Distributed Energy Management for Networked Microgrids Embedded Modern Distribution System Using ADMM Algorithm
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24 POWER TRANSMISSION AND DISTRIBUTION↗
SEARCH · Engineering Papers
Results for “Embedded Systems”
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.
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Graphene embedded seal
Systems and processes for forming seals. The seals have graphene embedded in the seal. The graphene-embedded seals exhibit improved wear resistance over seals without graphene.
Erdemir, Ali↗
An Fe-Ni-Cr embedded atom method potential for austenitic and ferritic systems: An Fe-Ni-Cr embedded atom method potential for austenitic and ferritic systems
Fe-Ni-Cr stainless-steels are important structural materials because of their superior strength and corrosion resistance. Atomistic studies of mechanical properties of stainless-steels, however, have been limited by the lack of high-fidelity interatomic potentials. Here in this paper, using density functional theory as a guide, we have developed a new Fe-Ni-Cr embedded atom method potential. We demonstrate that our potential enables stable molecular dynamics simulations of stainless-steel alloys at high temperatures, accurately reproduces the stacking fault energy—known to strongly influence the mode of plastic deformation (e.g., twinning vs. dislocation glide vs. cross-slip)—of these alloys over a range of compositions, and gives reasonable elastic constants, energies, and volumes for various compositions. The latter are pertinent for determining short-range order and solute strengthening effects. Our results suggest that our potential is suitable for studying mechanical properties of austenitic and ferritic stainless-steels which have vast implementation in the scientific and industrial communities.
36 MATERIALS SCIENCE↗
Module Embedded Micro-inverter Smart Grid Ready Residential Solar Electric System
The “Module Embedded Micro-inverter Smart Grid Ready Residential Solar Electric System” program is focused on developing innovative concepts for residential photovoltaic (PV) systems with the following objectives: to create an Innovative micro-inverter topology that reduces the cost from the best in class micro-inverter and provides high efficiency (>96% CEC - California Energy Commission), and 25+ year warranty, as well as reactive power support; integrate micro-inverter and PV module to reduce system price by at least $0.25/W through a) accentuating dual use of the module metal frame as a large area heat spreader reducing operating temperature, and b) eliminating redundant wiring and connectors; and create micro-inverter controller handles smart grid and safety functions to simplify implementation and reduce cost.
14 SOLAR ENERGY↗
Sparse Hardware Embedding of Spiking Neuron Systems for Community Detection
We study the applicability of spiking neural networks and neuromorphic hardware for solving general optimization problems without the use of adaptive training or learning algorithms. We leverage the dynamics of Hopfield networks and spin-glass systems to construct a fully connected spiking neural system to generate synchronous spike responses indicative of the underlying community structure in an undirected, unweighted graph. Mapping this fully connected system to current generation neuromorphic hardware is done by embedding sparse tree graphs to generate only the leading-order spiking dynamics. Here, we demonstrate that for a chosen set of benchmark graphs, the spike responses generated on a current generation neuromorphic processor can improve the stability of graph partitions and non-overlapping communities can be identified even with the loss of higher-order spiking behavior if the graphs are sufficiently dense. For sparse graphs, the loss of higher-order spiking behavior improves the stability of certain graph partitions but does not retrieve the known community memberships.
97 MATHEMATICS AND COMPUTING↗
Data‐driven variational method for discrepancy modeling: Dynamics with small‐strain nonlinear elasticity and viscoelasticity
Abstract The effective inclusion of a priori knowledge when embedding known data in physics‐based models of dynamical systems can ensure that the reconstructed model respects physical principles, while simultaneously improving the accuracy of the solution in the previously unseen regions of state space. This paper presents a physics‐constrained data‐driven discrepancy modeling method that variationally embeds known data in the modeling framework. The hierarchical structure of the method yields fine scale variational equations that facilitate the derivation of residuals which are comprised of the first‐principles theory and sensor‐based data from the dynamical system. The embedding of the sensor data via residual terms leads to discrepancy‐informed closure models that yield a method which is driven not only by boundary and initial conditions, but also by measurements that are taken at only a few observation points in the target system. Specifically, the data‐embedding term serves as residual‐based least‐squares loss function, thus retaining variational consistency. Another important relation arises from the interpretation of the stabilization tensor as a kernel function, thereby incorporating a priori knowledge of the problem and adding computational intelligence to the modeling framework. Numerical test cases show that when known data is taken into account, the data driven variational (DDV) method can correctly predict the system response in the presence of several types of discrepancies. Specifically, the damped solution and correct energy time histories are recovered by including known data in the undamped situation. Morlet wavelet analyses reveal that the surrogate problem with embedded data recovers the fundamental frequency band of the target system. The enhanced stability and accuracy of the DDV method is manifested via reconstructed displacement and velocity fields that yield time histories of strain and kinetic energies which match the target systems. The proposed DDV method also serves as a procedure for restoring eigenvalues and eigenvectors of a deficient dynamical system when known data is taken into account, as shown in the numerical test cases presented here.
