Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “complexity theory”

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 163 records · Page 9

Constant Overhead Entanglement Distillation via Scrambling

High-fidelity quantum entanglement enables key quantum networking capabilities such as secure communication and distributed quantum computing, but long-distance entanglement distribution is limited by noise and loss. Entanglement distillation protocols address this problem by extracting high-fidelity Bell pairs from multiple noisy ones. The primary objective is minimizing the resource overhead: the number of noisy input pairs needed to distill each high-fidelity output pair. While protocols achieving optimal overhead are known in theory, they often require complex decoding operations that make practical implementation challenging. We circumvent this challenge by introducing protocols that use quantum scrambling—the spreading of quantum information under chaotic dynamics—through random Clifford operations. Based on this scrambling mechanism, our protocol maintains asymptotically constant overhead, independent of the desired output error rate $\bar{𝜖}$ , and can be implemented with shallow quantum circuits of depth 𝑂⁡(poly log log⁡ $\bar{𝜖}$ −1 ) and memory 𝑂⁡(poly log⁡ $\bar{𝜖}$ −1 ). Our protocol remains effective even with noisy quantum gates. By incorporating error correction, our protocol achieves state-of-the-art performance: starting with pairs of 10% initial infidelity, we require only seven noisy inputs per output pair to distill a single Bell pair with infidelity $\bar{𝜖}$ =10 −12 , substantially outperforming existing schemes. We demonstrate the utility of our protocols for quantum repeater networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Reduced-basis method for few-body bound-state emulation

Recent advances in both theoretical and computational methods have enabled large-scale, precision calculations of the properties of atomic nuclei. With the growing complexity of modern nuclear theory, however, also comes the need for novel methods to perform systematic studies and quantify the uncertainties of models when confronted with experimental data. Here, this study presents an application of such an approach, the reduced basis method, to substantially lower computational costs by constructing a significantly smaller Hamiltonian subspace informed by previous solutions. Our method shows comparable efficiency and accuracy to other dimensionality reduction techniques on an artificial three-body bound system while providing a richer representation of physical information in its projection and training subspace. This methodological advancement can be applied in other contexts and has the potential to greatly improve our ability to systematically explore theoretical models and thus enhance our understanding of the fundamental properties of nuclear systems.

cluster models↗

Modeling Spatial Asymmetries in Teleconnected Extreme Temperatures

Abstract Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.

Krock, Mitchell L.↗

Zentropy Theory for Transformative Functionalities of Magnetic and Superconducting Materials

The proposed research developed the zentropy theory through applications to complex magnetic materials and superconductors under the hypothesis that the emergent properties of complex magnetic materials and superconductors can be predicted by statistical mechanics of ergodic microstates with their partition functions computed from DFT-predicted free energies. The key objective is to develop approaches to systematically determine the types and number of microstates and the supercell size in DFT-based calculations through convergency of macroscopic functionalities, with the incorporation of our mixed-space approach accounting for the interactions between periodic supercells. In addition to use scientific intuitions to guide the design of important microstates, the key innovation of the proposed research is to integrate the domain knowledge and the material-property-descriptor database (MPDD) with 4 million microstates, which is supported by our deep neural network machine learning models (SIPFENN: structure-informed prediction of formation energy using neural networks) and integrated with our high throughput DFT Tool Kit (DFTTK). For complex magnetic materials, one of the objectives is to develop approaches to calculate short-range ordering from the statistical distribution of each microstate. For superconductors, the divergency of quasiparticle effective mass at a quantum critical point will be investigated, and the superconducting and non-superconducting microstates will be delineated through analysis of electronic band structure, density of states, charge density, and Fermi surface.

