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At least 1,531 records · Page 85

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Robust Trajectory Optimization and GN&C Performance Analysis for NRHO Rendezvous

This paper evaluates several candidate Near-Rectilinear Halo Orbits (NRHO) rendezvous trajectory designs using linear covariance (LinCov) analysis and determines the optimal locations for NRHO rendezvous translational maneuver locations. The performance of several candidate relative trajectory designs are determined as a function of relative navigation accuracy (angles only), inertial optical navigation (OpNav), range observability maneuvers, maneuver execution errors, relative maneuver targeting, and environment uncertainties. Further, the optimal locations of rendezvous maneuvers are determined for each of the candidate reference trajectories. The long-term goal of this research is to utilize LinCov and a genetic optimization algorithm (GA) to determine a complete end-to-end optimal NRHO trajectory design that is robust to navigation errors, maneuver execution errors, and environment uncertainties. This paper represents a first step toward this goal. Three candidate rendezvous trajectories with varying numbers of range-observability maneuvers are evaluated for their robustness to uncertainties, errors, and total trajectory correction delta-v performance. Some key elements of this analysis include relative navigation performance in an NRHO, relative trajectory dispersion performance, and total 3-sigma delta-v performance. This development provides the foundation to then determine an optimal and robust end-to-end NRHO rendezvous trajectory, including the determination of the optimal locations of range observability maneuvers, if needed.

Linear Covariance Analysis↗

NASA NTRSDate not supplied

Trajectory Optimization Methods and Applications at NASA Goddard

The process of spacecraft trajectory design frequently incorporates an optimization step in which one or more objectives, such as propellant consumption or time of flight, is minimized. Optimization is especially crucial for low-thrust mission design, due to the need to specify a thrust vector at every instant in time along a trajectory. At NASA's Goddard Space Flight Center (GSFC) a number of open-source tools have been developed for spacecraft trajectory optimization that utilize direct shooting and collocation methods. These tools have been effectively applied to cislunar, libration point, and interplanetary mission design for the Lunar IceCube, SWFO, and DAVINCI missions, among others. This presentation outlines the theory underlying these optimization tools along with the details of their application to several trajectory design problems, with a focus on the Lunar IceCube (LIC) mission. The LIC mission required a low-thrust trajectory from a high-energy deployment state to a lunar orbit, and its limited control authority necessitated the use of optimization tools and low-energy trajectory design techniques. The tools discussed demonstrate how the use of optimization methods expands mission capabilities and enables transformational science.

Robert Pritchett↗

NASA NTRSDate not supplied

Optimization of Plug Nozzles Using CFD Informed Design Space Querying

This paper presents an axisymmetric CFD approach based on Reynolds-averaged Navier-Stokes (RANS) calculations for the shape optimization of plug nozzle contours for both supersonic cruise and landing/takeoff (LTO) conditions. Three key external plug design parameters were defined as independent variables in the optimization scheme, while nozzle performance was evaluated using thrust coefficient (Cfg) and discharge coefficient (Cd) as the primary dependent variables. By varying these parameters, a wide range of axisymmetric conic section plug profiles can be achieved, simplifying the problem. The paper also discusses the supersonic plug nozzle optimization approach, design space seeding, meshing, and CFD simulations for the optimization process. The results demonstrate that the approach is effective in exploring the parameter space and reducing computational expenses, making it a viable option for optimizing nozzle plug contours. Using this optimization toolchain, the study yielded preliminary designs for LTO, cruise, and LTO-cruise balanced plug nozzles.

