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There is a lack of state-of-the-art quantum computing simulation software that scales on heterogeneous systems like Titan. Tensor Network Quantum Virtual Machine (TNQVM) provides a quantum simulator that leverages a distributed network of GPUs to simulate quantum circuits in a manner that leverages recent results from tensor network theory.
Aerosol deposition (AD) can lead to the formation of dense coatings via deposition of particles from a gas flow; in AD the aerosol is passed through a converging-diverging nozzle, facilitating inertial particle impaction on a desired substrate at supersonic particle velocities. Unique from thermal spray methods, AD can be applied near room temperature and unique from cold spray, in AD the aerosol is typically at atmospheric pressure upstream of the nozzle. Though AD has been successfully demonstrated previously, a number of aspects related to particle motion in AD systems remain poorly understood. In this work, we simulated compressible flow field profiles and particle trajectories for typical AD working conditions for a slit type converging-diverging nozzle with a planar substrate. In examining the fluid flow profile, we show that the velocity and pressure profiles, as well as the shock structure are sensitive to the upstream and downstream operating pressures of the nozzle. These ultimately affect particle impaction speed. Importantly, in AD, the particle drag regime is dynamic; both particle Knudsen numbers and Mach numbers can vary by orders of magnitude. To aid particle trajectory simulations, we trained a neural network to predict the drag force on the particles based on existing experimental data, theoretical limits, and new direct simulation Monte Carlo (DMSC) results. The neural network based drag law, which depends upon both Mach and Knudsen numbers, shows better agreement with the DSMC simulation data than pre-existing correlations. With it, particle trajectory simulation results reveal that for a given particle density, there exists an optimal particle diameter to maximize particle impaction speed. We also find that in AD particles undergo size dependent inertial focusing, i.e. there is a particular particle diameter where the particle deposition linewidth is minimized. Particles smaller than this diameter are underfocused, and particles larger than this are overfocused, and hence have larger deposition linewidths in both cases. Using trajectory simulations, we additionally develop a framework that can be used to evaluate the position-dependent mass, momentum and kinetic energy fluxes to the deposition substrate for any aerosol size distribution function upstream the nozzle. It is shown for typical aerosol concentrations achievable in the laboratory, the kinetic energy flux can approach a magnitude normally observed in convective heat transfer with phase change, hence translational kinetic energy to thermal energy transfer in AD is likely the key contributor to the formation of dense coatings.
This study presents a modeling approach for studying hydro-mechanical coupled processes, including fracture development, within geological formations. This is accomplished through the novel linking of two codes: TOUGH2, which is a widely used simulator of subsurface multiphase flow based on the finite volume method; and an implementation of the Rigid-Body-Spring Network (RBSN) method, which provides a discrete (lattice) representation of material elasticity and fracture development. The modeling approach is facilitated by a Voronoi-based discretization technique, capable of representing discrete fracture networks. The TOUGH–RBSN simulator is intended to predict fracture evolution, as well as mass transport through permeable media, under dynamically changing hydrologic and mechanical conditions. Numerical results are compared with those of two independent studies involving hydro-mechanical coupling: (1) numerical modeling of swelling stress development in bentonite; and (2) experimental study of desiccation cracking in a mining waste. The comparisons show good agreement with respect to moisture content, stress development with changes in pore pressure, and time to crack initiation. Finally, the observed relationship between material thickness and crack patterns (e.g., mean spacing of cracks) is captured by the proposed modeling approach.
The density and configurational changes of crystal dislocations during plastic deformation influence the mechanical properties of materials. These influences have become clearest in nanoscale experiments, in terms of strength, hardness and work hardening size effects in small volumes. The mechanical characterization of a model crystal may be cast as an inverse problem of deducing the defect population characteristics (density, correlations) in small volumes from the mechanical behavior. In this work, we demonstrate how a deep residual network can be used to deduce the dislocation characteristics of a sample of interest using only its surface strain profiles at small deformations, and then statistically predict the mechanical response of size-affected samples at larger deformations. As a testbed of our approach, we utilize high-throughput discrete dislocation simulations for systems of widths that range from nano- to micro- meters. We show that the proposed deep learning model significantly outperforms a traditional machine learning model, as well as accurately produces statistical predictions of the size effects in samples of various widths. By visualizing the filters in convolutional layers and saliency maps, we find that the proposed model is able to learn the significant features of sample strain profiles.
