Search NASA⌕ Search

SEARCH · Search NASA

Results for “Complexity bounding”

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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Photoionization and Photofragmentation Dynamics of I 2 in Intense Laser Fields: A Velocity-Map Imaging Study

The photoionization and photofragmentation dynamics of I 2 in intense femtosecond near-infrared laser fields were studied using velocity-map imaging of cations, electrons, and anions. A series of photofragmentation pathways originating from different cationic electronic states were observed following single ionization, leading to I + fragments with distinct kinetic energies, which could not be resolved in previous studies. Photoelectron spectra indicate that these high-lying dissociative states are primarily produced through nonresonant ionization from several molecular orbitals (MO) of the neutral. The photoelectron spectra also show clear signatures of resonant ionization pathways (Freeman resonances) to low-lying bound ionic states via Rydberg states of the neutral moiety. To investigate the role of these Rydberg states further, we imaged anionic products (I – ) formed through ion-pair dissociations of neutral molecules excited to these Rydberg states by the intense femtosecond laser pulse. Collectively, these results shed significant new light on the complex dynamics of I 2 molecules in intense laser fields and on the important role of neutral Rydberg states in a full description of strong-field phenomena in molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Polarity-Tolerant Chloride Binding in Foldamer Capsules by Programmed Solvent-Exclusion

Persistent anion binding in a wide range of solution environments is a key challenge that continues to motivate and demand new strategies in synthetic receptor design. Though strong binding in low-polarity solvents has become routine, our ability to maintain high affinities in high-polarity solvents has not yet reached the standard set by nature. Anions are bound and transported regularly in aqueous environments by proteins that use secondary and tertiary structure to isolate anion binding sites from water. Inspired by this principle of solvent exclusion, we created a sequence-defined foldameric capsule whose global minimum conformation displays a helical folded state and is preorganized for 1:1 anion complexation. The high stability of the folded geometry and its ability to exclude solvent were supported by solid-state and solution phase studies. This capsule then withstood a 4-fold increase in solvent dielectric constant (εr) from dichloromethane (9) to acetonitrile (36) while maintaining a high and solvent-independent affinity of 10 5 M –1 ; ΔG ~ 28 kJ mol –1 . This behavior is unusual. More typical of solvent-dependent behavior, Cl – affinities were seen to plummet in control compounds, such as aryl-triazole macrocycles and pentads, with their solvent-exposed binding cavities susceptible to dielectric screening. Finally, dimethyl sulfoxide denatures the foldamer by putative solvent binding, which then lowers the foldamer’s Cl – affinity to normal levels. Furthermore, the design of this capsule demonstrates a new prototype for the development of potent receptors that can operate in polar solvents and has the potential to help manage hydrophilic anions present in the hydrosphere and biosphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

NO Coupling at Copper to cis -Hyponitrite: N 2 O Formation via Protonation and H-Atom Transfer

Copper nitrite reductases (CuNIRs) convert NO 2 – to NO as well as NO to N 2 O under high NO flux at a mononuclear type 2 Cu center. While model complexes illustrate N–N coupling from NO that results in symmetric trans-hyponitrite [Cu II ]– ONNO–[Cu II ] complexes, in this work we report NO assembly at a single Cu site in the presence of an external reductant Cp* 2 M (M = Co, Fe) to give the first copper cis-hyponitrites [Cp* 2 M]{[Cu II ](κ 2 - O 2 N 2 )[Cu I ]}. Importantly, the κ 1 –N-bound [Cu I ] fragment may be easily removed by the addition of mild Lewis bases such as CNAr or pyridine to form the spectroscopically similar anion {[Cu II ](κ 2 -O 2 N 2 )} – . The addition of electrophiles such as H + to these anionic copper(II) cis-hyponitrites leads to N 2 O generation with the formation of the dicopper(II)-bis-μ-hydroxide [Cu II ] 2 (μ- OH) 2 . One-electron oxidation of the {[Cu II ](κ 2 -O 2 N 2 )} – core turns on H-atom transfer reactivity, enabling the oxidation of 9,10- dihydroanthracene to anthracene with concomitant formation of N 2 O and [Cu II ] 2 (μ-OH) 2 . These studies illustrate both the reductive coupling of NO at a single copper center and a way to harness the strong oxidizing power of nitric oxide via the neutral cis- hyponitrite [Cu](κ 2 -O 2 N 2 ).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Bayesian Framework for Multi-Timescale State Estimation in Low-Observable Distribution Systems

