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At least 1,189 records · Page 66

Family of neural wiring receptors in bilaterians defined by phylogenetic, biochemical, and structural evidence

The evolution of complex nervous systems was accompanied by the expansion of numerous protein families, including cell-adhesion molecules, surface receptors, and their ligands. These proteins mediate axonal guidance, synapse targeting, and other neuronal wiring-related functions. Recently, 32 interacting cell surface proteins belonging to two newly defined families of the Ig superfamily (IgSF) in fruit flies were discovered to label different subsets of neurons in the brain and ventral nerve cord. They have been shown to be involved in synaptic targeting and morphogenesis, retrograde signaling, and neuronal survival. Here, we show that these proteins, Dprs and DIPs, are members of a widely distributed family of two- and three-Ig domain molecules with neuronal wiring functions, which we refer to as Wirins. Beginning from a single ancestral Wirin gene in the last common ancestor of Bilateria, numerous gene duplications produced the heterophilic Dprs and DIPs in protostomes, along with two other subfamilies that diversified independently across protostome phyla. In deuterostomes, the ancestral Wirin evolved into the IgLON subfamily of neuronal receptors. We show that IgLONs interact with each other and that their complexes can be broken by mutations designed using homology models based on Dpr and DIP structures. The nematode orthologs ZIG-8 and RIG-5 also form heterophilic and homophilic complexes, and crystal structures reveal numerous apparently ancestral features shared with Dpr-DIP complexes. The evolutionary, biochemical, and structural relationships we demonstrate here provide insights into neural development and the rise of the metazoan nervous system.

59 BASIC BIOLOGICAL SCIENCES↗

Generic Natural Systems Evaluation - Thermodynamic Database Development and Data Management

Thermodynamic data are essential for understanding and evaluating geochemical processes, as by speciation-solubility calculations, reaction-path modeling, or reactive transport simulation. These data are required to evaluate both equilibrium states and the kinetic approach to such states (via the affinity term or its equivalent in commonly used rate laws). These types of calculations and the data needed to carry them out are a central feature of geochemistry in many applications, including water-rock interactions in natural systems at low and high temperatures. Such calculations are also made in engineering studies, for example studies of interactions involving man-made materials such as metal alloys and concrete. They are used in a fairly broad spectrum of repository studies where interactions take place among water, rock, and man-made materials (e.g., usage on YMP and WIPP). Waste form degradation, engineered barrier system performance, and near-field and far-field transport typically incorporate some level of thermodynamic modeling, requiring the relevant supporting data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Production Facility System Reliability Analysis Report

This document describes the reliability, maintainability, and availability (RMA) modeling of the Los Alamos National Laboratory (LANL) design for the Closed Loop Helium Cooling System (CLHCS) planned for the NorthStar accelerator-based 99 Mo production facility. The current analysis incorporates a conceptual helium recovery system, beam diagnostics, and prototype control system into the reliability analysis. The results from the 1000 hr blower test are addressed.

Accelerator Design, Technology, and Operations↗

Sodium Pumping via Condensation within a Non-Wetting Porous Structure

The sodium thermal electrochemical converter (Na-TEC) is a heat engine that generates electricity via the isothermal expansion of sodium ions through a beta”-alumina solid-electrolyte. The transfer of liquid sodium from a low-pressure condenser to a high-pressure evaporator is necessary to complete the thermodynamic cycle for this conversion process. A unique sodium capillary pump for the Na-TEC is proposed, whereby low-pressure sodium vapor is condensed within a non-wetting (i.e. contact angle > 90o) stainless steel porous structure. Due to the curvature of the non-wetting liquid-vapor interface, the liquid adjacent to the interface is at a higher pressure than the vapor. This is in contrast to traditional wicks, where the liquid adjacent to the interface has a lower pressure. Thus, the curvature created at this non-wetting condensation interface supplies a directed force upon the liquid sodium that effectively “pushes” it towards a high pressure region, whereas a traditional capillary wick “pulls” the liquid. Porous structures made of stainless steel 316 are commercially available, and this material is non-wetting to sodium at the relevant temperature range of the Na-TEC condenser (500K – 673K). An experimental set-up is used to measure several interfacial and transport properties of the condensed sodium within this porous structure. These properties include the temperature dependent Laplace pressure generated at the phase change interface and the permeability of the porous structure. Liquid sodium is in contact with the porous structure and is progressively pressurized with argon gas until the sodium “breaks through” and the liquid begins to flow. This break-through pressure is determined when electrical contact is established between the flowing liquid metal and a set of electrodes cemented along the flow path. The velocity of the flowing sodium can also be measured with these electrodes, and the permeability is then determined by fitting velocity data to the classical Darcy law. These interfacial properties are then applied to a conjugate heat transfer model, which treats the coupled momentum and thermal transport processes within the non-wetting porous structure as sodium vapor is condensed. Due to the low mass flowrates in the Na-TEC (< 0.1 mg/s), higher order friction terms are neglected and the classical Darcy’s law is used for non-isothermal, compressible sodium vapor flow. At lower temperatures, the sodium vapor density becomes sufficiently small and there is a transition from classic Poiseuille flow (Kn < 0.1) to Knudsen flow (Kn > 10). Furthermore, transport due to binary diffusion is considered for situations where there is a substantial presence of non-condensable argon inside the sodium vapor plenum. Initial modeling results showing expected mass flowrates, temperature profiles, and liquid-vapor interface locations are presented for various physical conditions. Finally, the main design features of an experimental set-up used to demonstrate this capillary pumping is described in detail, including the method used to measure the low sodium mass flowrates. Initial experimental results demonstrating sodium pumping for a range of temperatures (800K – 1100K) will be presented and compared against the model predictions. A pump curve used to gauge the performance of this pumping mechanism is also created.