36 MATERIALS SCIENCE↗

Sound propagation in and radiation from acoustically lined flow ducts: A comparison of experiment and theory

The results of an experimental and theoretical study of many of the fundamental details of sound propagation in hard wall and soft wall annular flow ducts are reported. The theory of sound propagation along such ducts and the theory for determining the complex radiation impedance of higher order modes of an annulus are outlined, and methods for generating acoustic duct modes are developed. The results of a detailed measurement program on propagation in rigid wall annular ducts with and without airflow through the duct are presented. Techniques are described for measuring cut-on frequencies, modal phase speed, and radial and annular mode shapes. The effects of flow velocity on cut-on frequencies and phase speed are measured. Comparisons are made with theoretical predictions for all of the effects studies. The two microphone method of impedance is used to measure the effects of flow on acoustic liners. A numerical study of sound propagation in annular ducts with one or both walls acoustically lined is presented.

Plumblee, H. E., Jr.↗

Frequency-dependent FDTD methods using Z transforms

While the frequency-dependent finite-difference time-domain, or (FD)2TD, method can correctly calculate EM propagation through media whose dielectric properties are frequency-dependent, more elaborate applications lead to greater (FD)2TD complexity. Z-transform theory is presently used to develop the mathematical bases of the (FD)2TD method, simultaneously obtaining a clearer formulation and allowing researchers to draw on the existing literature of systems analysis and signal-processing.

Sullivan, Dennis M.↗

Preliminary Work for Examining the Scalability of Reinforcement Learning

Researchers began studying automated agents that learn to perform multiple-step tasks early in the history of artificial intelligence (Samuel, 1963; Samuel, 1967; Waterman, 1970; Fikes, Hart & Nilsonn, 1972). Multiple-step tasks are tasks that can only be solved via a sequence of decisions, such as control problems, robotics problems, classic problem-solving, and game-playing. The objective of agents attempting to learn such tasks is to use the resources they have available in order to become more proficient at the tasks. In particular, each agent attempts to develop a good policy, a mapping from states to actions, that allows it to select actions that optimize a measure of its performance on the task; for example, reducing the number of steps necessary to complete the task successfully. Our study focuses on reinforcement learning, a set of learning techniques where the learner performs trial-and-error experiments in the task and adapts its policy based on the outcome of those experiments. Much of the work in reinforcement learning has focused on a particular, simple representation, where every problem state is represented explicitly in a table, and associated with each state are the actions that can be chosen in that state. A major advantage of this table lookup representation is that one can prove that certain reinforcement learning techniques will develop an optimal policy for the current task. The drawback is that the representation limits the application of reinforcement learning to multiple-step tasks with relatively small state-spaces. There has been a little theoretical work that proves that convergence to optimal solutions can be obtained when using generalization structures, but the structures are quite simple. The theory says little about complex structures, such as multi-layer, feedforward artificial neural networks (Rumelhart & McClelland, 1986), but empirical results indicate that the use of reinforcement learning with such structures is promising. These empirical results make no theoretical claims, nor compare the policies produced to optimal policies. A goal of our work is to be able to make the comparison between an optimal policy and one stored in an artificial neural network. A difficulty of performing such a study is finding a multiple-step task that is small enough that one can find an optimal policy using table lookup, yet large enough that, for practical purposes, an artificial neural network is really required. We have identified a limited form of the game OTHELLO as satisfying these requirements. The work we report here is in the very preliminary stages of research, but this paper provides background for the problem being studied and a description of our initial approach to examining the problem. In the remainder of this paper, we first describe reinforcement learning in more detail. Next, we present the game OTHELLO. Finally we argue that a restricted form of the game meets the requirements of our study, and describe our preliminary approach to finding an optimal solution to the problem.

Clouse, Jeff↗

Applying Graph Theory to Problems in Air Traffic Management

Graph theory is used to investigate three different problems arising in air traffic management. First, using a polynomial reduction from a graph partitioning problem, it is shown that both the airspace sectorization problem and its incremental counterpart, the sector combination problem are NP-hard, in general, under several simple workload models. Second, using a polynomial time reduction from maximum independent set in graphs, it is shown that for any fixed e, the problem of finding a solution to the minimum delay scheduling problem in traffic flow management that is guaranteed to be within n1-e of the optimal, where n is the number of aircraft in the problem instance, is NP-hard. Finally, a problem arising in precision arrival scheduling is formulated and solved using graph reachability. These results demonstrate that graph theory provides a powerful framework for modeling, reasoning about, and devising algorithmic solutions to diverse problems arising in air traffic management.

