Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ITERATIVE NETWORK”

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.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Joint Sparsity Pattern Recovery With 1-b Compressive Sensing in Distributed Sensor Networks

In this paper, we study the problem of joint sparse support recovery with 1-b quantized compressive measurements in a distributed sensor network. Multiple nodes in the network are assumed to observe sparse signals having the same but unknown sparse support. Each node quantizes its measurement vector element-wise to 1 b. First, we consider that all the quantized measurements are available at a central fusion center. We derive performance bounds for sparsity pattern recovery using 1-bit quantized measurements from multiple sensors when the maximum likelihood decoder is employed. We further develop two computationally tractable algorithms for joint sparse support recovery in the centralized setting. One algorithm minimizes a cost function defined as the sum of the likelihood function and the l-∝ quasi-norm, while the other algorithm extends the binary iterative hard thresholding algorithm to the multiple measurement vector case. Second, we consider a decentralized setting where each node transmits 1-b measurements to its one-hop neighbors. The basic idea behind the algorithms developed in the decentralized setting is to embed collaboration among nodes and fusion strategies. We show that even with noisy 1-b compressed measurements, joint support recovery can be carried out accurately in both centralized and decentralized settings. Additionally, we further show that the performance of the proposed 1-bit compressive sensing-based algorithms is very close to that of their real-valued counterparts except when the signal-to-noise ratio is very small.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Aerodynamic Optimization of Rocket Control Surface Geometry Using Cartesian Methods and CAD Geometry

Aerodynamic design is an iterative process involving geometry manipulation and complex computational analysis subject to physical constraints and aerodynamic objectives. A design cycle consists of first establishing the performance of a baseline design, which is usually created with low-fidelity engineering tools, and then progressively optimizing the design to maximize its performance. Optimization techniques have evolved from relying exclusively on designer intuition and insight in traditional trial and error methods, to sophisticated local and global search methods. Recent attempts at automating the search through a large design space with formal optimization methods include both database driven and direct evaluation schemes. Databases are being used in conjunction with surrogate and neural network models as a basis on which to run optimization algorithms. Optimization algorithms are also being driven by the direct evaluation of objectives and constraints using high-fidelity simulations. Surrogate methods use data points obtained from simulations, and possibly gradients evaluated at the data points, to create mathematical approximations of a database. Neural network models work in a similar fashion, using a number of high-fidelity database calculations as training iterations to create a database model. Optimal designs are obtained by coupling an optimization algorithm to the database model. Evaluation of the current best design then gives either a new local optima and/or increases the fidelity of the approximation model for the next iteration. Surrogate methods have also been developed that iterate on the selection of data points to decrease the uncertainty of the approximation model prior to searching for an optimal design. The database approximation models for each of these cases, however, become computationally expensive with increase in dimensionality. Thus the method of using optimization algorithms to search a database model becomes problematic as the number of design variables is increased.

Nelson, Andrea↗
DOE OSTI2018

Geometric Analysis Based Double Closed-loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process

In order to improve the pulp quality and to reduce the energy consumption, the fiber length distribution (FLD) is generally employed as one of the important technological indexes in the refining process. Considering that the traditional mean and variance of fiber length are unable to adequately characterize the non-Gaussian distribution properties, this paper proposes a novel geometric analysis based double closed-loop iterative learning control (ILC) method for probability density function (PDF) shaping of output FLD in the refining process. Primarily, a RBF neural network (NN) with Gaussian-type is utilized to approximate the square root PDF in the inner loop, where the RBF basis function parameters (center and width) are tuned between any two adjacent batches by using an ILC law, and the subspace identification method can be applied to establish the state-space model of weight vector. Then, for the sake of accelerating the convergence rate of the closed-loop sysyem, a geometric analysis based ILC method is adopted in the outer loop. Lastly, both simulation and experiments demonstrate the effectiveness and practicability of the proposed approach.

