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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SU-E-T-592: Relationship Between Dose of Distribution and Area of Segment Fields Among Different Intensity-Modulated Radiotherapy Planning in Cervix Cancer

Purpose: In premise of uninfluenced to dose distribution of tumor target and organ at risk(OAR) in cervical cancer,area of segment fields was changed to increase efficacy and optimize treatment method by designing different plan of intensity modulated radiotherapy(IMRT). Methods: 12 cases of cervical cancer were confirmed in pathology and treated with step and shoot IMRT. Dose of PTV was 50Gy/25fractions. Every patient was designed 9 treatment plans of IMRT by Pinnacle 8.0m planning system,each plan was used with 9 beams of uniform distribution and fixing incidence direction(200°,240°,280°,320°,0°,40°,80°,120°and 160°respectively),and designed for delivery on Elekta Synergy linear accelerator. All plans were optimized with the direct machine parameter optimization(DMPO) algorithm using the same set of optimization objectives. Number of maximum segment field was defined at 80 and minimum MU in each segment was 5MU,and minimal segment area was 2*1cm{sup 2},2*2cm{sup 2},3*3cm{sup 2},4*4cm{sup 2},5*5cm{sup 2},6*6cm{sup 2},7*7cm{sup 2},8*8cm{sup 2}and 9*9cm{sup 2},respectively.Coverage,homogeneity and conformity of PTV,sparing of OAR, MU and number of segment were compared. Results: In this group, mean volume of PTV was 916.8±228.7 cm{sup 3}. Compared with the area of minimal segment field increased from 2*1cm{sup 2} to 9*9 cm{sup 2},the number of mean MU was decreased from 1405±170 to 490±47 and the number of segment field was reduced from 76±4 to 39±7 respectively(p<0.05). When the limit of minimal segment area was increased from 2*1cm{sup 2} to 7*7 cm{sup 2},dose distribution of PTV,OAR,CI,HI and V{sub 2} {sub 3} were not different (p>0.05),but when the minimal segment area was 8*8 cm{sup 2} and 9*9 cm{sup 2},they were changed compared with 7*7 cm{sup 2} and below(p<0.05). Conclusion: The minimal segment field of IMRT plan designed by Pinnacle 8.0m planning system in cervical carcinoma should be enlarge reasonably and minimal segment area of 7*7 cm{sup 2} was recommend.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Hybrid Particle Swarm-Interpolation Algorithm for Improved Critical Experiment Design

The typical goal of designing a critical experiment is twofold: a system that is both critical and optimized for some other value. This value could be an energy-integrated sensitivity, percent fissions in a certain energy range, or some other value that can be calculated by a transport code. By simulating different combinations of reflector, moderator, and fuel thicknesses a designer can identify such a desirable configuration. The domain of all possible combinations of these thicknesses is referred to as the experiment search space. As more dimensions are added, the search space increases in size exponentially. For a three-dimensional case, which includes three thickness values between zero and ten centimeters to the nearest tenth of a millimeter, a total of 1,000 3 , configurations exists. Rather than check each configuration individually, which would be extremely computationally expensive, it has been shown to be more efficient to use a conventional particle swarm optimization (PSO) algorithm to identify critical and optimal configurations. This work presents the theory and implementation of a novel hybrid PSO interpolation algorithm to perform these optimizations faster than a conventional PSO algorithm. To demonstrate this, an example optimization will be carried out by the conventional and hybrid PSO algorithms and their performances will be compared.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Optimal Coordination of Building Loads and Energy Storage for Power Grid and End User Services

Demand response and energy storage play a profound role in the smart grid. The focus of this paper is to evaluate benefits of coordination of flexible loads and energy storage for power grid and end user services. We present a Generalized Battery Model (GBM) to describe the flexibility of building loads and energy storage. A Model Predictive Control (MPC) approach is proposed to characterize the GBM parameters (power and energy limits) for flexible building loads. Moreover, we develop optimal coordination algorithms for them to provide ancillary and end user services such as energy arbitrage, frequency regulation, spinning reserve, as well as energy and demand charge reduction. Several case studies have been performed to demonstrate the efficacy of the GBM and coordination algorithms, and evaluate the benefits of using their flexibility to provide grid and end user services. We show that optimal coordination yields significant cost saving and revenue. Moreover, the best option for grid services is to provide energy arbitrage and frequency regulation, because spinning reserve cannot compete with regulation service. Furthermore, when coordinating flexible loads with energy storage to provide end user services, it is recommended to consider both the time-of-use (TOU) price and demand charge rather than the TOU price only in order to flat the aggregate power profile.