Sustaining fusion reactions in tokamaks requires heating plasma to thermonuclear temperatures while maintaining confinement and stability. Neutral beam injection (NBI) provides heating, current drive, torque, and fueling, while electron cyclotron (EC) waves are widely used for heating and current drive; together, these actuators shape the plasma current, temperature, and density profiles. The control-oriented tokamak simulator (COTSIM), a predictive, control-oriented code, has been enhanced with neural-network surrogates for transport and sources. Turbulent transport is predicted by MMMnet—a neural-network version of the updated multimode model (MMM 9.0.10)—with significantly reduced computation time relative to MMM; neoclassical transport follows the Chang–Hinton model. NUBEAMnet, a surrogate of the Monte Carlo NUBEAM module, predicts beam-driven heating, current, and torque. EC heating and current drive use a control-oriented, empirically scaled source model; plasma resistivity follows the Spitzer formulation; bootstrap current uses the Sauter model. Equilibrium is computed using both prescribed and fixed-boundary solvers (FBSs), and the pedestal structure is modeled with an empirical pedestal model. For a representative DIII-D discharge, COTSIM predicts electron and ion temperature and safety-factor profiles in close agreement with TRANSP predictive and interpretive simulations while extending predictions through the pedestal region to the plasma edge (versus 80% of the minor radius in TRANSP). Furthermore, the equivalent COTSIM simulation runs in under 3 min compared to about 2 h for TRANSP, enabling rapid scenario planning, optimization of tokamak operation, and between-pulse control design.
Earth's climate stability, characterized by the global radiative feedback parameter (𝜆), varies decadally due to changing surface temperature patterns. Recent variations in 𝜆 are poorly understood as coordinated model simulations typically end in 2014. We apply a convolutional neural network trained on climate model simulations to observation-based surface temperature reconstructions to estimate variations in 𝜆 up to 2025. We find that 𝜆 reached a minimum (maximum stability) around the mid 1990s (𝜆 ≃ −3 Wm −2 /K), but has since weakened significantly (𝜆 ≃ −2 Wm −2 /K). We confirm these results with climate model simulations extended to 2022. The recent 𝜆 weakening is not significantly affected by El Niño Southern Oscillation or Pacific Decadal Oscillation. Attribution reveals that warming in the subtropical Northeast Pacific is an important driver of the recently weakened feedback, confirmed by targeted experiments in E3SMv2. Our approach enables near real-time monitoring of Earth's climate stability.
This LETTER REPORT has been prepared by Idaho National Laboratory for US Navy NAVFAC EXWC to support in testing pre-commercial SIREN (Simulated Integration of Renewable Energy Networks) computer software models. In the logistics mode SIREN software simulates the combination of renewable power sources (solar arrays, wind turbines, and energy storage systems) in supplying an electrical demand. NAVFAC EXWC will create SIREN software logistics models of existing or planned renewable energy projects at five Navy locations (San Nicolas Island, AUTEC, New London, & China Lake), and INL will deliver additional HOMER computer models for comparative analysis. In the transient mode SIREN simulates the short time-scale variation of electrical parameters when a power outage or other destabilizing event occurs. In the HOMER model, a variety of inputs are entered such as location coordinates, Generators, PV arrays, Wind Turbines, Batteries, Converters, Grid costs/usage, Solar resources, Wind resources, Temperatures, Fuels, and Electric Loads. HOMER's optimization and sensitivity analysis algorithms then evaluate the economic and technical feasibility of these technology options and account for variations in technology costs, electric load, and energy resource availability. The Navy can then use HOMER’s optimization and sensitivity results to compare to those of the SIREN model. The U.S. Department of Energy (DOE) Idaho National Laboratory (INL) possesses unique expertise and experience in the software, hardware, and systems design for the integration of renewable energy into the electrical grid. NAVFAC EXWC will draw upon this expertise to complete mission requirements.