14 SOLAR ENERGY↗

Ensemble Kalman filter for data assimilation coupled with low-resolution computations techniques applied in fluid dynamics

This paper presents an innovative Reduced-order model (ROM) for merging experimental and simulation data using data assimilation (DA) to estimate the "True" state of a fluid dynamics system, leading to more accurate predictions. Our methodology introduces a novel approach by implementing the ensemble Kalman filter (EnKF) within a reduced-dimensional framework, grounded in a robust theoretical foundation and applied to fluid dynamics. To address the substantial computational demands of DA, the proposed ROM employs low-resolution (LR) techniques to drastically reduce computational costs. This innovative approach involves downsampling datasets for DA computations, followed by an advanced reconstruction technique based on low-cost singular value decomposition (lcSVD). The lcSVD method, a key innovation in this paper, has never been applied to DA before and offers a highly efficient way to enhance resolution with minimal computational resources. Our results demonstrate significant reductions in both computation time and RAM usage through these LR techniques without compromising the accuracy of the estimations. For instance, in a turbulent test case, for a data compression rate of 15.9, the LR approach can achieve a speed-up of 13.7 and a RAM compression of 90.9% while maintaining a low relative root mean square error (RRMSE) of 2.6%, compared to 0.8% in the high-resolution (HR) reference. Furthermore, we highlight the effectiveness of the EnKF in estimating and predicting the state of fluid flow systems based on limited observations and given low-fidelity numerical data. This paper highlights the potential of the proposed DA method in fluid dynamics applications, particularly for improving computational efficiency in CFD and related fields. Its ability to balance accuracy with low computational and memory costs makes it especially suitable for large-scale and real-time applications, such as environmental monitoring or engineering design. This method will be incorporated into ModelFLOWs-app.

Data Assimilation↗

Electronic properties of bimetallic metal–organic frameworks (MOFs): Tailoring the density of electronic states through MOF modularity

The development of porous well-defined hybrid materials (e.g., metal-organic frameworks or MOFs) will add a new dimension to a wide number of applications ranging from supercapacitors and electrodes to 'smart' membranes and thermoelectrics. From this perspective, the understanding and tailoring of the electronic properties of MOFs are key fundamental challenges that could unlock the full potential of these materials. In this work, we focused on the fundamental insights responsible for the electronic properties of three distinct classes of bimetallic systems, M x-y M' y -MOFs, M x M' y - MOFs, and M x (ligand-M' y )-MOFs, in which the second metal (M') incorporation occurs through (i) metal (M) replacement in the framework nodes (type I), (ii) metal node extension (type II), and (iii) metal coordination to the organic ligand (type III), respectively. We employed microwave conductivity, X-ray photoelectron spectroscopy, diffuse reflectance spectroscopy, powder X-ray diffraction, inductively coupled plasma atomic emission spectroscopy, pressed-pellet conductivity, and theoretical modeling to shed light on the key factors responsible for the tunability of MOF electronic structures. Experimental prescreening of MOFs was performed based on changes in the density of electronic states near the Fermi edge, which was used as a starting point for further selection of suitable MOFs. As a result, we demonstrated that the tailoring of MOF electronic properties could be performed as a function of metal node engineering, framework topology, and/or the presence of unsaturated metal sites while preserving framework porosity and structural integrity. Finally, these studies unveil the possible pathways for transforming the electronic properties of MOFs from insulating to semiconducting, as well as provide a blueprint for the development of hybrid porous materials with desirable electronic structures.

