Design and Validation of Advanced Controls for Networked Power Systems.
Abstract not provided.
Cook, Marvin A.↗
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NET DESIGNER: A visual tool for writing nuclear networks for MESA and other network codes
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73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Optimal design of mixed-media packet-switching networks - Routing and capacity assignment
This paper considers a mixed-media packet-switched computer communication network which consists of a low-delay terrestrial store-and-forward subnet combined with a low-cost high-bandwidth satellite subnet. We show how to route traffic via ground and/or satellite links by means of static, deterministic procedures and assign capacities to channels subject to a given linear cost such that the network average delay is minimized. Two operational schemes for this network model are investigated: one is a scheme in which the satellite channel is used as a slotted ALOHA channel; the other is a new multiaccess scheme we propose in which whenever a channel collision occurs, retransmission of the involved packets will route through ground links to their destinations. The performance of both schemes is evaluated and compared in terms of cost and average packet delay tradeoffs for some examples. The results offer guidelines for the design and optimal utilization of mixed-media networks.
Huynh, D.↗
Hybrid Mobile Communication Networks for Planetary Exploration
A paper discusses the continuing work of the Mobile Exploration System Project, which has been performing studies toward the design of hybrid communication networks for future exploratory missions to remote planets. A typical network could include stationary radio transceivers on a remote planet, mobile radio transceivers carried by humans and robots on the planet, terrestrial units connected via the Internet to an interplanetary communication system, and radio relay transceivers aboard spacecraft in orbit about the planet. Prior studies have included tests on prototypes of these networks deployed in Arctic and desert regions chosen to approximate environmental conditions on Mars. Starting from the findings of the prior studies, the paper discusses methods of analysis, design, and testing of the hybrid communication networks. It identifies key radio-frequency (RF) and network engineering issues. Notable among these issues is the study of wireless LAN throughput loss due to repeater use, RF signal strength, and network latency variations. Another major issue is that of using RF-link analysis to ensure adequate link margin in the face of statistical variations in signal strengths.
Alena, Richard↗
SimCCS: An open-source tool for optimizing CO 2 capture, transport, and storage infrastructure
Commercial-scale carbon capture and storage (CCS) technology will involve deploying infrastructure on a massive and costly scale. This effort will require careful and comprehensive planning to ensure that capture locations, storage sites, and the dedicated CO 2 distribution pipelines are selected in a robust and cost-effective manner. Presented in 2009, SimCCS is an optimization model for integrated system design that enables researchers, stakeholders, and policy makers to design CCS infrastructure networks. SimCCS 2.0 is a complete, ground-up redesign that is now a portable software package, useable and shareable by the CCS research, industrial, policy, and public communities. SimCCS 2.0 integrates multiple new capabilities including a refined optimization model, novel candidate network generation techniques, and optional integration with high-performance computing platforms. Accessing user-provided CO 2 source, sink, and transportation data, SimCCS 2.0 creates candidate transportation routes and formalizes an optimization problem that determines the most cost-effective CCS system design. This optimization problem is further solved either through a high-performance computing interface, or through third-party software on a local desktop computing platform. Finally, SimCCS 2.0 employs an open-access geographic information system framework to enable analysis and visualization capabilities. SimCCS 2.0 is written in Java and is publicly available via GitHub to encourage collaboration, modification, and community development.
42 ENGINEERING↗
The Transactive Network Template Metamodel
While transactive energy—an allocation of electricity based on dynamically discovered values or prices—has been extensively studied, its uptake and use has been slow. This report describes a tool, the transactive network template, which should hasten the creation and uptake of transactive energy networks. The transactive network template is a metamodel meant to guide implementers as they configure their own transactive agent within a network of such agents. The transactive network template’s object–oriented design approach provides implementers basic code object types, which may be used and extended to represent each of the assets in the implementer’s circuit region. These objects further facilitate the transactive agent’s necessary computations, which are divided among responsibilities to schedule assets’ power usage, to balance electric supply and demand, and to coordinate the exchange of electricity with the other transactive agents. This report addresses the conceptual transactive network template design. Implementers are directed to more formal design documents and reference implementations. A Python™-based [Python 2018] reference implementation of the transactive network template has been coded, and three implementations have been configured to represent a national laboratory and two university campuses. In the future, the author wishes to make the transactive network template more generally applicable to networks that require more accurate power flow and those employing alternative transactive coordination mechanisms.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Dynamic ADMM for Real-Time Optimal Power Flow
This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearization of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation of the AC power flows, and it avoids ubiquitous metering to gather the state of noncontrollable resources. Optimality and convergence of the proposed algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Dynamic ADMM for Real-Time Optimal Power Flow: Preprint
This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearizations of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation of the AC power flows, and it avoids ubiquitous metering to gather the state of non-controllable resources. Optimality and convergence of the propose algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.
