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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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NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters will be added to the decision space of the GA, and specify the range of desired values for each optimization parameter. For performance objectives, the user is presented with a list of available coverage and revisit performance based calculation options from which two metrics are chosen to serve as the objective functions that the GA will use to evaluate solutions during the optimization process. Finally, the definition of the GA parameters provides user control over the number of generations (number of optimization iterations), the population size (number of candidate constellations created in each generation), and the adaptive mutation and crossover threshold values (control parameters for how frequently each process occurs during the optimization). GenETOC has been extensively tested to verify the individual components of the optimization process. The GA has been tested against a suite of GA test problems to confirm convergence to the known two and three-dimensional Pareto fronts. The coverage and revisit performance metrics obtained in GenETOC are compared with STK desktop scenarios, confirming the constellations are being appropriately modeled within GenETOC simulations. A walkthrough of a simple, example problem is provided to illustrate the workings of GenETOC and to demonstrate the output available to the mission designer.

mission design↗
NASA NTRS2015

Comparison of Optimal Small Spacecraft Micro Electric Propulsion Technologies for Mission Opportunities

The goal of this paper is to explore the mission opportunities that are uniquely enabled by U-class Solar Electric Propulsion (SEP) technologies. Small SEP thrusters offers significant advantages relative to existing technologies and will revolutionize the class of mission architectures that small spacecraft can accomplish by enabling trajectory maneuvers with significant change in velocity requirements and reaction wheel-free attitude control. This paper aims to develop and apply a common system-level modeling framework to evaluate these thrusters for relevant upcoming mission scenarios, taking into account the mass, power, volume, and operational constraints of small highly-constrained missions. We will identify the optimal technology for broad classes of mission applications for different U-class spacecraft sizes and provide insights into what constrains the system performance to identify technology areas where improvements are needed.

Spangelo, Sara↗
NASA NTRS1983

Design of sidewall treatment of cabin noise control of a twin engine turboprop aircraft

An analytical procedure was used to predict the noise transmission into the cabin of a twin engine general aviation aircraft. This model was then used to optimize the interior A weighted noise levels to an average value of about 85 dBA. The surface pressure noise spectral levels were selected utilizing experimental flight data and empirical predictions. The add on treatments considered in this optimization study include aluminum honeycomb panels, constrained layer damping tape, porous acoustic blankets, acoustic foams, septum barriers and limp trim panels which are isolated from the vibration of the main sidewall structure. To reduce the average noise level in the cabin from about 102 kBA (baseline) to 85 dBA (optimized), the added weight of the noise control treatment is about 2% of the total gross takeoff weight of the aircraft.

Vaicaitis, R.↗
NASA NTRS2001

Robust Airfoil Optimization to Achieve Consistent Drag Reduction Over a Mach Range

We prove mathematically that in order to avoid point-optimization at the sampled design points for multipoint airfoil optimization, the number of design points must be greater than the number of free-design variables. To overcome point-optimization at the sampled design points, a robust airfoil optimization method (called the profile optimization method) is developed and analyzed. This optimization method aims at a consistent drag reduction over a given Mach range and has three advantages: (a) it prevents severe degradation in the off-design performance by using a smart descent direction in each optimization iteration, (b) there is no random airfoil shape distortion for any iterate it generates, and (c) it allows a designer to make a trade-off between a truly optimized airfoil and the amount of computing time consumed. For illustration purposes, we use the profile optimization method to solve a lift-constrained drag minimization problem for 2-D airfoil in Euler flow with 20 free-design variables. A comparison with other airfoil optimization methods is also included.

