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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Co-orchestration of multiple instruments to uncover structure–property relationships in combinatorial libraries

The rapid growth of automated and autonomous instrumentation brings forth opportunities for the co-orchestration of multimodal tools that are equipped with multiple sequential detection methods or several characterization techniques to explore identical samples. This is exemplified by combinatorial libraries that can be explored in multiple locations via multiple tools simultaneously or downstream characterization in automated synthesis systems. In co-orchestration approaches, information gained in one modality should accelerate the discovery of other modalities. Correspondingly, an orchestrating agent should select the measurement modality based on the anticipated knowledge gain and measurement cost. Herein, we propose and implement a co-orchestration approach for conducting measurements with complex observables, such as spectra or images. The method relies on combining dimensionality reduction by variational autoencoders with representation learning for control over the latent space structure and integration into an iterative workflow via multi-task Gaussian Processes (GPs). This approach further allows for the native incorporation of the system's physics via a probabilistic model as a mean function of the GPs. We illustrate this method for different modes of piezoresponse force microscopy and micro-Raman spectroscopy on a combinatorial Sm-BiFeO3 library. However, the proposed framework is general and can be extended to multiple measurement modalities and arbitrary dimensionality of the measured signals.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Machine Learning for Optimized Polarization at Jefferson Lab

Polarized cryo-targets and polarized photon beams are widely used in experiments at Jefferson Lab. Traditional methods for maintaining the optimal polarization involve manual adjustments throughout data taking by human shift takers. This may introduce some level of inconsistency simply due to the wide variety of experience and expertise of the shift takers themselves. Implementing machine learning-based control systems can improve the stability of the polarization without relying on human intervention. The cryo-target polarization is influenced by temperature, microwave energy, the distribution of paramagnetic radicals, as well as operational conditions including the radiation dose. Diamond radiators are used to generate linearly polarized photons from a primary electron beam. The energy spectrum of these photons can drift over time due to changes in the primary electron beam conditions and diamond degradation. As a first step towards automating the continuous optimization and control processes, uncertainty aware surrogate models have been developed to predict the polarization based on historical data. This talk will provide an overview of the use cases and models developed, highlighting the collaboration between data scientists and physicists at Jefferson Lab.

Jeske, Torri [Thomas Jefferson National Accelerato↗
DOE OSTI2019

Impact of imposed mode 2 laser drive asymmetry on inertial confinement fusion implosions

Low-mode asymmetries have emerged as one of the primary challenges to achieving high-performing inertial confinement fusion implosions. These asymmetries seed flows in the implosions, which will manifest as modifications to the measured ion temperature (T ion ) as inferred from the broadening of primary neutron spectra. The effects are important to understand (i) to learn to control and mitigate low-mode asymmetries and (ii) to experimentally more closely capture thermal T ion used as input in implosion performance metric calculations. Here, in this paper, results from and simulations of a set of experiments with a seeded mode 2 in the laser drive are described. The goal of this intentionally asymmetrically driven experiment was to test our capability to predict and measure the signatures of flows seeded by the low-mode asymmetry. The results from these experiments [first discussed in M. Gatu Johnson et al., Phys. Rev. E 98, 051201(R) (2018)] demonstrate the importance of interplay of flows seeded by various asymmetry seeds. In particular, measured T ion and self-emission x-ray asymmetries are expected to be well captured by interplay between flows seeded by the imposed mode 2 and the capsule stalk mount. Measurements of areal density asymmetry also indicate the importance of the stalk mount as an asymmetry seed in these implosions. The simulations brought to bear on the problem (1D LILAC, 2D xRAGE, 3D ASTER, and 3D Chimera) show how thermal T ion is expected to be significantly lower than T ion as inferred from the broadening of measured neutron spectra. They also show that the electron temperature is not expected to be the same as T ion for these implosions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

An overview of 3D field optimization for control of transport and edge instabilities on KSTAR

An international team from several laboratories and universities has made key advances over the last few years in the control of plasma transport and edge instabilities with applied 3D fields in the KSTAR tokamak to optimize long pulse operation scenarios. This overview begins with the optimization of both core and edge resonant magnetic perturbations (RMP) to improve fast ion confinement to avoid excessive limiter heat loads due to fast ion losses and successful modeling of the experimental results. Integrated and advanced plasma control techniques with machine learning (ML) and adaptive control were then used to optimize the 3D field spectrum in real-time to control edge localized modes (ELMs) while avoiding core locked modes that could disrupt the plasma. Accelerating the offline model of 3D fields with a surrogate ML model can optimize ELM suppression in the edge while limiting the impact of the applied RMP fields deeper in the plasma core in real-time. In addition, the impact of the 3D fields on the divertor heat load has been modeled and compared with experimental measurements. An analysis of a multi-machine database including KSTAR has been performed to better understand the metrics for the observed RMP thresholds for ELM suppression and the resulting plasma performance. Predictive modeling of the operational space for ELM suppression and density pumpout due to RMP has shown the importance of magnetic islands in the plasma edge and their impact on plasma turbulence. This research has culminated in the development of successful long pulse operational scenarios on KSTAR while attempting to overcome challenges of the new tungsten divertor.