To support the smart grid paradigm, there has been a significant increase in sensor deployments and metering infrastructure in distribution systems. However, the measurements provided by these sensors and metering devices are typically sampled at different rates and could suffer from losses during the aggregation process. It is crucial to effectively reconcile the time-series measurements for a reliable state estimation. While weighted least squares has been the traditional approach for state estimation, sparsity-based approaches like matrix completion have become popular due to their superior performance in low-observability conditions. This paper proposes a Bayesian framework for both multi-timescale data aggregation and matrix completion based state estimation. Specifically, the multiscale time-series data aggregated from heterogenous sources are reconciled using a multitask Gaussian process that exploits the spatio-temporal correlations. Here, the resulting consistent timeseries alongwith the confidence bound on the imputations are fed into a Bayesian matrix completion method augmented with linearized power-flow constraints to accurately estimate the states in low-observability conditions. Results on three phase unbalanced IEEE 37 and IEEE 123 bus test systems reveal the superior performance of the proposed Bayesian framework. The computational complexity for the proposed Bayesian framework is also quantified.

42 ENGINEERING↗
DOE OSTI2020

Optimal polynomial based quantum eigenstate filtering with application to solving quantum linear systems

We present a quantum eigenstate filtering algorithm based on quantum signal processing (QSP) and minimax polynomials. The algorithm allows us to efficiently prepare a target eigenstate of a given Hamiltonian, if we have access to an initial state with non-trivial overlap with the target eigenstate and have a reasonable lower bound for the spectral gap. We apply this algorithm to the quantum linear system problem (QLSP), and present two algorithms based on quantum adiabatic computing (AQC) and quantum Zeno effect respectively. Both algorithms prepare the final solution as a pure state, and achieves the near optimal $\mathcal{\tilde{O}}$(dκlog(1/ϵ)) query complexity for a d -sparse matrix, where κ is the condition number, and ϵ is the desired precision. Neither algorithm uses phase estimation or amplitude amplification.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1975

Design of Energy Storage Reactors for Dc-To-Dc Converters

Two methodical approaches to the design of energy-storage reactors for a group of widely used dc-to-dc converters are presented. One of these approaches is based on a steady-state time-domain analysis of piecewise-linearized circuit models of the converters, while the other approach is based on an analysis of the same circuit models, but from an energy point of view. The design procedure developed from the first approach includes a search through a stored data file of magnetic core characteristics and results in a list of usable reactor designs which meet a particular converter's requirements. Because of the complexity of this procedure, a digital computer usually is used to implement the design algorithm. The second approach, based on a study of the storage and transfer of energy in the magnetic reactors, leads to a straightforward design procedure which can be implemented with hand calculations. An equation to determine the lower-bound volume of workable cores for given converter design specifications is derived. Using this computer lower-bound volume, a comparative evaluation of various converter configurations is presented.

Chen, D. Y.↗
NASA NTRS1991

A time delay controller for magnetic bearings

The control of systems with unknown dynamics and unpredictable disturbances has raised some challenging problems. This is particularly important when high system performance needs to be guaranteed at all times. Recently, the Time Delay Control has been suggested as an alternative control scheme. The proposed control system does not require an explicit plant model nor does it depend on the estimation of specific plant parameters. Rather, it combines adaptation with past observations to directly estimate the effect of the plant dynamics. A control law is formulated for a class of dynamic systems and a sufficient condition is presented for control systems stability. The derivation is based on the bounded input-bounded output stability approach using L sub infinity function norms. The control scheme is implemented on a five degrees of freedom high speed and high precision magnetic bearing. The control performance is evaluated using step responses, frequency responses, and disturbance rejection properties. The experimental data show an excellent control performance despite the system complexity.

Youcef-Toumi, K.↗
NASA NTRS1991

Multi-stage decoding for multi-level block modulation codes

In this paper, we investigate various types of multi-stage decoding for multi-level block modulation codes, in which the decoding of a component code at each stage can be either soft-decision or hard-decision, maximum likelihood or bounded-distance. Error performance of codes is analyzed for a memoryless additive channel based on various types of multi-stage decoding, and upper bounds on the probability of an incorrect decoding are derived. Based on our study and computation results, we find that, if component codes of a multi-level modulation code and types of decoding at various stages are chosen properly, high spectral efficiency and large coding gain can be achieved with reduced decoding complexity. In particular, we find that the difference in performance between the suboptimum multi-stage soft-decision maximum likelihood decoding of a modulation code and the single-stage optimum decoding of the overall code is very small: only a fraction of dB loss in SNR at the probability of an incorrect decoding for a block of 10(exp -6). Multi-stage decoding of multi-level modulation codes really offers a way to achieve the best of three worlds, bandwidth efficiency, coding gain, and decoding complexity.

