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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Neural Lyapunov Control for Power System Transient Stability: A Deep Learning-Based Approach

We report that power system control and transient stability analysis play essential roles in secure system operation. Control of power systems typically involves highly nonlinear and complex dynamics. Most of the existing works address such problems with additional assumptions in system dynamics, leading to a requirement for a complete and general solution. This paper, therefore, proposes a novel control framework for various power system control and stability problems leveraging a learning-based approach. The proposed framework includes a two-module structure that iteratively and jointly learns the candidate Lyapunov function and control law via deep neural networks in a learning module. Meanwhile, it guides the learning procedure towards valid results satisfying Lyapunov conditions in a falsification module. The introduced termination criteria ensure provable system stability. This control framework is verified through several studies handling different types of power system control problems. The results show that the proposed framework is generalizable and can simplify the control design for complex power systems with the stability guarantee and enlarged region of attraction.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Nonlinear inductive response of pinned superconducting vortices in artificial pinning sites

Using the time dependent Ginzburg-Landau equations, we simulate the inductive responses of a variety of thin-film systems containing patterned antidots with different sizes and shapes. The results for all shapes show that the kinetic inductance diverges as the applied current approaches a critical current that is below the BCS depairing current. In conclusion, exploiting the similarity of the observed current-voltage behavior to that of Josephson junctions, we obtain an empirical equation that well fits the inductivity curve as a function of the applied current.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Roadmap on thermodynamics and thermal metamaterials

Thermal metamaterials represent a transformative paradigm in modern physics, synergizing thermodynamic principles with metamaterial engineering to master heat flow at will. As next-generation technologies demand multi-scale thermal control, this field urgently requires systematic frameworks to unify its multidisciplinary advances. Curated through a global collaboration involving over 50 specialists across 25 subdisciplines, this review primarily summarizes two decades of advancements, ranging from theoretical breakthroughs to functional implementations. The review reveals groundbreaking innovations in heat manipulation through the exploration of both classical and non-classical transport regimes, topological thermal control mechanisms, and quantum-informed phonon engineering strategies. By bridging physical insights like non-Hermitian thermal dynamics and valleytronic phonon transport with cutting-edge applications, we demonstrate paradigm-shifting capabilities: environment-adaptive thermal cloaks, AI-optimized metamaterials, and nonlinear thermal circuits enabling heat-based computation. Experimental milestones include 3D thermal null media with reconfigurable invisibility and thermal designs breaking classical conductivity limits. Here, this collaborative effort establishes an indispensable roadmap for physicists, highlighting pathways to quantum thermal management, entropy-controlled energy systems, and topological devices. As thermal metamaterials transition from laboratory marvels to technological cornerstones, this work provides the foundational lexicon and design principles for the coming era of intelligent thermal matter.

heat conduction control↗
DOE OSTI2023

Spatio-temporally shaped deep UV laser source for ultrabright photocathodes using novel upconversion techniques (Final Report)

The goal of this project was to take a VUV laser system designed by Kapteyn-Murnane Labs (KMLabs)—the Hyperion VUV, and increase its functionality, efficiency, and applicability by further probing the fundamental physics taking place in the system and optimizing the system performance. More specifically, DOE accelerator applications—particularly for XFELs that require low-emittance electron beams, require precisely controlled ultrashort pulses in the deep-UV at 250-260 nm to drive the photocathode electron source. This is conventionally done using an ultrafast infrared laser, with subsequent stages of nonlinear upconversion in solid-state nonlinear-optical crystal media (principally BBO, which is commonly used for UV upconversion). However, maintaining high spatial mode beam quality, as well as precisely controlled temporal profile, has proven a persistent challenge. As a possible alternative to this approach, KMLabs has demonstrated a system that generates vacuum ultraviolet light (VUV) by upconverting pulses from an infrared ultrafast laser in a gas-filled hollow waveguide, driving a process of highly cascaded harmonic generation (HCHG). Because HCHG upconversion is done in a gas, rather than a solid, at high intensity in a guided-wave geometry, it retains an excellent beam spatial profile as well as a large spectral bandwidth that allows for control over the temperal structure of the pulse. A challenge, however, is in energy scaling of this HCHG technique to the ~1-20 μJ pulses required for photocathode applications. To-date, the pulse energies used in published HCHG work are sub-10-nJ. This project proposed to explore ways to generate higher-energy pulses using HCHG, as well as to investigate spectral and temporal shaping of the deep UV pulses by pulse shaping the infrared laser used to seed the process. Although we showed that our approach works for generating light at 259 nm, the total pulse energy achieved seems limited to approximately the same value as past work. However, the techniques we investigated may well have benefits for extending this source to much shorter wavelength output.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Isogeometric Bézier dual mortaring: The Kirchhoff–Love shell problem