24 POWER TRANSMISSION AND DISTRIBUTION↗
ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers
Deep neural networks (DNNs) have heavily relied on traditional computational units, such as CPUs and GPUs. However, this conventional approach brings significant computational burden, latency issues, and high power consumption, limiting their effectiveness. This has sparked the need for lightweight networks such as ExtremeC3Net. Meanwhile, there have been notable advancements in optical computational units, particularly with metamaterials, offering the exciting prospect of energy-efficient neural networks operating at the speed of light. Yet, the digital design of metamaterial neural networks (MNNs) faces precision, noise, and bandwidth challenges, limiting their application to intuitive tasks and low-resolution images. In this study, we proposed a large kernel lightweight segmentation model, ExtremeMETA. Based on ExtremeC3Net, our proposed model, ExtremeMETA maximized the ability of the first convolution layer by exploring a larger convolution kernel and multiple processing paths. With the large kernel convolution model, we extended the optic neural network application boundary to the segmentation task. To further lighten the computation burden of the digital processing part, a set of model compression methods was applied to improve model efficiency in the inference stage. The experimental results on three publicly available datasets demonstrated that the optimized efficient design improved segmentation performance from 92.45 to 95.97 on mIoU while reducing computational FLOPs from 461.07 MMacs to 166.03 MMacs. The large kernel lightweight model ExtremeMETA showcased the hybrid design’s ability on complex tasks.
large convolution kernel↗
COMPASS Final Report: 2008 International Lunar Network (ILN)
The Compass team developed three designs for a soft lander for the International Lunar Network (ILN) using different power systems. These systems included solar arrays and batteries and two radioisotope power system (RPS) options scaled down from previous designs. These are a half Stirling radioisotope generator and a one-eighth Radioisotope Thermoelectric Generator (RTG). The COMPASS design consists of a soft-lander capable of network science (seismic, magnetospheric, laser reflector, and thermal transfer) that can land anywhere on the near side of the Moon and operate for at least six years. The baseline version, using only batteries and solar arrays, requires a very large battery (~80 kg) for the roughly 20 W night-time power requirements. The lander launch mass (with solid rocket breaking motor) is almost 900 kg. With such a large battery, the lander will require a launcher larger than the desired Minotaur V; a Taurus II (being developed for NASA Commercial Orbital Transportation Services (COTS)) or larger (Delta II or Atlas V) would be needed. The two RPS cases, half an Advanced Stirling Radioisotope Generators (ASRG) and one-eighth RTG have much smaller launch masses of 500 and 600 kg respectively. (The one-eighth RTG case lacks the power for the full science package, so a solar array battery is still needed.) Unfortunately, the Minotaur V’s launch capability is still not large enough (~430 kg to trans-lunar injection (TLI)) for the RPS systems. Consequently, a fourth case was developed to fly a limited science floor suite of instruments (seismometer only) which reduced the RPS Lander launch mass to fit on a Minotaur V.
Radioisotope Power Systems↗
Value Gradient Learning Approach in Power and Frequency Regulation of Grid-Connected Synchronverters
In this paper, a neural network adaptive critic design (ACD) method based on value gradient learning (VGL) is used to optimally control a grid-connected synchronverter. The main drawback of the traditional synchronverters is their infeasibility to face non-inductive grids. To be able to implement a synchronverter technique in any impedance angle, a neural network-based adaptive controller is used. The advantage of adaptive dynamic programing is its ability to adjust itself when it faces changes and uncertainties in the power system. The proposed VGL consists of two subnetworks: the critic network and the action network. The action network is trained during the operation, and the critic network can be pretrained offline or can be simultaneously trained with the action network. To compare the effectiveness of the traditional synchronverter to the VGL-based synchronverter, the simulation results are provided.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Composability-Centered Convolutional Neural Network Pruning
This work studies the composability of the building blocks of structural CNN models (e.g., GoogleLeNet and Residual Networks) in the context of network pruning. We empirically validate that a network composed of pre-trained building blocks (e.g. residual blocks and Inception modules) not only gives a better initial setting for training, but also allows the training process to converge at a significantly higher accuracy in much less time. Based on that insight, we propose a composability-centered design for CNN network pruning. Experiments show that this new scheme shortens the configuration process in CNN network pruning by up to 186.8X for ResNet-50 and up to 30.2X for Inception-V3, and meanwhile, the models it finds that meet the accuracy requirement are significantly more compact than those found by default schemes.