Lin, Shu↗
NASA NTRSDate not supplied

SLS Integrated Modal Test Uncertainty Quantification using the Hybrid Parametric Variation Method

Uncertainty in structural loading during launch is a significant concern in the development of spacecraft and launch vehicles. Small variations in launch vehicle and payload mode shapes and their interaction can result in significant variation in system loads. In many cases involving large aerospace systems it is difficult, not economical, or impossible to perform a system modal test. However, it is still vital to obtain test results that can be compared with analytical predictions to validate models. Instead, the “Building Block Approach” is used in which system components are tested individually. Component models are correlated and updated to agree as best they can with test results. The Space Launch System consists of a number of components that are assembled into a launch vehicle. Finite element models of the components are developed, reduced to Hurty/Craig-Bampton models and assembled to represent different phases of flight. The only opportunity to obtain modal test data from an assembled Space Launch System will be during the Integrated Modal Test. There is always uncertainty in every model, which flows into uncertainty in predicted system results. Uncertainty Quantification is used to determine statistical bounds on prediction accuracy based on model uncertainty. For the Space Launch System, model uncertainty is at the Hurty/Craig-Bampton component level. Uncertainty in the Hurty/Craig-Bampton components is quantified using the hybrid parametric variation approach that combines parametric and nonparametric uncertainty. Uncertainty in model form is one of the biggest contributors to uncertainty in complex built-up structures. This type of uncertainty cannot be represented by variations infinite element model input parameters and thus cannot be included in a parametric approach. However, model-form uncertainty can be modeled using a nonparametric approach based on random matrix theory. The hybrid parametric variation method requires the selection of dispersion values for the Hurty/Craig-Bampton fixed-interface eigenvalues, and the Hurty/Craig-Bampton stiffness matrices. Component test/analysis frequency error is used to identify the fixed-interface eigenvalue dispersions, while test/analysis cross-orthogonality is used to identify stiffness dispersion values. The hybrid parametric variation uncertainty quantification approach is applied to the Space Launch System Integrated Modal Test configuration. Monte Carlo analysis is performed, and statistics are determined for modal correlation metrics, frequency response from Integrated Modal Test shakers to selected accelerometers, as well as other metrics for determining how well target modes are excited and identified. If the predicted uncertainty envelopes future Integrated Modal Test results, then there will be increased confidence in the utility of the component-based hybrid parametric variation uncertainty quantification approach.

Uncertainty Quantification↗
DOE OSTI2020

Chemical and structural investigation of the paroxetine-human serotonin transporter complex

Antidepressants target the serotonin transporter (SERT) by inhibiting serotonin reuptake. Structural and biochemical studies aiming to understand binding of small-molecules to conformationally dynamic transporters like SERT often require thermostabilizing mutations and antibodies to stabilize a specific conformation, leading to questions about relationships of these structures to the bonafide conformation and inhibitor binding poses of wild-type transporter. To address these concerns, we determined the structures of ΔN72/ΔC13 and ts2-inactive SERT bound to paroxetine analogues using single-particle cryo-EM and x-ray crystallography, respectively. We synthesized enantiopure analogues of paroxetine containing either bromine or iodine instead of fluorine. We exploited the anomalous scattering of bromine and iodine to define the pose of these inhibitors and investigated inhibitor binding to Asn177 mutants of ts2-active SERT. These studies provide mutually consistent insights into how paroxetine and its analogues bind to the central substrate-binding site of SERT, stabilize the outward-open conformation, and inhibit serotonin transport.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1990

Strategies for concurrent processing of complex algorithms in data driven architectures

The performance modeling and enhancement for periodic execution of large-grain, decision-free algorithms in data flow architectures is examined. Applications include real-time implementation of control and signal processing algorithms where performance is required to be highly predictable. The mapping of algorithms onto the specified class of data flow architectures is realized by a marked graph model called ATAMM (Algorithm To Architecture Mapping Model). Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of resource (computing element) requirements. A systematic design procedure is described for generating operating conditions for predictable performance both with and without resource constraints. An ATAMM simulator is used to test and validate the performance prediction by the design procedure. Experiments on a three resource testbed provide verification of the ATAMM model and the design procedure.

