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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Photoneutron Production Using an Electron Linear Accelerator for Applications in Neutron Imaging

Photoneutron production is possible using an electron linear accelerator and a target capable of generating photonuclear reactions. A short pulse neutron source can be useful for neutron imaging dynamic experiments. This study is aimed at the feasibility of photoneutron production using a 20 MeV electron linear accelerator and tungsten and depleted uranium targets of various thicknesses. MCNP6 (Monte Carlo N-Particle) code will be used to develop a computational model to estimate total neutron yield, and this will be verified at the Idaho State University’s Accelerator Center. After verification of the neutron yield and energy spectra, an additional MCNP6 model will be developed to analyze the neutron imaging processes. This study will potentially prove it is possible to conduct multi-mode imaging experiments on the anticipated Scorpius electron linear accelerator at the Nevada National Security Sites.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Broadband Asymmetric Transmission of Linearly Polarized Mid‐Infrared Light Based on Quasi‐3D Metamaterials

Abstract Metamaterials consisting of subwavelength resonators offer an exciting opportunity for realizing asymmetric transmission (AT) of linearly polarized light. However, to date, only moderate/narrow‐band AT responses have been obtained in metadevices based on stacked planar nanostructures. Here, leveraging a combination of a genetic algorithm (GA) based optimization method and a membrane projection lithography (MPL) fabrication approach, a quasi‐3D metamaterial for broadband AT of linearly polarized mid‐infrared light is demonstrated. Facilitated by the customized GA, an efficient exploration of 3D plasmonic meta‐atoms with broken mirror symmetry in the light propagation direction allows the satisfaction of the rigorous conditions for AT of linearly polarized waves over a broad wavelength range. Confirmed by surface current analysis, the observed AT behavior is attributed to the resonant coupling between the plasmonic nanostructures located on the two orthogonal walls of the MPL cavities. Incorporating an advanced inverse‐design method and a state‐of‐art fabrication technique, the methodology used in the present study provides a promising route for exploiting 3D metamaterials with sophisticated functionalities via effectively exploring the high‐dimensional parametric space offered by true 3D meta‐atoms.

Whiting, Eric B.↗
DOE OSTI2021

A non‐intrusive domain‐decomposition model reduction method for linear steady‐state partial differential equations with random coefficients

Abstract Domain decomposition methods have been proved to be an effective strategy to reduce the dimension of parametric partial differential equations (PDEs). However, existing domain decomposition methods for parametric PDEs are usually intrusive, which means domain decomposition based solvers need to be implemented from scratch for each target parametric PDE. To address this issue, we develop a new non‐intrusive domain‐decomposition model reduction method for linear steady‐state PDEs with random‐field coefficients. As a variant of our previous work by Mu and Zhang, the new method only needs access to the final linear system, that is, the global stiffness matrix and the right hand side, of a deterministic PDE solver, in order to build a domain‐decomposition‐based reduced model without intrusive implementation from scratch. The key idea is to remove the interface condition between sub‐domains and rely on the correlation between columns of the linear system to couple the sub‐domains. The non‐intrusive feature enables the applicability of the proposed method to a broader class of uncertainty quantification problems, where many legacy codes/solvers can be fully reused by our method. Two numerical examples including diffusion equations with random diffusivity and convection‐dominated transport with random velocity, are provided to demonstrate the effectiveness and efficiency of our method.

Zhang, Guannan↗
DOE OSTI2024

Experimental validation of a Kalman observer using linearized OpenFAST and a fully instrumented 1:70 model

Abstract To enable real‐time monitoring and control strategies for floating offshore wind turbines, accurate information about the state of the system is needed. This paper details the application of a Kalman filter to the UMaine VolturnUS‐S floating wind platform to provide accurate state estimates in real time using minimal system measurements. The midfidelity nonlinear simulation tool OpenFAST was used to generate the underlying linear state‐space model for the Kalman filter. This linear model and its limitations are demonstrated through comparison with experimental data collected on a 1:70 froude‐scaled model of the floating platform and tower. Using a selection of five measurements from the real system, a Kalman filter was developed to provide estimates for the remaining system states and measurements. These estimates were then validated against the experimental values collected from testing of the scale model. Validation of the Kalman filter produced accurate estimates of surge, heave, and tower base bending moment, measurements of which were not available to the Kalman filter. Performance of the Kalman filter was tested and validated over a range of sea conditions from rated wind speed to storm events and demonstrated robustness in the Kalman filter to maintain accuracy across all operating conditions despite significant error in the underlying linear model for extreme conditions.

