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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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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Integrated path planning and control through proximal policy optimization for a marine current turbine

This paper presents an integrated path planning and tracking control framework for a marine current turbine (MCT), where the MCT is treated as an energy-harvesting autonomous underwater vehicle (AUV). Considering the ocean (space of action) is continuous, the proposed framework employs two modules to address path planning and path tracking enabled by the proximal policy optimization (PPO) algorithm, which is a policy gradient deep reinforcement learning (RL) method. Further, to enable fully autonomous operation in a stochastic oceanic environment, the proposed path planning seeks a primary objective of maximizing the harvested energy; then, the path tracking module is designed to minimize the tracking error and avoid collisions with static and dynamic obstacles. Using field-collected acoustic Doppler current profiler (ADCP) data, the performance of the proposed framework is evaluated. Comparative studies with baseline algorithms in three different scenarios of path planning, path tracking without an obstacle, and path tracking with collision avoidance verify the effectiveness of our proposed approach.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

Super-resolution for scanning light stimulation systems

Super-resolution (SR) is a technique used in digital image processing to overcome the resolution limitation of imaging systems. In this process, a single high resolution image is reconstructed from multiple low resolution images. SR is commonly used for CCD and CMOS (Complementary Metal-Oxide-Semiconductor) sensor images, as well as for medical applications, e.g., magnetic resonance imaging. Here, we demonstrate that super-resolution can be applied with scanning light stimulation (LS) systems, which are common to obtain space-resolved electro-optical parameters of a sample. For our purposes, the Projection Onto Convex Sets (POCS) was chosen and modified to suit the needs of LS systems. To demonstrate the SR adaption, an Optical Beam Induced Current (OBIC) LS system was used. The POCS algorithm was optimized by means of OBIC short circuit current measurements on a multicrystalline solar cell, resulting in a mean square error reduction of up to 61% and improved image quality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Investigation of fast and efficient lossless compression algorithms for macromolecular crystallography experiments

Structural biology experiments benefit significantly from state-of-the-art synchrotron data collection. One can acquire macromolecular crystallography (MX) diffraction data on large-area photon-counting pixel-array detectors at framing rates exceeding 1000 frames per second, using 200 Gbps network connectivity, or higher when available. In extreme cases this represents a raw data throughput of about 25 GB s −1 , which is nearly impossible to deliver at reasonable cost without compression. Our field has used lossless compression for decades to make such data collection manageable. Many MX beamlines are now fitted with DECTRIS Eiger detectors, all of which are delivered with optimized compression algorithms by default, and they perform well with current framing rates and typical diffraction data. However, better lossless compression algorithms have been developed and are now available to the research community. Here one of the latest and most promising lossless compression algorithms is investigated on a variety of diffraction data like those routinely acquired at state-of-the-art MX beamlines.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optimizing Fast Charging and Wetting in Lithium-Ion Batteries with Optimal Microstructure Patterns Identified by Genetic Algorithm

To sustain the high-rate current required for fast charging electric vehicle batteries, electrodes must exhibit sufficiently high effective ionic diffusion. Additionally, to reduce battery manufacturing costs, wetting time must decrease. Both of these issues can be addressed by structuring the electrodes with mesoscale pore channels. However, their optimal spatial distribution, or patterns, is unknown. Herein, a genetic algorithm has been developed to identify these optimal patterns using a CPU-cheap proxy distance-based model to evaluate the impact of the added pore networks. Both coin-cell and pouch cell form factors have been considered for the wetting analysis, with their respective electrolyte infiltration mode. Regular hexagonal and mud-crack-like patterns, respectively, for fast charging and fast wetting were found to be optimal and have been compared with pre-determined, easier to manufacture, patterns. The model predicts that using cylindrical channels arranged in a regular hexagonal pattern is ∼6.25 times more efficient for fast charging as compared to grooved lines with both structuring strategies being restricted to a 5% electrode total volume loss. The model also shows that only a very limited electrode volume loss (1%–2%) is required to dramatically improve the wetting (5–20 times) compared to an unstructured electrode.