Here in this paper we develop an isogeometric Bézier dual mortar method for coupling multi-patch Kirchhoff–Love shell structures. The proposed approach weakly enforces the continuity of the solution at patch interfaces through a dual mortar method and can be applied to both conforming and non-conforming discretizations. As the employed dual basis functions have local supports and satisfy the biorthogonality property, the resulting stiffness matrix is sparse. In addition, the coupling accuracy is optimal because the dual basis possesses the polynomial reproduction property. We also formulate the continuity constraints through the Rodrigues’ rotation operator which gives a unified framework for coupling patches that are intersected with G 1 continuity as well as patches that meet at a kink. Several linear and nonlinear examples demonstrated the performance and robustness of the proposed coupling techniques.

42 ENGINEERING↗
DOE OSTI2026

Latent space dynamics identification for interface tracking with application to shock-induced pore collapse

Capturing sharp, evolving interfaces remains a central challenge in reduced-order modeling, especially when data is limited and the system exhibits localized nonlinearities or discontinuities. Here, we propose LaSDI-IT (Latent Space Dynamics Identification for Interface Tracking), a data-driven framework that combines low-dimensional latent dynamics learning with explicit interface-aware encoding to enable accurate and efficient modeling of physical systems involving moving material boundaries. At the core of LaSDI-IT is a revised autoencoder architecture that jointly reconstructs the physical field and an indicator function representing material regions or phases, allowing the model to track complex interface evolution without requiring detailed physical models or mesh adaptation. The latent dynamics are learned through linear regression in the encoded space and generalized across parameter regimes using Gaussian process interpolation with greedy sampling. We demonstrate LaSDI-IT on the problem of shock-induced pore collapse in high explosives, a process characterized by sharp temperature gradients and dynamically deforming pore geometries. The method achieves relative prediction errors below 9% across the parameter space, accurately recovers key quantities of interest such as pore area and hot spot formation, and matches the performance of dense training with only half the data. This latent dynamics prediction was 10 6 times faster than the conventional high-fidelity simulation, proving its utility for multi-query applications. These results highlight LaSDI-IT as a general, data-efficient framework for modeling discontinuity-rich systems in computational physics, with potential applications in multiphase flows, fracture mechanics, and phase change problems.

Gaussian process↗
DOE OSTI2025

Density Functional Tight-Binding Enables Tractable Studies of Quantum Plasmonics

Routine investigations of plasmonic phenomena at the quantum level present a formidable computational challenge due to the large system sizes and ultrafast time scales involved. This Feature Article highlights the use of density functional tight-binding (DFTB), particularly its real-time time-dependent formulation (RT-TDDFTB), as a tractable approach to study plasmonic nanostructures from a quantum mechanical purview. We begin by outlining the theoretical framework and limitations of DFTB, emphasizing its efficiency in modeling systems with thousands of atoms over picosecond time scales. Applications of RT-TDDFTB are then explored in the context of optical absorption, nonlinear harmonic generation, and plasmon-mediated photocatalysis. We demonstrate how DFTB can reconcile classical and quantum descriptions of plasmonic behavior, capturing key phenomena such as size-dependent plasmon shifts and plasmon coupling in nanoparticle assemblies. Lastly, we showcase DFTB’s ability to model hot carrier generation and reaction dynamics in plasmon-driven H2 dissociation, underscoring its potential to model photocatalytic processes. Collectively, these studies establish DFTB as a powerful, yet computationally efficient tool to probe the emergent physics of materials at the limits of space and time.

Electrical energy↗
DOE OSTI2026

Supramolecular assembly of molecular wires alternating crown ethers and metal–halide complexes

Metal–halide complexes serve as key emissive centres in halide perovskites; however, precise control over their spatial organization through bottom-up assembly is challenging. Here we show that a crown-ether-assisted supramolecular assembly strategy can alternatingly connect metal–halide complexes and (crown ether@A) 2+ (where ‘A’ is an alkaline earth metal cation) complexes into a one-dimensional molecular wire, which can then be packed into a hexagonal crystal structure. This process resulted in the creation of an (18C6@Ba)MnBr 4 single crystal with green emission, achieving over 80% photoluminescence quantum yield and a narrow full width at half maximum. In addition, the non-centrosymmetric crystal structure gave rise to strong nonlinear optical responses, including second-harmonic generation. This versatile supramolecular assembly approach could be generalized to create various [M(I)X 2 ] − , [M(I)X 3 ] 2− , [M(II)X 4 ] 2− and [M(III)X 5 ] 2− molecular wires, broadening the potential for diverse emission colours and distinct optical properties. This strategy provides a general design principle for constructing supramolecular metal–halide building blocks with diverse optical functionalities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Adjoint DSMC for nonlinear spatially-homogeneous Boltzmann equation with a general collision model

