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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Spectral method for a kinetic swarming model

Here we present the first numerical method for a kinetic description of the Vicsek swarming model. The kinetic model poses a unique challenge, as there is a distribution dependent collision invariant to satisfy when computing the interaction term. We use a spectral representation linked with a discrete constrained optimization to compute these interactions. To test the numerical scheme we investigate the kinetic model at different scales and compare the solution with the microscopic and macroscopic descriptions of the Vicsek model. Lastly, we observe that the kinetic model captures key features such as vortex formation and traveling waves.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Development of Density Functional Tight-Binding Parameters Using Relative Energy Fitting and Particle Swarm Optimization

We provide a strategy to optimize density functional tight-binding (DFTB) parameterization for the calculation of the structures and properties of organic molecules consisting of hydrogen, carbon, nitrogen, and oxygen. We utilize an objective function based on similarity measurements and the Particle Swarm Optimization (PSO) method to find an optimal set of parameters. This objective function considers not only the common DFTB descriptors of binding energies and atomic forces but also incorporates relative energies of isomers into the fitting procedure for more chemistry-driven results. The quality in the description of the binding energies and atomic forces is measured based on the Ballester similarity index and relative energies through a similarity index induced by the Levenshtein edit distance to quantify the correct energetic order of isomers. Training and testing datasets were created to include all relevant chemical functional groups. Finally, the accuracy of this strategy is assessed, and its range of applicability is discussed by comparison against our previous parameterization. The improved performance of the new DFTB parameterization is validated with respect to the density functional theory large datasets QM-9 and ANI-1, where excellent agreement is found between the structures and properties available in these datasets, and the ones obtained with DFTB.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Binary particle swarm optimisation‐based optimal substation coverage algorithm for phasor measurement unit installations in practical systems

Phasor measurement units (PMUs) play an important role in the wide‐area monitoring and protection of modern power systems. Historically, their deployment was limited by the prohibitive cost of the device itself. Therefore, the objective of the conventional optimal PMU placement problem was to find minimum number of devices, which when carefully placed throughout the network, maximised observability subject to different constraints. Due to improvements in relay technology, digital relays can now serve as both relays and PMUs. Under such circumstances, the substation installations consume the largest portion of the deployment cost, and not the devices themselves. Thus, for minimising cost of synchrophasor deployment, number of substation installations must be minimised. This study uses binary particle swarm optimisation to minimise number of substations in which installations must be performed for making all voltage levels observable, while being subject to various practical constraints. Standard IEEE systems have been used to explain the technique. Finally, a large‐scale network of Dominion Virginia Power is used as the test bed for implementation.

Mishra, Chetan↗
DOE OSTI2015

3D model generation using an airborne swarm

Using an artificial kinematic field to provide co-ordination between multiple inspection UAVs, the authors herein demonstrate full 3D modelling capability based on a photogrammetric system. The operation of the system is demonstrated by generating a full 3D surface model of an intermediate level nuclear waste storage drum. Such drums require periodic inspection to ensure that drum distortion or corrosion is carefully monitored. Performing this inspection with multiple airborne platforms enables rapid inspection of structures that are inaccessible to on-surface remote vehicles and are in human-hazardous environments. A three-dimensional surface-meshed model of the target can then be constructed in post-processing through photogrammetry analysis of the visual inspection data. The inspection environment uses a tracking system to precisely monitor the position of each aerial vehicle within the enclosure. The vehicles used are commercially available Parrot AR. Drone quadcopters, controlled through a computer interface connected over an IEEE 802.11n (WiFi) network, implementing a distributed controller for each vehicle. This enables the autonomous and distributed elements of the control scheme to be retained, while alleviating the vehicles of the control algorithm’s computational load. The control scheme relies on a kinematic field defined with the target at its centre. This field defines the trajectory for all the drones in the volume relative to the central target, enabling the drones to circle the target at a set radius while avoiding drone collisions. This function enables complete coverage along the height of the object, which is assured by transitioning to another inspection band only after completing circumferential coverage. Using a swarm of vehicles, the time until complete coverage can be significantly reduced.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Conformational sampling enhancement of replica exchange molecular dynamics simulations using swarm particle intelligence

