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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Integration of a Decentralized Linear-Quadratic-Gaussian Control into GSFC's Universal 3-D Autonomous Formation Flying Algorithm

A decentralized control is investigated for applicability to the autonomous formation flying control algorithm developed by GSFC for the New Millenium Program Earth Observer-1 (EO-1) mission. This decentralized framework has the following characteristics: The approach is non-hierarchical, and coordination by a central supervisor is not required; Detected failures degrade the system performance gracefully; Each node in the decentralized network processes only its own measurement data, in parallel with the other nodes; Although the total computational burden over the entire network is greater than it would be for a single, centralized controller, fewer computations are required locally at each node; Requirements for data transmission between nodes are limited to only the dimension of the control vector, at the cost of maintaining a local additional data vector. The data vector compresses all past measurement history from all the nodes into a single vector of the dimension of the state; and The approach is optimal with respect to standard cost functions. The current approach is valid for linear time-invariant systems only. Similar to the GSFC formation flying algorithm, the extension to linear LQG time-varying systems requires that each node propagate its filter covariance forward (navigation) and controller Riccati matrix backward (guidance) at each time step. Extension of the GSFC algorithm to non-linear systems can also be accomplished via linearization about a reference trajectory in the standard fashion, or linearization about the current state estimate as with the extended Kalman filter. To investigate the feasibility of the decentralized integration with the GSFC algorithm, an existing centralized LQG design for a single spacecraft orbit control problem is adapted to the decentralized framework while using the GSFC algorithm's state transition matrices and framework. The existing GSFC design uses both reference trajectories of each spacecraft in formation and by appropriate choice of coordinates and simplified measurement modeling is formulated as a linear time-invariant system. Results for improvements to the GSFC algorithm and a multiple satellite formation will be addressed. The goal of this investigation is to progressively relax the assumptions that result in linear time-invariance, ultimately to the point of linearization of the non-linear dynamics about the current state estimate as in the extended Kalman filter. An assessment will then be made about the feasibility of the decentralized approach to the realistic formation flying application of the EO-1/Landsat 7 formation flying experiment.

Folta, David C.↗
DOE OSTI2024

Optimal experimental design: Formulations and computations

Questions of ‘how best to acquire data’ are essential to modelling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental design (OED) formalizes these questions and creates computational methods to answer them. This article presents a systematic survey of modern OED, from its foundations in classical design theory to current research involving OED for complex models. We begin by reviewing criteria used to formulate an OED problem and thus to encode the goal of performing an experiment. We emphasize the flexibility of the Bayesian and decision-theoretic approach, which encompasses information-based criteria that are well-suited to nonlinear and non-Gaussian statistical models. We then discuss methods for estimating or bounding the values of these design criteria; this endeavour can be quite challenging due to strong nonlinearities, high parameter dimension, large per-sample costs, or settings where the model is implicit. A complementary set of computational issues involves optimization methods used to find a design; we discuss such methods in the discrete (combinatorial) setting of observation selection and in settings where an exact design can be continuously parametrized. Finally we present emerging methods for sequential OED that build non-myopic design policies, rather than explicit designs; these methods naturally adapt to the outcomes of past experiments in proposing new experiments, while seeking coordination among all experiments to be performed. Throughout, we highlight important open questions and challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Two-epoch Orbit Estimation for Wide Binaries Resolved in Hipparcos and Gaia

The Hipparcos catalog and its Double and Multiple System Annex (DMSA) lists 4099 components with individual proper motions and coordinates on the epoch 1991.25. Many of these long-period binary stars are also present in Gaia Data Release 2 (DR2). Using the available relative positions and proper motions separated by 24.25 yr, the equations of relative orbital motion can be solved for the two epoch eccentric anomalies, orbital period, and eccentricity. This method employs elimination of the linear Thiele–Innes unknowns and nonlinear optimization of the remaining condition equations. The quality of these solutions is compromised by the insufficient condition and modest precision of the Hipparcos astrometric data, as revealed by Monte Carlo simulations with artificially perturbed data points. The presence of multiple systems and optical pairs can also perturb the results. Limited experiments with artificial data indicate that useful estimates can be obtained with a 25 yr epoch difference for wide binaries with orbital periods up to ~500 yr. The prospects of this method dramatically improve with the proposed next-generation space astrometry missions such as Gaia-NIR and Theia, especially when additional conditions are included from astrometric or spectroscopic measurements. An ancillary catalog of cross-identification and astrometric information for 1295 double-star pairs cross-matched in Gaia DR2 and Hipparcos is also published.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

An active optimal control strategy of rotor vibrations using external forces

An active control strategy for lateral rotor vibrations using external forces is proposed. An extended state observer is used to reconstruct the full states and the unbalance distribution. An optimal controller which accommodates persistent unbalance excitation is derived with feedback of estimated states and unbalances. Numerical simulations were conducted for two separate four degree of freedom rotor systems. These simulations indicated that the proposed strategy can achieve almost complete vibration cancellation. This was shown to be true even when the number of external control forces was less than the system order so long as coordinate coupling was present. Both steady state and transient response at a constant speed are presented.