25 ENERGY STORAGE↗
DOE OSTI2024

Studying Transient Phenomena in Thin Films with Reinforcement Learning

Neutron reflectometry has long been a powerful tool to study the interfacial properties of energy materials. Recently, time-resolved neutron reflectometry has been used to better understand transient phenomena in electrochemical systems. Those measurements often comprise a large number of reflectivity curves acquired over a narrow q range, with each individual curve having lower information content compared to a typical steady-state measurement. In this work, we present an approach that leverages existing reinforcement learning tools to model time-resolved data to extract the time evolution of structure parameters. Further, by mapping the reflectivity curves taken at different times as individual states, we use the Soft Actor-Critic algorithm to optimize the time series of structure parameters that best represent the evolution of an electrochemical system. We show that this approach constitutes an elegant solution to the modeling of time-resolved neutron reflectometry data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Enabling DER Participation in Frequency Regulation Markets

Distributed energy resources (DERs) are playing an increasing role in ancillary services for the bulk grid, particularly in frequency regulation. In this article, we propose a framework for collections of DERs, combined to form microgrids and controlled by aggregators, to participate in frequency regulation markets. Our approach covers both the identification of bids for the market clearing stage and the mechanisms for the real-time allocation of the regulation signal. The proposed framework is hierarchical, consisting of a top layer and a bottom layer. The top layer consists of the aggregators communicating in a distributed fashion to optimally disaggregate the regulation signal requested by the system operator. The bottom layer consists of the DERs inside each microgrid whose power levels are adjusted so that the tie line power matches the output of the corresponding aggregator in the top layer. The coordination at the top layer requires the knowledge of cost functions, ramp rates, and capacity bounds of the aggregators. We develop meaningful abstractions for these quantities respecting the power flow constraints and taking into account the load uncertainties and propose a provably correct distributed algorithm for optimal disaggregation of regulation signals among the microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

"Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs"

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest.

Lee, Jonathan↗
DOE OSTI2017

Planning Framework for Mesolevel Optimization of Urban Runoff Control Schemes

A planning framework is developed to optimize runoff control schemes at scales relevant for regional planning at an early stage. The framework employs less sophisticated modeling approaches to allow a practical application in developing regions with limited data sources and computing capability. The methodology contains three interrelated modules: (1)the geographic information system (GIS)-based hydrological module, which aims at assessing local hydrological constraints and potential for runoff control according to regional land-use descriptions; (2)the grading module, which is built upon the method of fuzzy comprehensive evaluation. It is used to establish a priority ranking system to assist the allocation of runoff control targets at the subdivision level; and (3)the genetic algorithm-based optimization module, which is included to derive Pareto-based optimal solutions for mesolevel allocation with multiple competing objectives. The optimization approach describes the trade-off between different allocation plans and simultaneously ensures that all allocation schemes satisfy the minimum requirement on runoff control. Our results highlight the importance of considering the mesolevel allocation strategy in addition to measures at macrolevels and microlevels in urban runoff management. (C) 2016 American Society of Civil Engineers.

Zhou, Qianqian↗
DOE OSTI2018

Optimal Water-Power Flow Problem: Formulation and Distributed Optimal Solution

This paper formalizes an optimal water-power flow (OWPF) problem to optimize the use of controllable assets across power and water systems while accounting for the couplings between the two infrastructures. Tanks and pumps are optimally managed to satisfy water demand while improving power grid operations; {for the power network, an AC optimal power flow formulation is augmented to accommodate the controllability of water pumps.} Unfortunately, the physics governing the operation of the two infrastructures and coupling constraints lead to a nonconvex (and, in fact, NP-hard) problem; however, after reformulating OWPF as a nonconvex, quadratically-constrained quadratic problem, a feasible point pursuit-successive convex approximation approach is used to identify feasible and optimal solutions. In addition, a distributed solver based on the alternating direction method of multipliers enables water and power operators to pursue individual objectives while respecting the couplings between the two networks. The merits of the proposed approach are demonstrated for the case of a distribution feeder coupled with a municipal water distribution network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Artificial Neural Network as the FPGA Trigger in the Cyclone V based Front-End for a Detection of Neutrino-Origin Showers