17 WIND ENERGY↗
DOE OSTI2023

Linear growth of circuit complexity from Brownian dynamics

How rapidly can a many-body quantum system generate randomness? Using path integral methods, we demonstrate that Brownian quantum systems have circuit complexity that grows linearly with time. In particular, we study Brownian clusters of N spins or fermions with time-dependent all-to-all interactions, and calculate the Frame Potential to characterize complexity growth in these models. In both cases the problem can be mapped to an effective statistical mechanics problem which we study using path integral methods. Within this framework it is straightforward to show that the kth Frame Potential comes within ϵ of the Haar value after a time of order t ~ kN + k log k + log ϵ –1 . Using a bound on the diamond norm, this implies that such circuits are capable of coming very close to a unitary k-design after a time of order t ~ kN. We also consider the same question for systems with a time-independent Hamiltonian and argue that a small amount of time-dependent randomness is sufficient to generate a k-design in linear time provided the underlying Hamiltonian is quantum chaotic. These models provide explicit examples of linear complexity growth that are analytically tractable and are directly applicable to practical applications calling for unitary k-designs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Linear Reciprocating Tribometer for In Situ Neutron Reflectometry of Soft Matter

Neutron reflectometry is a technique for measuring structure near planar interfaces that has been previously used to non-destructively characterize the polymer density of hydrated, dilute, and soft materials. Previous investigations have conducted neutron reflectometry measurements of liquids, gels, emulsion, and polymer solutions at rest, in compression, and subject to shear stress. However, correlating structure with tribological properties of soft materials presents significant experimental challenges for prior instruments due to wall slip, sample thickness, and structural heterogeneity (e.g., depth-wise gradients). A linear reciprocating tribometer offers several advantages for in situ neutron reflectometry studies, including uniform velocity profiles, constant shear stress over large regions of interest, and independent control of normal force and sliding velocity during measurements. This work outlines basic considerations for the design of a custom linear reciprocating tribometer that operates in a neutron beamline and includes commissioning measurements. The tribometer is designed to compress soft and hydrated materials against linearly reciprocating silicon disks. The three key design considerations for this tribometer are (1) safety, (2) neutron transmission, and (3) sample positioning. This instrument design will enable in situ studies of soft matter and illuminate the role of interfacial structure on tribological phenomena.

42 ENGINEERING↗
DOE OSTI2022

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO 2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 10 6 because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder

Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evaluation of alkali–silica reaction damage in concrete using linear and nonlinear resonance techniques

Here in this study, the initiation and development of alkali–silica reaction (ASR) in concrete prisms containing reactive coarse (RCA) and reactive fine (RFA) aggregates were monitored over one year using linear and nonlinear resonance techniques. Although the linear resonance test could detect initiation and development of ASR damage in RCA specimens, it failed in the RFA specimens. Petrographic analysis confirmed the presence of early-stage ASR damage and the presence of different microcracking patterns in the RFA and RCA specimens, which may explain differences in the linear resonance data. For both RCA and RFA specimens, the nonlinear resonance test results were able to detect the initiation of ASR damage and showed a strong correlation with the ASR expansion for all specimens from different mix designs. The results demonstrate the high sensitivity of the nonlinear resonance test and its potential for quantitative evaluation of the ASR damage state of concrete specimens without a baseline measurement.

36 MATERIALS SCIENCE↗
DOE OSTI2026

An efficient explicit implementation of a near-optimal quantum algorithm for simulating linear dissipative differential equations

