Reconfigurable Distribution Automation and Control Laboratory: Solar Microgrid Experiments
Not provided.
14 SOLAR ENERGY↗
SEARCH · Engineering Papers
Results for “network flow”
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.
Superconducting disordered neural networks for neuromorphic processing with fluxons
In superconductors, magnetic fields are quantized into discrete fluxons (flux quanta Φ 0 ), made of microscopic circulating supercurrents. We introduce a multiterminal synapse network comprising a disordered array of superconducting loops with Josephson junctions. The loops can trap fluxons defining memory, while the junctions allow their movement between loops. Dynamics of fluxons through such a disordered system through a complex reconfigurable energy landscape represents brain-like spiking information flow. In this work, we experimentally demonstrate a three-loop network using YBa 2 Cu 3 O 7 –δ -based superconducting loops and Josephson junctions, which exhibit stable memory configurations of trapped flux in loops that determine the rate of flow of fluxons through synaptic connections. The memory states are, in turn, affected by the applied input signals but can also be externally configured electrically through control current/feedback terminals. These results establish a previously unexplored, biologically similar architectural approach to neuromorphic computing that is scalable while dissipating energy of atto Joules/spike.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Region-based convolutional neural network for wind turbine wake characterization from scanning lidars
A convolutional neural network is applied to lidar scan images from three experimental campaigns to identify and characterize wind turbine wakes. Initially developed as a proof-of-concept model and applied to a single data set in complex terrain, the model is now improved and generalized and applied to two other unique lidar data sets, one located near an escarpment and one located offshore. The model, initially developed using lidar scans collected in predominantly westerly flow, exhibits sensitivity to wind flow direction. The model is thus successfully generalized through implementing a standard rotation process to scan images before input into the convolutional neural network to ensure the flow is westerly. The sample size of lidar scans used to train the model is increased, and along with the generalization process, these changes to the model are shown to enhance accuracy and robustness when characterizing dissipating and asymmetric wakes. Applied to the offshore data set in which nearly 20 wind turbine wakes are included per scan, the improved model exhibits a 95% success rate in characterizing wakes and a 74% success rate in characterizing dissipating wake fragments. The improved model is shown to generalize well to the two new data sets, although an increase in wake characterization accuracy is offset by an increase in model sensitivity and false positive wake identifications.
17 WIND ENERGY↗
Quantum algorithms for geologic fracture networks
Abstract Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approximation of the solution. Unfortunately, fractured systems cannot be accurately coarse-grained, as critical network topology exists at the smallest scales, including topology that can push the network across a percolation threshold. Therefore, new techniques are necessary to accurately model important fracture systems. Quantum algorithms for solving linear systems offer a theoretically-exponential improvement over their classical counterparts, and in this work we introduce two quantum algorithms for fractured flow. The first algorithm, designed for future quantum computers which operate without error, has enormous potential, but we demonstrate that current hardware is too noisy for adequate performance. The second algorithm, designed to be noise resilient, already performs well for problems of small to medium size (order 10–1000 nodes), which we demonstrate experimentally and explain theoretically. We expect further improvements by leveraging quantum error mitigation and preconditioning.
58 GEOSCIENCES↗
Perspective—Hydrogen Bonded Concentrated Electrolytes for Redox Flow Batteries: Limitations and Prospects
This perspective provides a brief overview of the recent work on electrolytes with hydrogen (H)-bonding network, specifically the deep eutectic solvents (DESs), and outlines important factors to consider when adapting these electrolytes in redox flow batteries (RFBs). The redox behavior, solubility, and stability of several redox molecules of relevance to RFBs in DESs are presented, including some of our work within the Breakthrough Electrolytes for Energy Storage (BEES)—an Energy Frontier Research Center of the United States Department of Energy. Particularly, the challenges and opportunities for further development of DESs for energy storage are discussed.