42 ENGINEERING↗
DOE OSTI2020

Identification of a network of nonlinear interactions as a mechanism triggering the onset of edge localized modes

Edge localized modes (ELMs) remain a critical issue for the lifetime of in-vessel components of fusion reactors such as ITER. We study ELMs triggered when the pedestal parameters are below the peeling-ballooning limit. These ELMs occur while the pedestal recovers after an ELM crash. We identify a mechanism by which a non-linear perturbation involving many active triads—a network of non-linear interactions – leads to the triggering of these ELMs. Each active triad leads to the non-linear transfer of energy between three waves, such that the network of non-linear interactions lead to a transfer of energy between many waves. The triggering of this non-linear perturbation systematically coincides with a pedestal perturbation induced by the neutral beam injection beams and localized near the magnetic surface of safety factor q = 5. Among the non-linear interactions detected during the ELM by a bi-coherence analysis, the most active triad involves a slow frequency that is qualitatively consistent with a geodesic acoustic mode located at the same location, i.e., near the q = 5 surface.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Energetic particle marginal stability profile for HL-2M integrated simulation based on neural network module

Abstract A critical gradient model is employed to develop a module of energetic particle (EP) marginal stability profiles in OMFIT integrated simulations for studying EP transport. Currently, each iteration of transport evolution is approximately 10 min in the integrated simulation, whereas, the EP marginal stability profile, which serves as an input in the integrated simulation could take much longer; the reason being a combination of the TGLFEP and EPtran codes is employed in our previous investigation. To reduce the simulation time, the critical gradient is predicted by a neural network instead of the TGLFEP code, and the EPtran code is revised with parallel computing, so that the running time of this module can be controlled to within 5 min. The predictions are in good agreement with previous approaches. The integrated simulation of HL-2M with Alfven eigenmodes transported by neutral beam EP profiles indicates that EP transport reduces the total pressure and current as expected, but could also under some conditions raise the safety factor in the core, which is favorable for reversed magnetic shear and high-performance plasmas.

Physics↗
DOE OSTI2018

Real-time coherent diffraction inversion using deep generative networks

Abstract Phase retrieval, or the process of recovering phase information in reciprocal space to reconstruct images from measured intensity alone, is the underlying basis to a variety of imaging applications including coherent diffraction imaging (CDI). Typical phase retrieval algorithms are iterative in nature, and hence, are time-consuming and computationally expensive, making real-time imaging a challenge. Furthermore, iterative phase retrieval algorithms struggle to converge to the correct solution especially in the presence of strong phase structures. In this work, we demonstrate the training and testing of CDI NN, a pair of deep deconvolutional networks trained to predict structure and phase in real space of a 2D object from its corresponding far-field diffraction intensities alone. Once trained, CDI NN can invert a diffraction pattern to an image within a few milliseconds of compute time on a standard desktop machine, opening the door to real-time imaging.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

American cities in a time of global environmental change: the case of the Baltimore Social-Environmental Collaborative

The Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory seeks a new paradigm for urban climate research. Motivated by deep uncertainties in urban climate and the future of urban systems, BSEC works collaboratively across institutions and stakeholder groups to co-generate the science needed to advance energy security and resilience to extreme events across the city of Baltimore, Maryland, USA, and to do so in a manner that can inform similar efforts in other cities. BSEC begins with stakeholder priorities (health, affordable energy, etc) and designs observation networks and models to deliver climate science to address them. This takes the form of an iterative collaborative cycle, in which an initial research strategy is repeatedly updated in conversation with community partners, and researchers and stakeholders learn from each other. To date, this cycle has included multiple rounds of collaborative deliberation on urban heat mitigation, in which a multicriteria decision tool has been updated with more community-relevant spatial structure and modified optimization metrics. The guiding objective of this cycle is to inform potential ‘secure and resilient pathways’ for energy and infrastructure. In doing so, BSEC addresses fundamental urban science questions in natural and social sciences. It also tests our ability to integrate this science in a manner that advances participatory decision-making for urban resilience.

climate↗
DOE OSTI2024

Final CRADA Report – NFE-21-08693

TAE Technologies is developing a magnetic fusion energy concept known as the beam-driven field-reversed configuration (FRC) with the ultimate goal of developing a reactor for commercial electricity production capable of burning aneutronic pB11 fuel. To achieve the high plasma temperatures this requires, auxiliary radiofrequency (RF) heating will likely be needed. High Harmonic Fast Wave (HHFW) heating has been identified as a candidate RF heating scheme to overcome the unique challenges posed to RF heating by the FRC, including the large distance from the plasma edge to the last closed flux surface and a magnetic field profile with strength decreasing from edge to core and reversing sign at a null point inside the plasma. The purpose of this project was to develop the experimental capabilities to test HHFW on TAE’s C-2W device through the design of a phased array antenna and accompanying matching network. The design was performed by ORNL and informed by experiments with a prototype four-strap phased antenna-array that was manufactured and installed on the LArge Plasma Device (LAPD) at UCLA and simulations conducted with the Petra-M code under the purview of a previous INFUSE grant. The ORNL team completed the conceptual design of the antenna and matching network which was then handed off to the TAE Mechanical Design team. The design was then iterated on to ensure changes to the mechanical design did not interfere with the RF performance. This process is now complete, and, with mechanical design in hand, TAE is proceeding with plans for final integration.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1973

Dynamic power load simulator

Two independent models simulate dynamic and steady-state responses of electrical and electronic equipment under power load. One is resistance/capacitance/inductance network, and the other is variable resistance analog device. Resistance, inductance, and/or capacitance are selected by iterative process; time-domain response is compared with that of real equipment to select optimal values.