nozzles↗

NASA NTRSDate not supplied

Optimization of Plug Nozzles Using CFD Informed Design Space Querying

CFD↗

NASA NTRSDate not supplied

Angles-Only Robust Trajectory Optimization for NRHO Rendezvous

This study demonstrates a robust trajectory optimization approach for rendezvous and proximity operations with angles-only navigation measurements. Often, sensors that directly measure relative range and velocity require communication or coordination between the chaser and target vehicle and can have limiting pointing accuracy, mass, or power requirements compared to angle measurement sensors. Thus, the capability to perform a rendezvous with only angle measurements can be advantageous for vehicle design and to improve robustness to failures. However, the well studied limitation of angles-only navigation in measuring range results in large uncertainties in the navigation system that must be reduced with chaser vehicle thrust maneuvers to induce observability in range for the navigation filter. This analysis presents a trajectory optimization problem for a lunar ascent rendezvous during a crewed lunar mission in a Near-Rectilinear Halo Orbit (NRHO) that is limited to only angle measurements. The objective of this study is to show that an angles-only rendezvous is feasible in an NRHO and to present the sensitivity to an assortment of constraints generated from a systematic optimization process using linear covariance analysis and particle swarm optimization. Linearized NRHO dynamics and linearized relative targeting are applied to use linear covariance analysis to determine the expected delta-v and trajectory dispersions due to initial state uncertainty, sensor errors, maneuver execution errors, and unmodeled dynamics. The delta-v and trajectory dispersions are passed into a particle swarm optimization algorithm to find the optimized maneuver profile that minimizes fuel use while satisfying constraints such as free drift and underburn to 3-sigma certainty. The trajectory constraints including time available, desired final uncertainty, and initial uncertainty are varied to ascertain sensitivity and desirable engineering trades.

Linear Covariance Analysis↗

NASA NTRS2024

Runtime Thread-Block Optimization for Custom Multistream CUDA Kernels for the Glenn Research Center Communication Analysis Suite

In preparation of the return of humans to the Moon with the coming Artemis missions, NASA scientists must evaluate proposed landing site locations for terrain and communications viability. The Glenn Research Center Communication Analysis Suite (GCAS) combines sophisticated communication network models with accurate lunar terrain to access sites across the Moon’s south pole. Given the importance of proper site selection to crew safety and mission success, many locations need to be analyzed resulting in a large computational load needing to be performed. To meet the growing project demands, development has begun to improve the runtime efficiency of GCAS with GPU parallelization by way of multi-stream CUDA kernels. One of the most prominent factors in kernel optimization is the proper selection of thread-block dimensions in order to maximize the concurrent operation on the device. Typically, thread-block dimensions are optimized by hand requiring many stages of benchmarking and iteration. Additionally, given the main conditions to optimization are the physical GPU architecture and problem size, these optimal dimensions are non-portable and fragile in their scope. As such, a novel optimization routine was developed to generate the optimal thread-block dimensions during runtime with considerations to hardware specifications and problem size resolving the issues of portability and enabling the function of more dynamic routines.

Aden Bergstresser↗

DOE OSTI2013

Optimal Multicarrier Phase-Coded Waveform Design for Detection of Extended Targets

We design a parametric multicarrier phase-coded (MCPC) waveform that achieves the optimal performance in detecting an extended target in the presence of signal-dependent interference. Traditional waveform design techniques provide only the optimal energy spectral density of the transmit waveform and suffer a performance loss in the synthesis process of the time-domain signal. Therefore, we opt for directly designing an MCPC waveform in terms of its time-frequency codes to obtain the optimal detection performance. First, we describe the modeling assumptions considering an extended target buried within the signal-dependent clutter with known power spectral density, and deduce the performance characteristics of the optimal detector. Then, considering an MCPC signal transmission, we express the detection characteristics in terms of the phase-codes of the MCPC waveform and propose to optimally design the MCPC signal by maximizing the detection probability. Our numerical results demonstrate that the designed MCPC signal attains the optimal detection performance and requires a lesser computational time than the other parametric waveform design approach.

Sen, Satyabrata↗

DOE OSTI2013

Energy optimization system

A system for optimizing customer utility usage in a utility network of customer sites, each having one or more utility devices, where customer site is communicated between each of the customer sites and an optimization server having software for optimizing customer utility usage over one or more networks, including private and public networks. A customer site model for each of the customer sites is generated based upon the customer site information, and the customer utility usage is optimized based upon the customer site information and the customer site model. The optimization server can be hosted by an external source or within the customer site. In addition, the optimization processing can be partitioned between the customer site and an external source.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2013

PARLO: PArallel Run-Time Layout Optimization for Scientific Data Explorations with Heterogeneous Access Pattern