25 ENERGY STORAGE↗
Behavior of an air-cooled thermosiphon during a non-air gas ingress event
Here, this study investigates the transient response of an air-cooled thermosiphon subjected to a sudden non-air gas ingress event. Results presented are from experimental testing conducted on a large-scale thermal hydraulic facility that examines heat removal performance of the Reactor Cavity Cooling System (RCCS) concept, a passive safety system for advanced nuclear reactors. Relying on buoyancy driven natural circulation flow, these systems feature a series of riser standpipes and coupled chimney network that provide a pathway for heat rejection to the atmosphere. In this work, the facility was modified to allow a transition of the draft intake from normal ambient air, to draft intake from an open-top volume containing high purity argon. Testing began by establishing normal operation and allowing the facility to reach steady-state thermal hydraulic flow conditions with natural air draft. The transitioned sequence was then initiated which simultaneously changed the inlet boundary from open air to a pathway from the quiescent argon volume. This event caused 1,200 cu-feet of the heavy gas, twice the internal volume of total facility flow path, to be drawn into the inlet plenum and ingress into the heated riser standpipes. With the upper chimney network still containing residual air, there was insufficient density difference to maintain buoyancy driven natural circulation. After a period of only 90 s past the ingress event, the facility experienced complete flow stagnation before entering an extended period of severely degraded system flow. Due to the cessation of bulk fluid movement and subsequent failure of its heat removal function, fluid and structural temperatures began to rise sharply. Re-circulation patterns developed within the multiple parallel riser standpipes, where hot gas near the outlets was observed to travel downward and re-enter the inlet of adjacent channels. After approximately 18-minutes, fluid temperatures and their associated density difference rose to a level sufficient to allow reestablishment of buoyancy driven system flow, and ultimately, recovery of facility operation to normal behavior.
42 ENGINEERING↗
Scalable Risk Assessment of Rare Events in Power Systems With Uncertain Wind Generation and Loads
Risk assessment of rare events has become increasingly important in power system planning and operation with the increasing integration of renewable energy and the presence of system uncertainties. However, quantifying the risk posed by rare events via the traditional method, i.e., Monte Carlo sampling (MCS), incurs substantial computational expense stemming from the vast ensemble of power flow simulations. To accelerate the assessment, this paper proposes a Deep Neural Network (DNN)-kernelized vector-valued Gaussian Process (VVGP) approach with excellent computational efficiency while maintaining high accuracy. Consequently, serving as a surrogate model for the power flow solver, the DNN-kernelized VVGP enables significantly faster but accurate risk assessment compared to the power flow solver. The developed surrogate model evaluates low-order N - k events that contain more than 90% instances by adeptly capturing the topological features while the high-order N - k events are assessed via a power flow solver, thereby striking a balance between computational efficiency and uncertainty quantification accuracy. Moreover, the model incorporates a Support Vector Machine (SVM) classifier to resample concerning low-probability tail events to counteract the biases potentially introduced during the DNN-kernelized VVGP evaluations. Simulations conducted on the modified IEEE 24-bus, 118-bus, and European 1354-bus systems demonstrate that the proposed method maintains the accuracy benchmark set by MCS while significantly reducing computational demands in large-scale power systems as compared to other state-of-the-art methods.
17 WIND ENERGY↗
Using 100G Network Technology in Support of Petascale Science
NASA in collaboration with a number of partners conducted a set of individual experiments and demonstrations during SC 10 that collectively were titled "Using 100G Network Technology in Support of Petascale Science". The partners included the iCAIR, Internet2, LAC, MAX, National LambdaRail (NLR), NOAA and SCinet Research Sandbox (SRS) as well as the vendors Ciena, Cisco, ColorChip, cPacket, Extreme Networks, Fusion-io, HP and Panduit who most generously allowed some of their leading edge 40G/100G optical transport, Ethernet switch and Internet Protocol router equipment and file server technologies to be involved. The experiments and demonstrations featured different vendor-provided 40G/100G network technology solutions for full-duplex 40G and 100G LAN data flows across SRS-deployed single-node fiber-pairs among the Exhibit Booths of NASA, the National Center for Data lining, NOAA and the SCinet Network Operations Center, as well as between the NASA Exhibit Booth in New Orleans and the Starlight Communications Exchange facility in Chicago across special SC 10- only 80- and 100-Gbps wide area network links provisioned respectively by the NLR and Internet2, then on to GSFC across a 40-Gbps link. provisioned by the Mid-Atlantic Crossroads. The networks and vendor equipment were load-stressed by sets of NASA/GSFC High End Computer Network Team-built, relatively inexpensive, net-test-workstations that are capable of demonstrating greater than 100Gbps uni-directional nuttcp-enabled memory-to-memory data transfers, greater than 80-Gbps aggregate--bidirectional memory-to-memory data transfers, and near 40-Gbps uni-directional disk-to-disk file copying. This paper will summarize the background context, key accomplishments and some significances of these experiments and demonstrations.