precision arrival scheduling↗

Testing First-Order Logic Axioms in AutoCert

AutoCert [2] is a formal verification tool for machine generated code in safety critical domains, such as aerospace control code generated from MathWorks Real-Time Workshop. AutoCert uses Automated Theorem Provers (ATPs) [5] based on First-Order Logic (FOL) to formally verify safety and functional correctness properties of the code. These ATPs try to build proofs based on user provided domain-specific axioms, which can be arbitrary First-Order Formulas (FOFs). These axioms are the most crucial part of the trusted base, since proofs can be submitted to a proof checker removing the need to trust the prover and AutoCert itself plays the part of checking the code generator. However, formulating axioms correctly (i.e. precisely as the user had really intended) is non-trivial in practice. The challenge of axiomatization arise from several dimensions. First, the domain knowledge has its own complexity. AutoCert has been used to verify mathematical requirements on navigation software that carries out various geometric coordinate transformations involving matrices and quaternions. Axiomatic theories for such constructs are complex enough that mistakes are not uncommon. Second, adjusting axioms for ATPs can add even more complexity. The axioms frequently need to be modified in order to have them in a form suitable for use with ATPs. Such modifications tend to obscure the axioms further. Thirdly, speculating validity of the axioms from the output of existing ATPs is very hard since theorem provers typically do not give any examples or counterexamples.

Ahn, Ki Yung↗

Application of narrow band control to reduce vibrations in magnetic bearing systems

The benefits of narrowband control theory for simple, open-loop stable systems are illustrated, and how the approach changes for magnetic bearing systems, which are open-loop unstable, is discussed. Magnetic bearing systems are good applications for narrowband control theory. Two sources of synchronous forces, the measurement error and the magnetic unbalance, are discussed. Both the measurement error and the magnetic unbalance manifest themselves as synchronous disturbances. It is shown that narrowband control theory for simple, open-loop stable systems provides excellent performance and good stability robustness. Because magnetic bearing systems are open-loop stable, the narrowband control approach becomes more complex. Disturbance accommodating control (DAC) theory is introduced as an effective approach to reduce vibrations in magnetic bearing systems. It is used to develop a control/estimation scheme that enables the rotor to spin about its center of mass in the presence of the measurement error disturbances.

Gaffney, Monique S.↗

Hydrogenation of ethylene over molybdenum–sulfur complexes supported on UiO-66

Development of supported single-site catalysts using small metal sulfide complexes could significantly help in the development of cost-effective catalytic materials to drive selective hydrogenation and hydrogenolysis. The goal of this study is to contribute to the development of metal sulfide catalysts by calculating the thermodynamics of a catalytic cyclic involving a metal organic framework functionalized by insertion of metal sulfide. Anchored metal sulfide complexes can potentially be designed with ligands with distinctly different electronic and catalytic properties for specific catalytic applications. Here we examine the hydrogenation of ethylene as a model. We use density functional theory to investigate molybdenum–sulfur complexes as active catalysts anchored on the metal–organic framework UiO-66 as a stable support. Our calculations show that the anchored complexes with more than two sulfur ligands are unfavorable for ethylene adsorption, so we study complexes with one or two sulfur ligands. Hydrogenation of the unsaturated carbon double bond requires the transfer of two hydrogen atoms, which can occur via heterolytic activation of hydrogen to form a Mo-hydride and a protonated sulfur – either by hydride transfer followed by proton transfer or via proton transfer followed by hydride transfer, and we find that both mechanisms proceed via two-state reactivity involving two spin states along the reaction path. Of the two catalysts studied in gas the phase, the MoS single-sulfur–ligand complex with lower oxidation states produces thermodynamically more favorable intermediates along the pathway for the first hydrogen transfer for both the hydride-first mechanism and the proton-first mechanism. As a result, the quantum mechanical calculations provide experimentally inaccessible partial atomic charges and geometries of the various intermediates encountered along the steps of the reaction mechanisms.