97 MATHEMATICS AND COMPUTING↗
Design considerations for beamwaveguide in the NASA Deep Space Network
A generalized solution is found for retrofitting a large dual-shaped reflector antenna for a beamwaveguide. The design is termed as a bypass beamwaveguide. Both highpass design feed imaging and bandpass design feed imaging are considered. Each design was studied using geometrical optics, Gaussian wave analysis, and both low-frequency and high-frequency diffraction analysis. An important extension of the Mizusawa-Kitsuregawa criteria was discovered (Zusama and Kitsuregawa, 1973). The principle revealed shows how a two-reflector cell, although in itself distorting, may be combined with a second cell which compensates for the first and delivers an output beam which is a good image of the input beam.
Veruttipong, Thavath↗
A National Virtual Specimen Database for Early Cancer Detection
Access to biospecimens is essential for enabling cancer biomarker discovery. The National Cancer Institute's (NCI) Early Detection Research Network (EDRN) comprises and integrates a large number of laboratories into a network in order to establish a collaborative scientific environment to discover and validate disease markers. The diversity of both the institutions and the collaborative focus has created the need for establishing cross-disciplinary teams focused on integrating expertise in biomedical research, computational and biostatistics, and computer science. Given the collaborative design of the network, the EDRN needed an informatics infrastructure. The Fred Hutchinson Cancer Research Center, the National Cancer Institute,and NASA's Jet Propulsion Laboratory (JPL) teamed up to build an informatics infrastructure creating a collaborative, science-driven research environment despite the geographic and morphology differences of the information systems that existed within the diverse network. EDRN investigators identified the need to share biospecimen data captured across the country managed in disparate databases. As a result, the informatics team initiated an effort to create a virtual tissue database whereby scientists could search and locate details about specimens located at collaborating laboratories. Each database, however, was locally implemented and integrated into collection processes and methods unique to each institution. This meant that efforts to integrate databases needed to be done in a manner that did not require redesign or re-implementation of existing system
distributed↗
Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units
Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs such as social networks. We demonstrate a novel hardware-software co-design approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations including a planner and vectorization for the gather-scatter operations targeted for Graphcore’s Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed co-design approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes and sparsity. Here, we demonstrate that such a co-design approach can reduce the training time of atomistic GNNs and can improve the performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Analysis and Testing of Mobile Wireless Networks
Wireless networks are being used to connect mobile computing elements in more applications as the technology matures. There are now many products (such as 802.11 and 802.11b) which ran in the ISM frequency band and comply with wireless network standards. They are being used increasingly to link mobile Intranet into Wired networks. Standard methods of analyzing and testing their performance and compatibility are needed to determine the limits of the technology. This paper presents analytical and experimental methods of determining network throughput, range and coverage, and interference sources. Both radio frequency (BE) domain and network domain analysis have been applied to determine wireless network throughput and range in the outdoor environment- Comparison of field test data taken under optimal conditions, with performance predicted from RF analysis, yielded quantitative results applicable to future designs. Layering multiple wireless network- sooners can increase performance. Wireless network components can be set to different radio frequency-hopping sequences or spreading functions, allowing more than one sooner to coexist. Therefore, we ran multiple 802.11-compliant systems concurrently in the same geographical area to determine interference effects and scalability, The results can be used to design of more robust networks which have multiple layers of wireless data communication paths and provide increased throughput overall.
Alena, Richard↗
Universal linear intensity transformations using spatially incoherent diffractive processors
Abstract Under spatially coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number ( N ) of optimizable phase-only diffractive features is ≥~2 N i N o , where N i and N o refer to the number of useful pixels at the input and the output FOVs, respectively. Here we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output FOVs. Under spatially incoherent monochromatic light, the spatially varying intensity point spread function ( H ) of a diffractive network, corresponding to a given, arbitrarily-selected linear intensity transformation, can be written as H ( m , n ; m ′, n ′) = | h ( m , n ; m ′, n ′)| 2 , where h is the spatially coherent point spread function of the same diffractive network, and ( m , n ) and ( m ′, n ′) define the coordinates of the output and input FOVs, respectively. Using numerical simulations and deep learning, supervised through examples of input-output profiles, we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N ≥ ~2 N i N o . We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths, operating simultaneously. Finally, we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination, achieving a test accuracy of >95%. Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light.
36 MATERIALS SCIENCE↗