Masud, Arif↗
Application of 3D Printing for Smart Objects with Embedded Electronic Sensors and Systems
Applications of a 3D printing process are presented. This process integrates liquid-state printed components and interconnects with IC chips in all three dimensions, various orientations, and multiple printing layers to deliver personalized system-level functionalities. Here, as an example application, a form-fitting glove is demonstrated with embedded programmable heater, temperature sensor, and the associated control electronics for thermotherapeutic treatment.
36 MATERIALS SCIENCE↗
Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision
Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.
Jha, Sumit Kumar↗
Relativistic two-component projection-based quantum embedding for open-shell systems
In this work, we present a relativistic quantum embedding formalism capable of variationally treating relativistic effects, including scalar-relativity and spin–orbit coupling. We extend density functional theory (DFT)-in-DFT projection-based quantum embedding to a relativistic two-component formalism, where the full spin magnetization vector form is retained throughout the embedding treatment. To benchmark various relativistic embedding schemes, spin–orbit splitting of the nominally t 2g valence manifold of W(CO) 6 , exchange coupling of [(H 3 N) 4 Cr(OH) 2 Cr(NH 3 ) 4 ] 4+ , and the dissociation potential curve of WF 6 are investigated. The relativistic embedding formalism introduced in this work is well suited for efficient modeling of open-shell systems containing late transition metal, lanthanide, and actinide molecular complexes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Privacy-Preserving Control of Partitioned Energy Resources
Distributed energy resources are an increasingly important part of the electric grid. We examine the problem of partitioning a distributed energy resource among many users while providing privacy to them. In this model, clients can send requests to a server, the server can verify that the requests are valid and aggregate them, but it cannot see the actual values in the requests. Without privacy, each user is forced to reveal their daily schedule or energy use. Energy resources add a novel challenge that prior systems do not address: they require verifying limits on private power (a rate over time) and energy (a sum) values. Furthermore, the cryptographic mechanisms must run on embedded energy control systems. We describe Weft, a novel cryptographic system that verifies both power (rate) and energy (integral) constraints on private client values and aggregates them. The key insight behind the approach is to rely on additively homomorphic secret shares, which allows servers to compute sums from rates. We present 3 cryptographic proof systems with different system trade-off for embedded systems: bit-splitting proofs minimize memory use, sorting proofs minimize computation, and commitment proofs minimize network communication. Using bit-splitting proofs, it takes an IoT client using a CortexM microcontroller 4 minutes of compute time to privately control its share of an energy resource for a day at 20s granularity.
Laufer, Evan↗
Structure of a 28.5 kDa duplex-embedded G-quadruplex system resolved to 7.4 Å resolution with cryo-EM
Genomic regions with high guanine content can fold into non-B form DNA four-stranded structures known as G-quadruplexes (G4s). Extensive in vivo investigations have revealed that promoter G4s are transcriptional regulators. Little structural information exists for these G4s embedded within duplexes, their presumed genomic environment. Here, we report the 7.4 Å resolution structure and dynamics of a 28.5 kDa duplex-G4-duplex (DGD) model system using cryo-EM, molecular dynamics, and small-angle X-ray scattering (SAXS) studies. The DGD cryo-EM refined model features a 53° bend induced by a stacked duplex-G4 interaction at the 5’ G-tetrad interface with a persistently unstacked 3’ duplex. The surrogate complement poly dT loop preferably stacks onto the 3’ G-tetrad interface resulting in occlusion of both 5’ and 3’ tetrad interfaces. Structural analysis shows that the DGD model is quantifiably more druggable than the monomeric G4 structure alone and represents a new structural drug target. Our results illustrate how the integration of cryo-EM, MD, and SAXS can reveal complementary detailed static and dynamic structural information on DNA G4 systems.
59 BASIC BIOLOGICAL SCIENCES↗
Large-Scale First-Principles Molecular Dynamics Simulations with Electrostatic Embedding: Application to Acetylcholinesterase Catalysis
Enzymes are complicated solvated systems that typically require many atoms to simulate their function with any degree of accuracy. We have recently developed numerical techniques for large scale First-Principles molecular dynamics simulations and applied them to study the enzymatic reaction catalyzed by acetylcholinesterase. We carried out Density functional theory calculations for a quantum mechanical (QM) sub- system consisting of 612 atoms with an O(N) complexity finite-difference approach. The QM sub-system is embedded inside an external potential field representing the electrostatic effect due to the environment. We obtained finite temperature sampling by First-Principles molecular dynamics for the acylation reaction of acetylcholine catalyzed by acetylcholinesterase. Our calculations shows two energies barriers along the reaction coordinate for the enzyme catalyzed acylation of acetylcholine. In conclusion, the second barrier (8.5 kcal/mole) is rate-limiting for the acylation reaction and in good agreement with experiment.