Li, Wu↗
NASA NTRS1993

Trajectory optimization for the National Aerospace Plane

The objective of this second phase research is to investigate the optimal ascent trajectory for the National Aerospace Plane (NASP) from runway take-off to orbital insertion and address the unique problems associated with the hypersonic flight trajectory optimization. The trajectory optimization problem for an aerospace plane is a highly challenging problem because of the complexity involved. Previous work has been successful in obtaining sub-optimal trajectories by using energy-state approximation and time-scale decomposition techniques. But it is known that the energy-state approximation is not valid in certain portions of the trajectory. This research aims at employing full dynamics of the aerospace plane and emphasizing direct trajectory optimization methods. The major accomplishments of this research include the first-time development of an inverse dynamics approach in trajectory optimization which enables us to generate optimal trajectories for the aerospace plane efficiently and reliably, and general analytical solutions to constrained hypersonic trajectories that has wide application in trajectory optimization as well as in guidance and flight dynamics. Optimal trajectories in abort landing and ascent augmented with rocket propulsion and thrust vectoring control were also investigated. Motivated by this study, a new global trajectory optimization tool using continuous simulated annealing and a nonlinear predictive feedback guidance law have been under investigation and some promising results have been obtained, which may well lead to more significant development and application in the near future.

Lu, Ping↗
NASA NTRS2012

Direct Method Transcription for a Human-Class Translunar Injection Trajectory Optimization

This paper presents a new trajectory optimization software package developed in the framework of a low-to-high fidelity 3 degrees-of-freedom (DOF)/6-DOF vehicle simulation program named Mission Analysis Simulation Tool in Fortran (MASTIF) and its application to a translunar trajectory optimization problem. The functionality of the developed optimization package is implemented as a new "mode" in generalized settings to make it applicable for a general trajectory optimization problem. In doing so, a direct optimization method using collocation is employed for solving the problem. Trajectory optimization problems in MASTIF are transcribed to a constrained nonlinear programming (NLP) problem and solved with SNOPT, a commercially available NLP solver. A detailed description of the optimization software developed is provided as well as the transcription specifics for the translunar injection (TLI) problem. The analysis includes a 3-DOF trajectory TLI optimization and a 3-DOF vehicle TLI simulation using closed-loop guidance.

Witzberger, Kevin E.↗
NASA NTRS2018

The Number of Support Constraints for Overlapping Set Optimization with Nested Admissible Sets Is Equal to One

This paper reports on the formalization of a recent result by Crespo, et al., as found in the references. The formalized result bounds the number of support constraints in a particular type of optimization problem. The problem involves discovering an optimal member of a family of sets that overlaps each member of a constraining collection of sets. The particular case addressed here concerns optimizations in which the family of sets is nested. The primary results were formalized in the interactive theorem prover PVS and support the claim that a single support constraint exists in very general circumstances.

Siratt, John↗
NASA NTRS1980

Pareto-optimal multi-objective design of airplane control systems

A constrained minimization algorithm for the computer aided design of airplane control systems to meet many requirements over a set of flight conditions is generalized using the concept of Pareto-optimization. The new algorithm yields solutions on the boundary of the achievable domain in objective space in a single run, whereas the older method required a sequence of runs to approximate such a limiting solution. However, Pareto-optimality does not guarantee a satisfactory design, since such solutions may emphasize some objectives at the expense of others. The designer must still interact with the program to obtain a well-balanced set of objectives. Using the example of a fighter lateral stability augmentation system (SAS) design over five flight conditions, several effective techniques are developed for obtaining well-balanced Pareto-optimal solutions. For comparison, one of these techniques is also used in a recently developed algorithm of Kreisselmeier and Steinhauser, which replaces the hard constraints with soft constraints, using a special penalty function. It is shown that comparable results can be obtained.

Schy, A. A.↗
NASA NTRS2001

A Method of Integrating Aeroheating into Conceptual Reusable Launch Vehicle Design: Evaluation of Advanced Thermal Protection Techniques for Future Reusable Launch Vehicles

