A Low Cost and Rugged Graphene Sensor Network for Characterizing Phosphorus Levels Across Terrestrial-Aquatic Interfaces Final Report
Explore the source record for details and available documents.
Anibal, Jacob↗
SEARCH · Engineering Papers
Results for “network interfaces”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Using Direct Numerical Simulation of Pore-Level Events to Improve Pore-Network Models for Prediction of Residual Trapping of CO2
Direct numerical simulation and pore-network modeling are common approaches to study the physics of two-phase flow through natural rocks. For assessment of the long-term performance of geological sequestration of CO 2 , it is important to model the full drainage-imbibition cycle to provide an accurate estimate of the trapped CO 2 . While direct numerical simulation using pore geometry from micro-CT rock images accurately models two-phase flow physics, it is computationally prohibitive for large rock volumes. On the other hand, pore-network modeling on networks extracted from micro-CT rock images is computationally efficient but utilizes simplified physics in idealized geometric pore elements. This study uses the lattice-Boltzmann method for direct numerical simulation of CO 2 -brine flow in idealized pore elements to develop a new set of pore-level flow models for the pore-body filling and snap-off events in pore-network modeling of imbibition. Lattice-Boltzmann simulations are conducted on typical idealized pore-network configurations, and the interface evolution and local capillary pressure are evaluated to develop modified equations of local threshold capillary pressure of pore elements as a function of shape factor and other geometrical parameters. The modified equations are then incorporated into a quasi-static pore-network flow solver. The modified model is applied on extracted pore-network of sandstone samples, and saturation of residual trapped CO 2 is computed for a drainage-imbibition cycle. The modified model yields different statistics of pore-level events compared with the original model; in particular, the occurrence of snap-off in pore-throats is reduced resulting in a more frontal displacement pattern along the main injection direction. Compared to the original model, the modified model is in closer agreement with the residual trapped CO 2 obtained from core flow experiments and direct numerical simulation.
Kohanpur, Amir H.↗
Enabling scientific machine learning in MOOSE using Libtorch
A neural-network-based machine learning interface has been developed for the Multiphysics Object-Oriented Simulation Environment (MOOSE). The interface relies on Libtorch, the C++ front-end of PyTorch, and enables an online interaction between modern machine learning algorithms and all the existing simulation, modeling, and analysis processes available in MOOSE. New capabilities in MOOSE include the native generation and training of artificial neural networks together with options to load pretrained neural networks in TorchScript format. Furthermore, the MOOSE stochastic tools module (MOOSE-STM) has been enhanced with neural network-based surrogate and reduced-order model generation options for efficient stochastic analyses. Lastly, a reinforcement learning capability has been added to MOOSE-STM for the interactive control and optimization of complex multiphysics problems.
97 MATHEMATICS AND COMPUTING↗
Mechanical rolling formation of interpenetrated lithium metal/lithium tin alloy foil for ultrahigh-rate battery anode
To achieve good rate capability of lithium metal anodes for high-energy-density batteries, one fundamental challenge is the slow lithium diffusion at the interface. Here we report an interpenetrated, three-dimensional lithium metal/lithium tin alloy nanocomposite foil realized by a simple calendering and folding process of lithium and tin foils, and spontaneous alloying reactions. The strong affinity between the metallic lithium and lithium tin alloy as mixed electronic and ionic conducting networks, and their abundant interfaces enable ultrafast charger diffusion across the entire electrode. Furthermore, we demonstrate that a lithium/lithium tin alloy foil electrode sustains stable lithium stripping/plating under 30 mA cm -2 and 5 mAh cm -2 with a very low overpotential of 20 mV for 200 cycles in a commercial carbonate electrolyte. Cycled under 6 C (6.6 mA cm -2 ), a 1.0 mAh cm -2 LiNi 0.6 Co 0.2 Mn 0.2 O 2 electrode maintains a substantial 74% of its capacity by pairing with such anode.
25 ENERGY STORAGE↗
Reinforcement Learning for Intentional Islanding in Resilient Power Transmission Systems
Intentional islanding is the process of identifying and deliberately decomposing the transmission network to form self-sustained islands from an endangered network during disruptions to improve resilience and security. Most existing intentional islanding models are offline resilience decision tools and hence do not provide outage responses in a timely manner. In this paper, a reinforcement learning (RL) based model for intentional islanding is developed, which offers real-time switching control, online deployability, and adaptability to varying system conditions. The intentional islanding process is formulated as a Markov decision process, where the optimal transmission switching policy is learned using the RL approach. The control policy is learned over an environment that encompasses a Power System Simulator for Engineering (PSS/E) model of the transmission network, facilitated by an interface to the standard openAI Gym framework. The proposed RL-based methodology aims to form stable and self-sustainable islands by ensuring voltage stability while reducing the power mismatch in the formed islands. A proximal policy optimization algorithm is designed, which is suitable for controlling the on/off status of the switches with multi-layer perceptron as value and actor networks. The effectiveness of the proposed framework in the self-recovery of the grid by island formation is applied on the modified IEEE 39-bus test network and validated by dynamic simulations.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Physics-informed neural networks for heterogeneous poroelastic media
This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs (I-PINNs) framework (Sarma et al., Comput. Methods Appl. Mech. Eng. 429: 117135, 2024), allowing different activation functions across material interfaces. Further, this ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.