36 MATERIALS SCIENCE↗

A Computational Framework for Control Co-Design of Resilient Cyber–Physical Systems With Applications to Microgrids

Critical infrastructure networks, such as power and transportation networks, can be modelled as cyber-physical systems. As the complexity of such systems grow, there is need for developing metrics and design tools that will co-optimize the physical components of the system and the control policies to guarantee resilience against cyber and natural threats. To that end, we develop a simulation-based control co-design computational framework that will concurrently determine the system and control parameters of a cyber-physical system to meet pre-specified resilience, operational and economic objectives. Here, the capabilities of the developed co-design engine is demonstrated by designing the physical components and control parameters of a microgrid system that will meet its resiliency objectives when subjected to various cyber and physical threats.

97 MATHEMATICS AND COMPUTING↗

Carbon Dioxide Plume Evolution Following Injection into a Depleted Natural Gas Reservoir: Modeling of Conformance Uncertainty Reduction Over Time

The uncertainty in the long-term fate of CO 2 injected for geologic carbon sequestration (GCS) is a significant barrier to the adoption of GCS as a greenhouse gas emission mitigation approach for industry and regulatory agencies alike. Here we present a modeling study that demonstrates that the uncertainty in forecasts of GCS site performance decreases over time as monitoring data are used to inform and update operational models. The approach we take is to consider a case study consisting of a depleted natural gas reservoir that is used for GCS with CO 2 injection occurring over 20 years, with a 50-year post-injection site care (PISC) period. We constructed a detailed model of the system and ran this model out to 200 years to generate the actual site data. A series of simpler operational models based on limited data and assumptions about how an actual operator would model such a site are then run and compared against the actual model output at various specific monitoring points after one year, two years, etc. The operational model is then updated and improved using the observations (synthetic data from the actual model) at the same time intervals. We found that both model parameter values and model features needed to be added over time to improve matches to the actual system. These kinds of model adjustments are expected to be a normal part of reservoir engineering and site management at GCS sites. We found that the uncertainty in two key measures related to site performance at various locations decreases with time. This overall conclusion should help allay the concerns of industry and regulators about the uncertainty in GCS operations.

03 NATURAL GAS↗

Integrated Molten Salt Reactor Modeling Capabilities in NEAMS Thermal Hydraulics Tools

The DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program supports a full range of computational thermal fluids analysis capabilities and code developments for a broad range of advanced reactor concepts. The research and development approach under the thermal fluids technical area synergistically combines three length and time scales in a hierarchical multi-scale approach. To enable multi-scale thermal fluids capability using these codes, a key joint effort has been underway to develop an integrated system- and engineering-scale thermal fluids analysis capability, through integration of SAM and Pronghorn codes, both based on the MOOSE framework.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Parallel Grid-Forming Inverter-Driven Black Start: Power-Hardware-in-the-Loop Validation: Preprint

With power systems encountering increasing deployment levels of inverter-based resources (IBRs), system restoration using grid-forming (GFM) IBRs has gained attention. Engineered to establish grid voltages in the absence of a stiff grid, black-start-capable GFM IBRs are expected to enhance power system resilience by playing a critical role in bottom-up system restoration. This paper experimentally studies the feasibility of the novel approach through power-hardware-in-the-loop (PHIL) testing and validation with a commercial GFM inverter. The PHIL test setup demonstrates an inverter-driven black start of a 5-MW unbalanced distribution feeder where two GFM inverters collectively black start the feeder: one is a commercial hardware GFM inverter interacting through the PHIL interface, and the other is a software GFM emulated by a real-time simulation with full electromagnetic transient (EMT) models. Both GFM inverters are equipped with negative-sequence voltage control to suppress the voltage imbalance resulting from the unbalanced loading, allowing us to study the dynamic interactions between the GFMs with their control parameters unknown. To evaluate the dynamic behavior of the GFM inverters under the entire black-start process, the EMT model of the distribution system details the transformer and motor dynamics to emulate their inrush and startup behaviors. It abstracts conventional grid-connected inverter dynamics, i.e., grid-following inverters. Oscilloscope measurements of the hardware GFM inverter are also presented for critical steps. Takeaways for further study and field deployment are provided.

grid-forming inverter↗

Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators

Abstract Although projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad adoption has been limited by their intrusivity: implementing such a reduced-order model typically requires significant modifications to the underlying simulation code. To address this, we propose a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive manner. Specifically, the approach approximates the low-dimensional operators associated with projection-based reduced-order models (ROMs) using modern machine-learning regression techniques. The only requirement of the simulation code is the ability to export the velocity given the state and parameters; this functionality is used to train the approximated low-dimensional operators. In addition to enabling nonintrusivity, we demonstrate that the approach also leads to very low computational complexity, achieving up to $$10^3{\times }$$ 10 3 × in run time. We demonstrate the effectiveness of the proposed technique on two types of PDEs. The domain of applications include both parabolic and hyperbolic PDEs, regardless of the dimension of full-order models (FOMs).