3D fields↗
DOE OSTI2025

ACTIVE

The Automated Control Testbed for Integration, Verification, and Emulation (ACTIVE) framework is a software platform designed to support the optimized operation and management of a wide range of building types. It enables the development, testing, and validation of diverse control strategies, including AI-based, rule-based, and model-based approaches. The platform facilitates a seamless transition from simulation-based evaluation of control strategies to real-world field validation and deployment. ACTIVE supports the full building management lifecycle, encompassing data acquisition and management, system monitoring, optimized control, adaptive learning services, device dispatch and coordination, as well as advanced analytics and visualization. Together, these capabilities provide an integrated environment for improving building performance, operational efficiency, reducing energy cost, and reliability.

Smith, Robert [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2023

Controlling Optical Cavities using Machine Learning

The GQuEST experiment endeavors to investigate the unification of Quantum Mechanics and General Relativity within the realm of quantum gravity. This pursuit constitutes a well-established conundrum in the field of physics, characterized by its formidable nature due to the infinitesimal effects manifesting at the Planck scale, which measures a mere 10 35 meters. A remarkable facet of the GQuEST experiment is its capacity to predict observable manifestations of Quantum Gravity on a larger and more accessible scale.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem

The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a power network. DED produces a more dynamic supervisory control policy than traditional economic dispatch (T-ED) that reduces overall generation costs. However, the incorporation of differential equations that govern the system dynamics makes DED an optimization problem that is computationally prohibitive to solve. In this work, we present a new data-driven approach based on differentiable programming to efficiently obtain offline parametric solutions to the underlying DED problem. In particular, we employ the recently proposed differentiable predictive control (DPC) for offline learning of explicit neural control policies based on identified Koopman operator (KO) model of the system dynamics. We demonstrate the high solution quality and five orders of magnitude computational-time savings of the DPC method over the original optimization-based DED approach on a 9-bus test power grid network.

King, Ethan↗
DOE OSTI2016

THE MURCHISON WIDEFIELD ARRAY 21 cm POWER SPECTRUM ANALYSIS METHODOLOGY

We present the 21 cm power spectrum analysis approach of the Murchison Widefield Array Epoch of Reionization project. In this paper, we compare the outputs of multiple pipelines for the purpose of validating statistical limits cosmological hydrogen at redshifts between 6 and 12. Multiple independent data calibration and reduction pipelines are used to make power spectrum limits on a fiducial night of data. Comparing the outputs of imaging and power spectrum stages highlights differences in calibration, foreground subtraction, and power spectrum calculation. The power spectra found using these different methods span a space defined by the various tradeoffs between speed, accuracy, and systematic control. Lessons learned from comparing the pipelines range from the algorithmic to the prosaically mundane; all demonstrate the many pitfalls of neglecting reproducibility. We briefly discuss the way these different methods attempt to handle the question of evaluating a significant detection in the presence of foregrounds.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Impact of imposed mode 2 laser drive asymmetry on inertial confinement fusion implosions

Low-mode asymmetries have emerged as one of the primary challenges to achieving high-performing inertial confinement fusion implosions. These asymmetries seed flows in the implosions, which will manifest as modifications to the measured ion temperature (Tion) as inferred from the broadening of primary neutron spectra. The effects are important to understand both to learn to control and mitigate low-mode asymmetries, and to experimentally more closely capture thermal Tion used as input in implosion performance metric calculations. In this paper, results from and simulations of a set of experiments with a seeded mode 2 in the laser drive are described. The goal of this intentionally asymmetrically driven experiment was to test our capability to predict and measure the signatures of flows seeded by the low-mode asymmetry. The results from these experiments (first discussed in M Gatu Johnson et al, Phys. Rev. E 98, 051201(R) (2018)) demonstrate the importance of interplay of flows seeded by various asymmetry seeds. In particular, measured Tion and self-emission x-ray asymmetries are expected to be well captured by interplay between flow seeded by the imposed mode 2 and the capsule stalk mount. Measurements of areal density asymmetry also indicate the importance of the stalk mount as an asymmetry seed in these implosions. The simulations brought to bear on the problem (1D LILAC, 2D xRAGE, 3D ASTER and 3D Chimera) show how thermal Tion is expected to be significantly lower than Tion as inferred from the broadening of measured neutron spectra. They also show that the electron temperature is not expected to be the same as Tion for these implosions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1990

Program Aids Simulation Of Neural Networks

Computer program NETS - Tool for Development and Evaluation of Neural Networks - provides simulation of neural-network algorithms plus software environment for development of such algorithms. Enables user to customize patterns of connections between layers of network, and provides features for saving weight values of network, providing for more precise control over learning process. Consists of translating problem into format using input/output pairs, designing network configuration for problem, and finally training network with input/output pairs until acceptable error reached. Written in C.

Baffes, Paul T.↗
NASA NTRS1990

Intelligent control and adaptive systems; Proceedings of the Meeting, Philadelphia, PA, Nov. 7, 8, 1989

Various papers on intelligent control and adaptive systems are presented. Individual topics addressed include: control architecture for a Mars walking vehicle, representation for error detection and recovery in robot task plans, real-time operating system for robots, execution monitoring of a mobile robot system, statistical mechanics models for motion and force planning, global kinematics for manipulator planning and control, exploration of unknown mechanical assemblies through manipulation, low-level representations for robot vision, harmonic functions for robot path construction, simulation of dual behavior of an autonomous system. Also discussed are: control framework for hand-arm coordination, neural network approach to multivehicle navigation, electronic neural networks for global optimization, neural network for L1 norm linear regression, planning for assembly with robot hands, neural networks in dynamical systems, control design with iterative learning, improved fuzzy process control of spacecraft autonomous rendezvous using a genetic algorithm.

Rodriguez, Guillermo↗