Microgravity, or an altered gravity environment from the static 1g, has been shown to influence global gene expression patterns and protein levels in living organisms. However, it is unclear how these changes in gene and protein expressions are related to each other or are related to other factors regulating such changes. A different class of RNA, the small non-coding microRNA (miRNA), can have a broad effect on gene expression networks by mainly inhibiting the translation process. Previously, we investigated changes in the expression of miRNA and related genes under simulated microgravity conditions on the ground using the NASA invented bioreactor. In comparison to static 1 g, simulated microgravity altered a number of miRNAs in human lymphoblastoid cells. Pathway analysis with the altered miRNAs and RNA expressions revealed differential involvement of cell communication and catalytic activity, as well as immune response signaling and NGF activation of NF-kB pathways under simulated microgravity condition. The network analysis also identified several projected networks with c- Rel, ETS1 and Ubiquitin C as key factors. In a flight experiment on the International Space Station (ISS), we will investigate the effects of actual spaceflight on miRNA expressions in nondividing human fibroblast cells in mostly G1 phase of the cell cycle. A fibroblast is a type of cell that synthesizes the extracellular matrix and collagen, the structural framework for tissues, and plays a critical role in wound healing and other functions. In addition to miRNA expressions, we will investigate the effects of spaceflight on the cellular response to DNA damages from bleomycin treatment.
Proton transfer in macromolecular systems is a fascinating yet elusive process. In the last ten years, molecular simulations have shown to be a useful tool to unveil the atomistic mechanism. Notwithstanding, the large number of degrees of freedom involved make the accurate description of the process very hard even for the case of proton diffusion in bulk water. Here, multi-state empirical valence bond molecular dynamics simulations in conjunction with complex network analysis are applied to study proton transfer in liquid water. Making use of a transition network formalism, this approach takes into account the time evolution of several coordinates simultaneously. Our results provide evidence for a strong dependence of proton transfer on the length of the hydrogen bond solvating the Zundel complex, with proton transfer enhancement as shorter bonds are formed at the acceptor site. We identify six major states (nodes) on the network leading from the “special pair” to a more symmetric Zundel complex required for transferring the proton. Moreover, the second solvation shell specifically rearranges to promote the transfer, reiterating the idea that solvation beyond the first shell of the Zundel complex plays a crucial role in the process.
Interatomic potential development using machine learning (ML) approaches has attracted a lot of attention in recent years because these potentials can effectively describe the structural and dynamical properties of complex materials at the atomistic level. Here, in this work, we present the development of a neural network (NN) deep ML interatomic potential for Fe-Si alloys, and we demonstrate the effectiveness of the NN-ML potential in predicting the structures and energies of liquid and crystalline phases of Fe-Si alloys in comparison with the results from ab initio molecular dynamics simulations or experimental data. The developed NN-ML potential is also used to perform molecular dynamics simulations to study the structures of Fe-Si alloys with various compositions under rapid solidification conditions. The short-ranged orders in the rapidly solidified Fe-Si alloys are also analyzed by a cluster alignment method.
The CubeSat platform is finding increasing use in space science applications due to its low cost and comparative ease of launch. It is becoming a key scientific discovery tool in low Earth orbit (LEO) and beyond, including geosynchronous equatorial orbit (GEO), the Lagrange Points, Lunar missions, and more. The increasing complexity of these missions and their scientific goals must be supported by equal advancements in communications technology. Higher data rates and greater reliability are required every year. However, the reduced Size, Weight, and Power (SWaP) constraints of CubeSat platforms introduce unique challenges in the area of satellite communications. There is currently a lack of communication equipment tailored specifically to the CubeSat platform. This lack of standardized, tested equipment extends development time and reduces mission confidence. Furthermore, missions utilizing the CubeSat platform are often subject to more difficult design constraints. Antenna placement, size, and pointing are often subordinate to the requirements of the payload instruments and mission goals. Traditional link margin estimation techniques are insufficient in these cases, as they emphasize worst case scenarios. In reality the actual link parameters may vary widely even during a single pass. This presents new challenges in predicting communications performance and scheduling ground station contacts, but also new opportunities for improving efficiency. This paper presents the integration, testing, and validation process for a new software defined radio (SDR) designed for the CubeSat platform in conjunction with Vulcan Wireless, Inc. The SDR is planned for use on 5 upcoming CubeSat missions at NASAs Goddard Space Flight Center (GSFC) including a Geosynchronous Transfer Orbit (GTO) mission and it may also serve as a standard and well-tested option for future missions by enabling a standardized, rapid and low cost CubeSat communication system network integration process. Detailed simulations have been developed to estimate the communication performance of these missions, taking the unique antenna placements and attitude behavior of each satellite into account. These simulations allow a much more accurate analysis of the expected link margin, which varies considerably during each pass for the NASA Space Relay (SR) and Direct to Earth (DTE) network. The modelling procedures are outlined, and the results are used to predict communications performance of the missions.
