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Rachel Wood Consulting - Viga Span Tables

Bosiljevac, Thomas B.

The purpose and scope of the viga span tables project for Rachel Wood Consulting (RWC) is focused on producing tabulated beam span tables for three species of wood vigas commonly used in New Mexico to allow producers, designers and builders to incorporate vigas into their building designs in a prescriptive manner similar to the span tables for sawn lumber incorporated into the International Residential Code (IRC) or the International Log Builders Association (ILBA) publication. The information provided in this report and the associated viga span tables also attempts to address and clarify questions raised by RWC during their review of the 2018 Los Alamos National Laboratory (LANL) New Mexico Small Business Assistance (NMSBA) program report by August Mosimann pertaining to span lengths, loading, deflection calculations, and log grading certification prior to submitting the span tables to the Construction Industries Division (CID) of New Mexico.

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TYPE Other Report YEAR 2022
OSTIDOI

Mini-DAQ: A lightweight, low-cost, high resolution, data acquisition system for wave energy converter testing

HardwareX

Bosma, Bret; Coe, Ryan G.; Bacelli, Giorgio; Brekken, Ted; Gunawan, Budi

As part of the development process, scaled testing of wave energy converter devices are necessary to prove a concept, study hydrodynamics, and validate control system approaches. Creating a low-cost, small, lightweight data acquisition system suitable for scaled testing is often a barrier for wave energy converter developers’ ability to test such devices. This paper outlines an open-source solution to these issues, which can be customized based on specific needs. This will help developers with limited resources along a path toward commercialization.

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TYPE Conference Poster YEAR 2022
DOIOSTIScopus

Magnetized High-Energy-Density Plasma Experiments at MIT

Hare, Jack; Datta, Rishabh; Varnish, Thomas; Lebedev, Sergey; Jerry, Chittenden; Crilly, Aidan; Halliday, Jack; Russell, Danny; Chandler, Katherine M.; Fox, Will; Hantao, Ji; Myers, Clayton; Aragon, Carlos; Jennings, Christopher A.; Ampleford, David; Hansen, Stephanie B.; Yager-Elorriaga, David A.; Harding, Eric; Shipley, Gabriel A.; Harmon, Roger; Gonzalez, Josue; Molina, Leo

Abstract not provided.

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TYPE Conference Presentation YEAR 2022
DOIOSTI

The effects of dose, dose rate, and irradiation type and their equivalence on radiation-induced segregation in binary alloy systems via phase-field simulations

Journal of Nuclear Materials

Vizoso, Daniel; Deo, Chaitanya; Dingreville, Remi P.M.

Radiation-induced segregation is a phenomenon commonly observed in many alloys which consists of the redistribution of elements (solute or interstitial impurities) under irradiation. The onset and development of radiation-induced segregation can only occur when a sufficient flux of defects is sustained and defect sinks are present. Irradiation dose, dose rate, and particle types all affect defect flux. In this work, we employ a phase-field model to examine the effects of dose, dose rate, and type of incident particles on radiation-induced segregation behavior in a model binary alloy. The phase-field model takes into account the formation and evolution of point defects as well as defect clusters, the diffusion and clustering of alloy species, the presence of additional extrinsic defect sinks in the form of dislocations, and two different methods of radiation-damage insertion, which are intended to simulate either light-ion/electron irradiation via Frenkel pairs or heavy-ion irradiation in the form of cascades. Our results show a dose-rate and particle-type dependence on the amount of solute segregation. We show that the material systems exposed to higher dose rates are less subjected to solute segregation at equivalent doses. We also show that such dose-rate-dependence behavior is due to a delay of the incubation dose at which radiation-induced segregation effectively starts. Particle type and the presence of dislocations can accentuate this behavior. Our model predictions correlate with many experimental observations made over the years on radiation-induced segregation providing credence to the simulation results. The methodology presented in this study allows for a first-order prediction of the dose rate at which proxy irradiation experiments could be performed to approximate radiation-induced segregation behaviors seen in targeted irradiation conditions.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Grey Zone Test Range Social Model

Doyle, Casey L.; Gunda, Thushara; Bernard, Michael

The Grey Zone Test Range (GZTR) social model operates as a piece of the overall GZTR modeling effort. It works in conjunction with supply models for resources, an electric grid model for power availability, and a traffic model for road congestion, as well as a general controller framework that allows external system effects. The social model functions as an aggregate model where the entire population of the city is divided into groups based on the Transportation Analysis Zones (TAZs), a common geospatial boundary present in all GZTR models. These groups will act as a singular community; each time step the state of the system around them will be assessed and then community will come up with a general plan of action that they will attempt to follow for the day. Additionally, they will track values for their general emotional state and memory about negative impacts in the near past.

