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Results for “Algorithmic Optimization”

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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Stellar and Accretion Disk Parameters of the Close Binary HD 50526

We present a photometric and spectroscopic study of HD 50526, an ellipsoidal binary member of the group Double Periodic Variable stars. Performing data mining in photometric surveys and conducting new spectroscopic observations with several spectrographs during 2008–2015, we obtained orbital and stellar parameters of the system. The radial velocities were analyzed with the genetic PIKAIA algorithm, whereas Doppler tomography maps for the Hα and Hβ lines were constructed with the Total Variation Minimization code. An optimized simplex algorithm was used to solve the inverse problem adjusting the light curve with the best stellar parameters for the system. We find an orbital period of 6.ͩ701 ± 0.ͩ001 and a long photometric cycle of 191 ± 2 days. We detected the spectral features of the coldest star and modeled it with a logg=2.79±0.02 dex giant of mass 1.13 ± 0.02 M {sub ⊙} and effective temperature 10500 ± 125 K. In addition, we determine a mass ratio q = 0.206 ± 0.033 and that the hot star is a B-type dwarf of mass 5.48 ± 0.02 M {sub ⊙}. The V-band orbital light curve can be modeled including the presence of an accretion disk around the hotter star. This fills the Roche lobe of the hotter star and has a radius 14.74 ± 0.02 R {sub ⊙} and the temperature at the outer edge is 9400 K. Two bright spots located in the disk account for the global morphology of the light curve. The Doppler tomography maps of Hα and Hβ reveal complex structures of mass fluxes in the system.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1991

Lanczos eigensolution method for high-performance computers

The theory, computational analysis, and applications are presented of a Lanczos algorithm on high performance computers. The computationally intensive steps of the algorithm are identified as: the matrix factorization, the forward/backward equation solution, and the matrix vector multiples. These computational steps are optimized to exploit the vector and parallel capabilities of high performance computers. The savings in computational time from applying optimization techniques such as: variable band and sparse data storage and access, loop unrolling, use of local memory, and compiler directives are presented. Two large scale structural analysis applications are described: the buckling of a composite blade stiffened panel with a cutout, and the vibration analysis of a high speed civil transport. The sequential computational time for the panel problem executed on a CONVEX computer of 181.6 seconds was decreased to 14.1 seconds with the optimized vector algorithm. The best computational time of 23 seconds for the transport problem with 17,000 degs of freedom was on the the Cray-YMP using an average of 3.63 processors.

Bostic, Susan W.↗
DOE OSTI2014

SU-E-T-85: Comparison of Treatment Plans Calculated Using Ray Tracing and Monte Carlo Algorithms for Lung Cancer Patients Having Undergone Radiotherapy with Cyberknife

Purpose: The latest publications indicate that the Ray Tracing algorithm significantly overestimates the dose delivered as compared to the Monte Carlo (MC) algorithm. The purpose of this study is to quantify this overestimation and to identify significant correlations between the RT and MC calculated dose distributions. Methods: Preliminary results are based on 50 preexisting RT algorithm dose optimization and calculation treatment plans prepared on the Multiplan treatment planning system (Accuray Inc., Sunnyvale, CA). The analysis will be expanded to include 100 plans. These plans are recalculated using the MC algorithm, with high resolution and 1% uncertainty. The geometry and number of beams for a given plan, as well as the number of monitor units, is constant for the calculations for both algorithms and normalized differences are compared. Results: MC calculated doses were significantly smaller than RT doses. The D95 of the PTV was 27% lower for the MC calculation. The GTV and PTV mean coverage were 13 and 39% less for MC calculation. The first parameter of conformality, as defined as the ratio of the Prescription Isodose Volume to the PTV Volume was on average 1.18 for RT and 0.62 for MC. Maximum doses delivered to OARs was reduced in the MC plans. The doses for 1000 and 1500 cc of total lung minus PTV, respectively were reduced by 39% and 53% for the MC plans. The correlation of the ratio of air in PTV to the PTV with the difference in PTV coverage had a coefficient of −0.54. Conclusion: The preliminary results confirm that the RT algorithm significantly overestimates the dosages delivered confirming previous analyses. Finally, subdividing the data into different size regimes increased the correlation for the smaller size PTVs indicating the MC algorithm improvement verses the RT algorithm is dependent upon the size of the PTV.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A novel triple power cycle featuring a gas turbine cycle with supercritical carbon dioxide and organic Rankine cycles: Thermoeconomic analysis and optimization