42 ENGINEERING↗
Cybersecurity Considerations for Grid-Connected Batteries with Hardware Demonstrations
The share of renewable and distributed energy resources (DERs), like wind turbines, solar photovoltaics and grid-connected batteries, interconnected to the electric grid is rapidly increasing due to reduced costs, rising efficiency, and regulatory requirements aimed at incentivizing a lower-carbon electricity system. These distributed energy resources differ from traditional generation in many ways including the use of many smaller devices connected primarily (but not exclusively) to the distribution network, rather than few larger devices connected to the transmission network. DERs being installed today often include modern communication hardware like cellular modems and WiFi connectivity and, in addition, the inverters used to connect these resources to the grid are gaining increasingly complex capabilities, like providing voltage and frequency support or supporting microgrids. To perform these new functions safely, communications to the device and more complex controls are required. The distributed nature of DER devices combined with their network connectivity and complex controls interfaces present a larger potential attack surface for adversaries looking to create instability in power systems. To address this area of concern, the steps of a cyberattack on DERs have been studied, including the security of industrial protocols, the misuse of the DER interface, and the physical impacts. These different steps have not previously been tied together in practice and not specifically studied for grid-connected storage devices. In this work, we focus on grid-connected batteries. We explore the potential impacts of a cyberattack on a battery to power system stability, to the battery hardware, and on economics for various stakeholders. We then use real hardware to demonstrate end-to-end attack paths exist when security features are disabled or misconfigured. Our experimental focus is on control interface security and protocol security, with the initial assumption that an adversary has gained access to the network to which the device is connected. We provide real examples of the effectiveness of certain defenses. This work can be used to help utilities and other grid-connected battery owners and operators evaluate the severity of different threats and the effectiveness of defense strategies so they can effectively deploy and protect grid-connected storage devices.
25 ENERGY STORAGE↗
Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses
Scientific computations are expected to be increasingly distributed across wide-area networks, and Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. Application-level measurements of MPI operations reflect the connection Round-Trip Time (RTT) and loss rate, and machine learning methods have been previously developed to estimate them under deterministic periodic losses. In this paper, we consider more complex, random losses with unform, Poisson and Gaussian distributions. We study five disparate machine leaning methods, with linear and non-linear, and smooth and non-smooth properties, to estimate RTT and loss rate over 10Gbps connections with 0-366ms RTT. The diversity and complexity of these estimators combined with the randomness of losses and TCP’s non-linear response together rule out the selection of a single best among them; instead, we fuse them to retain their design diversity. Overall, the results show that accurate estimates can be generated at low loss rates but become inaccurate at loss rates 10% and higher, thereby illustrating both their strengths and limitations.
Generalization Bounds↗
Sequentially coupled flow and geomechanical simulation with a discrete fracture model for analyzing fracturing fluid recovery and distribution in fractured ultra-low permeability gas reservoirs
More accurate characterization and prediction of the in-situ distribution of fracturing fluid in fractured reservoirs are needed for enhancing well productivity. In this study, an implicit-sequentially coupled flow/geomechanics simulator incorporating an efficient discrete fracture model is developed to model fluid distribution and recovery performance of ultra-low permeability gas reservoirs. The finite-volume and finite-element methods are used for space discretization of the flow and geomechanics equations, respectively, while the backward Euler method is employed for time discretization. The flow and geomechanics equations are solved sequentially based on fixed-stress splitting. An efficient discrete-fracture model is used to explicitly model the fractured system. Flexible unstructured gridding is employed to model arbitrarily-oriented fractures. The interrelations among pore volume, permeability and geomechanical conditions are considered dynamically using two-way coupled flow and geomechanics computations. The geometry of fracture (networks) due to hydraulic fracturing has significant impacts on the fracturing fluid recovery efficiency and ensuing fluid distribution. Under the same injection volume, the fracturing fluid recovery is higher when the fracture geometry is planar. Fluid recovery is relatively lower whenever natural fractures are activated during fracturing treatments; flowback time is also shortened when complex fracture network with enlarged fracture interface is present. Fracturing fluid in hydraulic fractures may leak off into the natural fractures and subsequently imbibes into the surrounding matrix due to capillarity effects. The fracturing fluid recovery and in-situ fluid distribution are sensitive to the shut-in duration and fracture closure behavior. This study analyzes the coupled flow-geomechanical responses of fractured gas reservoirs during the post-fracturing periods. Understanding the fate of the fracturing fluid can provide insights on, to some extent, the stimulated fracture volume, size of the water invasion zone, and efficiency of the fracturing design. The simulation predictions can also provide more accurate initial reservoir conditions (e.g. distributions of different phases and pressure) for long-term well performance estimation.