42 ENGINEERING↗

3D-Printed pHEMA Materials for Topographical and Biochemical Modulation of Dorsal Root Ganglion Cell Response

Understanding and controlling the interactions occurring between cells and engineered materials are central challenges toward progress in the development of biomedical devices. In this work, we describe materials for direct ink writing (DIW), an extrusion-based type of 3D printing, that embed a custom synthetic protein (RGD-PDL) within the microfilaments of 3D-hydrogel scaffolds to modify these interactions and differentially direct tissue-level organization of complex cell populations in vitro. The RGD-PDL is synthesized by modifying poly-d-lysine (PDL) to varying extents with peptides containing the integrin-binding motif Arg-Gly-Asp (RGD). Compositional gradients of the RGD-PDL presented by both patterned and thin-film poly(2-hydroxyethyl) methacrylate (pHEMA) substrates allow the patterning of cell-growth compliance in a grayscale form. The surface chemistry-dependent guidance of cell growth on the RGD-PDL-modified pHEMA materials is demonstrated using a model NIH-3T3 fibroblast cell line. The formation of a more complex cellular system—organotypic primary murine dorsal root ganglion (DRG)—in culture is also achieved on these scaffolds, where distinctive forms of cell growth and migration guidance are seen depending on their RGD-PDL content and topography. In conclusion, this experimental platform for the study of physicochemical factors on the formation and the reorganization of organotypic cultures offers useful capabilities for studies in tissue engineering, regenerative medicine, and diagnostics.

36 MATERIALS SCIENCE↗

Duty-Cycle Predictive Control of Quasi-Z-Source Modular Cascaded Converter Based Photovoltaic Power System

A duty-cycle predictive control is proposed for dc grid integration of front-end isolated quasi-Z-source modular cascaded converter (qZS-MCC) photovoltaic (PV) power system. The post-stage qZS half-bridge dc-dc converter deals with PV maximum power point tracking, dc grid integration, and dc-link voltage balance; whereas, the front-end isolation converters operate at a constant duty cycle of 50%. Thus, it saves control hardware resources while overcoming challenges from PV-panel voltage variations and dc-bus voltage limit. The proposed control uses the derived circuit model to predict the global active-state duty cycle for grid-connected current control and predict the shoot-through duty cycles for dc-link voltage balance, achieving a fast and accurate tracking target. The proposed control method has advantages of: i) eliminating weighting factors that exist in conventional model predictive control (MPC), ii) no sophisticated loop parameters design that exists in proportional-integral (PI) control, iii) operating at constant switching frequency that is different from the conventional MPC with variable switching frequency. Simulation and experimental tests are carried out to verify the effectiveness of the proposed control method and compare with the PI-based control system.

42 ENGINEERING↗

Minimum entropy filtering for a single output non-Gaussian stochastic system using state transformation

This paper presents a novel filter design for the single-output stochastic non-linear systems subjected to non-Gaussian noises and the proposed assumptions. Based on a state transformation, the unmeasurable states of the systems can be estimated where non-linear terms in the systems have been eliminated. It has been shown that the estimation error is linearly dynamical regarding to the presented vector-valued filter gain which can be optimised by minimising the entropy-based performance criterion. In addition, the convergence of the presented algorithm is analysed in mean-square sense and a numerical example is given to verify the effectiveness of the presented filtering algorithm. Meanwhile, the extended Kalman filter, unscented particle filter and minimum entropy filter are given for the comparisons of the filtering performance. Following the presented framework, some extensions of the presented filtering algorithm are discussed to indicate the flexibility of the filter design. The contribution of this paper can be summarised as establishing a novel minimum entropy filtering framework which consists of model transformation, entropy optimisation and convergence analysis.

