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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improving Progressive Retrieval for HPC Scientific Data using Deep Neural Network

As the disparity between compute and I/O on high-performance computing systems has continued to widen, it has become increasingly difficult to perform post-hoc data analytics on full-resolution scientific simulation data due to the high I/O cost. Error-bounded data decomposition and progressive data retrieval framework has recently been developed to address such a challenge by performing data decomposition before storage and reading only part of the decomposed data when necessary. However, the performance of the progressive retrieval framework has been suffering from the over-pessimistic error control theory, such that the achieved maximum error of recomposed data is significantly lower than the required error. Therefore, more data than required is fetched for recomposition, incurring additional I/O overhead. In order to tackle this issue, we propose a DNN-based progressive retrieval framework that can better identify the minimum amount of data to be retrieved. Our contributions are as follows: 1) We provide an in-depth investigation of the recently developed progressive retrieval framework; 2) We propose two designs of prediction models (named D-MGARD and E-MGARD) to estimate the amount of retrieved data size based on error bounds. 3) We evaluate our proposed solutions using scientific datasets generated by real-world simulations from two domains. Evaluation results demonstrate the effectiveness of our solution in accurately predicting the amount of retrieval data size, as well as the advantages of our solution over the traditional approach to reducing the I/O overhead. Based on our evaluation, our solution is shown to read significantly less data (5% - 40% with D-MGARD, 20% - 80% with E-MGARD).

Wang, Jinzhen↗
DOE OSTI2024

Directing Nanoparticle Organization in Response to Diverse Chemical Inputs

Signaling cascades are crucial for transducing stimuli in biological systems, enabling multiple stimuli to regulate a downstream target with precisely controlled timing and amplifying signals through a series of intermediary reactions. Developing a robust signaling system with such capabilities would be pivotal for programming complex behaviors in synthetic DNA-based molecular devices. However, although “software” such as nucleic acid circuits could potentially be harnessed to relay signals to DNA-based nanostructure hardware, such explorations have been limited. Here, in this study, we develop a platform for transducing a variety of stimuli via messenger-mediated reactions to regulate the release and reloading of gold nanoparticles (AuNPs) in a 3D DNA framework. In the first step, an in vitro transcription circuit is engineered to sense and amplify chemical stimuli, including arbitrary DNA sequences and proteins, producing RNA. In the second step, the RNA releases the DNA-coated AuNPs from the DNA framework via a strand displacement reaction. AuNP reloading is controlled by a separate step driven by degradation of the RNA. Our platform holds promise for applications requiring dynamic multiagent control over DNA-based devices, offering a versatile tool for advanced molecular device engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluation of automated stability testing in machining through closed-loop control and Bayesian machine learning

Here, this paper describes a system for automated identification of the optimal stable cutting parameters in milling through Bayesian machine learning and closed-loop control. The closed-loop control system consists of a process monitoring architecture, an analysis framework, and a feedback mechanism. The analysis framework consists of a Bayesian machine learning algorithm that learns a stability map given test results. The learned stability map is used to select parameters for stability testing using an expected improvement in the material removal rate criterion. The test parameters are communicated to the machine controller to complete the test cut through a feedback mechanism. The test cuts were monitored using an audio signal; the stability of the test cut was determined by analyzing the frequency content of the audio signal. The test result was fed back to the Bayesian learning algorithm to complete the loop. Experimental results demonstrate that the system can identify the optimal stable parameters without information about the cutting force model or the structural dynamics. The system provides a low-cost method for optimal stable parameter identification in an industrial environment.

Chatter↗
DOE OSTI2020

An 860 MHz Wireless Surface Acoustic Wave Sensor with a Metal-Organic Framework Sensing Layer for CO2 and CH4

Wireless and passive surface acoustic wave (SAW) devices with nanoporous metal-organic framework (MOF) sensing layers are attractive gas sensors for applications in many fields such as energy industries and air pollution control. Here, we report on enhancing the sensitivity and detection limit of zeolitic imidazolate framework-8 (ZIF-8) MOF-coated SAW reflective delay line mass sensors by increasing the operating frequency for sensitive detection of carbon dioxide (CO2) and methane (CH4) at ambient conditions. In particular, we show at least four times higher sensitivity of an 860 MHz (4-μm periodicity) SAW reflective delay line coated with a 240 nm thick ZIF-8 compared to the sensitivity of a 430 MHz (8-μm periodicity) otherwise identical sensor device to the targeted gases. The detection limits of the higher frequency wireless devices for CO2 and CH4 were estimated to be 0.91 vol-% and 7.01 vol-%, respectively. The enhanced sensitivity for higher frequency devices is explained in terms of the frequency dependent acoustic wave energy confinement.