In this study, a novel triple power cycle is proposed where waste heat from a gas turbine cycle is utilized to drive a supercritical carbon dioxide (s-CO 2 ) recompression cycle and a recuperative organic Rankine cycle (ORC) in sequence. A detailed thermoeconomic model is developed and implemented in MATLAB to evaluate the performance of the proposed cycle under different operating conditions. Optimization using a particle swarm optimization (PSO) algorithm is performed to minimize the levelized cost of electricity (LCOE) and determine the optimum design conditions of the cycle. The optimization results show that for a 100 MW cycle, the overall thermal efficiency and LCOE are 0.521 and $\$52.819$/MWh, respectively. The turbine inlet temperature of the gas turbine and s-CO 2 cycles are found as the most influential parameters on the thermoeconomic performance of the triple cycle. The proposed triple cycle shows an excellent waste energy recovery potential and superiority over a very thermodynamically efficient cycle from the literature, which included a gas turbine topping cycle and a complex cascade s-CO 2 power cycle used a bottoming cycle. As a result, the proposed triple cycle shows up to 0.9% points higher efficiency while it has fewer heat exchangers and turbomachinery than the cycle from the literature.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

A unified pseudo- C ℓ framework

The pseudo- C ℓ is an algorithm for estimating the angular power and cross-power spectra that is very fast and in realistic cases also nearly optimal. The algorithm can be extended to deal with contaminant deprojection and E/B purification, and can therefore be applied in a wide variety of scenarios of interest for current and future cosmological observations. This paper presents NAMASTER, a public, validated, accurate and easy-to-use software package that, for the first time, provides a unified framework to compute angular cross-power spectra of any pair of spin-0 or spin-2 fields, contaminated by an arbitrary number of linear systematics and requiring B- or E-mode purification, both on the sphere or in the flat-sky approximation. We describe the mathematical background of the estimator, including all the features above, and its software implementation in NAMASTER. We construct a validation suite that aims to resemble the types of observations that next-generation large-scale structure and ground-based CMB experiments will face, and use it to show that the code is able to recover the input power spectra in the most complex scenarios with no detectable bias. NAMASTER can be found at https://github.com/LSSTDESC/NaMaster, and is provided with comprehensive documentation and a number of code examples.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Validation of Microgrid Algorithms using At-Scale Simulation and Emulation Real-Time (ASSERT) Framework

This paper presents a novel framework for validating microgrid algorithms using a At-Scale Simulation and Emulation Real-Time (ASSERT) setup. The proposed universal microgrid validation framework was developed at Advanced Research on Integrated Energy Systems (ARIES) platform at National Renewable Energy Laboratory in Golden, Colorado, USA. As the number of intercommunicating devices grow in power systems, along with more integration of inverter-based resources, developing and validating microgrid operation and control algorithms is becoming complex and time-consuming task. The ASSERT framework is designed to provide a comprehensive, accurate, and efficient solution for validating microgrid algorithms. It combines at-scale simulations and real-time emulation to create a virtual environment that closely mimics the behavior of a physical microgrids which are characteristic of future smart distribution systems, such as: (1) increasing variability in the physical size of new energy technologies; (2) requiring control large numbers of interconnected devices, and (3) integrating diverse technologies that have not previously worked together. The paper describes the technical details of the ASSERT framework, its validation process, and its benefits over traditional validation methods. The framework's ability to validate the performance of various algorithms is demonstrated through a case study, which shows that it can effectively identify the weaknesses of an algorithm and optimize its performance. Overall, the ASSERT framework is a promising tool that can significantly enhance the development and validation of microgrid algorithms, leading to more reliable and efficient microgrid operation.

at-scale↗
DOE OSTI2015

Next-generation Algorithms for Assessing Infrastructure Vulnerability and Optimizing System Resilience