Download Citation Email Print Request Permissions Save to Project The size and scope of cutting-edge scientific simulations are growing much faster than the I/O and storage capabilities of their run-time environments. The growing gap is exacerbated by exploratory, data-intensive analytics, such as querying simulation data with multivariate, spatio-temporal constraints, which induces heterogeneous access patterns that stress the performance of the underlying storage system. Previous work addresses data layout and indexing techniques to improve query performance for a single access pattern, which is not sufficient for complex analytics jobs. We present PARLO a parallel run-time layout optimization framework, to achieve multi-level data layout optimization for scientific applications at run-time before data is written to storage. The layout schemes optimize for heterogeneous access patterns with user-specified priorities. PARLO is integrated with ADIOS, a high-performance parallel I/O middleware for large-scale HPC applications, to achieve user-transparent, light-weight layout optimization for scientific datasets. It offers simple XML-based configuration for users to achieve flexible layout optimization without the need to modify or recompile application codes. Experiments show that PARLO improves performance by 2 to 26 times for queries with heterogeneous access patterns compared to state-of-the-art scientific database management systems. Compared to traditional post-processing approaches, its underlying run-time layout optimization achieves a 56% savings in processing time and a reduction in storage overhead of up to 50%. PARLO also exhibits a low run-time resource requirement, while also limiting the performance impact on running applications to a reasonable level.

Gong, Zhenhuan↗

DOE OSTI2013

Role of Design Standards in Wind Plant Optimization (Presentation)

When a turbine is optimized, it is done within the design constraints established by the objective criteria in the international design standards used to certify a design. Since these criteria are multifaceted, it is a challenging task to conduct the optimization, but it can be done. The optimization is facilitated by the fact that a standard turbine model is subjected to standard inflow conditions that are well characterized in the standard. Examples of applying these conditions to rotor optimization are examined. In other cases, an innovation may provide substantial improvement in one area, but be challenged to impact all of the myriad design load cases. When a turbine is placed in a wind plant, the challenge is magnified. Typical design practice optimizes the turbine for stand-alone operation, and then runs a check on the actual site conditions, including wakes from all nearby turbines. Thus, each turbine in a plant has unique inflow conditions. The possibility of creating objective and consistent inflow conditions for turbines within a plant, for used in optimization of the turbine and the plant, are examined with examples taken from LES simulation.

17 WIND ENERGY↗

DOE OSTI2015

A new hybrid genetic algorithm for optimizing the single and multivariate objective functions

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the most improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.

feedstock↗

DOE OSTI2018

Optimal Structures for Voltage Controllers in Inverters: Preprint

Output voltage regulation is a primary performance objective in power electronics systems which are not supported by a stiff voltage source. In this paper, we pose an optimal voltage control problem for ac inverter systems and study the structure of the resulting feedback laws. Here, it is demonstrated that the solution to the optimal voltage regulation control problem exhibits an inner current-controller structure even though there are no explicit objectives on tracking current which are targeted. Furthermore, the outer-loop voltage control and inner-loop current control structure is insensitive to the weighting transfer functions used in the optimal control problem. Although the inner-outer structure has been used in prior works, the optimal nature of such a structure was not known. In deriving the optimal controller, we also present a systematic design framework which is cognizant of the physical properties of inverters. Case studies are used to study the optimal controller and its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

On-line optimization scheme for HVAC demand response

A computer-implemented method of optimizing demand-response (DR) of a heating, ventilation, and air-conditioning (HVAC) system of a building, includes determining (30, 31, 32) a value of an objective function F.sub.ij of a HVAC system for each of a plurality of DR strategies j for each of a plurality of weather patterns i that is a weighted sum of an energy cost of the HVAC system and a thermal comfort loss of the HVAC system, assigning (33, 34, 35, 36) a likelihood score L.sub.i,j to each of a selected subset of near-optimal DR strategies j for each weather pattern i, and selecting (37, 38) those near-optimal DR strategies with large overall likelihood scores L.sub.j to create an optimal strategy pool of DR strategies. An optimal strategy pool can be searched (39) in real-time for an optimal DR strategy for a given weather pattern.

42 ENGINEERING↗

DOE OSTI2019

Optimized thionin protects plants against bacterial infections

Two small proteins with anti-bacterial activity are generated and called optimized thionin and optimized pro-thionin. Optimized pro-thionin contains a signal sequence for intracellular trafficking of the protein and is cleaved off to yield optimized thionin. Genetically altered plants and their progeny expressing a polynucleotide encoding optimized pro-thionin or optimized thionin resist diseases caused by bacteria.