Gary, James P.↗
Robust hierarchical dispatch for residential distribution network management considering home thermal flexibility and model predictive control
In the transactive energy (TE) paradigm, the devices of participative consumers, or prosumers, may be aggregated and employed to drive operational objectives at the network level. Home heating, ventilation and air-conditioning (HVAC) systems in particular are well-suited to modulate their behaviours based on both home thermal flexibility and requests from the utility grid. This paper develops a robust, hierarchical power dispatch scheme in the context of a residential distribution network. The formulation couples a unique, multiphase linear distribution optimal power flow (OPF) at the upper level with model predictive control (MPC)-based HVAC fleet controllers at the lower level. The proposed approach is tested on nearly 2000 homes with a three-phase distribution network in an intraday market setting, where two major applications are explored and analysed.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Multiphase Optimal and Non-Singular Power Flow by Successive Linear Approximations: Preprint
We propose an optimal power flow method for multiphase power systems. The method works for both radial and meshed networks, and is able to accommodate different types of load/source connections such as wye, delta, etc. Moreover, the method generates a sequence of non-singular points in the electrical state space and a corresponding sequence of power injections, which can be used in real-time control settings. Under certain conditions, the sequences attain the local minimum. The method has been tested using several typical networks, including the IEEE 37-bus and 123-bus test feeders.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Multimodal Freight Energy Model for Emerging Freight Technology Analysis
While freight movement represents a small portion of the volume in the transportation sector, they are a critical contributor for energy consumed in that sector. To reduce logistics costs, energy consumption and negative environmental impact, emerging technologies, such as digitalization of logistics and alternative powertrain, have been developed and extended applications in freight. These trends are expected to grow and provide opportunities for greater efficiencies in freight movement and corresponding energy use. Still, the complexity of freight systems present challenges in evaluating these benefits, especially in the multimodal inter-city freight. Addressing the research need, this paper develops a multimodal freight energy modeling framework for the analysis of emerging freight technology scenarios. The framework is a bi-level optimization problem: network cost minimization problem (lower-level) and energy minimization problem (upper-level). The lower-level problem is a mode-path assignment problem in multimodal inter-city freight networks, where commodity-specific congestion effects on trans-shipment links are considered. For this model, an inverse modeling approach is applied to infer parameters of the lower-level model. The upper-level problem is designed to search for an optimal scenario that has the lowest energy consumption among different levels of technology applications. The proposed model is empirically tested to analyze truck load-pooling and multimodal load-pooling scenarios through stand-alone and mixed applications using freight shipments originating from or destined to the Chicago region. This framework can be used to explore the impact of emerging freight technologies on mode-path freight flow and energy consumption in the national multimodal freight network.
ADVANCED PROPULSION SYSTEMS↗
Simulation of gas mixture dynamics in a pipeline network using explicit staggered-grid discretization
Here we develop an explicit staggered finite difference discretization scheme for simulating the transport of highly heterogeneous gas mixtures through pipeline networks. This study is motivated by the proposed blending of hydrogen into natural gas pipelines to reduce end use carbon emissions while using existing pipeline systems throughout their planned lifetimes. Our computational method accommodates an arbitrary number of constituent gases with very different physical properties that may be injected into a network with significant spatiotemporal variation. In this setting, the gas flow physics are highly location- and time- dependent, so that local composition and nodal mixing must be accounted for. The resulting conservation laws are formulated in terms of pressure, partial densities and flows, and volumetric and mass fractions of the constituents. We include non-ideal equations of state that employ linear approximations of gas compressibility factors, so that the pressure dynamics propagate locally according to a variable wave speed that depends on mixture composition and density. We derive compatibility relationships for network edge boundary values that are more complex than for a homogeneous gas. The simulation method is evaluated on initial boundary value problems for a single pipe and a small network, is cross-validated with a lumped element simulation, and used to demonstrate a local monitoring and control policy for maintaining allowable concentration levels.