Zhu, W.↗
NASA NTRS1992

Dietary protein deficiency reduces lysosomal and nonlysosomal ATP-dependent proteolysis in muscle

When rats are fed a protein deficient (PD) diet for 7 days, rates of proteolysis in skeletal muscle decrease by 40-50% (N. E. Tawa, Jr., and A. L. Goldberg. Am. J. Physiol. 263 (Endocrinol. Metab. 26): E317-325, 1992). To identify the underlying biochemical adaptations, we measured different proteolytic processes in incubated muscles. The capacity for intralysosomal proteolysis, as shown by sensitivity to methylamine or lysosomal protease inhibitors, fell 55-75% in muscles from PD rats. Furthermore, extracts of muscles of PD rats showed 30-70% lower activity of many lysosomal proteases, including cathepsins B, H, and C, and carboxypeptidases A and C, as well as other lysosomal hydrolases. The fall in cathepsin B and proteolysis was evident by 3 days on the PD diet, and both returned to control levels 3 days after refeeding of the normal diet. In muscles maintained under optimal conditions, 80-90% of protein breakdown occurs by nonlysosomal pathways. In muscles of PD rats, this ATP-dependent process was also 40-60% slower. Even though overall proteolysis decreased in muscles of PD rats, their capacity for Ca(2+)-dependent proteolysis increased (by 66%), as did the activity of the calpains (+150-250%). Thus the lysosomal and the ATP-dependent processes decrease coordinately and contribute to the fall in muscle proteolysis in PD animals.

Non-NASA Center↗
DOE OSTI2018

Distributed gradient descent for solving optimal power flow in radial networks

Node controllers and power distribution networks in accordance with embodiments of the invention enable distributed power control. One embodiment includes a node controller comprising a memory containing: a plurality of node operating parameters; and a plurality of node operating parameters describing operating parameters for a set of at least one node selected from the group consisting of at least one downstream node and at least one upstream node; wherein the processor is configured by the node controller application to: receive and store in memory a plurality of coordinator parameters describing operating parameters of a node coordinator by the network interface; and calculate updated node operating parameters using an iterative gradient projection process to determine updated node parameters using node operating parameters that describe operating parameters of node and operating parameters of the set of at least one node, where each iteration is determined by the coordinator parameters.

Gan, Lingwen↗
DOE OSTI2022

PARETO: An open-source produced water optimization framework

PARETO is an optimization framework for onshore produced water management that is meant to empower practitioners, researchers, and policymakers to identify cost-effective and environmentally sustainable ways to manage, treat, and – when possible – beneficially reuse produced water from oil & gas operations. Given user-provided water production, demand, and transportation data, PARETO can help determine where and how to build out produced water infrastructure while simultaneously improving the coordination of water deliveries over time. As shown here, the framework is innately designed to help organizations recognize opportunities for minimizing fresh and brackish water consumption by maximizing produced water reuse in active oil & gas development areas. PARETO is Python-based and is publicly available via GitHub.

42 ENGINEERING↗
DOE OSTI2025

IoT-based retrofit information diffusion in future smart communities

Community-scale building retrofits are not merely scaled-up versions of single-building retrofits. They involve complex challenges, such as reconciling individual interests with collective goals and managing the dynamic interplay between buildings through mechanisms like power grids and social connections. Internet of Things (IoT) connectivity holds the potential to leverage these interplays to balance individual and collective interests effectively in smart communities. One critical aspect of this interplay is information diffusion, which shapes how retrofit decisions spread among neighbors, influencing individual choices and ultimately impacting community-level retrofit outcomes. In other words, IoT-based smart devices automatically push tailored retrofit notifications to homeowners, which completely changes the format of information diffusion in the future. To investigate this influence by such information diffusion, the study used CityBES to simulate energy performance for different retrofits and applied an information diffusion model to analyze how decisions spread in a networked community of 192 buildings. The diffusion process was modeled on a weighted, directed network, capturing the dynamics of information flow and decision-making across 16 scenarios. Individual retrofit benefits were evaluated through payback years, while community-level retrofit outcomes were assessed using greenhouse gas (GHG) emission reductions. The results demonstrate that easier information diffusion among neighbors encourages households to prioritize retrofit measures that align with the majority’s optimal choices, even at the expense of individual financial benefits. In this case, such collective prioritization enhanced community-level retrofit performance, increasing GHG emission reductions by up to 29.4 %. However, this improvement came with trade-offs, as the average payback period for households extended by approximately 1.74 years. These findings highlight the potential of IoT-based information diffusion in future smart communities to coordinate individual interests with collective goals, ultimately accelerating community-level building retrofits.