Stoughton, John W.↗
NASA NTRS1990

Strategies for concurrent processing of complex algorithms in data driven architectures

Performance modeling and performance enhancement for periodic execution of large-grain, decision-free algorithms in data flow architectures are discussed. Applications include real-time implementation of control and signal processing algorithms where performance is required to be highly predictable. The mapping of algorithms onto the specified class of data flow architectures is realized by a marked graph model called algorithm to architecture mapping model (ATAMM). Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of resource (computing element) requirements. A systematic design procedure is described for generating operating conditions for predictable performance both with and without resource constraints. An ATAMM simulator is used to test and validate the performance prediction by the design procedure. Experiments on a three resource testbed provide verification of the ATAMM model and the design procedure.

Som, Sukhamoy↗
DOE OSTI2020

Consistency-Enhanced Evolution for Variable Selection Can Identify Key Chemical Information from Spectroscopic Data

In the last few decades, spectroscopic techniques such as near-infrared (NIR) spectroscopy have gained wide applications in several industries, such as the pharmaceutical, agricultural, oil, and gas industries. As a result, various soft sensors have been developed to predict sample properties from spectroscopic readings. Because the spectroscopic readings at different wavelengths, especially at the adjacent wavelengths, are highly correlated, it has been shown that variable selection could significantly improve a soft sensor’s prediction performance while reducing the model complexity. To improve the prediction performance, most variable selection methods focus on identifying the variables (i.e., wavelengths or wavelength segments) that are strongly correlated with the dependent variable. Although many successful applications have been reported, these variable selection methods do have their limitations. Specifically, the selected wavelengths sometimes show little connection to the chemical bounds or functional groups presenting in the sample. In addition, the selected variables can be quite sensitive to the choice of the training samples. In this work, we address these limitations from a different perspective: if a variable selection algorithm can identify the truly relevant input variables, it should consistently identify the same subset of variables regardless of the choice of the training samples. Therefore, we propose a variable selection method that aims to improve the consistency of variable selection resulting from different training samples. Furthermore, the new algorithm is termed consistency-enhanced evolution for variable selection (CEEVS). To demonstrate the performance and robustness of CEEVS, we compare the proposed method with three representative variable selection methods using five published NIR data sets. These case studies clearly demonstrate that by improving the variable selection consistency, we can not only achieve improved prediction performance, but also identify key chemical information from spectroscopic data.

42 ENGINEERING↗
DOE OSTI2025

Heterobinuclear Molecular Precursors Direct the Formation of Supported Subnanometer Cu–M Clusters with Tunable Catalytic Behavior

Subnanometer bimetallic clusters hold great promise for catalytic applications due to their unique electronic properties and high surface-to-volume ratios. However, precise control over their composition and size remains a major challenge, particularly for immiscible metal pairs. Here, we report a surface-anchored molecular approach for synthesizing ∼0.7–0.8 nm Cu–M (M = Ru, Mo, W, Fe) bimetallic clusters on mesoporous silica supports, using heterobinuclear N-heterocyclic carbene (NHC)-based complexes as precursors. The NHC ligand functionalized with an alkoxysilane anchor enables robust grafting to the silica interface. Controlled calcination and reduction lead to subnanometer clusters with tunable composition, dictated by the metal–metal bond stability in the precursor. In situ transmission electron microscopy reveals cluster growth proceeds via sintering of adjacent surface-bound units, while elevated temperatures above 300 °C triggering diffusion and phase separation. Catalytic testing in ethylene hydrogenation demonstrates composition-dependent activity and kinetics, with CuRu and CuW clusters exhibiting lower apparent activation energy barriers compared with monometallic Cu nanoparticles. This study establishes a generalizable strategy for the interfacial synthesis of alloyed clusters from molecular precursors and provides mechanistic insight into how precursor design governs nanostructure formation and catalytic behavior.

N-heterocyclic carbene↗
DOE OSTI2023

Generating Synthetic Time Series Photovoltaic Data with Real-World Physical Challenges and Noise for Use in Algorithm Test and Validation

The PV Fleet Data Initiative and other projects seek the develop algorithms for automated analysis of PV time series data for extraction of statistical information and other parameters of the data such as degradation rates, soiling loss information, tracker performance, clipping or curtailment, system availability and other valuable information. While there is a vast body of PV data available for application of said extraction algorithms it is difficult to validate these algorithms because the true parameters to be extracted are not known. There has been a wide use of synthetic data in the literature for algorithm validation but this synthetic data is typically very bounded by the problem or topic at hand. The PV Fleet Data Initiative project has demonstrated that real time series PV data almost always includes a host of data quality and physical problems that, in reality, any automated PV abstraction algorithm must handle appropriately. For this reason, this work describes the development of a complex synthetic PV times series data set that includes data quality and physical problems that have been experienced in real world PV data. The various quality and physical problems are documented in the synthetic data so that users can test the validity of various PV extraction algorithms as well as develop new algorithms to solve problems this data set can support.