Infiltration processes in fractured-porous media remain a crucial, yet not very well understood component of recharge and vulnerability assessment. Under partially-saturated conditions flows in fractures, percolating fracture networks, and fault zones contribute to the fastest spectrum of infiltration velocities via preferential pathways. Specifically, the partitioning dynamics at fracture intersections determine the magnitude of flow fragmentation into vertical and horizontal components and hence the bulk flow velocity and dispersion of fracture networks. In this work, we derive an approximate analytical solution for the partitioning process and validate it using smoothed particle hydrodynamics simulations. The developed transfer function is conceptually based on simulation results and laboratory experiments carried out in previous works. It allows to efficiently simulate flow through fracture networks with simple cubic structure and arbitrary number of fractures and aperture sizes via linear response theory and convolution of a given input signal. We derive a non-dimensional bulk flow velocity ($\widetilde{v}$) and dispersion coefficient ($\widetilde{D}$) to characterize fracture networks in terms of dimensionless horizontal and vertical time scales $\tau_m$ and $\tau_0$. The dispersion coefficient is shown to strongly depend on the horizontal time scale and converges towards a constant value of $0.08$ within reasonable fluid and geometrical parameter ranges, while the non-dimensional velocity exhibits a characteristic $\widetilde{v} \sim \tau_m^{-1/2}$ scaling. Given that hydraulic information is often only available at limited places within (fractured-porous) aquifer system, such as boreholes or springs, our study intends to provide a rudimentary analytical concept to potentially reconstruct internal fracture network geometries from external boundary information, e.g., the dispersive properties of discharge (groundwater level fluctuations).
Chemistry plays an important role in the interstellar medium (ISM), regulating the heating and cooling of the gas and determining abundances of molecular species that trace gas properties in observations. Although solving the time-dependent equations is necessary for accurate abundances and temperature in the dynamic ISM, a full chemical network is too computationally expensive to incorporate into numerical simulations. In this paper, we propose a new simplified chemical network for hydrogen and carbon chemistry in the atomic and molecular ISM. We compare results from our chemical network in detail with results from a full photodissociation region (PDR) code, and also with the Nelson and Langer (NL99) network previously adopted in the simulation literature. We show that our chemical network gives similar results to the PDR code in the equilibrium abundances of all species over a wide range of densities, temperature, and metallicities, whereas the NL99 network shows significant disagreement. Applying our network to 1D models, we find that the CO-dominated regime delimits the coldest gas and that the corresponding temperature tracks the cosmic-ray ionization rate in molecular clouds. We provide a simple fit for the locus of CO-dominated regions as a function of gas density and column. We also compare with observations of diffuse and translucent clouds. We find that the CO, CH{sub x}, and OH{sub x} abundances are consistent with equilibrium predictions for densities n=100--1000 cm{sup −3}, but the predicted equilibrium C abundance is higher than that seen in observations, signaling the potential importance of non-equilibrium/dynamical effects.
nEXO is a proposed experiment to search for the neutrino-less double beta decay (0νββ) of 136 Xe in a tonne-scale liquid xenon time projection chamber (TPC). The nEXO TPC will be equipped with charge collection tiles to form the anode. In this work, the charge reconstruction performance of this anode design is studied with a dedicated simulation package. A multi-variate method and a deep neural network are developed to distinguish simulated 0νββ signals from backgrounds arising from trace levels of natural radioactivity in the detector materials. These simulations indicate that the nEXO TPC with charge-collection tiles shows promising capability to discriminate the 0νββ signal from backgrounds. Furthermore, the estimated half-life sensitivity for 0νββ decay is improved by 20 32)% with the multi-variate (deep neural network) methods considered here, e sensitivity estimated in the nEXO pre-conceptual design report.