In this paper, we present a new method based on swarm particle social intelligence for use in replica exchange molecular dynamics simulations. In this method, the replicas (representing the different system configurations) are allowed communicating with each other through the individual and social knowledge, in additional to considering them as a collection of real particles interacting through the Newtonian forces. The new method is based on the modification of the equations of motion in such way that the replicas are driven towards the global energy minimum. The method was tested for the Lennard-Jones clusters of N = 4, 5, and 6 atoms. Our results showed that the new method is more efficient than the conventional replica exchange method under the same practical conditions. In particular, the new method performed better on optimizing the distribution of the replicas among the thermostats with time and, in addition, ergodic convergence is observed to be faster. We also introduce a weighted histogram analysis method allowing analyzing the data from simulations by combining data from all of the replicas and rigorously removing the inserted bias.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

CALIBRATION OF SEMI-ANALYTIC MODELS OF GALAXY FORMATION USING PARTICLE SWARM OPTIMIZATION

We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observed galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Swarm Intelligence Based Optimal Design of Local Volt/Var Control Function for Distributed Energy Resources

The increasing penetration of renewable based distributed energy resources (DERs) in distribution network (DN) leads to larger and more frequent voltage variation in distributions network (DN), thus posing challenges on voltage control. Real-time local voltage control method is a promising solution for the above issue. However, the local voltage control function needs to be customized and optimized according to real distribution system condition. In this paper, a swarm intelligence based Volt/Var control optimal design method (SO-VVC) is proposed to optimize the control function. Compared with existing approaches, the proposed method can not only represent the nonlinear behaviour of power flow but is also computation efficient. The performance of the proposed SO-VVC is demonstrated by case studies on a modified IEEE-123 bus system.

Zhang, Zhengfa [University of Tennessee, Knoxville↗
DOE OSTI2014

SU-E-T-259: Particle Swarm Optimization in Radial Dose Function Fitting for a Novel Iodine-125 Seed

Purpose: To determine the coefficients of bi- and tri-exponential functions for the best fit of radial dose functions of the new iodine brachytherapy source: Iodine-125 Seed AgX-100. Methods: The particle swarm optimization (PSO) method was used to search for the coefficients of the biand tri-exponential functions that yield the best fit to data published for a few selected radial distances from the source. The coefficients were encoded into particles, and these particles move through the search space by following their local and global best-known positions. In each generation, particles were evaluated through their fitness function and their positions were changed through their velocities. This procedure was repeated until the convergence criterion was met or the maximum generation was reached. All best particles were found in less than 1,500 generations. Results: For the I-125 seed AgX-100 considered as a point source, the maximum deviation from the published data is less than 2.9% for bi-exponential fitting function and 0.2% for tri-exponential fitting function. For its line source, the maximum deviation is less than 1.1% for bi-exponential fitting function and 0.08% for tri-exponential fitting function. Conclusion: PSO is a powerful method in searching coefficients for bi-exponential and tri-exponential fitting functions. The bi- and tri-exponential models of Iodine-125 seed AgX-100 point and line sources obtained with PSO optimization provide accurate analytical forms of the radial dose function. The tri-exponential fitting function is more accurate than the bi-exponential function.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-A-9A-02: BEST IN PHYSICS (THERAPY) - 4D IMRT Planning Using Highly- Parallelizable Particle Swarm Optimization

Purpose: We present a particle swarm optimization (PSO)-based 4D IMRT planning technique designed for dynamic MLC tracking delivery to lung tumors. The key idea is to utilize the temporal dimension as an additional degree of freedom rather than a constraint in order to achieve improved sparing of organs at risk (OARs). Methods: The target and normal structures were manually contoured on each of the ten phases of a 4DCT scan acquired from a lung SBRT patient who exhibited 1.5cm tumor motion despite the use of abdominal compression. Corresponding ten IMRT plans were generated using the Eclipse treatment planning system. These plans served as initial guess solutions for the PSO algorithm. Fluence weights were optimized over the entire solution space i.e., 10 phases × 12 beams × 166 control points. The size of the solution space motivated our choice of PSO, which is a highly parallelizable stochastic global optimization technique that is well-suited for such large problems. A summed fluence map was created using an in-house B-spline deformable image registration. Each plan was compared with a corresponding, internal target volume (ITV)-based IMRT plan. Results: The PSO 4D IMRT plan yielded comparable PTV coverage and significantly higher dose—sparing for parallel and serial OARs compared to the ITV-based plan. The dose-sparing achieved via PSO-4DIMRT was: lung Dmean = 28%; lung V20 = 90%; spinal cord Dmax = 23%; esophagus Dmax = 31%; heart Dmax = 51%; heart Dmean = 64%. Conclusion: Truly 4D IMRT that uses the temporal dimension as an additional degree of freedom can achieve significant dose sparing of serial and parallel OARs. Given the large solution space, PSO represents an attractive, parallelizable tool to achieve globally optimal solutions for such problems. This work was supported through funding from the National Institutes of Health and Varian Medical Systems. Amit Sawant has research funding from Varian Medical Systems, VisionRT Ltd. and Elekta.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-06: 4D Particle Swarm Optimization to Enable Lung SBRT in Patients with Central And/or Large Tumors