We derive an adjoint method for the Direct Simulation Monte Carlo (DSMC) method for the spatially homogeneous Boltzmann equation with a general collision law. This generalizes our previous results in Caflisch et al., which was restricted to the case of Maxwell molecules, for which the collision rate is constant. The main difficulty in generalizing the previous results is that a rejection sampling step is required in the DSMC algorithm in order to handle the variable collision rate. We find a new term corresponding to the so-called score function in the adjoint equation and a new adjoint Jacobian matrix capturing the dependence of the collision parameter on the velocities. The new formula works for a much more general class of collision models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Deep transfer operator learning for partial differential equations under conditional shift

Transfer learning enables the transfer of knowledge gained while learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labelling, potential computational power limitations and dataset distribution mismatches. Here, we propose a new transfer learning framework for task-specific learning (functional regression in partial differential equations) under conditional shift based on the deep operator network (DeepONet). Task-specific operator learning is accomplished by fine-tuning task-specific layers of the target DeepONet using a hybrid loss function that allows for the matching of individual target samples while also preserving the global properties of the conditional distribution of the target data. Inspired by conditional embedding operator theory, we minimize the statistical distance between labelled target data and the surrogate prediction on unlabelled target data by embedding conditional distributions onto a reproducing kernel Hilbert space. We demonstrate the advantages of our approach for various transfer learning scenarios involving nonlinear partial differential equations under diverse conditions due to shifts in the geometric domain and model dynamics. Our transfer learning framework enables fast and efficient learning of heterogeneous tasks despite considerable differences between the source and target domains.

42 ENGINEERING↗
DOE OSTI2023

Detecting driving potentials at the buried SiO 2 nanolayers in solar cells by chemical-selective nonlinear x-ray spectroscopy

We present an approach to selectively examine an asymmetric potential in the buried layer of solar cell devices by means of nonlinear x-ray spectroscopy. Detecting second harmonic generation signals while resonant to the SiO 2 core level, we directly observe existence of the band bending effect in the SiO 2 nanolayer, buried in the heterostructures of Al/LiF/SiO 2 /Si, TiO 2 /SiO 2 /Si, and Al 2 O 3 /SiO 2 /Si. Furthermore, the results demonstrate high sensitivity of the method to the asymmetric potential that determines performance of functional materials for photovoltaics or other optoelectronic devices.

14 SOLAR ENERGY↗
DOE OSTI2023

A Barrier-Certificated Reinforcement Learning Approach for Enhancing Power System Transient Stability

Increasing integration of renewable resources brings more flexibility and poses new challenges to modern power systems, leading to highly nonlinear and complex dynamics. Here, this paper aims to provide a general solution framework to traditional control problems, such as frequency control and voltage control, which attempt to maintain the stability of either synchronous generators-governed or inverter-governed systems when subjected to a disturbance and simultaneously guarantee operational constraints, providing a complete complement to existing works on control design. Building on reinforcement learning (RL) and control barrier functions, the framework includes two subsystems, i.e., a model-free controller and a barrier-certification system, which discover RL-based control actions and sequentially filter them using a barrier certificate to satisfy operational constraints. Calculating a barrier function is generally challenging for a complex power system. This is addressed by representing the barrier function using neural networks (NNs) and data-based approaches. An adaptive method is introduced to certify the neural barrier function that perseveres barrier conditions, which is more compatible with online implementation. The proposed framework synthesizes a stabilizing controller that satisfies predefined safety regions. The effectiveness of the proposed framework is demonstrated via several comparative case studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Reinforcement learning for online adaptation of model predictive controllers: Application to a selective catalytic reduction unit

Here we present a novel application of reinforcement learning (RL) for online dynamic tuning of model predictive controllers (MPC). Applying a state-action-reward-state-action (SARSA) algorithm for temporal difference learning with a control-specific reward function improves the error tracking performance of a standard MPC formulation. The proposed RL approach is also readily adaptable to other MPCs, or entirely different control approaches. Practical details for the implementation of the RL-MPC algorithm are also presented. The proposed algorithm is applied to a case study of controlling nitrogen oxide (NO x ) emissions in an industrial selective catalytic reduction (SCR) unit, a control problem characterized by significant nonlinearity and time delay. Along with an RL-MPC formulation for NOx control, another MPC is proposed to mitigate ammonia slip and decrease ammonia consumption in the SCR. Results showing the efficacy of the RL-MPC for NO x control through learning and implementation on the nonlinear SCR dynamic model are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Accurate and Efficient Parametric Model-Order Reduction for Turbulent Thermal Transport