Hao, He↗
DOE OSTI2018

Optimization methodology for the global 10 Hz orbit feedback in RHIC

To combat beam oscillations induced by triplet vibrations at the Relativistic Heavy Ion Collider (RHIC), a global orbit feedback system was developed and applied at injection and top energy in 2011, and during beam acceleration in 2012. Singular Value Decomposition (SVD) was employed to determine the strengths and currents of the applied corrections. The feedback algorithm was optimized for different magnetic configurations (lattices) at fixed beam energies and during beam acceleration. While the orbit feedback performed well since its inception, corrector current transients and feedback-induced beam oscillations were observed during the polarized proton program in 2015. In this paper, we present the feedback algorithm, the optimization of the algorithm for various lattices and the solution adopted to mitigate the observed current transients during beam acceleration.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

SU-F-T-377: Monte Carlo Re-Evaluation of Volumetric-Modulated Arc Plans of Advanced Stage Nasopharygeal Cancers Optimized with Convolution-Superposition Algorithm

Background: Commercial treatment planning system Pinnacle3 (Philips, Fitchburg, WI, USA) employs a convolution-superposition algorithm for volumetric-modulated arc radiotherapy (VMAT) optimization and dose calculation. Study of Monte Carlo (MC) dose recalculation of VMAT plans for advanced-stage nasopharyngeal cancers (NPC) is currently limited. Methods: Twenty-nine VMAT prescribed 70Gy, 60Gy, and 54Gy to the planning target volumes (PTVs) were included. These clinical plans achieved with a CS dose engine on Pinnacle3 v9.0 were recalculated by the Monaco TPS v5.0 (Elekta, Maryland Heights, MO, USA) with a XVMC-based MC dose engine. The MC virtual source model was built using the same measurement beam dataset as for the Pinnacle beam model. All MC recalculation were based on absorbed dose to medium in medium (Dm,m). Differences in dose constraint parameters per our institution protocol (Supplementary Table 1) were analyzed. Results: Only differences in maximum dose to left brachial plexus, left temporal lobe and PTV54Gy were found to be statistically insignificant (p> 0.05). Dosimetric differences of other tumor targets and normal organs are found in supplementary Table 1. Generally, doses outside the PTV in the normal organs are lower with MC than with CS. This is also true in the PTV54-70Gy doses but higher dose in the nasal cavity near the bone interfaces is consistently predicted by MC, possibly due to the increased backscattering of short-range scattered photons and the secondary electrons that is not properly modeled by the CS. The straight shoulders of the PTV dose volume histograms (DVH) initially resulted from the CS optimization are merely preserved after MC recalculation. Conclusion: Significant dosimetric differences in VMAT NPC plans were observed between CS and MC calculations. Adjustments of the planning dose constraints to incorporate the physics differences from conventional CS algorithm should be made when VMAT optimization is carried out directly with MC dose engine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. We report a systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Comparison of Optimal Energy Management Strategies Using Dynamic Programming, Model Predictive Control, and Constant Velocity Prediction

Due to the recent advancements in autonomous vehicle technology, future vehicle velocity predictions are becoming more robust which allows fuel economy (FE) improvements in hybrid electric vehicles through optimal energy management strategies (EMS). A real- world highway drive cycle (DC) and a controls-oriented 2017 Toyota Prius Prime model are used to study potential FE improvements. We proposed three important metrics for comparison: (1) perfect full drive cycle prediction using dynamic programming, (2) 10-second prediction horizon model predictive control (MPC), and (3) 10-second constant velocity prediction. These different velocity predictions are put into an optimal EMS derivation algorithm to derive optimal engine torque and engine speed. The results show that the constant velocity prediction algorithm outperformed the baseline control strategy but underperformed the MPC strategy with an average 1.58% and 2.45% of FE improvement with highway and city-highway DC. Also, using a 10-second prediction window MPC strategy provided FE improvement results close to the full drive cycle prediction case. MPC has the potential to achieve 60%-65% and 70% - 80% of global FE improvement over highway and city-highway DC respectively.

Patel, Amol Arvind↗
DOE OSTI2020

Comparison of Optimal Energy Management Strategies Using Dynamic Programming, Model Predictive Control, and Constant Velocity Prediction

Due to the recent advancements in autonomous vehicle technology, future vehicle velocity predictions are becoming more robust which allows fuel economy (FE) improvements in hybrid electric vehicles through optimal energy management strategies (EMS). A realworld highway drive cycle (DC) and a controls-oriented 2017 Toyota Prius Prime model are used to study potential FE improvements. We proposed three important metrics for comparison: (1) perfect full drive cycle prediction using dynamic programming, (2) 10-second prediction horizon model predictive control (MPC), and (3) 10-second constant velocity prediction. These different velocity predictions are put into an optimal EMS derivation algorithm to derive optimal engine torque and engine speed. The results show that the constant velocity prediction algorithm outperformed the baseline control strategy but underperformed the MPC strategy with an average 1.58% and 2.45% of FE improvement with highway and city-highway DC. Also, using a 10-second prediction window MPC strategy provided FE improvement results close to the full drive cycle prediction case. MPC has the potential to achieve 60%-65% and 70% - 80% of global FE improvement over highway and city-highway DC respectively.