Neutrinos play a fundamental role in the understanding of the origin of ultra-high-energy cosmic rays. They interact through charged and neutral currents in the atmosphere generating extensive air showers. However, their a very low rate of events potentially generated by neutrinos is a significant challenge for a detection technique and requires both sophisticated algorithms and high-resolution hardware. A trigger based on a artificial neural network was implemented into the Cyclone{sup R} V E FPGA 5CEFA9F31I7 - the heart of the prototype Front-End boards developed for tests of new algorithms in the Pierre Auger surface detectors. Showers for muon and tau neutrino initiating particles on various altitudes, angles and energies were simulated in CORSICA and Offline platforms giving pattern of ADC traces in Auger water Cherenkov detectors. The 3-layer 12-8-1 neural network was taught in MATLAB by simulated ADC traces according the Levenberg-Marquardt algorithm. Results show that a probability of a ADC traces generation is very low due to a small neutrino cross-section. Nevertheless, ADC traces, if occur, for 1-10 EeV showers are relatively short and can be analyzed by 16-point input algorithm. We optimized the coefficients from MATLAB to get a maximal range of potentially registered events and for fixed-point FPGA processing to minimize calculation errors. New sophisticated triggers implemented in Cyclone{sup R} V E FPGAs with large amount of DSP blocks, embedded memory running with 120 - 160 MHz sampling may support a discovery of neutrino events in the Pierre Auger Observatory. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

TU-FG-BRB-03: Basis Vector Model Based Method for Proton Stopping Power Estimation From Experimental Dual Energy CT Data

Purpose: This work aims at reducing the uncertainty in proton stopping power (SP) estimation by a novel combination of a linear, separable basis vector model (BVM) for stopping power calculation (Med Phys 43:600) and a statistical, model-based dual-energy CT (DECT) image reconstruction algorithm (TMI 35:685). The method was applied to experimental data. Methods: BVM assumes the photon attenuation coefficients, electron densities, and mean excitation energies (I-values) of unknown materials can be approximated by a combination of the corresponding quantities of two reference materials. The DECT projection data for a phantom with 5 different known materials was collected on a Philips Brilliance scanner using two scans at 90 kVp and 140 kVp. The line integral alternating minimization (LIAM) algorithm was used to recover the two BVM coefficient images using the measured source spectra. The proton stopping powers are then estimated from the Bethe-Bloch equation using electron densities and I-values derived from the BVM coefficients. The proton stopping powers and proton ranges for the phantom materials estimated via our BVM based DECT method are compared to ICRU reference values and a post-processing DECT analysis (Yang PMB 55:1343) applied to vendorreconstructed images using the Torikoshi parametric fit model (tPFM). Results: For the phantom materials, the average stopping power estimations for 175 MeV protons derived from our method are within 1% of the ICRU reference values (except for Teflon with a 1.48% error), with an average standard deviation of 0.46% over pixels. The resultant proton ranges agree with the reference values within 2 mm. Conclusion: Our principled DECT iterative reconstruction algorithm, incorporating optimal beam hardening and scatter corrections, in conjunction with a simple linear BVM model, achieves more accurate and robust proton stopping power maps than the post-processing, nonlinear tPFM based DECT analysis applied to conventional reconstructions of low and high energy scans. Funding Support: NIH R01CA 75371; NCI grant R01 CA 149305.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Lowest-energy cage structures of medium-sized (ZnO){sub n} clusters with n = 15 − 24

Fullerene-like cage structures of medium-sized (ZnO){sub n} clusters with n = 15 − 24 were generated by spiral algorithm and optimized using density functional theory calculations. Most of these lowest-energy cage structures contain only four-membered and six-membered rings, whereas eight-membered rings were found in the lowest-energy cages of (ZnO){sub n} (n = 19, 20, 23, 24). Our best cage configurations either reproduce or prevail the previously reported ones. The size-dependent electronic properties were also discussed.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements

Abstract. Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments that are at least 2 orders of magnitude greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require the extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar-attenuated backscatter (Rmax⁡) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of Rmax⁡ to within 5 m can constrain Nd to within several tens of percent. However, operational lidar networks provide vertical resolutions of > 15 m, making a direct calculation of Nd from Rmax⁡ very uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion such as lidar-derived extinction and radar reflectivity near the cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30 %, respectively. By comparing surface-derived cloud properties with MODIS satellite and aircraft data collected during the MARCUS and CAPRICORN II campaigns, we demonstrate the utility of the methodology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Optimizing Multi-Robot Placements for Wire Arc Additive Manufacturing