42 ENGINEERING↗
Elucidating proximity magnetism through polarized neutron reflectometry and machine learning
Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge for parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from polarized neutron reflectometry data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator–ferromagnetic insulator heterostructure Bi2Se3/EuS exhibiting proximity magnetism in good agreement with the results of conventional fitting. We further analyze a more challenging reflectometry profile of the topological insulator–antiferromagnet heterostructure (Bi,Sb)2Te3/Cr2O3 and identify possible interfacial proximity magnetism in this material. We anticipate that the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
PyNetUQ
SAND2021-3242 O PyNetUQ is a Python library for network uncertainty propagation. Custom interface classes for components are modeled individually with potentially distinct simulation tools. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Tencer, John↗
Electro-optic transduction in silicon via gigahertz-frequency nanomechanics
Interfacing electronics with optical fiber networks is key to the long-distance transfer of classical and quantum information. Piezo-optomechanical transducers enable such interfaces by using gigahertz-frequency acoustic vibrations as mediators for converting microwave photons to optical photons via the combination of optomechanical and piezoelectric interactions. However, despite successful demonstrations, efficient quantum transduction remains out of reach due to the challenges associated with hybrid material integration and increased loss from piezoelectric materials when operating in the quantum regime. Here, we demonstrate an alternative approach in which we actuate 5-GHz phonons in a conventional silicon-on-insulator platform. In our experiment, microwave photons resonantly drive a phononic crystal oscillator via the electrostatic force realized in a charge-biased narrow-gap capacitor. The mechanical vibrations are subsequently transferred via a phonon waveguide to an optomechanical cavity, where they transform into optical photons in the sideband of a pump laser field. Operating at room temperature and atmospheric pressure, we measure a microwave-to-optical photon conversion efficiency of 1.72±0.14×10 −7 in a 3.3 MHz bandwidth. Our results mark a stepping stone towards quantum transduction with integrated devices made from crystalline silicon, which promise efficient high-bandwidth operation and integration with superconducting qubits. Additionally, the lack of need for piezoelectricity or other intrinsic nonlinearities makes our approach applicable to a wide range of materials for potential applications beyond quantum technologies.
42 ENGINEERING↗
Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)
Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations. To make advancements in all the aforementioned scientific areas, many technical aspects need to be more thoroughly studied and deeply understood in parallel discrete event simulation (PDES). The unique dynamics inherent in a discrete event modeling approach, by their very nature, intersect and influence the entire stack of the computing system, including (a) the unique nature of the instruction sets exercised in PDES workloads without a predominance of high-precision floating point operations, (b) virtual time-constrained multi-threaded execution of many logical processes per processor, (c) extremely variable and difficult to predict network traffic characteristics, (d) interfaces and inter-dependencies with machine learning and artificial intelligence codes at higher software layers, and (e) highly challenging load balancing needs, especially in effectively accounting for accelerated/extremely heterogeneous computing in current and future high-performance computing systems. Efficient and accurate parallel execution of PDES workloads is also dominated by challenges in dealing with their asynchronous concurrency fundamentally present at the model level. Conservative synchronization, optimistic/speculative synchronization, and their hybrid schemes open new questions in fundamental computer science with respect to reversibility of computation and prediction (lookahead) of behaviors inherent within model codes. On the implementation front, there are relatively few scalable, general-purpose parallel discrete event simulators in the world, and even fewer have been studied on emerging hardware platforms. To enable scientific advances using PDES, the research needs in computer science must also be pursued and met in the intersection of the algorithmic and hardware-aware aspects of scalable PDES engines. This report is aimed at capturing a computer science-oriented view of this important area of research in PDES, presenting a sample of important applications with their inherent discrete event technology elements. Needs are outlined in core areas of parallel discrete event research as well as cross-cutting directions in computer science research that positively impact scientific advancements across several important application areas. A selection of priority research opportunities in advanced computing for PDES is identified to serve as reference for key research topics and their order of importance for scientific advancements.