Purpose: Patients presenting with large and/or centrally-located lung tumors are currently considered ineligible for highly potent regimens such as SBRT due to concerns of toxicity to normal tissues and organs-at-risk (OARs). We present a particle swarm optimization (PSO)-based 4D planning technique, designed for MLC tracking delivery, that exploits the temporal dimension as an additional degree of freedom to significantly improve OAR-sparing and reduce toxicity to levels clinically considered as acceptable for SBRT administration. Methods: Two early-stage SBRT-ineligible NSCLC patients were considered, presenting with tumors of maximum dimensions of 7.4cm (central-right lobe; 1.5cm motion) and 11.9cm (upper-right lobe; 1cm motion). In each case, the target and normal structures were manually contoured on each of the ten 4DCT phases. Corresponding ten initial 3D-conformal plans (Pt#1: 7-beams; Pt#2: 9-beams) were generated using the Eclipse planning system. Using 4D-PSO, fluence weights were optimized over all beams and all phases (70 and 90 apertures for Pt1&2, respectively). Doses to normal tissues and OARs were compared with clinicallyestablished lung SBRT guidelines based on RTOG-0236. Results: The PSO-based 4D SBRT plan yielded tumor coverage and dose—sparing for parallel and serial OARs within the SBRT guidelines for both patients. The dose-sparing compared to the clinically-delivered conventionallyfractionated plan for Patient 1 (Patient 2) was: heart Dmean = 11% (33%); lung V20 = 16% (21%); lung Dmean = 7% (20%); spinal cord Dmax = 5% (16%); spinal cord Dmean = 7% (33%); esophagus Dmax = 0% (18%). Conclusion: Truly 4D planning can significantly reduce dose to normal tissues and OARs. Such sparing opens up the possibility of using highly potent and effective regimens such as lung SBRT for patients who were conventionally considered SBRT non-eligible. Given the large, non-convex solution space, PSO represents an attractive, parallelizable tool to successfully achieve a globally optimal solution for this problem. This work was supported through funding from the National Institutes of Health and Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-500: Initial Implementation of GPU-Based Particle Swarm Optimization for 4D IMRT Planning in Lung SBRT

Purpose 4D-IMRT planning, combined with dynamic MLC tracking delivery, utilizes the temporal dimension as an additional degree of freedom to achieve improved OAR-sparing. The computational complexity for such optimization increases exponentially with increase in dimensionality. In order to accomplish this task in a clinically-feasible time frame, we present an initial implementation of GPU-based 4D-IMRT planning based on particle swarm optimization (PSO). Methods The target and normal structures were manually contoured on ten phases of a 4DCT scan of a NSCLC patient with a 54cm3 right-lower-lobe tumor (1.5cm motion). Corresponding ten 3D-IMRT plans were created in the Eclipse treatment planning system (Ver-13.6). A vendor-provided scripting interface was used to export 3D-dose matrices corresponding to each control point (10 phases × 9 beams × 166 control points = 14,940), which served as input to PSO. The optimization task was to iteratively adjust the weights of each control point and scale the corresponding dose matrices. In order to handle the large amount of data in GPU memory, dose matrices were sparsified and placed in contiguous memory blocks with the 14,940 weight-variables. PSO was implemented on CPU (dual-Xeon, 3.1GHz) and GPU (dual-K20 Tesla, 2496 cores, 3.52Tflops, each) platforms. NiftyReg, an open-source deformable image registration package, was used to calculate the summed dose. Results The 4D-PSO plan yielded PTV coverage comparable to the clinical ITV-based plan and significantly higher OAR-sparing, as follows: lung Dmean=33%; lung V20=27%; spinal cord Dmax=26%; esophagus Dmax=42%; heart Dmax=0%; heart Dmean=47%. The GPU-PSO processing time for 14940 variables and 7 PSO-particles was 41% that of CPU-PSO (199 vs. 488 minutes). Conclusion Truly 4D-IMRT planning can yield significant OAR dose-sparing while preserving PTV coverage. The corresponding optimization problem is large-scale, non-convex and computationally rigorous. Our initial results indicate that GPU-based PSO with further software optimization can make such planning clinically feasible. This work was supported through funding from the National Institutes of Health and Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-FG-BRA-08: Swarm Intelligence-Based Personalized Respiratory Gating in Lung SAbR