Wire arc additive manufacturing is a metal additive manufacturing process in which the material is deposited using arc welding technology. It is gaining popularity due to high material deposition rates and faster build time. It is en-abled using robotic manipulators and can build relatively large-scale parts faster when compared with other metal additive manufacturing processes. However, the size of the large-scale parts is limited by the size of the industrial manipulator being used for the process. This limitation is overcome by using a fixed configuration multi-robot cell in which manipulators work cooperatively to build large-scale parts quickly. A fixed multi-robot cell with closely spaced industrial manipulators has high flexibility, but it restricts the part size that can be built. If the manipulators are spread out, the cell loses its flexibility but can build relatively larger parts. This issue can be avoided by using larger size manipulators, which are expensive, or by moving the modest size manipulators based on the part geometries. This paper presents a novel algorithm to generate multi-robot placements for different part geometries to be built using wire arc additive manufacturing. Furthermore, the algorithm hierarchically optimizes the build time and the inverse kinematics consistency in robot paths to improve the process efficiency and part quality. We compare the results with fixed multi-robot cells and provide insights to users to make an informed decision on whether to use a fixed or a flexible multi-robot cell for wire arc additive manufacturing.

Bhatt, Prahar↗
DOE OSTI2020

Applying Constrained Bayesian Optimization to the Design of Critical Experiments

Often when planning a criticality experiment, many design configurations are iteratively investigated with a Monte Carlo transport code. The goal is that the experiment will be optimal with respect to some variable, like the fraction of fissions occurring at a certain energy range, while simultaneously being critical. Unfortunately, the Monte Carlo transport simulations are expensive, which can ultimately limit the number of configurations that can be explored. In this work, we present how Gaussian processes (GPs) can be used as a reduced-order model in a constrained Bayesian optimization (CBO) algorithm to design a criticality experiment. The GPs replace the Monte Carlo transport simulations that explore the design space. The CBO algorithm efficiently identifies new points in the design space to run the Monte Carlo transport code while respecting the criticality constraint. It does so in a manner that both improves the accuracy of the GP and finds the approximate global optimum. We demonstrate the performance of CBO with the design of a Thermal Epithermal eXperiment (TEX) for the criticality safety validation of nuclear waste models of the Hanford Tank Farm.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.

R. Kulkarni, Shruti↗
DOE OSTI2017

Harmonic control using matrix converter of unbalanced input voltage supplied from solar power system

Most of the renewable energy sources are not distributed directly to consumer. It gone through several section to get optimum output voltage and current with the almost zero distortion. Direct Matrix converters are well known of its better power quality and low power distortion to the reactive loads. Conversely, multilevel H bridge inverters are broadly used to attain a sinusoidal output voltage in solar photovoltaic application. Reactive power is subject to high importance for the operation of alternating current (AC) power systems. Moreover there is always a challenge to obtain the balance current control from an unbalanced supply voltage with minimum harmonic distortion for reactive load application from solar power system. The main objective of the proposed system is to maximize the power usage by maintaining the current and power factor nearby unity. There are many different optimization techniques are used so far in matrix converter, a novel method of optimization BAT algorithm is proposed for the power quality to get the best result. This work mainly deals with the compensation of reactive power used in Solar Photovoltaic Power System. Bidirectional energy flow is possible with matrix converter for battery charging. Simulation results are presented to confirm the appropriate operation of the system under different of operating conditions. Also, in the proposed method the percentage of the higher order harmonics can reduced less than 9% of matrix converter for solar photovoltaic application.

14 SOLAR ENERGY↗
DOE OSTI2019

Synchronous and asynchronous optimized Schwarz methods for one-way subdivision of bounded domains

Convergence of both synchronous and asynchronous optimized Schwarz algorithms for the shifted Laplacian operator on a bounded rectangular domain, in a one-way subdivision of the computational domain, with overlap, is shown. Convergence results are obtained under very mild conditions on the size of the subdomains and on the amount of overlap. A couple of results are also given, relating the convergence rate of the asynchronous method to changes in the size of the domain. Numerical experiments illustrate the theoretical results.

97 MATHEMATICS AND COMPUTING↗