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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 163 records · Page 9

Source-encoded waveform inversion in the Northern Hemisphere

SUMMARY We use source-encoded waveform inversion to image Earth’s Northern Hemisphere. The encoding method is based on measurements of Laplace coefficients of stationary wavefields. By assigning to each event a unique frequency, we compute Fréchet derivatives for all events simultaneously based on one ‘super’ forward and one ‘super’ adjoint simulation for a small fraction of the computational cost of classical waveform inversion with the same data set. No cross-talk noise is introduced in the process, and the method does not require all events to be recorded by all stations. Starting from global model GLAD_M25, we performed 100 conjugate gradient iterations using a data set consisting of 786 earthquakes recorded by 9846 stations. Synthetic inversion tests show that we achieve good convergence based on this data set, and we see a consistent misfit reduction during the inversion. The new model, named SE100, has much higher spatial resolution than GLAD_M25, revealing details of the Yellowstone and Iceland hotspots, subduction beneath the Western United States and the upper mantle structure beneath the Arctic Ocean.

Cui, Congyue↗

Properties of Heavy Cosmic Nuclei Phosphorus, Chlorine, Argon, Potassium, and Calcium: Results from the Alpha Magnetic Spectrometer

We report the unique properties of cosmic phosphorus (P), chlorine (Cl), argon (Ar), potassium (K), and calcium (Ca) fluxes in the GV to TV rigidity range collected by the Alpha Magnetic Spectrometer (AMS) on the International Space Station. With a total of one million events collected over 13.5 years, we observed that the rigidity dependencies of the five fluxes are well described by the sums of a primary cosmic ray component and a secondary cosmic ray component. The abundance ratios of all five elements to Si at the source are accurately determined independent of cosmic ray propagation. The source abundance of Ar and Ca (even-𝑍 elements) is larger than P, Cl, and K (odd-𝑍 elements). The secondary components of the P and the Cl fluxes are each ∼1/3 of the F flux, and the secondary components of the Ar, K, and Ca fluxes are each ∼1/2 of the F flux. The twenty elements measured by AMS, from He to Ca and Fe, can be categorized into four classes, two primary and two secondary, based on their rigidity dependence.

cosmic ray acceleration↗

Transient Stability Preventive Control via Tuning the Parameters of Virtual Synchronous Generators

This paper presents an optimal preventive control (OPC) method to improve the power system transient stability via tuning the transient parameter of virtual synchronous generators. The novelty of this work is that we formulate the preventive control as an optimization problem so that inverter parameters can be adjusted at the pre-contingency stage. A reinforcement learning (RL)-driven method is proposed to solve the OPC problem with the fault energy-based reward function. An ANDES-based RL environment is also developed. Versatile functions included in the proposed environment have been presented in this paper. The proposed OPC formulation, the RLdriven method, and the fault energy-based reward function are verified on several standard test systems.

Inverter-based resources, virtual synchronous gene↗

Parameter Reduction of Composite Load Model Using Active Subspace Method

Over the past decades, the increasing penetration of distributed energy resources (DERs) has dramatically changed the power load composition in the distribution networks. The traditional static and dynamic load models can hardly capture the dynamic behavior of modern loads especially for fault-induced delayed voltage recovery (FIDVR) events. Thus, a more comprehensive composite load model with combination of static load, different types of induction motors, single-phase A/C motor, electronic load and DERs has been proposed by Western Electricity Coordinating Council (WECC). However, due to the large number of parameters and model complexity, the WECC composite load model (WECC CMLD) raises new challenges to power system studies. To overcome these challenges, in this paper, a cutting-edge parameter reduction (PR) approach for WECC CMLD based on active subspace method (ASM) is proposed. Firstly, the WECC CMLD is parameterized in a discrete-time manner for the application of the proposed method. Then, parameter sensitivities are calculated by discovering the active subspace, which is a lower-dimensional linear subspace of the parameter space of WECC CMLD in which the dynamic response is most sensitive. The interdependency among parameters can be taken into consideration by our approach. Finally, the numerical experiments validate the effectiveness and advantages of the proposed approach for WECC CMLD model.

active subspace↗

Molecular Polaritonics: Chemical Dynamics Under Strong Light–Matter Coupling

Chemical manifestations of strong light–matter coupling have recently been a subject of intense experimental and theoretical studies. Here we review the present status of this field. Section 1 is an introduction to molecular polaritonics and to collective response aspects of light–matter interactions. Section 2 provides an overview of the key experimental observations of these effects, while Section 3 describes our current theoretical understanding of the effect of strong light–matter coupling on chemical dynamics. A brief outline of applications to energy conversion processes is given in Section 4. Pending technical issues in the construction of theoretical approaches are briefly described in Section 5. Finally, the summary in Section 6 outlines the paths ahead in this exciting endeavor.

Chemistry↗

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