Search NASA⌕ Search

SEARCH · Search NASA

Results for “Adaptive tuning”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Sky-Scanning Sun-Tracking Airborne Radiometer (3STAR): Instrument Design, Flight Testing, and Tracking Performance

The Sky-Scanning, Sun-Tracking Airborne Radiometer (3STAR) adapts commercial radiometer technology developed for the ocean color research community to airborne measurement of spectrally resolved solar irradiance and sky radiance. These atmospheric observations are used to derive aerosol optical depth (AOD), supporting localized AOD inputs for atmospheric correction of satellite and airborne data over terrestrial and aquatic (including optically dark) targets. The ability to regionally “tune” atmospheric correction schemes with relevant spatial AOD supports constraining atmospheric correction of remote sensing reflectance. Very wide dynamic range has been achieved for multi-channel band-pass-filter-radiometers originally designed for deployment into the water column. By actively tracking and directly pointing to the Sun, the light attenuation by aerosol particles in the atmospheric column can be quantified. These measurements improve knowledge of atmospheric constituents and the atmospheric corrections required to improve remote sensing capabilities for interpreting reflectance measurements from the Earth surface. 3STAR incorporates a custom Sun-tracking/sky-scanning pointing head, a Sun-tracking camera, and a commercially available, cylindrical, 19-channel radiometer tube assembly. An accurate and responsive mount and tracking system has been developed and certified to NASA and Naval Air Systems Command (NAVAIR) airworthiness standards for deployment into the aircraft slipstream. Ground and flight testing indicate typical tracking errors of less than 0.1 degrees, well within the field of view of the radiometer as required to minimize measurement uncertainty from alignment error. Preliminary AOD measurements compare to within 0.013 with 15 measurements from an Aerosol Robotic Network (AERONET) Cimel instrument at 500 nm wavelength and low solar angle.

Atmosphere↗
DOE OSTI2021

Ferromagnetic liquid droplets with adjustable magnetic properties

Significance Structured functional liquids combine mechanical versatility of fluids with solid-state properties, such as ferromagnetism, and offer a route to synthesize and control magnetic liquids for adaptive liquid robotics. Studies on these intriguing materials are only in their nascent state, and a profound understanding of the physical state is still lacking. We use hydrodynamics experiments to probe how the magnetization of ferromagnetic liquid droplets, governed by the assembly and jamming of magnetic nanoparticles at liquid–liquid interfaces, and their response to external stimuli can be tuned by chemical, structural, and magnetic means. Our results highlight the leading role of structural short-range order on magnetic properties, which provide a path toward nano-patterning structured liquids.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Beam optics ramping in underconstrained lattice design: Application to Electron-Ion Collider hadron storage ring cooling section

This paper presents the lattice design and optics ramping strategy for the cooling section of the hadron storage ring at the electron-ion collider. The main challenge is that available tuning knobs exceed beam-optics constraints. Independently optimized injection and top-energy optics often yield disconnected solutions, making interpolation impossible. To address this, we propose two new methods. The first is an intermediate-penalty scheme that ensures ramping path continuity by penalizing constraint violations at intermediate states. The second is a continuation-based approach that adapts high-energy optics to low energy, guided by an adaptive weighting scheme to balance injection and ramping constraints. The solutions meet all beam dynamics and hardware limits. The two methods offer a general strategy for ramping in systems where the solution space is under-constrained and the starting and target configurations are far apart.

43 PARTICLE ACCELERATORS↗
NASA NTRS1999

A Survey of Collective Intelligence

This chapter presents the science of "COllective INtelligence" (COIN). A COIN is a large multi-agent systems where: i) the agents each run reinforcement learning (RL) algorithms; ii) there is little to no centralized communication or control; iii) there is a provided world utility function that, rates the possible histories of tile full system. Tile conventional approach to designing large distributed systems to optimize a world utility does not use agents running RL algorithms. Rather that approach begins with explicit modeling of the overall system's dynamics, followed by detailed hand-tuning of the interactions between the components to ensure that they "cooperate" as far as the world utility is concerned. This approach is labor-intensive, often results in highly non-robust systems, and usually results in design techniques that, have limited applicability. In contrast, with COINs we wish to solve the system design problems implicitly, via the 'adaptive' character of the RL algorithms of each of the agents. This COIN approach introduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we do not have phenomena like the tragedy of the commons, or Braess's paradox? Although still very young, the science of COINs has already resulted in successes in artificial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur's "El Farol bar problem". It is expected that as it matures not only will COIN science expand greatly the range of tasks addressable by human engineers, but it will also provide much insight into already established scientific fields, such as economics, game theory, or population biology.