The Interplanetary Overlay Network (ION) software at JPL is an implementation of Delay/Disruption Tolerant Networking (DTN) which has been proposed as an interplanetary protocol to support space communication. The JPL Deep Impact Network (DINET) is a technology development experiment intended to increase the technical readiness of the JPL implemented ION suite. The DINET Experiment Operations Center (EOC) developed by JPL's Protocol Technology Lab (PTL) was critical in accomplishing the experiment. EOC, containing all end nodes of simulated spaces and one administrative node, exercised publish and subscribe functions for payload data among all end nodes to verify the effectiveness of data exchange over ION protocol stacks. A Monitor and Control System was created and installed on the administrative node as a multi-tiered internet-based Web application to support the Deep Impact Network Experiment by allowing monitoring and analysis of the data delivery and statistics from ION. This Monitor and Control System includes the capability of receiving protocol status messages, classifying and storing status messages into a database from the ION simulation network, and providing web interfaces for viewing the live results in addition to interactive database queries.
This paper presents preliminary analysis using a finite volume based computational method for network flow analysis for the simulation of chill and fill of a cryogenic tank. An axisymmetric two-dimensional model of a spherical tank was developed using a nodal approach. No Vent Filling (NVF) was accomplished using a Thermodynamic Vent System assisted injector where the injector was cooled by the cold gas generated by Joule-Thompson expansion of the liquid propellant. The numerical model accounts for a) different regimes of pool boiling heat transfer, b) condensation of vapor around spray droplets, and c) condensation of vapor at the interface of the cooled injector and ullage. The numerical predictions of tank pressure, filling rate, and wall temperatures were compared with the test data and one-dimensional (1D) model predictions. It was demonstrated that the axisymmetric 2D model provided a more realistic prediction than the 1D model.
Simulated galaxy distributions are suitable for developing filament detection algorithms. However, samples of observed galaxies, being of limited size, cause difficulties that lead to a discontinuous distribution of filaments. We created a new galaxy filament catalog composed of a continuous cosmic web with no lone filaments. The core of our approach is a ridge filter used within the framework of image analysis. We considered galaxies from the HyperLeda database with redshifts 0.02 ≤ z ≤ 0.1, and in the solid angle 120° ≤ R.A. ≤ 240°, 0° ≤ decl. ≤ 60°. We divided the sample into 16 two-dimensional celestial projections with redshift bin Δ z = 0.005, and compared our continuous filament network with a similar recent catalog covering the same region of the sky. We tested our catalog on two application scenarios. First, we compared the distributions of the distances to the nearest filament of various astrophysical sources (Seyfert galaxies and other active galactic nuclei, radio galaxies, low-surface-brightness galaxies, and dwarf galaxies), and found that all source types trace the filaments well, with no systematic differences. Next, among the HyperLeda galaxies, we investigated the dependence of the g - r color distribution on the distance to the nearest filament, and confirmed that early-type galaxies are located on average further from the filaments than late-type ones.
In electromagnetic transient (EMT) simulations for power systems and inverter-based resources (IBRs), the arrangement of states within the system's linear equations, represented by matrix A in Ax=b, is critical. The state ordering in matrix A can highlight distinct characteristics of the system's graph, and identifying an optimal state ordering is crucial for efficient computation. The choice of state ordering, however, is dependent on the solver used, as each solver may perform optimally with different matrix patterns. With a wide array of matrix reordering algorithms available, selecting the most suitable one becomes challenging without insights into the matrix's ideal configuration. To address this, the paper proposes a fully convolutional network (FCN) to evaluate the reordering potential of the A matrix into a bordered block diagonal (BBD) pattern, which is commonly observed in power system and IBR modeling. The FCN's assessment aims to streamline the solver's operation, which in turn could substantially reduce the computational time required to find a solution.