97 MATHEMATICS AND COMPUTING↗
Fluid System Stability Analysis Techniques
Fluid systems, or networks, consist of multiple components that work together to achieve some desired thermofluid state. For any generic application, this fluid state can be a combination of the fluid pressure, flow rate, enthalpy, or species concentration. Fluid system components, such as pumps and valves, are often governed by nonlinear differential equations, resulting in complex component-to-component interactions. System-level fluid network stability occurs when the flow through the system can maintain a steady-state solution in the presence of small perturbations, which depends on these component interactions. System instability, however, can go undetected until issues arise during integrated system testing. This presentation explores a method for system designers to think of the fluid network as an assembly of components, each with their own thermofluid surfaces of partial stability, called nullclines. The intersections of all nullclines yields system-level solutions, called equilibrium points. When designers define operating points, they are tuning system parameters so that these equilibrium points move to the desired location in the thermofluid state plane. However, linearization theory shows us that the dynamic behavior around these points can be unstable. The local stability of these equilibrium points can be assessed analytically with eigen-analysis, or numerically by propagating state-plane samples to construct a phase portrait. Investigating a phase portrait can help designers gain a qualitative understanding of a system’s dynamic performance. This understanding can then help inform requirement definitions, component selection, and operational procedures. This presentation includes an example of the phase portrait technique on a system featuring a centrifugal pump and a back-pressure regulator (BPR). Numerical modeling of this system suggests that equilibrium points on the left-hand side of the pump curve are dynamically unstable.
Fluid System↗
Fluid System Stability Analysis Techniques
Fluid systems, or networks, consist of multiple components that work together to achieve some desired thermofluid state. For any generic application, this fluid state can be a combination of the fluid pressure, flow rate, enthalpy, or species concentration. Fluid system components, such as pumps and valves, are often governed by nonlinear differential equations, resulting in complex component-to-component interactions. System-level fluid network stability occurs when the flow through the system can maintain a steady-state solution in the presence of small perturbations, which depends on these component interactions. System instability, however, can go undetected until issues arise during integrated system testing. This presentation explores a method for system designers to think of the fluid network as an assembly of components, each with their own thermofluid surfaces of partial stability, called nullclines. The intersections of all nullclines yields system-level solutions, called equilibrium points. When designers define operating points, they are tuning system parameters so that these equilibrium points move to the desired location in the thermofluid state plane. However, linearization theory shows us that the dynamic behavior around these points can be unstable. The local stability of these equilibrium points can be assessed analytically with eigen-analysis, or numerically by propagating state-plane samples to construct a phase portrait. Investigating a phase portrait can help designers gain a qualitative understanding of a system’s dynamic performance. This understanding can then help inform requirement definitions, component selection, and operational procedures. This presentation includes an example of the phase portrait technique on a system featuring a centrifugal pump and a back-pressure regulator (BPR). Numerical modeling of this system suggests that equilibrium points on the left-hand side of the pump curve are dynamically unstable.