This project produced new algorithms and software for low-cost exploration of turbulent thermal-fluids behavior under parametric variation by using reduced-order models (ROMs). The ROMs numerically solve the governing equations for fluid motion by using a small set of basis functions (typically, N=20-200 modes) to represent the solution. The base modes are computed as optimal combinations of solutions from expensive high-fidelity "anchor-point" solutions involving millions of unknowns, which are typically generated by solving the full Navier-Stokes equations on a supercomputer. The ROM solution is itself a combination of the base modes, where the basis coefficients are determined by evolving an NxN system of nonlinear equations. The overarching idea is to use the inexpensive ROM to predict solutions under conditions where the parameters differ from the anchor-point conditions. Several ingredients are required to make ROMs useful for thermal hydraulics analysis. These include: a stable and accurate ROM that is capable of reproducing the large-scale dynamics of turbulent flow, error indicators than can guide the choice of anchor points, and low-cost mechanisms for evaluating nonlinear terms in the reduced equations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Modeling information flow in a computer processor with a multi-stage queuing model

In this paper, we introduce a nonlinear stochastic model to describe the propagation of information inside a computer processor. In this model, a computational task is divided into stages, and information can flow from one stage to another. The model is formulated as a spatially-extended, continuous-time Markov chain where space represents different stages. This model is equivalent to a spatially-extended version of the M/M/s queue. The main modeling feature is the throttling function which describes the processor slowdown when the amount of information falls below a certain threshold. We derive the stationary distribution for this stochastic model and develop a closure for a deterministic ODE system that approximates the evolution of the mean and variance of the stochastic model. In conclusion, we demonstrate the validity of the closure with numerical simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Distinguishing Mesoscale Polar Order (Unidirectional vs Bidirectional) of Cellulose Microfibrils in Plant Cell Walls Using Sum Frequency Generation Spectroscopy

Cellulose in plant cell walls are synthesized as crystalline microfibrils with diameters of 3–4 nm and lengths of around 1–10 μm. These microfibrils are known to be the backbone of cell walls, and their multiscale three-dimensional organization plays a critical role in cell wall functions including plant growth and recalcitrance to degradation. The mesoscale organization of microfibrils over a 1–100 nm range in cell walls is challenging to resolve because most characterization techniques investigating this length scale suffer from low spatial resolution, sample preparation artifacts, or inaccessibility of specific cell types. Here, we report a sum frequency generation (SFG) study determining the mesoscale polarity of cellulose microfibrils in intact plant cell walls. SFG is a nonlinear optical spectroscopy technique sensitive to the molecular-to-mesoscale order of noncentrosymmetric domains in amorphous matrices. However, the quantitative theoretical model to unravel the effect of polarity in packing of noncentrosymmetric domains on SFG spectral features has remained unresolved. In this work, we show how the phase synchronization principle of the SFG process is used to predict the relative intensities of vibrational modes with different polar angles from the noncentrosymmetric domain axis. Applying this model calculation for the first time and employing SFG microscopy, we found that cellulose microfibrils in certain xylem cell walls are deposited unidirectionally (or biased in one direction) instead of the bidirectional polarity which was believed to be dominant in plant cell walls from volume-averaged characterizations of macroscopic samples. Here, with this advancement in SFG analysis, one can now determine the relative polarity of noncentrosymmetric domains such as crystalline biopolymers interspersed in amorphous polymer matrices, which will open opportunities to study new questions that have not been conceived in the past.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Elastic-instability–enabled locomotion

Locomotion of an organism interacting with an environment is the consequence of a symmetry-breaking action in space-time. Here we show a minimal instantiation of this principle using a thin circular sheet, actuated symmetrically by a pneumatic source, using pressure to change shape nonlinearly via a spontaneous buckling instability. This leads to a polarized, bilaterally symmetric cone that can walk on land and swim in water. In either mode of locomotion, the emergence of shape asymmetry in the sheet leads to an asymmetric interaction with the environment that generates movement––via anisotropic friction on land, and via directed inertial forces in water. Scaling laws for the speed of the sheet of the actuator as a function of its size, shape, and the frequency of actuation are consistent with our observations. The presence of easily controllable reversible modes of buckling deformation further allows for a change in the direction of locomotion in open arenas and the ability to squeeze through confined environments––both of which we demonstrate using simple experiments. Our simple approach of harnessing elastic instabilities in soft structures to drive locomotion enables the design of novel shape-changing robots and other bioinspired machines at multiple scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