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TYPE Other Report YEAR 2022
DOIOSTI

First-principles simulation of light-ion microscopy of graphene

2D Materials

Kononov, Alina K.; Olmstead, Alexandra; Baczewski, Andrew D.; Schleife, Andre

The extreme sensitivity of 2D materials to defects and nanostructure requires precise imaging techniques to verify presence of desirable and absence of undesirable features in the atomic geometry. Helium-ion beams have emerged as a promising materials imaging tool, achieving up to 20 times higher resolution and 10 times larger depth-of-field than conventional or environmental scanning electron microscopes. Here, we offer first-principles theoretical insights to advance ion-beam imaging of atomically thin materials by performing real-time time-dependent density functional theory simulations of single impacts of 10-200 keV light ions in free-standing graphene. We predict that detecting electrons emitted from the back of the material (the side from which the ion exits) would result in up to three times higher signal and up to five times higher contrast images, making 2D materials especially compelling targets for ion-beam microscopy. This predicted superiority of exit-side emission likely arises from anisotropic kinetic emission. The charge induced in the graphene equilibrates on a sub-fs time scale, leading to only slight disturbances in the carbon lattice that are unlikely to damage the atomic structure for any of the beam parameters investigated here.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Fundamentals of hydrogen storage in nanoporous materials

Progress in Energy

Zhang, Linda; Allendorf, Mark D.; Balderas-Xicohtencatl, Rafael; Broom, Darren P.; Fanourgakis, George S.; Froudakis, George E.; Gennett, Thomas; Hurst, Katherine E.; Ling, Sanliang; Milanese, Chiara; Parilla, Philip A.; Pontiroli, Daniele; Ricco, Mauro; Shulda, Sarah; Stavila, Vitalie; Steriotis, Theodore A.; Webb, Colin J.; Witman, Matthew D.; Hirscher, Michael

Physisorption of hydrogen in nanoporous materials offers an efficient and competitive alternative for hydrogen storage. At low temperatures (e.g. 77 K) and moderate pressures (below 100 bar) molecular H2 adsorbs reversibly, with very fast kinetics, at high density on the inner surfaces of materials such as zeolites, activated carbons and metal-organic frameworks (MOFs). This review, by experts of Task 40 ‘Energy Storage and Conversion based on Hydrogen’ of the Hydrogen Technology Collaboration Programme of the International Energy Agency, covers the fundamentals of H2 adsorption in nanoporous materials and assessment of their storage performance. The discussion includes recent work on H2 adsorption at both low temperature and high pressure, new findings on the assessment of the hydrogen storage performance of materials, the correlation of volumetric and gravimetric H2 storage capacities, usable capacity, and optimum operating temperature. The application of neutron scattering as an ideal tool for characterising H2 adsorption is summarised and state-of-the-art computational methods, such as machine learning, are considered for the discovery of new MOFs for H2 storage applications, as well as the modelling of flexible porous networks for optimised H2 delivery. The discussion focuses moreover on additional important issues, such as sustainable materials synthesis and improved reproducibility of experimental H2 adsorption isotherm data by interlaboratory exercises and reference materials.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Magnetically Ablated Reconnection on Z (MARZ) collaboration

Hare, Jack; Datta, Rishabh; Lebedev, Sergey; Chittenden, Jeremy P.; Crilly, Aidan; Halliday, Jack; Chandler, Katherine M.; Jennings, Christopher A.; Ampleford, David; Bland, Simon; Aragon, Carlos; Yager-Elorriaga, David A.; Hansen, Stephanie B.; Shipley, Gabriel A.; Webb, Timothy J.; Harding, Eric; Robertson, G.K.; Montoya, Michael M.; Kellogg, Jeffrey; Harmon, Roger; Molina, Leo

Abstract not provided.

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TYPE Conference Presentation YEAR 2022
DOIOSTI

A model for K-shell x-ray yield from magnetic implosions at Sandia's Z machine

Physics of Plasmas

Schwarz, Jens; Vesey, Roger A.; Ampleford, David; Schaeuble, Marc-Andre; Giuliani, J.L.; Esaulov, A.; Dasgupta, A.; Jones, Brent M.

A zero-dimensional magnetic implosion model with a coupled equivalent circuit for the description of an imploding nested wire array or gas puff is presented. Circuit model results have been compared with data from imploding stainless steel wire arrays, and good agreement has been found. The total energy coupled to the load, E j × B, has been applied to a simple semi-analytic K-shell yield model, and excellent agreement with previously reported K-shell yields across all wire array and gas puff platforms is seen. Trade space studies in implosion radius and mass have found that most platforms operate near the predicted maximum yield. In some cases, the K-shell yield may be increased by increasing the mass or radius of the imploding array or gas puff.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Computational Imaging for Intelligence in Highly Scattering Aerosols (Final Report)

Bentz, Brian Z.; Wright, Jeremy B.; Bays, Nathan R.; Sanchez, Andres L.; Pattyn, Christian A.; Bays, Nathan R.; Redman, Brian J.; Deneke, Elihu; Glen, Andrew; Westlake, Karl; Hastings, Ryan L.; Lacny, Christopher M.; Alexander, David W.; Webb, Kevin J.