We propose an efficient block-encoding technique for the implementation of the Linear Combination of Hamiltonian Simulations (LCHS) for simulating dissipative initial-value problems. This algorithm approximates a target nonunitary operator as a weighted sum of Hamiltonian evolutions, thereby emulating a dissipative problem by mixing various time scales. We introduce an efficient encoding of the LCHS into a quantum circuit based on a simple coordinate transformation that turns the dependence on the summation index into a trigonometric function. Classically, this method is equivalent to the use of a highly accurate Fejér-Clenshaw-Curtis quadrature formula. Quantumly, this significantly simplifies block-encoding of a dissipative problem and allows one to perform an exponential number of Hamiltonian simulations by a single Quantum Signal Processing (QSP) circuit. The resulting LCHS circuit has high success probability and the selector scales logarithmically with the number of terms in the LCHS sum and linearly with time. Careful analysis of error convergence proves that this method is more efficient than other LCHS circuits that have recently appeared in the literature. We verify the quantum circuit and its scaling by simulating it on a digital emulator of fault-tolerant quantum computers and, as a test problem, solve the advection-diffusion equation. The proposed algorithm can be used for simulating a wide class of nonunitary initial-value problems including the Liouville equation with added dissipation and linear embeddings of nonlinear systems, such as the Koopman-von Neumann and Carleman embeddings.

Novikau, I [Lawrence Livermore National Laboratory↗
DOE OSTI2026

Climate-eutrophication-anoxia interactions in Late Glacial Soppensee, Switzerland: Forcings, non-linear responses and recovery

Combined effects of climate warming and anthropogenic nutrient loadings lead to lake eutrophication and anoxia globally. Because of chemical feedbacks, lakes under multiple stressors often respond in non-linear ways. However, it remains unclear whether climate change alone can lead to non-linear lake responses in the absence of anthropogenic nutrient disturbances. Here, we investigate the interactions between climate variability, nutrient cycling and trophic state changes, mixing regimes, anoxia and related chemical feedback in a small kettle-hole lake in Switzerland during Late Glacial times (15.2–12.6 cal ka BP), a period known for high-amplitude climate change in pre-anthropogenic times. After its formation during Heinrich Stadial 1 (>15 cal ka BP), Soppensee was oligotrophic and well-mixed. Soppensee became eutrophic and developed anoxia at 14.25 cal ka BP. Phosphorus (P) was released from sediments through the reductive dissolution of Fe-oxyhydroxides, fuelling eutrophication. Eutrophication lagged the Bølling warming (14.65 cal ka BP) by 400 years, suggesting that rising temperatures were not the trigger for eutrophication. Instead, eutrophication responded non-linearly to forest closure (threshold at 76 % arboreal pollen AP), which shielded Soppensee from wind mixing, enhancing lake stratification, anoxia and P release, intensifying eutrophication. These conditions ended during the 200-years cold period of the Aegelsee Oscillation (GI-1d, ca. 14.0 cal ka BP) when the landscape regionally opened (AP<76 %); the lake became well-mixed, oxygenated and P was efficiently sequestered. Throughout the Allerød (13.9–12.8 cal ka BP), enhanced Fe input prompted diagenetic vivianite formation, sequestering P in sediments, naturally remediating lake eutrophication despite closed forests, warm temperatures, lake stratification and anoxia.

Environmental sciences↗
DOE OSTI2022

Electron pulse train accelerated by a linearly polarized Laguerre–Gaussian laser beam

A linearly polarized Laguerre–Gaussian (LP-LG) laser beam with a twist index $l$ = –1 has field structure that fundamentally differs from the field structure of a conventional linearly polarized Gaussian beam. Close to the axis of the LP-LG beam, the longitudinal electric and magnetic fields dominate over the transverse components. This structure offers an attractive opportunity to accelerate electrons in vacuum. It is shown, using three-dimensional particle-in-cell simulations, that this scenario can be realized by reflecting an LP-LG laser off a plasma with a sharp density gradient. The simulations indicate that a 600 TW LP-LG laser beam effectively injects electrons into the beam during the reflection. The electrons that are injected close to the laser axis experience a prolonged longitudinal acceleration by the longitudinal laser electric field. The electrons form distinct monoenergetic bunches with a small divergence angle. The energy in the most energetic bunch is 0.29 GeV. The bunch charge is 6 pC and its duration is approximately 270 as. The divergence angle is just 0.57° (10 mrad). By using a linearly polarized rather than a circularly polarized Laguerre–Gaussian beam, our scheme makes it easier to demonstrate the electron acceleration experimentally at a high-power laser facility.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

A quasi-linear model of electromagnetic turbulent transport and its application to flux-driven transport predictions for STEP