Patel, Amol Arvind↗
DOE OSTI2026

Refractory-based thermal energy storage for industrial process heat: one-dimensional modeling, control, and optimization

The variable and weather-dependent output of wind and solar power plants present a substantial challenge for planning and operating electricity-systems, particularly in the absence of cost-effective and dispatchable energy storage technologies. This study investigates a high-temperature, electrically heated, refractory-based thermal energy storage (RTES) system that stores electrical energy as sensible heat in dense ceramic bricks over the 950–1800 °C range. The stored heat can be discharged as a controlled hot-gas stream for industrial heating, fuel substitution in high-temperature processes, or electricity generation. The main novelty is a comprehensive modelling, control, mapping, and optimization framework that integrates one-dimensional transient gas–solid heat transfer, fan-assisted discharge, bypass-flow regulation, reheating logic, fan-power evaluation, insulation-loss assessment, and genetic-algorithm-based design optimization. The model uses feedback from outlet temperature and delivered power to regulate discharge, while a two-stage genetic algorithm optimizes brick-channel geometry, gas-flow operation, and multilayer insulation thicknesses. Storage capacities below 50 MWh and discharge powers of 5–30 MW are analyzed to evaluate hold time, thermal delivery, fan-power penalty, heat loss, state-of-charge evolution, and indicative capital cost. Results demonstrate that optimized and well-insulated refractory-based thermal energy storage units can provide stable, efficient, and repeatable heat delivery over multiple discharge cycles. The generated performance and cost maps support modular refractory thermal energy storage as a practical option for large-scale integration of wind and solar generation and for high-temperature industrial process heat.

25 ENERGY STORAGE↗
DOE OSTI2015

Feasibility of a unified approach to intensity-modulated radiation therapy and volume-modulated arc therapy optimization and delivery

Purpose: To study the feasibility of unified intensity-modulated arc therapy (UIMAT) which combines intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) optimization and delivery to produce superior radiation treatment plans, both in terms of dose distribution and efficiency of beam delivery when compared with either VMAT or IMRT alone. Methods: An inverse planning algorithm for UIMAT was prototyped within the PINNACLE treatment planning system (Philips Healthcare). The IMRT and VMAT deliveries are unified within the same arc, with IMRT being delivered at specific gantry angles within the arc. Optimized gantry angles for the IMRT and VMAT phases are assigned automatically by the inverse optimization algorithm. Optimization of the IMRT and VMAT phases is done simultaneously using a direct aperture optimization algorithm. Five treatment plans each for prostate, head and neck, and lung were generated using a unified optimization technique and compared with clinical IMRT or VMAT plans. Delivery verification was performed with an ArcCheck phantom (Sun Nuclear) on a Varian TrueBeam linear accelerator (Varian Medical Systems). Results: In this prototype implementation, the UIMAT plans offered the same target dose coverage while reducing mean doses to organs at risk by 8.4% for head-and-neck cases, 5.7% for lung cases, and 3.5% for prostate cases, compared with the VMAT or IMRT plans. In addition, UIMAT can be delivered with similar efficiency as VMAT. Conclusions: In this proof-of-concept work, a novel radiation therapy optimization and delivery technique that interlaces VMAT or IMRT delivery within the same arc has been demonstrated. Initial results show that unified VMAT/IMRT has the potential to be superior to either standard IMRT or VMAT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis

The classical Back-Propagation (BP) scheme with gradient-based optimization in training Artificial Neural Networks (ANNs) suffers from many drawbacks, such as the premature convergence, and the tendency of being trapped in local optimums. Therefore, as an alternative for the BP and gradient-based optimization schemes, various Evolutionary Algorithms (EAs), i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), have gained popularity in the field of ANN weight training. This study applied a new efficient and effective Shuffled Complex Evolutionary Global Optimization Algorithm with Principal Component Analysis – University of California Irvine (SP-UCI) to the weight training process of a three-layer feed-forward ANN. A large-scale numerical comparison is conducted among the SP-UCI-, PSO-, GA-, SA-, and DE-based ANNs on 17 benchmark, complex, and real-world datasets. Here the results show that SP-UCI-based ANN outperforms other EA-based ANNs in the context of convergence and generalization. Results suggest that the SP-UCI algorithm possesses good potential in support of the weight training of ANN in real-word problems. In addition, the suitability of different kinds of EAs on training ANN is discussed. The large-scale comparison experiments conducted in this paper are fundamental references for selecting proper ANN weight training algorithms in practice.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