Natural and man-made degraded visual environments pose major threats to national security. The random scattering and absorption of light by tiny particles suspended in the air reduces situational awareness and causes unacceptable down-time for critical systems and operations. To improve the situation, we have developed several approaches to interpret the information contained within scattered light to enhance sensing and imaging in scattering media. These approaches were tested at the Sandia National Laboratory Fog Chamber facility and with tabletop fog chambers. Computationally efficient light transport models were developed and leveraged for computational sensing. The models are based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes. After the new model was experimentally validated, statistical approaches for detection, localization, and imaging of objects hidden in fog were developed and demonstrated. A binary hypothesis test and the Neyman-Pearson lemma provided the highest theoretically possible probability of detection for a specified false alarm rate and signal-to-noise ratio. Maximum likelihood estimation allowed estimation of the fog optical properties as well as the position, size, and reflection coefficient of an object in fog. A computational dehazing approach was implemented to reduce the effects of scatter on images, making object features more readily discernible. We have developed, characterized, and deployed a new Tabletop Fog Chamber capable of repeatably generating multiple unique fog-analogues for optical testing in degraded visual environments. We characterized this chamber using both optical and microphysical techniques. In doing so we have explored the ability of droplet nucleation theory to describe the aerosols generated within the chamber, as well as Mie scattering theory to describe the attenuation of light by said aerosols, and correlated the aerosol microphysics to optical properties such as transmission and meteorological optical range (MOR). This chamber has proved highly valuable and has supported multiple efforts inclusive to and exclusive of this LDRD project to test optics in degraded visual environments. Circularly polarized light has been found to maintain its polarization state better than linearly polarized light when propagating through fog. This was demonstrated experimentally in both the visible and short-wave infrared (SWIR) by imaging targets made of different commercially available retroreflective films. It was found that active circularly polarized imaging can increase contrast and range compared to linearly polarized imaging. We have completed an initial investigation of the capability for machine learning methods to reduce the effects of light scattering when imaging through fog. Previously acquired experimental long-wave images were used to train an autoencoder denoising architecture. Overfitting was found to be a problem because of lack of variability in the object type in this data set. The lessons learned were used to collect a well labeled dataset with much more variability using the Tabletop Fog Chamber that will be available for future studies. We have developed several new sensing methods using speckle intensity correlations. First, the ability to image moving objects in fog was shown, establishing that our unique speckle imaging method can be implemented in dynamic scattering media. Second, the speckle decorrelation over time was found to be sensitive to fog composition, implying extensions to fog characterization. Third, the ability to distinguish macroscopically identical objects on a far-subwavelength scale was demonstrated, suggesting numerous applications ranging from nanoscale defect detection to security. Fourth, we have shown the capability to simultaneously image and localize hidden objects, allowing the speckle imaging method to be effective without prior object positional information. Finally, an interferometric effect was presented that illustrates a new approach for analyzing speckle intensity correlations that may lead to more effective ways to localize and image moving objects. All of these results represent significant developments that challenge the limits of the application of speckle imaging and open important application spaces. A theory was developed and simulations were performed to assess the potential transverse resolution benefit of relative motion in structured illumination for radar systems. Results for a simplified radar system model indicate that significant resolution benefits are possible using data from scanning a structured beam over the target, with the use of appropriate signal processing.

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TYPE SAND Report YEAR 2022
DOIOSTI

Perspectives on the integration between first-principles and data-driven modeling

Computers and Chemical Engineering

Bradley, William; Kim, Jinhyeun; Kilwein, Zachary A.; Blakely, Logan K.; Eydenberg, Michael S.; Jalvin, Jordan; Laird, Carl; Boukouvala, Fani

Efficiently embedding and/or integrating mechanistic information with data-driven models is essential if it is desired to simultaneously take advantage of both engineering principles and data-science. The opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive comparison of the hybridization techniques with respect to their differences and similarities, as well as advantages and limitations and future perspectives. Finally, we apply and illustrate hybrid modeling, physics-informed ML and model calibration via a chemical reactor case study.

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TYPE Journal Article YEAR 2022
DOIOSTIScopus
Results 6401–6600 of 101,000
Results 6401–6600 of 101,000
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