14 SOLAR ENERGY↗
NASA NTRS1992

A transport equation for eddy viscosity

A transport equation for eddy viscosity is proposed for wall bounded turbulent flows. The proposed model reduces to a quasi-homogeneous form far from surfaces. Near to a surface, the nonhomogeneous effect of the wall is modeled by an elliptic relaxation model. All the model terms are expressed in local variables and are coordinate independent; the model is intended to be used in complex flows. Turbulent channel flow and turbulent boundary layer flows with/without pressure gradient are calculated using the present model. Comparisons between model calculations and direct numerical simulation or experimental data show good agreement.

Durbin, P. A.↗
DOE OSTI2021

Ca2+ Effects on Fe(II) Interactions with Mn-Binding Sites in Mn-Depleted Oxygen-Evolving Complexes of Photosystem II and on Fe Replacement of Mn in Mn-Containing, Ca-Depleted Complexes

Fe(II) cations bind with high efficiency and specificity at the high-affinity (HA), Mn-binding site (termed the “blocking effect” since Fe blocks further electron donation to the site) of the oxygen-evolving complex (OEC) in Mn-depleted, photosystem II (PSII) membrane fragments (Semin et al. in Biochemistry 41:5854, 2002). Furthermore, Fe(II) cations can substitute for 1 or 2Mn cations (pH dependent) in Ca-depleted PSII membranes (Semin et al. in Journal of Bioenergetics and Biomembranes 48:227, 2016; Semin et al. in Journal of Photochemistry and Photobiology B 178:192, 2018). In the current study, we examined the effect of Ca2+ cations on the interaction of Fe(II) ions with Mn-depleted [PSII(-Mn)] and Ca-depleted [PSII(-Ca)] photosystem II membranes. We found that Ca2+ cations (about 50 mM) inhibit the light-dependent oxidation of Fe(II) (5 µM) by about 25% in PSII(-Mn) membranes, whereas inhibition of the blocking process is greater at about 40%. Blocking of the HA site by Fe cations also decreases the rate of charge recombination between QA- and YZ•+ from t1/2?=?30 ms to 46 ms. However, Ca2+ does not affect the rate during the blocking process. An Fe(II) cation (20 µM) replaces 1Mn cation in the Mn4CaO5 catalytic cluster of PSII(-Ca) membranes at pH 5.7 but 2 Mn cations at pH 6.5. In the presence of Ca2+ (10 mM) during the substitution process, Fe(II) is not able to extract Mn at pH 5.7 and extracts only 1Mn at pH 6.5 (instead of two without Ca2+). Measurements of fluorescence induction kinetics support these observations. Inhibition of Mn substitution with Fe(II) cations in the OEC only occurs with Ca2+ and Sr2+ cations, which are also able to restore oxygen evolution in PSII(-Ca) samples. Nonactive cations like La3+, Ni2+, Cd2+, and Mg2+ have no influence on the replacement of Mn with Fe. These results show that the location and/or ligand composition of one Mn cation in the Mn4CaO5 cluster is strongly affected by calcium depletion or rebinding and that bound calcium affects the redox potential of the extractable Mn4 cation in the OEC, making it resistant to reduction.

calcium↗
DOE OSTI2022

Model-based predictive control of multi-zone commercial building with a lumped building modelling approach

Here this study investigates the applicability of a lumped building modeling approach to model-based predictive control (MPC) to alleviate the complex modeling process of the grey-box multi-zone building model. Based on experimental data, two building models were estimated in this study. The detailed model as a reference case and a lumped model were estimated with decentralized and conventional approaches, respectively. Then, simulations were performed with two boundary conditions, including the comfort bound and electricity cost structure. The performances of the MPC with the detailed and lumped models were analyzed compared to the feedback control. More savings was achieved with a larger comfort bound and more aggressive electricity cost structure. The savings potential of the proposed lumped model approach was not as high as that of the detailed model. However, the proposed method yields good control performance, whose savings was approximately 8.6% over that of feedback control. These results suggest that the proposed method can be used to facilitate MPC implementation in multi-zone building applications.

42 ENGINEERING↗