This report summarizes the work performed under the project project Next-Generation Algorithms for Assessing Infrastructure Vulnerability and Optimizing System Resilience. The goal of the project was to improve mathematical programming-based optimization technology for infrastructure protection. In general, the owner of a network wishes to design a network a network that can perform well when certain transportation channels are inhibited (e.g. destroyed) by an adversary. These are typically bi-level problems where the owner designs a system, an adversary optimally attacks it, and then the owner can recover by optimally using the remaining network. This project funded three years of Deon Burchett's graduate research. Deon's graduate advisor, Professor Jean-Philippe Richard, and his Sandia advisors, Richard Chen and Cynthia Phillips, supported Deon on other funds or volunteer time. This report is, therefore. essentially a replication of the Ph.D. dissertation it funded in a format required for project documentation. The thesis had some general polyhedral research. This is the study of the structure of the feasible region of mathematical programs, such as integer programs. For example, an integer program optimizes a linear objective function subject to linear constraints, and (nonlinear) integrality constraints on the variables. The feasible region without the integrality constraints is a convex polygon. Careful study of additional valid constraints can significantly improve computational performance. Here is the abstract from the dissertation: We perform a polyhedral study of a multi-commodity generalization of variable upper bound flow models. In particular, we establish some relations between facets of single- and multi- commodity models. We then introduce a new family of inequalities, which generalizes traditional flow cover inequalities to the multi-commodity context. We present encouraging numerical results. We also consider the directed edge-failure resilient network design problem (DRNDP). This problem entails the design of a directed multi-commodity flow network that is capable of fulfilling a specified percentage of demands in the event that any G arcs are destroyed, where G is a constant parameter. We present a formulation of DRNDP and solve it in a branch-column-cut framework. We present computational results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (User's Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a users manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (V.6.16 User's Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Fast, Dynamic, and Scalable Algorithms for Large-Scale Constrained Optimization

Scientists and engineers constantly aim to optimize an objective subject to physical, environmental, and/or resource constraints. The technique of using mathematical models to formulate and find real solutions of such problems is known as mathematical optimization, a process that has become invaluable for design and discovery in numerous scientific fields. This project has focused on the design, analysis, and implementation of high-performance computing algorithms for solving cutting-edge optimization problems. These include problems that involve (1) data uncertainties, such as those in the future supply, demand, and capacity of a given power system, (2) extreme numbers of alternatives, such as in the design of electrical power grids to avoid network vulnerabilities, and (3) real-time decisions, such as in the control of chemical reactors. The key features of the new algorithms are that they are fast, dynamic, and scalable to meet the computational requirements of scientists and researchers working to optimize large-scale, complex systems. The algorithms that have been designed, analyzed, and tested as part of the project can be characterized based on the types of optimization problems they have been designed to solve. Much of the work has focused on algorithms for solving large-scale constrained optimization problems. Contributions have also been made in the design of algorithms for unconstrained optimization, with some specialized methods for solving problems that have inherent nonsmoothness. Contributions have also been made in areas that have grown to prominence in the field over the five-year award period. These include the design of algorithms for solving large-scale stochastic optimization problems, such as those that arise in large-scale machine learning, and in complexity analyses for solving nonconvex problems, which has been a focal point of much recent work in continuous optimization. Overall, the project has supported work that has led to almost 30 articles that have been published or accepted to top journals in the field of mathematical optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user’s manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.15 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Application of Wireless Charging at Seaports for Range Extension of Drayage Battery Electric Trucks

Even though heavy-duty battery electric trucks (BETs) have become commercially available, their range limitation still hinders widespread adoption. Drayage has been regarded as a suitable application for early BETs due to typically having limited daily mileage. However, drayage operation can vary widely and some form of range extension may still be needed for BETs operating in this application. Here, in this paper, wireless charging at port terminals is proposed for this purpose. Potential wireless charging zones at port terminals are identified, and efficacy of wireless charging to extend BET range in drayage operation is verified by simulating the activity of 20 BETs from a drayage operator serving the ports of Los Angeles and Long Beach, using a microscopic BET energy consumption model. Furthermore, an optimization problem is formulated for optimal wireless charging zone planning from the port authority's perspective, considering subsets of the identified zones, and charging power options to choose from, for different budget ranges. In this context, zone planning means determining which areas of the port terminals should be selected for installing wireless charging systems, and what level of charging power should be for each selected zone's system. For each budget range, the optimization problem is solved using genetic algorithm to determine an optimal zone plan that provides the maximum amount of energy through wireless charging per unit cost of installation. The results show that wireless charging can aid improving activity completion of the simulated fleet by 5%, and further optimizing the zone plan can achieve similar performance with lower cost.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Redesigning rain gauges network in Johor using geostatistics and simulated annealing

Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.

54 ENVIRONMENTAL SCIENCES↗