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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Climate-invariant machine learning

Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed “climate-invariant” ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Nonextensive hydrodynamics of boost-invariant plasmas

We use quasiparticle anisotropic hydrodynamics to study the non-conformal and non-extensive dynamics of a system undergoing boost-invariant Bjorken expansion. To introduce nonextensivity, we use an underlying Tsallis distribution with a time-dependent nonextensivity parameter q. By taking moments of the quasiparticle Boltzmann equation in the relaxation-time approximation, we obtain dynamical equations which allow us to determine the time evolution of all microscopic parameters including q. We compare numerical solutions for bulk observables obtained using the nonextensive evolution with results obtained using quasiparticle anisotropic hydrodynamics with a Boltzmann distribution function (q → 1). We show that the evolution of the temperature, pressure ratio, and scaled energy density, are quite insensitive to which distribution function is assumed. However, we find significant differences in the early-time evolution of the bulk pressure which are observed for even small deviations from the Boltzmann distribution function. Finally, we discuss the existence of non-conformal hydrodynamic attractors for the longitudinal and transverse pressures, the bulk and shear viscous corrections, and the nonextensivity parameter q.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Lorentz invariance of basis tensor gauge theory

Basis tensor gauge theory (BTGT) is a vierbein analog reformulation of ordinary gauge theories in which the vierbein field describes the Wilson line. After a brief review of the BTGT, we clarify the Lorentz group representation properties associated with the variables used for its quantization. In particular, we show that starting from an SO(1,3) representation satisfying the Lorentz-invariant U(1,3) matrix constraints, BTGT introduces a Lorentz frame choice to pick the Abelian group manifold generated by the Cartan subalgebra of U(1,3) for the convenience of quantization even though the theory is frame independent. This freedom to choose a frame can be viewed as an additional symmetry of BTGT that was not emphasized before. We then show how an [Formula: see text] permutation symmetry and a parity symmetry of frame fields natural in BTGT can be used to construct renormalizable gauge theories that introduce frame-dependent fields but remain frame independent perturbatively without any explicit reference to the usual gauge field.

Physics↗
DOE OSTI2025

Rotation-invariant-neural-networks

Rotational symmetries are a fundamental and inherent property of many sources of data. Ensuring NNs embody rotation invariance will enhance their accuracy and radically lower their data requirements across a transformatively wide spectrum of applications.

Lubbers, Nicholas [Los Alamos National Laboratory]↗
DOE OSTI2025

Universal structure of propagation-invariant optical pulses

Space–time structuring of light—where spatial and temporal degrees of freedom are deliberately coupled and controlled—is an emerging area of optics that enables novel configurations of electromagnetic fields. Of particular importance for applications are optical pulses whose peak intensity travels at an arbitrary, tunable velocity while maintaining its spatiotemporal profile. Space–time wave packets (STWPs) and the ideal flying focus (FF) are two prominent realizations of these pulses. Here, we show that these realizations share an identical spatiotemporal field structure and that this structure represents a universal solution for constant-velocity, propagation-invariant pulses.

Almeida, R. (ORCID:0000000288435855)↗
DOE OSTI2025

Construction of approximate invariants for non-integrable Hamiltonian systems

We present a method to construct high-order polynomial approximate invariants (AI) for non integrable Hamiltonian dynamical systems, and apply it to a modern ring-based particle accelerator. Taking advantage of a special property of one-turn transformation maps in the form of a square matrix, AIs can be constructed order-by-order iteratively. Evaluating AI with simulation data, we observe that AI’s fluctuation is actually a measure of chaos. Through minimizing the fluctuations, the stable region of long-term motions, i.e., the dynamic aperture of the accelerator, could be enlarged.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Experimental Study of the Invariance of Pressure Gain with Respect to the Dynamics of Multiple Competing Waves in a Rotating Detonation Combustor

Changes in the overall performance of a rotating detonation combustor with respect to changes in operation mode and wave dynamics arising by operation with fixed inlet/exit geometry but at different combustor, lengths are investigated experimentally. The air inlet, fuel injection, and exit constriction geometry are held constant while only the length of the detonation channel is varied from 71 to 137 mm (which corresponds to about 10 to 20 channel widths). Operation of H2/air over a range of air mass flow rates and equivalence ratios are considered for every chamber length. The number and speed of (primary) detonation and secondary waves are characterized through high-speed pressure measurements in the detonation channel and aft chemiluminescence videos. The number of waves is found to increase with length while detonation wave speed decreases significantly. Particular emphasis is given to characterize a phenomenon that is observed at operation with longer combustor lengths and higher mass flow rates. The phenomenon manifests as a super-cycle behavior with a period equal to many detonation wave rotational periods and is characterized by a periodic and structured ascending/descending sequence of the number, speed, and direction of both (primary) detonation and secondary waves. This phenomenon is likely a manifestation of the system failing in achieving operation with a higher number of detonation waves as length and/or mass flow rate are increased. The performance of the device is quantified in terms of measured thrust and pressure gain (through the use of the equivalent available pressure). Both metrics are essentially found to be invariant with respect to combustor length and most importantly, mode of operation. Surprisingly, even operation with complex wave dynamics arising from transitions between multiple competing wave systems does not appear to alter the overall global performance of the device but rather, it remains defined by the total (capture) air mass flow rate, equivalence ratio, and inlet/outlet areas.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).

Forestano, Roy T. (ORCID:0000000203552076)↗