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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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13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Single-Objective Airy Light-Sheet Imaging

Despite its massive potential, standard light-sheet imaging (LSI) faces key challenges, such as the incompatibility with common sample mounting techniques and low-resolution imaging. Single-objective LSI attempts to address these issues but often suffers from limited fields-of-view and throughput rates, or requires multiple optics that increase costs, alignment complexity, and losses. To overcome these challenges of standard single-objective LSI, we introduce single-objective Airy light-sheet imaging (SoALSI). SoALSI leverages the extraordinary self-acceleration properties of the Airy beam, achieving 5× higher imaging rates and enhanced imaging efficiency than standard single-objective LSI. Here, we demonstrate SoALSI’s versatility through rigorous contrast and resolution characterizations and by high-resolution imaging of diverse biological specimens, including malaria parasite-infected red blood cells and plant root tissue. SoALSI seamlessly integrates with any standard inverted microscope frame, enabling broader accessibility for the bioimaging community to explore biological processes in a wide range of specimens with enhanced resolution and imaging contrast.

airy beam↗
NASA NTRS2003

Evaluation of Genetic Algorithm Concepts using Model Problems: Single-Objective Optimization - Part 1

A genetic-algorithm-based optimization approach is described and evaluated using a simple hill-climbing model problem. The model problem utilized herein allows for the broad specification of a large number of search spaces including spaces with an arbitrary number of genes or decision variables and an arbitrary number hills or modes. In the present study, only single objective problems are considered. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all problems attempted. The most difficult problems - those with large hyper-volumes and multi-mode search spaces containing a large number of genes - require a large number of function evaluations for GA convergence, but they always converge.

Holst, Terry L.↗
DOE OSTI2025

Quantum approximate multi-objective optimization

The goal of multi-objective optimization is to understand optimal trade-offs between competing objective functions by finding the Pareto front, that is, the set of all Pareto-optimal solutions, where no objective can be improved without degrading another one. Multi-objective optimization can be challenging classically, even if the corresponding single-objective optimization problems are efficiently solvable. Thus, multi-objective optimization represents a compelling problem class to analyze with quantum computers. Here we use a low-depth quantum approximate optimization algorithm to approximate the optimal Pareto front of certain multi-objective weighted maximum-cut problems. We demonstrate its performance on an IBM Quantum computer, as well as with matrix product state numerical simulation, and show its potential to outperform classical approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Optimization of an Energy Tuning Assembly for High Explosives Detection

The Portable Isotopic Neutron Spectroscopy (PINS) system, employs neutron-induced gamma-ray spectroscopy and provides a nondestructive method for high explosives detection. In standard operation it uses Californium-252 as a neutron source. Operating PINS with a deuterium-tritium (DT) neutron generator has some advantages over Cf-252, including lifetime and ability to produce high-energy inelastic scattering gamma rays. However, current systems using DT neutron generators suffer from a high environmental background and reduced ability to induce neutron capture, reducing spectral quality and limiting nitrogen sensitivity. Here, this study presents the development of an energy-tuning assembly (ETA) designed to optimize the DT neutron energy spectrum to increase nitrogen reaction rates in a target, thereby improving high explosive detection capabilities. A metaheuristic optimization framework, MultiGNOWEE, coupled with MCNP, was employed to generate two ETA configurations: a single-objective ETA, which maximizes nitrogen capture reactions, and a multi-objective ETA, which balances neutron capture and inelastic scattering. Simulations demonstrated the optimized configurations achieved up to a 10-fold improvement in nitrogen capture rates compared to the bare configuration. Experimental validation was conducted using a DT neutron generator and a high-purity germanium (HPGe) detector. Two prototype ETAs were constructed and assessed on a melamine simulant. Measurements demonstrated improved nitrogen detection for both prototype ETA configurations when compared to the standard system.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

Calculation of Pareto-optimal solutions to multiple-objective problems using threshold-of-acceptability constraints

A technique is presented for the calculation of Pareto-optimal solutions to a multiple-objective constrained optimization problem by solving a series of single-objective problems. Threshold-of-acceptability constraints are placed on the objective functions at each stage to both limit the area of search and to mathematically guarantee convergence to a Pareto optimum.