59 BASIC BIOLOGICAL SCIENCES↗
Privacy Preserving Federated Learning for Advanced Scientific Ecosystems
We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC National Accelerator Laboratory and Spallation Neutron Source (SNS). We describe the software infrastructure developed to provide privacy for computational and experimental networks. We developed algorithmic privacy across the federated system by embedding database security, computation, and communication into the federation architecture, utilizing scientific tools developed by the experimental community.
Archibald, Rick [ORNL] (ORCID:0000000245389780)↗
Automated Adversary-in-the-Loop Cyber-Physical Defense Planning
Security of cyber-physical systems (CPS) continues to pose new challenges due to the tight integration and operational complexity of the cyber and physical components. To address these challenges, this article presents a domain-aware, optimization-based approach to determine an effective defense strategy for CPS in an automated fashion—by emulating a strategic adversary in the loop that exploits system vulnerabilities, interconnection of the CPS, and the dynamics of the physical components. Our approach builds on an adversarial decision-making model based on a Markov Decision Process (MDP) that determines the optimal cyber (discrete) and physical (continuous) attack actions over a CPS attack graph. The defense planning problem is modeled as a non-zero-sum game between the adversary and defender. We use a model-free reinforcement learning method to solve the adversary’s problem as a function of the defense strategy. We then employ Bayesian optimization (BO) to find an approximate best-response for the defender to harden the network against the resulting adversary policy. This process is iterated multiple times to improve the strategy for both players. We demonstrate the effectiveness of our approach on a ransomware-inspired graph with a smart building system as the physical process. Numerical studies show that our method converges to a Nash equilibrium for various defender-specific costs of network hardening.
97 MATHEMATICS AND COMPUTING↗
Fatigue Performance of Ti-6Al-4V Additively Manufactured Specimens with Integrated Capillaries of an Embedded Structural Health Monitoring System
Additive manufacturing (AM) of metals offers new possibilities for the production of complex structures. Up to now, investigations on the mechanical response of AM metallic parts show a significant spread and unexpected failures cannot be excluded. In this work, we focus on the detection of fatigue cracks through the integration of a Structural Health Monitoring (SHM) system in Ti-6Al-4V specimens. The working principle of the presented system is based on the integration of small capillaries that are capable of detecting fatigue cracks. Four-point bending fatigue tests have been performed on Ti-6Al-4V specimens with integrated capillaries and compared to the reference specimenswithout capillaries. Specimens were produced by conventional subtractive manufacturing of wrought material and AM, using the laser based Directed Energy Deposition (DED) process. In this study, we investigated the effect of the presence of the capillary on the fatigue strength and fatigue initiation location. Finite element (FEM) simulations were performed to validate the experimental test results. The presence of a drilled capillary in the specimens did not alter the fatigue initiation location. However, the laser based DED production process introduced roughness on the capillary surface that altered the fatigue initiation location to the capillary surface. The fatigue performance was greatly reduced when considering a printed capillary. It is concluded that the surface quality of the integrated capillary is of primary importance in order not to influence the structural integrity of the component to be monitored.
36 MATERIALS SCIENCE↗
Hub-filament System in IRAS 05480+2545: Young Stellar Cluster and 6.7 GHz Methanol Maser
To probe the star formation (SF) process, we present a multi-wavelength study of IRAS 05480+2545 (hereafter I05480+2545). Analysis of Herschel data reveals a massive clump (M {sub clump} ∼ 1875 M{sub ⊙}; peak N(H{sub 2}) ∼ 4.8 × 10{sup 22} cm{sup −2}; A {sub V} ∼ 51 mag) containing the 6.7 GHz methanol maser and I05480+2545, which is also depicted in a temperature range of 18–26 K. Several noticeable parsec-scale filaments are detected in the Herschel 250 μm image and seem to be radially directed to the massive clump. It resembles more of a “hub-filament” system. Deeply embedded young stellar objects (YSOs) have been identified using the 1–5 μm photometric data, and a significant fraction of YSOs and their clustering are spatially found toward the massive clump, revealing the intense SF activities. An infrared counterpart (IRc) of the maser is investigated in the Spitzer 3.6–4.5 μm images. The IRc does not appear as a point-like source and is most likely associated with the molecular outflow. Based on the 1.4 GHz and Hα continuum images, the ionized emission is absent toward the IRc, indicating that the massive clump harbors an early phase of a massive protostar before the onset of an ultracompact H ii region. Together, the I05480+2545 is embedded in a very similar “hub-filament” system to those seen in the Rosette Molecular Cloud. The outcome of the present work indicates the role of filaments in the formation of the massive star-forming clump and cluster of YSOs, which might help channel material to the central hub configuration and the clump/core.
79 ASTRONOMY AND ASTROPHYSICS↗
Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations
Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).
Chae, Junghoon↗