Wolpert, David H.↗
DOE OSTI2025

Dual-Responsive Macromolecular Surfaces with Binary Patterns

The development of nanopatterned interfaces incorporating multiple stimuli-responsive polymers has great potential for advancing smart sensors and molecular capture devices, yet it remains challenging due to limitations in patterning techniques and “grafting from” methods. Here, we present a binary-patterned surface featuring honeycomb-shaped photothermal polypyrrole (PPy) and thermally responsive poly(ethylene glycol methyl ether acrylate-co-poly(ethylene glycol) methyl ether acrylate) [poly(EGMEA-co-PEGMEA)] brushes within the cavities, fabricated using colloidal lithography and surface-initiated photoinduced electron transfer-reversible addition–fragmentation chain transfer (SI-PET-RAFT) polymerization. The morphology of the surface can be precisely tuned by adjusting the height of each domain. This design integrates unique topographical features with the stimuli-responsiveness of the polymer, enabling the collapse of the copolymer brushes through direct heating or photothermal conversion of PPy under near-IR light. In conclusion, this innovative system offers potential capture-release functionality, providing versatility for diverse applications and enhancing adaptability across different functional scenarios.

Copolymers↗
DOE OSTI2026

Flying focus with arbitrary directionality for spatiotemporal control of laser intensity

Flying focus techniques produce laser pulses whose focal points travel at arbitrary, controllable velocities. While this flexibility can enhance a broad range of laser-based applications, existing techniques constrain the motion of the focal point to the propagation direction of the pulse. Here, we introduce a flying focus configuration that decouples the motion of the focus from the propagation direction. Here, a chirped laser pulse focused and diffracted by a diffractive lens and grating creates a focal point that can move both along and transverse to the propagation direction. The focal length of the lens, grating period, and chirp can be tuned to control the direction and velocity of the focus. Simulations demonstrate this control for a holographic configuration suited to high-power pulses, in which two off-axis pump beams with different focal lengths encode the equivalent phase of a chromatic lens and grating in a gas or plasma. For low-power pulses, conventional solid-state or adaptive optics can be used instead. Multi-dimensional control over the focal trajectory enables new configurations for applications, including laser wakefield acceleration of ions, nonlinear Thomson scattering, and surface-plasmon emission of THz radiation.

Classical optics↗
NASA NTRSDate not supplied

Maximum-Likelihood Parameter Estimation for High-Contrast Wavefront Sensing & Control

Stellar coronagraphs use closed-loop focal-plane wavefront sensing and control algorithms to create high-contrast dark zones suitable for imaging exoplanets and exozodiacal dust clouds around nearby stars. At present, the deepest contrast has been achieved using model-based algorithms, which use the predicted focal-plane influence of the coronagraph's deformable mirrors to drive diffracted starlight toward zero over time in an optimal control framework. However, model-based algorithms are susceptible to model mismatch, wherein a departure of the coronagraph's true optical characteristics from the model predictions causes reduced control loop performance. Here, we report on a technique for maximum-likelihood estimation of the wavefront control Jacobian matrix and noise statistics of the coronagraph focal-plane electric field from data acquired in situ during closed-loop wavefront control operations. By empirically tuning the Jacobian and noise properties in a statistically rigorous fashion, the maximum-likelihood approach mitigates model mismatch and recovers near-optimal control loop performance.

coronagraphy↗
DOE OSTI2021

Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning

Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in real-world power systems under contingencies. However, existing emergency control schemes are rule-based and cannot be adaptively applied to uncertain and floating operating conditions. Here, we propose an adaptive UVLS algorithm for emergency control via deep reinforcement learning (DRL) and expert systems. We first construct dynamic components for picturing the power system operation as the environment. The transient voltage recovery criteria, which poses time-varying requirements to UVLS, is integrated into the states and reward function to advise the learning of deep neural networks. The proposed method has no tuning issue of coefficients in reward functions, and this issue was regarded as a deficiency in the existing DRL-based algorithms. Case studies illustrate that the proposed method outperforms the traditional UVLS relay in both the timeliness and efficacy for emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Model-predictive optimal control of ferrofluidic microrobots in three-dimensional space