Devkota, Jagannath↗
DOE OSTI2020

Functional metal–organic frameworks as effective sensors of gases and volatile compounds

Developing efficient sensor materials with superior performance for selective, fast and sensitive detection of gases and volatile organic compounds (VOCs) is essential for human health and environmental protection, through monitoring indoor and outdoor air pollutions, managing industrial processes, controlling food quality and assisting early diagnosis of diseases. Metal–organic frameworks (MOFs) are a unique type of crystalline and porous solid material constructed from metal nodes (metal ions or clusters) and functional organic ligands. They have been investigated extensively for possible use as high performance sensors for the detection of many different gases and VOCs in recent years, due to their large surface area, tunable pore size, functionalizable sites and intriguing properties, such as electrical conductivity, magnetism, ferroelectricity, luminescence and chromism. The high porosity of MOFs allows them to interact strongly with various analytes, including gases and VOCs, thus resulting in easily measurable responses to different physicochemical parameters. Although much of the recent work on MOF-based luminescent sensors have been summarized in several excellent reviews (up to 2018), a comprehensive overview of these materials for sensing gases and VOCs based on chemiresistive, magnetic, ferroelectric, and colorimertic mechanisms is missing. In this review, we highlight the most recent progress in developing MOF sensing and switching materials with an emphasis on sensing mechanisms based on electricity, magnetism, ferroelectricity and chromism. We provide a comprehensive analysis on the MOF–analyte interactions in these processes, which play a key role in the sensing performance of the MOF-based sensors and switches. We discuss in detail possible applications of MOF-based sensing and switching materials in detecting oxygen, water vapor, toxic industrial gases (such as hydrogen sulfide, ammonia, sulfur dioxide, nitrous oxide, carbon oxides and carbon disulfide) and VOCs (such as aromatic and aliphatic hydrocarbons, ketones, alcohols, aldehydes, chlorinated hydrocarbons and N,N'-dimethylformamide). Altogether, this review serves as a timely source of information and provides insight for the future development of advanced MOF materials as next-generation gas and VOC sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Control of Excitonic Energy Transfer in RGB Quantum Dot:Polymer Composites for Tunable White Emission

Tint-controlled white light is crucial for both illumination systems and display applications. Here, in this study, we demonstrate solution-processed quantum-dot light-emitting diodes (QD-LEDs) featuring a red–green–blue (RGB) QD–poly(methyl methacrylate) (PMMA) composite emissive layer (EML) for tunable white electroluminescence (EL). In this composite EML, PMMA functions as a dispersion matrix that modulates the interdot spacing (d), thereby controlling Förster resonance energy transfer (FRET) between QDs. By adjustment of the PMMA content, the balance of R, G, and B emissions is controlled, enabling systematic and continuous tuning of the EL color from greenish to reddish white at a fixed RGB ratio and constant driving bias. Time-resolved photoluminescence measurements confirm that the variation in the exciton lifetime with the PMMA fraction is the primary factor for tuning the EL color. Notably, nearly pure white EL with CIE coordinates close to (0.33, 0.33) is achieved using a diluted PMMA matrix without significant degradation of the electrical properties. Our results demonstrate d as an independent design parameter for decoupling color tuning from RGB composition and electrical operation, providing a versatile design framework for high-quality white- or tint-controlled QD-LEDs toward advanced solid-state lighting and display technologies.

36 MATERIALS SCIENCE↗
NASA NTRS1989

A neural network for controlling the configuration of frame structure with elastic members

A neural network for controlling the configuration of frame structure with elastic members is proposed. In the present network, the structure is modeled not by using the relative angles of the members but by using the distances between the joint locations alone. The relationship between the environment and the joints is also defined by their mutual distances. The analog neural network attains the reaching motion of the manipulator as a minimization problem of the energy constructed by the distances between the joints, the target, and the obstacles. The network can generate not only the final but also the transient configurations and the trajectory. This framework with flexibility and parallelism is very suitable for controlling the Space Telerobotic systems with many degrees of freedom.