97 MATHEMATICS AND COMPUTING↗
Municipal Adaptation to Changing Curbside Demands: Findings from Semi-Structured Interviews with Ten U.S. Cities
Emerging mobility services (e.g. ride-hailing, e-commerce, micro-mobility, etc.), are generating novel and rapidly growing demands to use curbside space, with potentially large impacts on mobility, energy consumption, and related outcomes. This presents both opportunities and challenges to municipal agencies responsible for managing this interface between the road network and adjacent land uses, as legacy practices require updating. In this study, we employ a semi-structured interviewing approach to establish how municipalities are adapting to these new pressures on their curbside. We interviewed senior staff responsible for curbside policy of ten large U.S. municipalities, with populations ranging from ~250,000 to ~5,000,000 (and the majority of which are the central city of their metropolitan region). We document a trend of organizational restructuring to more formally include curbside management teams, with the majority of interviewees also reporting increased staffing. Respondents reported that operational failures at their curbside (e.g. demand in excess of capacity) have impacts on safety, capacity, and emergency vehicle mobility, with impacts highly concentrated spatially and temporally (e.g. late evenings in nightlife districts). We document a diversity of data flows between ride-hailing operators (e.g. Uber, Lyft) and municipalities, with some cities reporting obtaining types of data that other cities report not receiving, despite requesting such data. Finally, respondents consistently expressed a desire for new data streams and methodologies to help manage the curbside of the future. It is hoped that establishing the state of practice in this rapidly changing context will be of use to practitioners facing similar pressures as those of our interviewees.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses
Scientific computations are expected to be increasingly distributed across wide-area networks, and Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. Application-level measurements of MPI operations reflect the connection Round-Trip Time (RTT) and loss rate, and machine learningmethods have been previously developed to estimate them under deterministic periodic losses. In this paper, we consider more complex, random losses with uniform, Poisson and Gaussian distributions. We study five disparate machine leaning methods, with linear and non-linear, and smooth and non-smooth properties, to estimate RTT and loss rate over 10 Gbps connections with 0–366 ms RTT. The diversity and complexity of these estimators combined with the randomness of losses and TCP’s non-linear response together rule out the selection of a single best among them; instead, we fuse them to retain their design diversity. Overall, the results show that accurate estimates can be generated at low loss ratesbut become inaccurate at loss rates 10% and higher, thereby illustrating both their strengths and limitations.
Rao, Nageswara S.↗
Ultrafast charge transfer coupled to quantum proton motion at molecule/metal oxide interface
Understanding how the nuclear quantum effects (NQEs) in the hydrogen bond (H-bond) network influence the photoexcited charge transfer at semiconductor/molecule interface is a challenging problem. By combining two kinds of emerging molecular dynamics methods at the ab initio level, the path integral–based molecular dynamics and time-dependent nonadiabatic molecular dynamics, and choosing CH 3 OH/TiO 2 as a prototypical system to study, we find that the quantum proton motion in the H-bond network is strongly coupled with the ultrafast photoexcited charge dynamics at the interface. The hole trapping ability of the adsorbed methanol molecule is notably enhanced by the NQEs, and thus, it behaves as a hole scavenger on titanium dioxide. The critical role of the H-bond network is confirmed by in situ scanning tunneling microscope measurements with ultraviolet light illumination. It is concluded the quantum proton motion in the H-bond network plays a critical role in influencing the energy conversion efficiency based on photoexcitation.
74 ATOMIC AND MOLECULAR PHYSICS↗
HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network
Machine learning (ML) is emerging as a promising tool for modeling hydro-ecological processes due to the increasing availability of large environmental data. However, the use of ML requires sufficient programming knowledge due to a lack of a graphical user interface (GUI). In this study, we introduced a GUI package, named HydroEcoLSTM, with the long short-term memory network (LSTM) as the core model, that allows non-ML experts to utilize their domain knowledge to construct complex ML models. We demonstrated the functionalities of HydroEcoLSTM with two practical examples, including (1) predictions of streamflow in both gauged and ungauged catchments and (2) predictions of multiple outputs (i.e., streamflow and isotope transport from two catchments). The simulation results obtained in both case experiments are satisfactory. In the first example, the average Nash–Sutcliffe Efficiency (NSE) for streamflow simulation during the testing period is 0.79 while the application of the trained model in two assumed ungauged catchments also achieves the average NSE of 0.68. In the second example, the average NSE for streamflow and instream isotope simulation during the testing period is 0.71. Ultimately, applications of HydroEcoLSTM with real-world examples demonstrate its potential use for practical applications and research without requiring extensive coding skills.
54 ENVIRONMENTAL SCIENCES↗