Fluid System↗
Response of deep aquifers to climate variability
There is a general agreement that deep aquifers experience significant lag time in their response to climatic variations. Analysis of Temporal Gravity Recovery and Climate Experiment (GRACE), Soil Moisture and Ocean Salinity mission (SMOS), satellite altimetry, stable isotopic composition of groundwater, and precipitation and static global geopotential models over the Nubian Sandstone Aquifer System (NSAS) revealed rapid aquifer response to climate variability. Findings include: (1) The recharge areas of the NSAS (Northern Sudan Platform subbasin) witnessed a dry period (2002-2012), where average annual precipitation (AAP) was modest (85 mm) followed by a wet period (2013-2016; AAP: 107 mm), and during both periods the APP remained negligible (<10 mm) over the northern parts of the NSAS (Dakhla subbasin); (2) the secular trends in terrestrial water storage (TWS) over the Dakhla subbasin were estimated at -3.8 ± 1.3 mm/yr and + 7.8 ± 1 mm/yr for the dry and wet periods, respectively; (3) spatial variations in TWS values and phase are consistent with rapid groundwater flow from the Northern Sudan Platform subbasin and Lake Nasser towards the Dakhla subbasin during the wet period and from the lake during the dry period; and (4) networks of densely fractured and karstified bedrocks provide preferential pathways for groundwater flow. The proposed model is supported by (1) rapid response in groundwater levels in distant wells (>280 km from source areas) and in soil moisture content in areas with shallow (<2 m) groundwater levels to fluctuations in Lake Nasser surface water, and (2) the isotopic composition (O, H) of groundwater along the preferred pathways, consistent with mixing of enriched (Lake Nasser water or precipitation over Sudan) and depleted (NSAS fossil water) end-members. Here, findings provide new insights into the response of large, deep aquifers to climate variability and address the sustainability of the NSAS and similar fossil aquifers worldwide.
54 ENVIRONMENTAL SCIENCES↗
Thermal Network Analyzer Program
Computer program solves transient or steady-state heat flow problems through the concept of lumped parameters expressed as the electrical analog of the heat transfer problem using finite differences techniques.
Sakakura, H. D.↗
Formulation and Comparison of Two Real-Time Predictive Gear Shift Algorithms for Connected/Automated Heavy-Duty Vehicles
Our work investigates the problem of predictive gear scheduling for fuel consumption minimization in connected/automated heavy trucks. The literature highlights the fuel economy benefits of such predictive scheduling, but there is a need to optimize such scheduling online, in real time. To address this need, we begin by using dynamic programming (DP) to schedule gear shifting offline, in a manner that achieves a globally optimal Pareto tradeoff between the conflicting objectives of minimizing fuel consumption and shift frequency. The computational cost of DP is unfavorable for online implementation, but we present two algorithms addressing this issue. Both algorithms rely on the fact that in the Pareto limit where fuel consumption minimization is the sole objective, DP furnishes a simple static shift map . Our first algorithm trains a recurrent neural network to prune the shift schedule generated by this map. The second algorithm performs this pruning in a direct manner tailored to reduce the schedule's rain flow count. We simulate these algorithms for different drive cycles. Both algorithms achieve a reasonable tradeoff between fuel consumption and gear shift frequency. Yet, the rain flow count algorithm is both more effective in approaching the DP-based Pareto front and more computationally efficient.
33 ADVANCED PROPULSION SYSTEMS↗
CONTROL AND DATA ACQUISITION IN A CYBER-PHYSICAL MIDSTREAM TESTBED
This thesis presents the development of a laboratory-scale cyber–physical midstream pipeline testbed designed to address this gap and support research in industrial control systems security. The platform integrates pumps, valves, sensors, programmable logic controllers (PLCs), and a human–machine interface (HMI) to emulate the monitoring and control architecture of real pipeline operations. The physical process is implemented as a closed-loop liquid circulation system designed to replicate flow behavior characteristic of midstream pipeline infrastructure. The testbed enables real-time data acquisition of key process variables, including flow rate and pressure facilitating the generation of datasets representative of normal pipeline operation. A threat model encompassing common ICS attack vectors was developed, including sensor spoofing, command injection, false data injection, denial-of-service attacks, and relay manipulation. Multiple attack scenarios were implemented and evaluated to demonstrate how cyber intrusions targeting sensors, actuators, networks, and software propagate into measurable physical consequences in pipeline flow and pressure. The developed platform serves as a practical, cost-effective environment for experimentation, education, and future cybersecurity research in midstream pipeline systems.
42 ENGINEERING↗