Tsutsumi, Kazuyoshi↗
DOE OSTI2023

Operator Growth in Open Quantum Systems

The spreading of quantum information in closed systems, often termed scrambling, is a hallmark of many-body quantum dynamics. In open systems, scrambling competes with noise, errors, and decoherence. Here, in this work, we provide a universal framework that describes the scrambling of quantum information in open systems: we predict that the effect of open-system dynamics is fundamentally controlled by operator size distributions and independent of the microscopic error mechanism. This framework allows us to demonstrate that open quantum systems exhibit universal classes of information dynamics that fundamentally differ from their unitary counterparts. Implications for the Loschmidt echo, nuclear magnetic resonance experiments, and the classical simulability of open quantum dynamics will be discussed.

Physics↗
NASA NTRS1994

Advanced technologies in the ASI MLRO towards a new generation laser ranging system

Matera Laser Ranging Observatory (MLRO) is a high performance, highly automated optical and astronomical observatory currently under design and development by AlliedSignal for the Italian Space Agency (ASI). It is projected to become operational at the Centro Geodesia Spaziale in Matera, Italy, in 1997. MLRO, based on a 1.5-meter astronomical quality telescope, will perform ranging to spacecraft in earthbound orbits, lunar reflectors, and specially equipped deep space missions. The primary emphasis during design is to incorporate state-of-the-art technologies to produce an intelligent, automated, high accuracy ranging system that will mimic the characteristic features of a fifth generation laser ranging system. The telescope has multiple ports and foci to support future experiments in the areas of laser communications, lidar, astrometry, etc. The key features providing state-of-the-art ranging performance include: a diode-pumped picosecond (50 ps) laser, high speed (3-5 GHz) optoelectronic detection and signal processing, and a high accuracy (6 ps) high resolution (less than 2 ps) time measurement capability. The above combination of technologies is expected to yield millimeter laser ranging precision and accuracy on targets up to 300,000 km, surpassing the best operational instrument performance to date by a factor of five or more. Distributed processing and control using a state-of-the-art computing environment provides the framework for efficient operation, system optimization, and diagnostics. A computationally intelligent environment permits optimal planning, scheduling, tracking, and data processing. It also supports remote access, monitor, and control for joint experiments with other observatories.

Varghese, Thomas↗
DOE OSTI2022

Metal-organic framework supported single-site nickel catalysts for butene dimerization

Homotopic sites in a well-controlled environment are not only ideal systems for mechanistic studies, but also allow optimal control of catalytic transformations. Sites having only a single metal cation and sites consisting of metal oxo complexes with few nickel (Ni) cations supported on the nodes of UiO-66 metal-organic framework (Ni-UiO-66) are studied for 1-butene dimerization. Monomeric Ni sites, which bind to the Zr 6 node via two Zr-OH(µ3) linkages, are active and selective for the dimerization of 1-butene to linear and mono-branched C 8 isomers. Ni oxo complexes with few Ni cations show lower activity and promote the oligomerization of transiently formed C 8 isomers. In conclusion, Kohn-Sham density function theory calculations combined with spectroscopic measurements and kinetic analyses indicate that dimerization follows a Cossee-Arlman reaction mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Quantum optimal control of superconducting qubits based on machine-learning characterization

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations in detailed numerical simulations of a superconducting transmon qubit. Furthermore, we demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of ∼99.99%.

Artificial neural networks↗
NASA NTRS1989

A spatial operator algebra for manipulator modeling and control

A recently developed spatial operator algebra, useful for modeling, control, and trajectory design of manipulators is discussed. The elements of this algebra are linear operators whose domain and range spaces consist of forces, moments, velocities, and accelerations. The effect of these operators is equivalent to a spatial recursion along the span of a manipulator. Inversion of operators can be efficiently obtained via techniques of recursive filtering and smoothing. The operator algebra provides a high level framework for describing the dynamic and kinematic behavior of a manipulator and control and trajectory design algorithms. The interpretation of expressions within the algebraic framework leads to enhanced conceptual and physical understanding of manipulator dynamics and kinematics. Furthermore, implementable recursive algorithms can be immediately derived from the abstract operator expressions by inspection. Thus, the transition from an abstract problem formulation and solution to the detailed mechanizaton of specific algorithms is greatly simplified. The analytical formulation of the operator algebra, as well as its implementation in the Ada programming language are discussed.