Joncas, K. P.↗
NASA NTRS1973

Design of nonlinear resistive networks with prescribed input-output behavior.

This paper considers the problem of designing nonlinear multiport resistive networks for prescribed driving point and transfer characteristics. It is shown that, if the topology of the desired network is given, then the i-v characteristics of the nonlinear elements can be obtained iteratively. This is achieved by implementation of a newly developed steepest descent criterion.

Peikari, B.↗
NASA NTRS1985

Methods for design and evaluation of integrated hardware-software systems for concurrent computation

Research activities and publications are briefly summarized. The major tasks reviewed are: (1) VAX implementation of the PISCES parallel programming environment; (2) Apollo workstation network implementation of the PISCES environment; (3) FLEX implementation of the PISCES environment; (4) sparse matrix iterative solver in PSICES Fortran; (5) image processing application of PISCES; and (6) a formal model of concurrent computation being developed.

Pratt, T. W.↗
DOE OSTI2025

Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation

We evaluate the performance of the Karhunen-Loève Deep Neural Network (KL-DNN) framework for surrogate modeling and approximate Bayesian parameter estimation in partial differential equation models. In the surrogate model, the Karhunen-Loève (KL) expansions are used for the dimensionality reduction of the number of unknown parameters and variables, and a deep neural network is employed to relate the reduced space of parameters to that of the state variables. The KL-DNN surrogate model is used to formulate a maximum-a-posteriori-like least-squares problem, which is randomized to draw samples of the posterior distribution of the parameters. We test the proposed framework for a hypothetical unconfined aquifer via comparison with the forward MODFLOW and inverse PEST++ iterative ensemble smoother (IES) solutions as well as the state-of-the-art Fourier neural operator (FNO) and deep operator networks (DeepONets) operator learning surrogate models. Our results show that the KL-DNN surrogate model outperforms FNO and DeepONet for forward predictions. For solving inverse problems, the randomized algorithm provides the same or more accurate Bayesian predictions of the parameters than IES as evidenced by the higher log-predictive probability of both the estimated parameter field and the forecast hydraulic head. The posterior mean obtained from the randomized algorithm is closer to the reference parameter field than that obtained with FNO as the maximum a posteriori estimate.

Approximate Bayesian inference↗
DOE OSTI2015

PROGRESS IN DESIGN OF THE INSTRUMENTATION AND CONTROL OF THE TOKAMAK COOLING WATER SYSTEM

This paper discusses progress in the design of the control, interlock and safety systems of the Tokamak Cooling Water System (TCWS) for the ITER fusion reactor. The TCWS instrumentation and control (I&C) is one of approximately 200 separate plant I&C systems (e.g., vacuum system I&C, magnets system I&C) that interface to a common central I&C system through standardized networks. Several aspects of the I&C are similar to the I&C of fission-based power plants. However, some of the unique features of the ITER fusion reactor and the TCWS (e.g., high quasi-static magnetic field, need for baking and drying as well as cooling operations), also demand some unique safety and qualification considerations. The paper compares the design strategy/guidelines of the TCWS I&C and the I&C of conventional nuclear power plants. Issues such as safety classifications, independence between control and safety systems, sensor sharing, redundancy, voting schemes, and qualification methodologies are discussed. It is concluded that independence and separation requirements are similar in both designs. However, the voting schemes for safety systems in nuclear power plants typically use 2oo4 (i.e., 4 divisions of safety I&C, any 2 of which is sufficient to trigger a safety action), while 2oo3 voting logic - within each of 2 independent trains - is used in the TCWS I&C. It is also noted that 2oo3 voting is also acceptable in nuclear power plants if adequate risk assessment and reliability is demonstrated. Finally, while qualification requirements provide similar guidance [e.g., both IEC 60780 (invoked in ITER-space), and IEEE 323 (invoked in fission power plant space) provide similar guidance], an important qualification consideration is the susceptibility of I&C to the magnetic fields of ITER. Also, the radiation environments are different. In the case of magnetic fields the paper discusses some options that are being considered.