An image classification deep-learning algorithm for shrapnel detection from ultrasound images

Ultrasound imaging is essential for non-invasively diagnosing injuries where advanced diagnostics may not be possible. However, image interpretation remains a challenge as proper expertise may not be available. In response, artificial intelligence algorithms are being investigated to automate image analysis and diagnosis. Here, we highlight an image classification convolutional neural network for detecting shrapnel in ultrasound images. As an initial application, different shrapnel types and sizes were embedded first in a tissue mimicking phantom and then in swine thigh tissue. The algorithm architecture was optimized stepwise by minimizing validation loss and maximizing F1 score. The final algorithm design trained on tissue phantom image sets had an F1 score of 0.95 and an area under the ROC curve of 0.95. It maintained higher than a 90% accuracy for each of 8 shrapnel types. When trained only on swine image sets, the optimized algorithm format had even higher metrics: F1 and area under the ROC curve of 0.99. Overall, the algorithm developed resulted in strong classification accuracy for both the tissue phantom and animal tissue. This framework can be applied to other trauma relevant imaging applications such as internal bleeding to further simplify trauma medicine when resources and image interpretation are scarce.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Droop Compensation with soft Switching for High Voltage Converter Modulator (HVCM)

High Voltage Converter Modulators (HVCM) offer significant performance advantages over conventional modulator technologies for long pulse applications. One of the key advantages of HVCM technology is the ability to compensate for capacitor bank voltage droop. Achieving droop compensation without incurring significant additional switching loss has not been possible in existing designs. This paper presents an analysis of the 'Y-point' variant of the HVCM topology using the Combined Phase and Frequency Modulation (CPFM) technique. This, combined with the addition of a 'lossless' snubber circuit, enables droop compensation while achieving soft switching over the entire pulse duration. The rise time and overshoot of the output voltage pulse is optimized using an iterative extreme seeking algorithm. The optimization reduces the rise time from 100 {micro}s to 50 {micro}s with no overshoot. This will lead to an increase in overall accelerator efficiency.

42 ENGINEERING↗
DOE OSTI2014

Genetic algorithm applied to the optimization of quantum cascade lasers with second harmonic generation

A computational model for the optimization of the second order optical nonlinearities in GaInAs/AlInAs quantum cascade laser structures is presented. The set of structure parameters that lead to improved device performance was obtained through the implementation of the Genetic Algorithm. In the following step, the linear and second harmonic generation power were calculated by self-consistently solving the system of rate equations for carriers and photons. This rate equation system included both stimulated and simultaneous double photon absorption processes that occur between the levels relevant for second harmonic generation, and material-dependent effective mass, as well as band nonparabolicity, were taken into account. The developed method is general, in the sense that it can be applied to any higher order effect, which requires the photon density equation to be included. Specifically, we have addressed the optimization of the active region of a double quantum well In{sub 0.53}Ga{sub 0.47}As/Al{sub 0.48}In{sub 0.52}As structure and presented its output characteristics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Multiobjective optimization of nuclear microreactor reactivity control system operation with swarm and evolutionary algorithms

To improve the marketability of novel microreactor designs, there is a need for automated and optimal control of these reactors. This paper presents a methodology for performing multiobjective optimization of control drum operation for a microreactor under normal and off-nominal conditions. Here, two different case studies are used where the control drum configuration is optimized for the reactor to be critical with some desired power distribution that would satisfy peaking limits. A surrogate model for power distribution is developed based on a feedforward neural network. The process for determining weights for scalarization of the multiobjective optimization problem is also detailed. Six optimization algorithms: evolutionary strategies, differential evolution, grey wolf optimization, Harris hawks optimization, moth flame optimization and particle swarm optimization, are all applied to these cases and the results analyzed. Although all these algorithms will demonstrate optima-seeking behavior, for real-time control it is necessary to identify the best algorithm to efficiently provide reasonable optima without operator interference. The moth flame optimization algorithm was found to perform particularly well on both cases. Overall, it was found that the algorithms capable of supplying the best optima were also the most consistent. Finally, the found optima were verified with the original model used to train surrogates.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection

Abstract Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar , which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Comparing, optimizing, and benchmarking quantum-control algorithms in a unifying programming framework

For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. Moreover, we introduce a unifying algorithmic framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