Ferrofluid microrobots have emerged as promising tools for minimally invasive medical procedures. Their unique properties to navigate complex fluids and reach otherwise inaccessible regions of the human body have enabled new applications in targeted drug delivery, tissue engineering, and diagnostics. Here, this paper proposes a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots in three dimensions (3D). The internal optimization routine of the controller determines appropriate changes in the applied electromagnetic field to minimize the deviation between the actual and desired trajectories of the microrobot. A linear system governing locomotion is derived and used as the equality constraints of the optimization problems associated with the feedback index. In addition to ferrofluid droplets, the controller presented in this work may be applied to other magnetically-pulled microrobots. Several experiments are performed to validate the controller and showcase its ability to adapt to changes in system parameters such as the desired tracking trajectory and the size, orientation, deformation, and velocity of the microrobot. The accuracy of the controller is analyzed for each experiment, and the average error is found to be within 0.25 mm for small velocities. An additional experiment is performed to demonstrate significant improvement over a PID controller that is optimally tuned using Bayesian optimization. The results presented in this paper suggest that the proposed control algorithm could enable new microrobotic capabilities in minimally invasive medical procedures, lab-on-a-chip applications, and microfluidics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

String instability mitigation of adaptive cruise control without modifying control laws: trajectory shaper and parameter estimation

Vehicle automation technologies equip vehicles with adaptive cruise control (ACC) systems, which relieve driving fatigue. However, recent studies have shown that the current ACC systems are string-unstable (i.e., exacerbate traffic congestion). To achieve string stability, most existing studies directly modify the control algorithms of ACC systems. Alternatively, this study proposes a trajectory shaper (TS)-based method, which only modifies the trajectory information of the predecessor vehicle, so that the ego vehicle driven by a string-unstable ACC system leverages the modified trajectory information to achieve string stability. To devise the TS-based method, an offline-online parameter estimation method integrating batch optimization and an extended Kalman filter is applied to estimate the parameters of an ACC system. The proposed TS-based method is cost-effective during implementation, as it avoids modifying existing ACC control algorithms (which entails a complex analysis of control systems and parameter tuning). In conclusion, the effectiveness of the proposed TS-based method is validated through extensive numerical experiments.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II

Deep neural network (DNN) potentials are an emerging tool for simulation of dynamical atomistic systems, with the promise of quantum mechanical accuracy at speedups of 10000$\times$. As with other DNN methods, hyperparameters used during training can make a substantial difference in model accuracy, and optimal settings vary with dataset. To enable rapid tuning of hyperparameters for DNN potential training, we developed a scalable multiobjective optimization evolutionary algorithm for supercomputers and tested it on the Summit system at the Oak Ridge Leadership Computing Facility (OLCF). The multiobjective approach is required due to the coupling of two learned values defining the potential: the energy and force. Using a large-scale implementation of the NSGA-II algorithm adapted for training DNN potentials, we discovered several optimal multiobjective combinations, including best choices of activation functions, learning rate scaling scheme, and pairing of the two radial cutoffs used in the three dimensional descriptor function.

Coletti, Mark↗
DOE OSTI2023

UnifyFS: A User-level Shared File System for Unified Access to Distributed Local Storage

We introduce UnifyFS, a user-level file system that aggregates node-local storage tiers available on high performance computing (HPC) systems and makes them available to HPC applications under a unified namespace. UnifyFS employs transparent I/O interception, so it does not require changes to application code and is compatible with commonly used HPC I/O libraries. The design of UnifyFS supports the predominant HPC I/O workloads and is optimized for bulk-synchronous I/O patterns. Furthermore, UnifyFS provides customizable file system semantics to flexibly adapt its behavior for diverse I/O workloads and storage devices. In this paper, we discuss the unique design goals and architecture of UnifyFS and evaluate its performance on a leadership-class HPC system. In our experimental results, we demonstrate that UnifyFS exhibits excellent scaling performance for write operations and can improve the performance of application checkpoint operations by as much as 3× versus a tuned configuration.

Brim, Michael↗
DOE OSTI2022

3D-printed electrode, gas diffusion electrode (GDE) and gas diffusion layer (GDL) for fuel cell applications

An electrocatalytically active ink composition is used with an additive manufacturing process, such as 3D printing, to produce electrodes having consistent, adaptable, and high surface area structures. The electrocatalytically active ink composition includes a mixed powdered precursor and a polymer matrix. The mixed powdered precursor includes a carbon source, a dopant source, and/or a metal-containing catalyst. The material and electrochemical properties of the ink composition may facilitate 3D printing of electrochemically active electrodes for energy conversion and storage devices, and may allow fine-tuning of macro- and microstructures to develop electrodes having improved activity and efficiency.