42 ENGINEERING↗

Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials

This paper provides a coherent and efficient computational framework for stochastic multiscale analysis of material systems in the presence of parametric uncertainties and modeling errors. Uncertainty in those model parameters that are not deduced as upscaled quantities is attributed to an uncertainty “germ”. While such parameters can appear at any scale, they are predominant at the finest analysis scale. Additional uncertainties stemming from statistical estimation, attributed to lack of data and model error, are associated with each submodel contributing to the multiscale system. Here, a robust and efficient framework based on a generalized extended polynomial chaos expansion (gEPCE) is proposed to simultaneously propagate all these uncertainties in order to provide a probabilistic representation of specific quantities of interest (QoI). We characterize the full probability distribution of the QoI and the uncertainty in the failure probability pertaining to its tails. By combining gEPCE with kernel density estimation (KDE) and directional derivatives, we construct sensitivity measures that connect these statistical metrics of QoI to the various sources of uncertainty to assess their individual and combined impacts. An illustrative problem featuring three-point bending of a composite beam is investigated to demonstrate the presented approach.

36 MATERIALS SCIENCE↗

A machine learning model for predicting the minimum miscibility pressure of CO 2 and crude oil system based on a support vector machine algorithm approach

CO 2 enhanced oil recovery (EOR) is a potential way for carbon capture, utilization and storage (CCUS). Though, the effect of CO 2 injection is greatly influenced by the reservoir conditions. Typically, Minimum miscible pressure (MMP) is selected as one of the key parameters for the screening and evaluation of prospective CO 2 flooding. Conventional slim tube test is both accurate and widely accepted but it is inefficient. Existing empirical formulas for MMPs are easy to be used but have been proved inaccurate and unreliable. Machine learning-based methods have great advantages in predicting MMP. However, only predication accuracy is discussed for most models without the screening of the main control factors and further validation of the model reliability. In this paper, a new prediction model based on support vector machine (SVM) was developed for pure/impure CO 2 and crude oil system. This study was based on 147 sets of MMP data from the literature with full information on reservoir temperature, oil composition and gas composition. The main control factors were screened by several statistical methods. Unlike the conventional prediction models that verified by only prediction accuracy, learning curve and single factor control variable analysis are further validated to obtain the optimum model.

02 PETROLEUM↗

Realization of a Hole-Doped Mott Insulator on a Triangular Silicon Lattice

The physics of doped Mott insulators is at the heart of some of the most exotic physical phenomena in materials research including insulator-metal transitions, colossal magnetoresistance, and high-temperature superconductivity in layered perovskite compounds. Advances in this field would greatly benefit from the availability of new material systems with a similar richness of physical phenomena but with fewer chemical and structural complications in comparison to oxides. In this paper, using scanning tunneling microscopy and spectroscopy, we show that such a system can be realized on a silicon platform. The adsorption of one-third monolayer of Sn atoms on a Si(111) surface produces a triangular surface lattice with half filled dangling bond orbitals. Modulation hole doping of these dangling bonds unveils clear hallmarks of Mott physics, such as spectral weight transfer and the formation of quasiparticle states at the Fermi level, well-defined Fermi contour segments, and a sharp singularity in the density of states. These observations are remarkably similar to those made in complex oxide materials, including high-temperature superconductors, but highly extraordinary within the realm of conventional $sp$-bonded semiconductor materials. Finally, it suggests that exotic quantum matter phases can be realized and engineered on silicon-based materials platforms.

36 MATERIALS SCIENCE↗

Development of Community, Capabilities and Understanding Through Unmanned Aircraft-Based Atmospheric Research: The LAPSE-RATE Campaign

Members of the international community developing unmanned aircraft for atmospheric research conducted a highly-successful coordinated flight week in Colorado during summer 2018 to collect measurements, develop capabilities, and enhance community in the field. Because unmanned aircraft systems (UAS) offer new perspectives on the atmosphere, their use in atmospheric science is expanding rapidly. In support of this growth, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) has been developed and have convened annual meetings and 'flight weeks'. The 2018 flight week, dubbed the Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE), involved a one-week deployment to Colorado's San Luis Valley. Between 14-20 July 2018 over 100 students, scientists, engineers, pilots and outreach coordinators conducted an intensive field operation using unmanned aircraft and ground-based assets to develop datasets, community, and capabilities. In addition to a coordinated 'community day' which offered a chance for groups to share their aircraft and science with the San Luis Valley community, LAPSE-RATE participants conducted nearly 1300 research flights totaling over 250 flight hours. The measurements collected have been used to advance capabilities (instrumentation, platforms, sampling techniques and modeling tools), conduct a detailed system intercomparison study, develop new collaborations, and foster community support for the use of UAS in atmospheric science.

54 ENVIRONMENTAL SCIENCES↗