Rodriguez, G.↗
DOE OSTI2021

OpenDSS-wrapper (Distribution System Co-simulator with Distributed Energy Resource Controls)

Electric grid transformation with the proliferation of distributed energy resources (DER), such as solar photovoltaic (PV), wind, advanced energy storage technologies, and electric vehicles, and the growing use of communication technologies in both transmission and distribution systems are increasing the need to capture the interactions among these systems. Advanced modeling, control, and simulation tools that can perform co-simulation of electric power systems with other domains become indispensable to accurately model these interactions. To address this need, we have developed a codebase that integrates an electric power distribution system simulator, DER models, and DER controls. The codebase is based on OpenDSS, a distribution system simulator, and an existing open source co-simulation framework called HELICS. The contribution and uniqueness of the proposed codebase is that it tailors the generic HELICS framework specifically for distribution grid-related applications. The codebase includes an OpenDSS wrapper that controls the simulation, implements advanced DER controls, and extracts power flows, voltages, and other power system element information from the the distribution network modeled in OpenDSS. Sample HELICS federates, including a federate for OpenDSS, are provided that can communicate messages through the HELICS interface. Sample federates can be modified and additional HELICS federates can be added by the user depending on their use case requirements. SEE ALSO: https://github.com/NREL/dss-cosim

Blonsky, Michael↗
DOE OSTI2020

Goldilocks and the three glymes: How Na+ solvation controls Na–O 2 battery cycling

In this work we report a framework to understand the role of solvent-salt interactions and how they mediate the performance of sodium-air/O 2 batteries. The utilization of suitable electrolyte materials remains a point of major concern within the research community, as their stability and decomposition pathways during cycling are intimately connected with capacity and cycle life. Glyme based solvents have been widely utilized in Na–O 2 batteries, however, to date no clear correlation between solvent/electrolyte properties and battery performance has been given. Herein, we have examined the effect of glyme chain length (ethylene glycol dimethyl ether DME; diethylene glycol dimethyl ether, DEGDME; and tetraethylene glycol dimethyl ether, TEGME) on the cycling behaviour of Na–O 2 batteries and conclude that overall cell performance is highly dependent on solvent selection, salt concentration and rate of discharge/charge. We demonstrate how solvent selection helps define cell chemistry and performance by linking salt-solvent interactions to enthalpy of dissolution - and subsequently to sodium battery electrolyte properties - through the combination of both experimental and theoretical methodologies. The approaches detailed in this study could be used to predictively prepare electrolytes for Li-air batteries, other glyme-based electrochemical systems and low temperature applications.

25 ENERGY STORAGE↗
DOE OSTI2025

LLM-Based Adaptive Distribution Voltage Regulation Under Frequent Topology Changes: An In-Context MPC Framework

This paper proposes a large language model (LLM) based adaptive inverter control for distribution voltage regulation under frequent topology changes. We leverage the ability of the LLM to perform in-context learning and create a topology-adaptive surrogate model for power flow calculation. The surrogate model is then integrated with a long short-term memory-based load forecaster and a model predictive control (MPC) scheme to achieve the optimal inverter control that adapts to frequent topology changes. Unlike many existing works that assume fixed-topology grids or require the knowledge of all possible topologies when training a model, the proposed in-context MPC method tackles the distribution voltage control problem under various topologies and adapts to unknown topologies with limited data requirement for fine-tuning. The effectiveness of our method is demonstrated on a modified IEEE 123-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

Output model-following control synthesis for an oblique-wing aircraft

Recent interest in oblique-wing aircraft has focused on the potential aerodynamic performance advantage of a variable-skew oblique wing over a conventional or symmetric sweep wing. Unfortunately, the resulting asymmetric configuration has significant aerodynamic and inertial cross-coupling between the aircraft longitudinal and lateral-directional axes. Presented here is a decoupling control law synthesis technique that integrates stability augmentation, decoupling, and the direct incorporation of desired handling qualities into an output feedback controller. The proposed design technique uses linear quadratic regulator concepts in the framework of explicit model following. The output feedback strategy used is a suboptimal projection from the state space to the output space. Dynamics are then introduced into the controller to improve steady-state performance and increase system robustness. Closed-loop performance is shown by application of the control laws to the linearized equations of motion and nonlinear simulation of an oblique-wing aircraft.

Pahle, Joseph W.↗