Stover, Eddie W.↗

DOE OSTI2021

Optimization of Refrigerant Compositions for Low-GWP Refrigerant Mixtures Using Segment-by-segment Heat Exchanger and Detailed System Models

The recently introduced hydrofluoroolefin (HFO) refrigerants, including R1234yf and R1234ze(E), have significantly lower global warming potentials (GWPs) than traditional hydrofluorocarbon (HFC) refrigerants like R410A. However, prior tests show that direct drop-in of pure R1234yf or R1234ze(E) into equipment designed for R410A results in a decrease in heat exchanger capacity and the system coefficient of performance. The primary reason is the lower in-tube heat transfer performance of R1234yf and R1234ze(E) compared with that of R410A. To address this issue, previous studies have mixed the mildly flammable HFC R32 with HFOs to improve system performance, with HFC R125 also added to suppress flammability. Previous studies selected compositions based on simple cycle analyses and did not consider modifications of the heat exchanger circuitry configuration to adapt to the new refrigerants. This study presents a novel multi-objective optimization approach to design a refrigerant composition that maximizes energy efficiency within flammability and GWP limits. The approach in this work simultaneously optimizes mixture composition and heat exchanger circuitry configuration. A case study on a rooftop unit indicates that, compared with mixture-only optimization, simultaneous optimization of mixture and heat exchanger circuitry yields a 5.9% improvement in cycle efficiency and a 48.6% reduction in refrigerant flammability with a GWP of 268. Circuitry optimization using refrigerants with different temperature glides shows that the larger the temperature glide is, the larger EER improvement is obtained. The results show that zeotropic blends with a large temperature glide are more sensitive to the refrigerant circuitry than pure refrigerants and may suffer significant performance degradation with subpar heat exchanger circuitry design. The proposed optimization approach is generally applicable to mixtures with any number of components. Using this approach to design a HVAC system can yield higher system efficiency within flammability and GWP constraints.

Li, Zhenning↗

DOE OSTI2022

Optimal Wind Turbine Design for H2 Production

NREL and industrial partners GE and Nel seek to advance affordable green hydrogen production by optimizing wind turbine design specifically for hydrogen production and validating designs using NREL's ARIES facilities. This project will couple wind turbine, wind plant, solar plant, and electrolyzer models to predict hydrogen production from variable, renewable power sources. It will then use gradient-based optimization with exact-analytic gradients to optimize wind turbine rotor diameter, hub height, and power rating for optimal hydrogen production. Finally, it will validate the optimal turbine designs using the ARIES research platform, where it will compare a baseline turbine to the optimal design for a variety of scenarios and resources.

electrolyzer↗

DOE OSTI2012

OPTIMIZATION FRAMEWORK FOR A RADIO FREQUENCY GUN BASED INJECTOR

Linear accelerator based light sources are used to produce coherent x-ray beams with unprecedented peak intensity. In these devices, the key parameters of the photon beam such as brilliance and coherence are directly dependent on the electron beam parameters. This leads to stringent beam quality requirements for the electron beam source. Radio frequency (RF) guns are used in such light sources since they accelerate electrons to relativistic energies over a very short distance, thus minimizing the beam quality degradation due to space charge effects within the particle bunch. Designing such sources including optimization of its beam parameters is a complex process where one needs to meet many requirements simultaneously. It is useful to have a tool to automate the design optimization in the context of the injector beam dynamics performance. Evolutionary and genetic algorithms are powerful tools to apply to nonlinear multi-objective optimization problems, and they have been successfully used in injector optimizations where the electric field profiles for the accelerating devices are fixed. Here the genetic algorithm based approach is extended to modify and optimize the electric field profile for an RF gun concurrently with the injector performance. Two field modification methods are used. This dissertation presents an overview of the optimization system and examples of its application to a state of the art RF gun. Results indicate improved injector performance is possible with unbalanced electric field profiles where the peak field in the cathode cell is larger than in subsequent cells.

Hofler, Alicia↗

DOE OSTI2022

Optimization control technology for building energy conservation

A simulation processor generates and stores a simulation model based on conditions associated with a physical structure, such as a building. A neural network processor implements a neural network, having an input layer coupled to receive sensor data from the structure and having an output layer coupled to supply control signals to the at least one electrically operable environmental control device. The neural network is trained using the simulation model. A particle swarm optimization processor programmed to receive the simulation results and perform particle swarm optimization, ascertains optimal parameters for controlling the at least one electrically operable environmental control device and supplies these optimal parameters to the neural network processor. The neural network processor uses the optimal parameters supplied by the particle swarm optimization processor to further train the neural network.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