Lee, Kwan-Soo↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗
DOE OSTI2022

OptZConfig: Efficient Parallel Optimization of Lossy Compression Configuration

Lossless compressors have very low compression ratios that do not meet the needs of today's large-scale scientific applications that produce vast volumes of data. Error-bounded lossy compression (EBLC) is considered a critical technique for the success of scientific research. Although EBLC allows users to set an error bound for the compression, users have been unable to specify the requirements on the compression quality, limiting practical use. Our contributions are: (1) We formulate the problem of configuring EBLC to preserve a user-defined metric as an optimization problem. This allows many classes of new metrics to be preserved, which improves over current practices. (2) We present a framework, OptZConfig, that can adapt to improvements in the search algorithm, compressor, and metrics with minimal changes, enabling future advancements in this area. (3) We demonstrate the advantages of our approach against the leading methods to configure compressors to preserve specific metrics. Here, our approach improves compression ratios against a specialized compressor by up to 3 x, has a 56x speedup over FRaZ, 1000x speedup over MGARD-QOI post tuning, and 110x speedup over systematic approaches which had not been bounded by compressors before.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Development and Airborne Demonstration of the Concurrent Artificially-Intelligent Spectrometry and Adaptive Lidar System: Advancing Lidar Capabilities for the STV Observing System

We report on the design, build and planned airborne demonstration of a spaceflight-prototype Concurrent Artificially-intelligent Spectrometry and Adaptive Lidar System (CASALS). The CASALS lidar is an Adaptive Wavelength Scanning Lidar (AWSL) operating in push broom mode. The demonstration has three major goals: advance the Technical Readiness Level of the AWSL hardware, validate its measurement performance and mature algorithms and methods needed for the Surface Topography and Vegetation (STV) observing system. AWSL acquires parallel tracks of surface heights by rapidly steering a laser beam across a swath. A 1040nm-centered laser is tuned across 30nm and carved into 2-ns pulses, the pulse energy is fiber amplified and the pulses are dispersed cross-track using a non-mechanical wavelength-to-angle grating. For the spaceflight system the beam will be pointable to 1200 10m footprints across a 7km swath. For the airborne demonstration there will be 256 0.7m footprints across a 110m swath. In both cases the footprints overlap across- and along-track for uniform target illumination. For the airborne demonstration a steering mirror will increase the accessible swath width to 4km. At the receiver, solar radiation is filtered with a narrow-slit grating-spectrometer and the footprints are imaged onto a linear-mode, photon-sensitive HgCdTe APD-array. The received pulses are time-division-multiplexed to a few high-speed analog-to-digital converters to record waveforms. Spaceflight and airborne CASALS are designed to nominally detect 20 photons per pulse and, by averaging 27 overlapping footprints, achieve 2cm flat target range precision and high-quality vegetation structure waveforms The AWSL will be flown in the summer of 2024, along with a Headwall VNIR-SWIR hyperspectral sensor imaging a 4km wide swath, at NEON eddy covariance flux towers in the U.S. mid-Atlantic where high resolution hyperspectral and lidar data, acquired annually, are available for validation.

Guangning Yang↗
NASA NTRS1993

Adaptive control strategies for flexible robotic arm

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity if not unstable closed-loop behavior. Therefore a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.

Bialasiewicz, Jan T.↗
DOE OSTI2024

Mini Report: Jailbreaking Attacks and Defenses

Overall, jailbreaking defenses are unreliable, and no defenses proposed thus far can completely stop such attacks in any verifiable way. Even manual attacks can trivially bypass some claimed ‘defenses’, and over the course of 2023 automated attacks on prior models have been adapted to work on LLMs while other attacks draw on ideas such as fuzz testing. At best, some defenses can make jailbreaks more difficult, but with the developing landscape of attacks existing papers have not robustly evaluated how effective they are against all these methods. However, our opinion is that given the way these large generative models are trained and ‘aligned’ to stated goals of safety via fine tuning, it will be exceedingly difficult if not impossible to eliminate the possibility of jailbreaking attacks. A major barrier is that the feature space of LLMs is not sufficiently understood in a way where guarantees can be made about the outputs. Barring major changes, the expectation around jailbreaking defenses should be that they can mitigate misuse, but not verifiably prevent it. However, one defense we believe merits further investigation depends on the fact as automated attacks produce text, they need an automated way to identify a successful jailbreak - a judgment model. We have seen some attempts to repurpose models like these to defend against jailbreaks, but the evaluations are small scale and not robust.

97 MATHEMATICS AND COMPUTING↗