Shu, Lei↗
DOE OSTI2020

Trifluoromethylated Phenanthroline Ligands Reduce Excited-State Distortion in Homoleptic Copper(I) Complexes

Herein, we report the synthesis and excited-state dynamics for a series of homoleptic copper(I) trifluoromethylated phenanthroline complexes with two, three, and four trifluoromethyl functional groups. Our analysis of the steady-state absorbance and emission, transient-absorption spectroscopy, and electronic-structure-theory calculations results enable in-depth analysis of the pseudo-Jahn–Teller distortion inhibition from increased steric hindrance of the trifluoromethyl functional group relative to the prototypical dimethyl phenanthroline complex. Surprisingly, our results demonstrate that the greatest degree of pseudo-Jahn–Teller distortion inhibition is achieved with trifluoromethylation of only the 2 and 9 positions by an unusual combination of steric hindrance and stabilization of a nondistorted 1 MLCT manifold observed by transient kinetic lifetimes and optimized excited-state structures. The intersystem-crossing (ISC) lifetime for the 2,9-bis(trifluoromethyl)-1,10-phenanthroline Cu(I) complex is 69 ps, while the triplet excited-state lifetime and emission quantum yield are 106 ns and 4 × 10 –3 , respectively. Further trifluoromethylation of the phenanthroline yields a greater σ bond inductive withdrawing force on the phenanthroline nitrogens, ultimately resulting in weaker coordination to the copper. Last, the surprising success of the 2,9-bis(trifluoromethyl)-1,10-phenanthroline Cu(I) complex by adjusting both ligand sterics and electronic properties outlines a new strategy for developing long-lived Cu(I) charge-transfer complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2025

The origin of the Stokes–Einstein relation in simple dense liquids

Here, we investigate the origin of the universal relation between structural relaxation and diffusion in simple dense liquids, known as the Stokes–Einstein (SE) relation. The fact that this relation, originally derived from a hydrodynamic model of a macroscopic particle in a viscous medium, can describe the microscopic-scale liquid dynamics still eludes understanding. We introduce a new universal measure of structural relaxation in a system of N identical particles based on an explicit decomposition of the configuration space into N! congruent convex polyhedra. This measure makes it possible to quantify the correlation between two distinct particle configurations in terms of their minimal Euclidean distance, optimized with respect to particle permutations. Using this measure alongside a model of independent random walkers under the single-occupancy constraint, we derive a master equation that quantifies the SE relation. It allows us to demonstrate that the universal relation between structural relaxation and diffusion in simple dense liquids is caused by two conditions: (a) the confinement of the dominant density fluctuations to the first coordination shell, manifested by de Gennes narrowing, and (b) Gaussianity of the diffusion process; the former is shown to be violated in low-density fluids, and the latter is known to be violated in supercooled liquids.

Physics - Condensed matter physics↗
DOE OSTI2024

Quantum Bipolaron Superconductivity from Quadratic Electron-Phonon Coupling

When the electron-phonon coupling is quadratic in the phonon coordinates, electrons can pair to form bipolarons due to phonon zero-point fluctuations, a purely quantum effect. Here we study superconductivity originating from this pairing mechanism in a minimal model and reveal that, in the strong coupling regime, the critical temperature (T c ) is only mildly suppressed by the coupling strength, in stark contrast to the exponential suppression in linearly coupled systems, thus implying higher optimal T c values. We demonstrate that large coupling constants of this flavor are achieved in known materials such as perovskites, and discuss strategies to realize such superconductivity using superlattices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Enhancing grid reliability and resilience through novel DER control, total situational awareness and integrated distribution-transmission representation

High penetration of distributed energy resources (DERs) can lead to overvoltage and thermal violations, miscoordination of protection devices and potential adverse control interactions. The challenges are exacerbated by a lack of operator situational awareness of behind-the-meter DERs, and T&D interactions. Furthermore, the large attack surface of a DER environment creates significant cyber security challenges. The motivation is to address these barriers. The main goal is to enable extreme levels of DERs in distribution systems while simultaneously enhancing their reliability and resiliency. This is achieved by a data-driven approach for operation, planning and control of distribution systems with total situational awareness and real-time DER control enabled by a network of edge intelligent devices, a cloud-based analytical platform, and secure communications.