Giesy, D. P.↗
NASA NTRS1982

Medusa spectroscopy of A400, A576, A1767, and A2124

Galaxy velocity data taken with the Steward Observatory multiple aperture fiber optic spectrograph are presented for four Abell clusters. The root-mean-square external errors in these velocities are about 100 km/s; accuracy which compares favorably with that obtained from single-object observations. It is expected that the recent adoption of a CCD detector should decrease external errors to about 50 km/s. All four of the clusters observed are known X-ray sources and the present data agree well with empirically derived velocity dispersion-X-ray luminosity relations for clusters of galaxies. Abell 400 is interesting in this regard, since both its X-ray luminosity and its velocity dispersion are quite small. Such objects are particularly important in determining the slope of the velocity dispersion-X-ray luminosity relation. The large microwave decrement observed in A576 was initially interpreted as due to Compton scattering of the microwave background by the X-ray-emitting intracluster gas. White and Silk have presented Einstein X-ray data which indicate that A576 contains too little gas to produce the observed microwave decrement by Compton scattering. The velocity dispersion obtained here for 47 members of this cluster strengthens their conclusion.

Hintzen, P.↗
NASA NTRS1993

Integrated design of the CSI evolutionary structure: A verification of the design methodology

One of the main objectives of the Controls-Structures Interaction (CSI) program is to develop and evaluate integrated controls-structures design methodology for flexible space structures. Thus far, integrated design methodologies for a class of flexible spacecraft, which require fine attitude pointing and vibration suppression with no payload articulation, have been extensively investigated. Various integrated design optimization approaches, such as single-objective optimization, and multi-objective optimization, have been implemented with an array of different objectives and constraints involving performance and cost measures such as total mass, actuator mass, steady-state pointing performance, transient performance, control power, and many more. These studies have been performed using an integrated design software tool (CSI-DESIGN CODE) which is under development by the CSI-ADM team at the NASA Langley Research Center. To date, all of these studies, irrespective of the type of integrated optimization posed or objectives and constraints used, have indicated that integrated controls-structures design results in an overall spacecraft design which is considerably superior to designs obtained through a conventional sequential approach. Consequently, it is believed that validation of some of these results through fabrication and testing of a structure which is designed through an integrated design approach is warranted. The objective of this paper is to present and discuss the efforts that have been taken thus far for the validation of the integrated design methodology.

Maghami, Peiman G.↗
NASA NTRS2002

Multiobjective Optimization Using a Pareto Differential Evolution Approach

Differential Evolution is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. In this paper, the Differential Evolution algorithm is extended to multiobjective optimization problems by using a Pareto-based approach. The algorithm performs well when applied to several test optimization problems from the literature.

Madavan, Nateri K.↗
NASA NTRS2004

Multiobjective Aerodynamic Shape Optimization Using Pareto Differential Evolution and Generalized Response Surface Metamodels

Differential Evolution (DE) is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. The DE algorithm has been recently extended to multiobjective optimization problem by using a Pareto-based approach. In this paper, a Pareto DE algorithm is applied to multiobjective aerodynamic shape optimization problems that are characterized by computationally expensive objective function evaluations. To improve computational expensive the algorithm is coupled with generalized response surface meta-models based on artificial neural networks. Results are presented for some test optimization problems from the literature to demonstrate the capabilities of the method.

Madavan, Nateri K.↗
NASA NTRS2010

Particle Swarm Optimization Toolbox

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.