Purpose: Respiratory gating is widely deployed as a clinical motion-management strategy in lung radiotherapy. In conventional gating, the beam is turned on during a pre-determined phase window; typically, around end-exhalation. In this work, we challenge the notion that end-exhalation is always the optimal gating phase. Specifically, we use a swarm-intelligence-based, inverse planning approach to determine the optimal respiratory phase and MU for each beam with respect to (i) the state of the anatomy at each phase and (ii) the time spent in that state, estimated from long-term monitoring of the patient’s breathing motion. Methods: In a retrospective study of five lung cancer patients, we compared the dosimetric performance of our proposed personalized gating (PG) with that of conventional end-of-exhale gating (CEG) and a previously-developed, fully 4D-optimized plan (combined with MLC tracking delivery). For each patient, respiratory phase probabilities (indicative of the time duration of the phase) were estimated over 2 minutes from lung tumor motion traces recorded previously using the Synchrony system (Accuray Inc.). Based on this information, inverse planning optimization was performed to calculate the optimal respiratory gating phase and MU for each beam. To ensure practical deliverability, each PG beam was constrained to deliver the assigned MU over a time duration comparable to that of CEG delivery. Results: Maximum OAR sparing for the five patients achieved by the PG and the 4D plans compared to CEG plans was: Esophagus Dmax [PG:57%, 4D:37%], Heart Dmax [PG:71%, 4D:87%], Spinal cord Dmax [PG:18%, 4D:68%] and Lung V13 [PG:16%, 4D:31%]. While patients spent the most time in exhalation, the PG-optimization chose end-exhale only for 28% of beams. Conclusion: Our novel gating strategy achieved significant dosimetric improvements over conventional gating, and approached the upper limit represented by fully 4D optimized planning while being significantly simpler and more clinically translatable. This work was partially supported through research funding from National Institutes of Health (R01CA169102) and Varian Medical Systems, Palo Alto, CA, USA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Application of a new hybrid particle swarm optimization-mixed kernels function-based support vector machine model for reservoir porosity prediction: A case study in Jacksonburg-Stringtown oil field, West Virginia, USA

Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. A support vector machine (SVM) model with mixed kernel function (MKF) is used to construct the relationship between limited conventional well-log suites and sparse core data. Porosity is the desired output, and two conventional well-log responses (gamma ray [GR] and bulk density) and three well-log-derived parameters (the slope of GR, the slope of density, and [Formula: see text]) are input parameters. A global stochastic searching algorithm, particle swarm optimization (PSO), is applied to improve the efficiency of locating the appropriate values of five control parameters in MKF-SVM model. The results of SVM with different traditional kernel functions were compared, and the MKF-SVM model provided an improvement over the traditional SVM model. To confirm the advantage of the hybrid PSO-MKF-SVM model, the results from three models: (1) radial basis function (RBF)-based least-squares SVM, (2) multilayer perceptron artificial neural network (ANN), and (3) RBF ANN, are compared with the result of the hybrid PSO-MKF-SVM model. The results indicate that the hybrid PSO-MKF-SVM model improves porosity prediction with the highest correlation coefficient ([Formula: see text] of 0.9560), the highest coefficient of determination ([Formula: see text] of 0.9140), the lowest root-mean-square error (1.6505), average absolute error value (1.4050), and maximum absolute error (2.717).