14 SOLAR ENERGY↗
DOE OSTI2026

Applications of Multibody for Everybody (M4E) in Marine Energy

Multibody for Everybody (M4E) is an open-source symbolic dynamics modeling framework designed to automate the derivation of equations of motion and simulation of constrained multibody systems using the joint coordinate formulation.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Realizing the Residential Electrification Opportunity: One Heat Pump at a Time

Space and water heating in residential buildings is a major contributor of greenhouse gas (GHG) emissions in the United States. Advanced electric heat pumps (HP) and heat pump water heaters (HPWH) are poised to provide a low carbon alternative to traditional fossil-based space and water heating. Widespread deployment of heat pumps could help address the significant portion of building emissions and primary energy used in American households, however much work needs to be done to close the knowledge gap between like-for-like fossil equipment replacement and switching to HPs for the average contractor. The Pacific Northwest National Laboratory (PNNL) has been working on closing this knowledge gap through the development of decision tools and resources targeted towards contractors and installers. Developed in coordination with stakeholders and experienced contractors from a variety of geographic regions, these tools help streamline the process of sizing and selecting residential HPs and HPWHs for key use cases. Along with the complementary Retrofit Decision Tool developed by PNNL, the decision tools will help contractors understand the importance of whole building considerations when choosing HPs including envelope upgrades, duct assessments, and electrical assessments to ensure optimal selection and performance of the HP or HPWH. They also provide direct links to resources and best practices developed by PNNL as well as external entities to further help contractor education and training. This paper describes the development of these tools, and their role in moving the existing space and water heating market towards HPs to help realize the country’s decarbonization goals.

heat pump, heat pump water heater, Residential bui↗
DOE OSTI2024

A GPU accelerated mixed-precision Smoothed Particle Hydrodynamics framework with cell-based relative coordinates

Smoothed Particle Hydrodynamics (SPH) is essential for modeling complex large-deformation problems across various applications, requiring significant computational power. A major portion of SPH computation time is dedicated to the Nearest Neighboring Particle Search (NNPS) process. While advanced NNPS algorithms have been developed to enhance SPH efficiency, the potential efficiency gains from modern computation hardware remain underexplored. Here, this study investigates the impact of GPU parallel architecture, low-precision computing on GPUs, and GPU memory management on NNPS efficiency. Our approach employs a GPU-accelerated mixed-precision SPH framework, utilizing low precision float-point 16 (FP16) for NNPS while maintaining high precision for other components. To ensure FP16 accuracy in NNPS, we introduce a Relative Coordinated-based Link List (RCLL) algorithm, storing FP16 relative coordinates of particles within background cells. Our testing results show three significant speedup rounds for CPU-based NNPS algorithms. The first comes from parallel GPU computations, with up to a 1000x efficiency gain. The second is achieved through low-precision GPU computing, where the proposed FP16-based RCLL algorithm offers a 1.5x efficiency improvement over the FP64-based approach on GPUs. By optimizing GPU memory bandwidth utilization, the efficiency of the FP16 RCLL algorithm can be further boosted by 2.7x, as demonstrated in an example with 1 million particles. Our code is released at https://github.com/pnnl/lpNNPS4SPH.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Platinum-complexed phosphorous-doped carbon nitride for electrocatalytic hydrogen evolution

Sustainable hydrogen gas production is critical for future fuel infrastructure. Here, a series of phosphorous-doped carbon nitride materials were synthesized by thermal annealing of urea and ammonium hexafluorophosphate, and platinum was atomically dispersed within the structural scaffold by thermal refluxing with Zeise's salt forming Pt–N/P/Cl coordination interactions, as manifested in X-ray photoelectron and absorption spectroscopic measurements. The resulting materials were found to exhibit markedly enhanced electrocatalytic activity towards the hydrogen evolution reaction (HER) in acidic media, as compared to the P-free counterpart. This was accounted for by P doping that led to a significantly improved charge carrier density within C 3 N 4 , and the sample with the optimal P content showed an overpotential of only –22 mV to reach the current density of 10 mA cm –2 , lower than that of commercial Pt/C (–26 mV), and a mass activity (7.1 mA μg –1 Pt at –70 mV vs. reversible hydrogen electrode) nearly triple that of the latter. Finally, results from the present study highlight the significance of P doping in the manipulation of the electronic structures of metal/carbon nitride nanocomposites for high-performance HER electrocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Data-Driven Pricing Strategy for Demand-Side Resource Aggregators

We consider a utility who seeks to coordinate the energy consumption of multiple demand-side flexible resource aggregators. For the purpose of privacy protection, the utility has no access to the detailed information of loads of resource aggregators. Instead, we assume that the utility can directly observe each aggregator's aggregate energy consumption outcomes. Furthermore, the utility can leverage resource aggregator energy consumption via time-varying electricity price profiles. Based on inverse optimization technique, we propose an estimation method for the utility to infer the energy requirement information of aggregators. Subsequently, we design a data-driven pricing scheme to help the utility achieve system-level control objectives (e.g., minimizing peak demand) by combining hybrid particle swarm optimizer with mutation algorithm and an iterative algorithm. Case studies have demonstrated the effectiveness of the proposed approach against two benchmark pricing strategies-a flat-rate scheme and a time-of-use scheme.

data-driven optimization↗