A quasi-linear reduced transport model is developed from a database of high-β electromagnetic nonlinear gyrokinetic simulations performed with spherical tokamak for energy production (STEP) relevant parameters. The quasi-linear model is fully electromagnetic and accounts for the effect of equilibrium flow shear using a novel approach. Its flux predictions are shown to agree quantitatively with predictions from local nonlinear gyrokinetic simulations across a broad range of STEP-relevant local equilibria. This reduced transport model is implemented in the T3D transport solver that is used to perform the first flux-driven simulations for STEP to account for transport from hybrid kinetic ballooning mode turbulence, which dominates over a wide region of the core plasma. Nonlinear gyrokinetic simulations of the final transport steady state from T3D return turbulent fluxes that are consistent with the reduced model, indicating that the quasi-linear model may also be appropriate for describing the transport steady state. Within the assumption considered here, our simulations support the existence of a transport steady state in STEP with a fusion power comparable to that in the burning flat top of the conceptual design, but do not demonstrate how this state can be accessed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Data-Driven Discovery of Linear Molecular Probes with Optimal Selective Affinity for PFAS in Water

Approaches to tackle the wide and growing variety of highly persistent per- and polyfluoroalkyl substances (PFAS) are of pressing global need because of their detrimental human health effects, such as cancer, birth defects, and hormone imbalance. Sensitive, selective, and easy-to-use real-time sensors to monitor and detect PFAS and sorbents to extract them are critical to meeting government-mandated environmental concentrations. In this work, we combine all-atom molecular dynamics simulations, enhanced sampling, deep representational learning, and Bayesian optimization to perform high-throughput virtual screening for highly sensitive and selective molecular probes. Our molecular design space consists of 3850 linear hydrocarbon chains with varying degrees of halogenation with and without amine- and phosphine-based headgroups. By employing a data-driven search process, we efficiently explore the molecular design space to optimize the sensitivity to perfluorooctanesulfonic acid (PFOS) as a prototypical PFAS analyte and selectivity relative to a sodium dodecyl sulfate (SDS) interferent. We calculate 504 Gibbs free energies of probe-analyte and probe-interferent interactions and identify probes with PFOS association free energies of up to (-ΔG PFOS ) = 9.8 ± 0.2 kJ/mol and selectivities relative to SDS of (-ΔΔG PFOS–SDS ) = 3.1 ± 1.5 kJ/mol. A C 11 Br 23 P(CH 3 ) 2 probe containing 11 backbone brominated carbons and a tertiary phosphine headgroup possesses the most sensitive binding constant to PFOS within the defined search space of K b PFOS = 177.4 ± 12.7, and a semibrominated probe C 5 H 11 C 7 Br 14 N(CH 3 ) 2 containing 12 backbone carbons and a tertiary amine headgroup possesses the highest selectivity relative to SDS of K b PFOS /K b SDS = 4.6 ± 1.7. A retrospective analysis of our data to extract interpretable design rules reveals that the sensitivity of linear hydrogenated probes increases by approximately 1 kJ/mol per C–C bond. The addition or removal of halogen atoms and amine or phosphine headgroups produces nonmonotonic changes in both sensitivity and selectivity with changes to the sensitivity of up to 2.5 kJ/mol. Finally, this work places empirical limitations on the performance of a wide range of linear probes for PFOS detection and offers a generic strategy for high-throughput computational screening to promote selective and sensitive binding.

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

Linear Discriminant Analysis-Based Machine Learning and All-Atom Molecular Dynamics Simulations for Probing Electro-Osmotic Transport in Cationic-Polyelectrolyte-Brush-Grafted Nanochannels

Deciphering the correct mechanisms governing certain phenomena in polyelectrolyte (PE) brush grafted systems, revealed through atomistic simulations, is an extremely challenging problem. In a recent study, our all-atom molecular dynamics (MD) simulations revealed a non-linearly large electroosmotic (EOS) flow (in the presence of an applied electric field) in nanochannels grafted with PMETAC [Poly(2-(methacryloyloxy)ethyl trimethylammonium chloride] brushes. Given the lack of any formal procedure that would have directed us to identify the correct factors responsible for such an occurrence, we needed to spend several months and devote significant analyses to unravel the involved mechanisms. In this paper, we propose a Linear Discriminant Analysis (LDA) based Machine Learning (ML) approach to address this gap. At first, we obtain data on certain basic features from the all-atom MD data. These basic features represent the number of atoms of certain species around one atom of another (or same) species. Here, we obtain such data on basic features for a reference case (case of an EOS flow in PMETAC-brush-grafted nanochannels with a smaller electric field) and a perturbed case (case of an EOS flow in PMETAC-brush-grafted nanochannels with a larger electric field) in bins in which the nanochannel half height has been divided into. These datasets are high-dimensional dataset, to which the LDA is applied. This leads to the projection of the data (between the reference and the perturbed states) in a highly separated form on a 1D line. From such LDA calculations, we are able to identify the relative importance of the different basic features in ensuring this separation of the data (between the reference and the perturbed states) on the 1D line. This relative importance of the different basic features is quantified as “importance scores” for the different features, which in turn tell us what to study and where to study. Such knowledge enables us to rapidly identify the key factors responsible for the non-linearly large EOS transport in PMETAC-brush-grafted nanochannels.