Grant, Michael J.↗
NASA NTRS2012

Phase Function Determination in Support of Orbital Debris Size Estimation

To recover the size of a space debris object from photometric measurements, it is necessary to determine its albedo and basic shape: if the albedo is known, the reflective area can be calculated; and if the shape is known, the shape and area taken together can be used to estimate a characteristic dimension. Albedo is typically determined by inferring the object s material type from filter photometry or spectroscopy and is not the subject of the present study. Object shape, on the other hand, can be revealed from a time-history of the object s brightness response. The most data-rich presentation is a continuous light-curve that records the object s brightness for an entire sensor pass, which could last for tens of minutes to several hours: from this one can see both short-term periodic behavior as well as brightness variations with phase angle. Light-curve interpretation, however, is more art than science and does not lend itself easily to automation; and the collection method, which requires single-object telescope dedication for long periods of time, is not well suited to debris survey conditions. So one is led to investigate how easily an object s brightness phase function, which can be constructed from the more survey-friendly point photometry, can be used to recover object shape. Such a recovery is usually attempted by comparing a phase-function curve constructed from an object s empirical brightness measurements to analytically-derived curves for basic shapes or shape combinations. There are two ways to accomplish this: a simple averaged brightness-versus phase curve assembled from the empirical data, or a more elaborate approach in which one is essentially calculating a brightness PDF for each phase angle bin (a technique explored in unpublished AFRL/RV research and in Ojakangas 2011); in each case the empirical curve is compared to analytical results for shapes of interest. The latter technique promises more discrimination power but requires more data; the former can be assembled in its essentials from fewer measurements but will be less definitive in its assignments. The goal of the present study is to evaluate both techniques under debris survey conditions to determine their relative performance and, additionally, to learn precisely how a survey should be conducted in order to maximize their performance. Because the distendedness of objects has more of an effect than their precise shape in calculating a characteristic dimension, one is interested in the techniques discrimination ability to distinguish between an elongated rectangular prism and a short rectangular prism or cube, or an elongated cylinder from a squat cylinder or sphere. Sensitivity studies using simulated data will be conducted to determine discrimination power for both techniques as a function of amount of data collected and range (and specific region) of phase angles sampled. Empirical GEODSS photometry data for distended objects (dead payloads with solar panels, rocket bodies) and compact objects (cubesats, calibration spheres, squat payloads) will also be used to test this discrimination ability. The result will be a recommended technique and data collection paradigm for debris surveys in order to maximize this type of discrimination.

Hejduk, M. D.↗
NASA NTRS2012

OpenMDAO: Framework for Flexible Multidisciplinary Design, Analysis and Optimization Methods

The OpenMDAO project is underway at NASA to develop a framework which simplifies the implementation of state-of-the-art tools and methods for multidisciplinary design, analysis and optimization. Foremost, OpenMDAO has been designed to handle variable problem formulations, encourage reconfigurability, and promote model reuse. This work demonstrates the concept of iteration hierarchies in OpenMDAO to achieve a flexible environment for supporting advanced optimization methods which include adaptive sampling and surrogate modeling techniques. In this effort, two efficient global optimization methods were applied to solve a constrained, single-objective and constrained, multiobjective version of a joint aircraft/engine sizing problem. The aircraft model, NASA's nextgeneration advanced single-aisle civil transport, is being studied as part of the Subsonic Fixed Wing project to help meet simultaneous program goals for reduced fuel burn, emissions, and noise. This analysis serves as a realistic test problem to demonstrate the flexibility and reconfigurability offered by OpenMDAO.

Kriging↗
NASA NTRSDate not supplied

Proposition to Optimize Fire Stations for Wildfires

The rising trend in wildfire occurrence and severity has put a strain on wildfire management organizations by spreading out limited resources to meet increasing demand. There has been extensive prior research and data collection to determine the areas of highest risk and to predict regional wildfire damages based on historical trends. Our group aims to utilize this data to best determine fire station placement, optimizing where resources are allocated to reduce the time and investment needed to effectively mitigate wildfires. Using existing research, we are able to calculate optimal fire station locations by utilizing a single-objective facility location problem algorithm incentivized for cost reduction. Further work would be needed to refine the algorithm to accommodate for more realistic factors including access to water and roadways as well as better accounting for the costs involved, but our work serves as a proof of concept and lays the foundation for future research. Implementation of this algorithm would allow public fire planning agencies (such as CAL FIRE and Forest Service) to shift resources to where they would be most effective.

Wildfires↗