaying Kolmogorov n-width. However, these networks are restricted to data on structured meshes. Unstructured meshes are often required for performing analyses of real systems with complex geometry. Our custom graph convolution operators based on the available differential operators for a given spatial discretization effectively extend the application space of deep convolutional autoencoders to systems with arbitrarily complex geometry that are typically discretized using unstructured meshes. We propose sets of convolution operators based on the spatial derivative operators for the underlying spatial discretization, making the method particularly well suited to data arising from the solution of partial differential equations. We demonstrate the method using examples from heat transfer and fluid mechanics and show better than an order of magnitude improvement in accuracy over linear methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Learning Nonlinear Reduced Models from Data with Operator Inference

This review discusses Operator Inference, a nonintrusive reduced modeling approach that incorporates physical governing equations by defining a structured polynomial form for the reduced model, and then learns the corresponding reduced operators from simulated training data. The polynomial model form of Operator Inference is sufficiently expressive to cover a wide range of nonlinear dynamics found in fluid mechanics and other fields of science and engineering, while still providing efficient reduced model computations. The learning steps of Operator Inference are rooted in classical projection-based model reduction; thus, some of the rich theory of model reduction can be applied to models learned with Operator Inference. This connection to projection-based model reduction theory offers a pathway toward deriving error estimates and gaining insights to improve predictions. Furthermore, through formulations of Operator Inference that preserve Hamiltonian and other structures, important physical properties such as energy conservation can be guaranteed in the predictions of the reduced model beyond the training horizon. This review illustrates key computational steps of Operator Inference through a large-scale combustion example.

Mechanics↗
DOE OSTI2024

Assessing Nonlinear Polarization in Electrochemical Cells using AC Impedance Spectroscopy

AC impedance spectroscopy is widely used to evaluate performance limitations in energy storage and conversion devices (e.g., batteries, supercapacitors, and fuel cells). This work shows that integrating the resistive elements in an equivalent circuit as functions of steady-state current enables one to recover overpotentials associated with different processes (e.g., ion migration, charge transfer, and diffusion) in nonlinear electrochemical power supplies. Closed form expressions for diffusion overpotentials are derived using this method for transmissive and reflective boundary conditions and three electrode symmetries (planar, cylindrical, and spherical). Discussion is also extended to macroscopically homogenous porous electrodes which are relevant for most real-world devices. Overall, the approach described herein is a powerful tool to identify rate-limiting steps and guide material/component design.

25 ENERGY STORAGE↗
DOE OSTI2020

Study on Intelligent Compaction-Equipment Logistics Scheduling and Propagation Characteristics of Vibration Wave in Nonlinear Systems with Multistability Based on Field Test

The equipment scheduling and propagation characteristics of vibration wave from vibratory roller ⟶ filling material nonlinear systems with multistability are the core problems of subgrade intelligent construction technology, and the logistics scheduling of the equipment is directly related to the construction efficiency. Aiming at the shortages, one typical subgrade located at the Gu’an station of Beijing-Xiong’an city railway is selected to research and finish the field tests; some findings are shown as follows: first, some valuable suggestions about the logistics scheduling of intelligent equipment are proposed, which can break the barriers between the organizations and improve construction efficiency; second, when the vibration wave propagates from the vibratory roller ⟶ surface of filling material ⟶ different buried depths of filling material, the peak acceleration of vibration wave gradually decreases and is hyperbolic distribution approximately. At the same time, the sensitive of attenuation is shown as follows: Z R 2 >0.9), and the concrete functional relationship among different amplitudes of harmonics can be summarized as y = Ae −BX ; fourth, the vibration energy is mainly concentrated near 10–30 Hz in the vibratory roller, but when the vibration wave propagates from vibratory roller⟶filling material, the vibration energy gradually decreases with the increase in depth, and the marginal spectrum gradually changes from one peak to two peaks, that is, 30–50 Hz and 50–100 Hz; fifth, the vibration energy in the vibrational wheel is distributed averagely in the compaction process, and the effective compaction time is two seconds, which will be helpful for revealing the propagation characteristics of vibration wave, optimizing the compaction quality control models and providing some support for the development of intelligent compaction theory of railway subgrade.

Han, Yixuan↗
DOE OSTI2024

A set of test problems for nonlinear optimization

The software is a set of test problems for nonlinear optimization algorithms, including subroutines such as linear algebra routines and automatic differentiation algorithms. The test problems come from chemical engineering open literature, and describe optimization tasks related to the design and operation of processes such as carbon capture, Hydrogen production, heat exchange, and distillation.

Parker, Robert↗