Korsah, Kofi↗
DOE OSTI2015

Optimal Voltage Regulation for Unbalanced Distribution Networks Considering Distributed Energy Resources

With increasing penetration of distributed generation in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative quadratic constrained quadratic programming model to minimize voltage deviations and maximize distributed energy resource (DER) active power output in a three phase unbalanced distribution system is developed. The optimization model is based on the linearized sensitivity coefficients between controlled variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DERs). To avoid the oscillation of solution when it is close to the optimum, a golden search method is introduced to control the step size. Numerical simulations on modified IEEE 13 nodes test feeders show the efficiency of the proposed model. Compared to the results solved by heuristic search (harmony algorithm), the proposed model converges quickly to the global optimum.

distributed energy resources↗
DOE OSTI2020

A Physics-Driven Deep-Learning Network for Solving Nonlinear Inverse Problems

Geosteering inversion, which can be viewed as a nonlinear inverse problem, is an important technique used by directional drilling. Traditional methods that rely on iterative procedures and regularization are sensitive to the selection of initial values and can be slow due to convergence issues. In industrial applications, a lookup table is used to produce fast predictions. However, performance is not guaranteed by this approach due to the limitation of the hardware. In this paper, we propose a novel physics-driven deep-learning framework for providing a fast, accurate surrogate to solve the inverse problem. Particularly, leveraged by the forward physical model and 1D convolutional neural network (1D-CNN), the proposed method provides more reliable solutions to the inverse problem with improved performance. In addition, a new physics-driven loss function is introduced to accommodate both the model misfit and the data misfit. In conclusion, our experiments demonstrate the effectiveness of our method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Adaptive pruning-based optimization of parameterized quantum circuits

Abstract Variational hybrid quantum–classical algorithms are powerful tools to maximize the use of noisy intermediate-scale quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space. In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call ‘parameter-efficient circuit training (PECT)’. Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms, in which each iteration of the algorithm activates and optimizes a subset of the total parameter set. To update the parameter subset between iterations, we adapt the Dynamic Sparse Reparameterization scheme which was originally proposed for training deep convolutional neural networks. We demonstrate PECT for the Variational Quantum Eigensolver, in which we benchmark unitary coupled-cluster ansatze including UCCSD and k -UpCCGSD, as well as the Low-Depth Circuit Ansatz (LDCA), to estimate ground state energies of molecular systems. We additionally use a layerwise variant of PECT to optimize a hardware-efficient circuit for the Sycamore processor to estimate the ground state energy densities of the one-dimensional Fermi-Hubbard model. From our numerical data, we find that PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).

Physics↗
DOE OSTI2026

An Iterative Machine Learning Framework for Event Classification and Monte Carlo Tuning in SpinQuest

The E1039/SpinQuest experiment at Fermi National Accelerator Laboratory uses a 120~GeV proton beam from the Main Injector incident on transversely polarized proton and deuteron targets, using $NH_3$ and $ND_3$, respectively. In addition to measuring the Sivers asymmetry in Drell--Yan $pp$ and $pd$ scattering from sea quarks, SpinQuest will study transverse-spin effects, particularly the transverse single-spin asymmetry (TSSA) in $J/\psi$ production. The angular distributions from the $J/\psi$ decay could play an important role in understanding the gluon contribution to the proton spin structure. However, before extracting these angular distributions, it is necessary to isolate signal events originating from the target from events produced by other sources and from the combinatorial background. To effectively and accurately classify the target events, it is important to ensure that the simulated events are properly tuned to the experimental physics channels. We have introduced an iterative technique to match simulated and experimental events and to classify the physics channels using deep neural networks and a generative model based on normalizing flows.

Hossain, Forhad [Virginia U. (main)] (ORCID:000000↗
NASA NTRS2006

Protograph LDPC Codes Over Burst Erasure Channels

In this paper we design high rate protograph based LDPC codes suitable for binary erasure channels. To simplify the encoder and decoder implementation for high data rate transmission, the structure of codes are based on protographs and circulants. These LDPC codes can improve data link and network layer protocols in support of communication networks. Two classes of codes were designed. One class is designed for large block sizes with an iterative decoding threshold that approaches capacity of binary erasure channels. The other class is designed for short block sizes based on maximizing minimum stopping set size. For high code rates and short blocks the second class outperforms the first class.

protgraph based codes↗