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

Optimal Linear Water Density for Proton Transport in Tunnel Oxides

Proton intercalation and transport is key to pseudocapacitive energy storage of oxide electrodes in acidic electrolytes. Although proton transport in water confined in layered or 2D materials is attracting great interest, much less is known about how the proton is transported in the 1D channels of tunnel oxides such as hexagonal WO 3 (h-WO 3 ). Here, we use first-principles molecular dynamics to reveal an optimal linear density of four water molecules per nanometer that yields the highest proton diffusivity. The volcano shape of proton diffusivity versus linear water density is a result of balancing the linear hydrogen-bond chain and the rotation of the water molecules to enable the Grotthuss mechanism. Furthermore, this insight provides a unifying view of proton transport along a single file of water molecules confined in hydrophilic 1D channels.

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

Altering Linear Scaling Relationships on Metal Catalysts via Ligand–Adsorbate Hydrogen Bonding

Linear scaling relationships for adsorption energies of related molecules provide a simple tool for the prediction of catalytic properties and also reveal inherent constraints in heterogeneous catalyst design. In an effort to predict materials that preferentially stabilize intermediates capable of forming hydrogen bonds (H-bonds) and thus reduce these constraints, scaling relations for thiolate-coated fcc (111) surfaces were developed. Here, we demonstrate how ligand–adsorbate H-bonds lead to the stabilization of certain adsorbates and how this affects the linear scaling of adsorption energies for similarly bound intermediates. For H-bonds that occur remote from the surface, ligand–adsorbate H-bond strength is independent of metal composition and instead depends only on the acidities of the H-bond-forming functional groups. As the distance of H-bond-accepting groups from the surface decreases and interactions with the metal strengthen, the effect of surface composition on H-bond strength increases while additional factors, such as conformational changes and steric hinderance, can offset the stabilizing effect of the ligands. In this work, these competing factors may influence both the slope and y-intercept of adsorption strength scaling. These findings aid the rational design of enhanced catalytic materials by enabling the screening of ligand- or spectator-modified materials that do not conform to the linear scaling relations of bare metal surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mapping the Interfacial Chemistry and Structure of Partially Fluorinated Bottlebrush Polymers and Their Linear Analogues

Polymer interfaces are key to a range of applications including membranes for chemical separations, hydrophobic coatings, and passivating layers for antifouling. While important, challenges remain in probing the interfacial monolayer where the molecular ordering and orientation can change depending on the chemical makeup or processing conditions. In this work, we leverage surface specific vibrational sum frequency generation (SFG) and the associated dependence on molecular symmetry to elucidate the ordering and orientations of key functional groups for poly(2,2,2-trifluoroethyl methacrylate) bottlebrush polymers and their linear polymer analogues. These measurements were framed by atomistic molecular dynamic simulations to provide a complementary physical picture of the gas–polymer interface. Simulations and SFG measurements show that methacrylate backbones are buried beneath a layer of trifluoroethyl containing side groups that result in structurally similar interfaces regardless of the polymer molecular weight or architecture. The average orientational angles of the trifluoroethyl containing side groups differ depending on polymer linear and bottlebrush architectures, suggesting that the surface groups can reorient via available rotational degrees of freedom. Results show that the surfaces of the bottlebrush and linear polymer samples do not strongly depend on molecular weight or architecture. As such, one cannot rely on increasing the molecular weight or altering the architecture to tune surface properties. This insight into the polymer interfacial structure is expected to advance the design of new material interfaces with tailored chemical/functional properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