Geochemistry & Geophysics↗
DOE OSTI2019

The evolution of a super-swarm of foot-and-mouth disease virus in cattle

Foot-and-mouth disease (FMD) is a highly contagious viral disease that severely impacts global food security and is one of the greatest constraints on international trade of animal products. Extensive viral population diversity and rapid, continuous mutation of circulating FMD viruses (FMDVs) pose significant obstacles to the control and ultimate eradication of this important transboundary pathogen. The current study investigated mechanisms contributing to within-host evolution of FMDV in a natural host species (cattle). Specifically, vaccinated and non-vaccinated cattle were infected with FMDV under controlled, experimental conditions and subsequently sampled for up to 35 days to monitor viral genomic changes as related to phases of disease and experimental cohorts. Consensus-level genomic changes across the entire FMDV coding region were characterized through three previously defined stages of infection: early, transitional, and persistent. The overall conclusion was that viral evolution occurred via a combination of two mechanisms: emergence of full-genomic minority haplotypes from within the inoculum super-swarm, and concurrent continuous point mutations. Phylogenetic analysis indicated that individuals were infected with multiple distinct haplogroups that were pre-existent within the ancestral inoculum used to infect all animals. Multiple shifts of dominant viral haplotype took place during the early and transitional phases of infection, whereas few shifts occurred during persistent infection. Overall, this work suggests that the establishment of the carrier state is not associated with specific viral genomic characteristics. These insights into FMDV population dynamics have important implications for virus sampling methodology and molecular epidemiology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Spectra Swarm Evaluation

Spectra Swarm product is an ethernet to Serial-attached-SCSI(SAS) bridge that allows a hostserver to communicate with SAS tape drives over ethernet using RDMA over ConvergedEthernet (RoCE).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

The Sagatu Ridge dike swarm, Ethiopian rift margin

A swarm of dikes forms the core of the Sagatu Ridge, a 70-km-long topographic feature elevated to more than 4000 m above sea level and 1500 m above the level of the Eastern (Somalian) plateau. The ridge trends NNE and lies about 50 km east of the northeasterly trending rift-valley margin. Intrusion of the dikes and buildup of the flood-lava pile, largely hawaiitic but with trachyte preponderant in the final stages, occurred during the late Pliocene-early Pleistocene and may have been contemporaneous with downwarping of the protorift trough to the west. The ensuing faulting that formed the present rift margin, however, bypassed the ridge. The peculiar situation and orientation of the Sagatu Ridge, and its temporary existence as a line of crustal extension and voluminous magmatism, are considered related to a powerful structural control by a major line of Precambrian crustal weakness, well exposed further south. Transverse rift structures of unknown type appear to have limited the development of the ridge to the north and south.

Mohr, P. A.↗
NASA NTRS1977

Study of the ablative effects on tektites: Atmosphere entry of a swarm of tektites

The large variety of ablation markings observed on recovered tektites lead to the previously proposed swarm wake model which states that the lead peripheral tektites bore the blunt of aerodynamic heating upon entry, and that the bulk of tektites in the wake enjoyed partial shielding at the expense of the leaders. Further considerations are presented in support of this model. Quantitative assessments indicate that wake shielding might indeed have provided for substantially less heating than would have been experienced by a tektite entering an undisturbed atmosphere along a similar trajectory. For the case of strong wake shielding it is even possible that the surface temperature of a falling tektite had barely reached its melting point. In the distribution of tektites, there is a size band (near R = 0.5 cm) which is least susceptible to melting.

Sepri, P.↗
NASA NTRS1984

Evolution of the Uranus-neptune Planetesimal Swarm: Consequences for the Earth

The evolution of planetesimals in the outer Solar System were evaluated, both stellar and planetary encounters. About 20% of the Uranus-Neptune planetesimals (UNP's) enter the comet cloud and are stored primarily in the region inside the observational limits of the Oort cloud. Half of the comets have suruived to the present time; the cloud now has a mass of the order of Jupiter's mass. Most UNP's are ejected from the Solar system, and about half of the planetesimal swarm is passed to the control of Jupiter prior to ejection. Jupiter's perturbations drive a large flux of these planetesimals into Earth-crossing orbits, and it now appears highly probable that UNP's account for most of the heavy bombardment of the Moon and Earth.

Shoemaker, E. M.↗