Kermani, Maryam Mansoori [University of Minnesota,↗

Theoretical Investigation of OCN(-) Charge Transfer Complexes in Condensed Phase Media: Spectroscopic Properties in Amorphous Ice

Density functional theory (DFT) calculations of cyanate (OCN(-)) charge-transfer complexes were performed to model the "XCN" feature observed in interstellar icy grain mantles. OCN(-) charge-transfer complexes were formed from precursor combinations of HNCO or HOCN with either NH3 or H2O. Three different solvation strategies for realistically modeling the ice matrix environment were explored, including (1) continuum solvation, (2) pure DFT cluster calculations, and (3) an ONIOM DFT/PM3 cluster calculation. The model complexes were evaluated by their ability to reproduce seven spectroscopic measurements associated with XCN: the band origin of the OCN(-) asymmetric stretching mode, shifts in that frequency due to isotopic substitutions of C, N, O, and H, plus two weak features. The continuum solvent field method produced results consistent with some of the experimental data but failed to account for other behavior due to its limited capacity to describe molecular interactions with solvent. DFT cluster calculations successfully reproduced the available spectroscopic measurements very well. In particular, the deuterium shift showed excellent agreement in complexes where OCN(-) was fully solvated. Detailed studies of representative complexes including from two to twelve water molecules allowed the exploration of various possible solvation structures and provided insights into solvation trends. Moreover, complexes arising from cyanic or isocyanic acid in pure water suggested an alternative mechanism for the formation of OCN(-) charge-transfer complexes without the need for a strong base such as NH3 to be present. An extended ONIOM (B3LYP/PM3) cluster calculation was also performed to assess the impact of a more realistic environment on HNCO dissociation in pure water.

Park, Jin-Young↗

The Development of the Ducted Fan Noise Propagation and Radiation Code CDUCT-LaRC

The development of the ducted fan noise propagation and radiation code CDUCT-LaRC at NASA Langley Research Center is described. This code calculates the propagation and radiation of given acoustic modes ahead of the fan face or aft of the exhaust guide vanes in the inlet or exhaust ducts, respectively. This paper gives a description of the modules comprising CDUCT-LaRC. The grid generation module provides automatic creation of numerical grids for complex (non-axisymmetric) geometries that include single or multiple pylons. Files for performing automatic inviscid mean flow calculations are also generated within this module. The duct propagation is based on the parabolic approximation theory of R. P. Dougherty. This theory allows the handling of complex internal geometries and the ability to study the effect of non-uniform (i.e. circumferentially and axially segmented) liners. Finally, the duct radiation module is based on the Ffowcs Williams-Hawkings (FW-H) equation with a penetrable data surface. Refraction of sound through the shear layer between the external flow and bypass duct flow is included. Results for benchmark annular ducts, as well as other geometries with pylons, are presented and compared with available analytical data.

Nark, Douglas M.↗

Gating Single Molecules with Counterions

We report atomic-scale gating and visualization of local charge distribution within individual rare-earth-based molecular complexes on a metallic surface. The complexes are formed by a positively charged lanthanum ion coordinated to a (pcam)3 molecule and a negatively charged counterion trapped underneath via electrostatic interactions on a Au(111) surface. Local gating is performed by adding an additional negatively charged counterion to one side of the complex, which results in the redistribution of charges within the complex and a positive shift of the frontier orbitals. This is caused by the internal Stark effect induced by the added counterion. This effect is directly captured using tunneling spectroscopy and spectroscopic mapping at 5 K substrate temperature. The polarizability of the complex is corroborated by density functional theory and analytical calculations based on experimental findings. Furthermore, the influence of charge polarization on nearby complexes is investigated in a cluster purposely assembled using three complexes, which reveals maintaining the charge states as in single complexes. These findings will enable the design of robust charged rare-earth complexes to be tailored for potential solid-state applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