A method for integrating Aeroheating analysis into conceptual reusable launch vehicle (RLV) design is presented in this thesis. This process allows for faster turn-around time to converge a RLV design through the advent of designing an optimized thermal protection system (TPS). It consists of the coupling and automation of four computer software packages: MINIVER, TPSX, TCAT, and ADS. MINIVER is an Aeroheating code that produces centerline radiation equilibrium temperatures, convective heating rates, and heat loads over simplified vehicle geometries. These include flat plates and swept cylinders that model wings and leading edges, respectively. TPSX is a NASA Ames material properties database that is available on the World Wide Web. The newly developed Thermal Calculation Analysis Tool (TCAT) uses finite difference methods to carry out a transient in-depth 1-D conduction analysis over the center mold line of the vehicle. This is used along with the Automated Design Synthesis (ADS) code to correctly size the vehicle's thermal protection system (TPS). The numerical optimizer ADS uses algorithms that solve constrained and unconstrained design problems. The resulting outputs for this process are TPS material types, unit thicknesses, and acreage percentages. TCAT was developed for several purposes. First, it provides a means to calculate the transient in-depth conduction seen by the surface of the TPS material that protects a vehicle during ascent and reentry. Along with the in-depth conduction, radiation from the surface of the material is calculated along with the temperatures at the backface and interior parts of the TPS material. Secondly, TCAT contributes added speed and automation to the overall design process. Another motivation in the development of TCAT is optimization. In some vehicles, the TPS accounts for a high percentage of the overall vehicle dry weight. Optimizing the weight of the TPS will thereby lower the percentage of the dry weight accounted for by the TPS. Also, this will lower the cost of the TPS and the overall cost of the vehicle.

Olds, John R.↗
NASA NTRS2001

Evaluation of Advanced Thermal Protection Techniques for Future Reusable Launch Vehicles

A method for integrating Aeroheating analysis into conceptual reusable launch vehicle RLV design is presented in this thesis. This process allows for faster turn-around time to converge a RLV design through the advent of designing an optimized thermal protection system (TPS). It consists of the coupling and automation of four computer software packages: MINIVER, TPSX, TCAT and ADS. MINIVER is an Aeroheating code that produces centerline radiation equilibrium temperatures, convective heating rates, and heat loads over simplified vehicle geometries. These include flat plates and swept cylinders that model wings and leading edges, respectively. TPSX is a NASA Ames material properties database that is available on the World Wide Web. The newly developed Thermal Calculation Analysis Tool (TCAT) uses finite difference methods to carry out a transient in-depth I-D conduction analysis over the center mold line of the vehicle. This is used along with the Automated Design Synthesis (ADS) code to correctly size the vehicle's thermal protection system JPS). The numerical optimizer ADS uses algorithms that solve constrained and unconstrained design problems. The resulting outputs for this process are TPS material types, unit thicknesses, and acreage percentages. TCAT was developed for several purposes. First, it provides a means to calculate the transient in-depth conduction seen by the surface of the TPS material that protects a vehicle during ascent and reentry. Along with the in-depth conduction, radiation from the surface of the material is calculated along with the temperatures at the backface and interior parts of the TPS material. Secondly, TCAT contributes added speed and automation to the overall design process. Another motivation in the development of TCAT is optimization.

Olds, John R.↗
NASA NTRS1979

Application of trajectory optimization principles to minimize aircraft operating costs

This paper summarizes various applications of trajectory optimization principles that have been or are being devised by both government and industrial researchers to minimize aircraft direct operating costs (DOC). These costs (time and fuel) are computed for aircraft constrained to fly over a fixed range. Optimization theory is briefly outlined, and specific algorithms which have resulted from application of this theory are described. Typical results which demonstrate use of these algorithms and the potential savings which they can produce are given. Finally, need for further trajectory optimization research is presented.

Sorensen, J. A.↗
NASA NTRS2001

Optimizing Orbit-Instrument Configuration for Global Precipitation Mission (GPM) Satellite Fleet

Following the scientific success of the Tropical Rainfall Measuring Mission (TRMM) spearheaded by a group of NASA and NASDA scientists, their external scientific collaborators, and additional investigators within the European Union's TRMM Research Program (EUROTRMM), there has been substantial progress towards the development of a new internationally organized, global scale, and satellite-based precipitation measuring mission. The highlights of this newly developing mission are a greatly expanded scope of measuring capability and a more diversified set of science objectives. The mission is called the Global Precipitation Mission (GPM). Notionally, GPM will be a constellation-type mission involving a fleet of nine satellites. In this fleet, one member is referred to as the "core" spacecraft flown in an approximately 70 degree inclined non-sun-synchronous orbit, somewhat similar to TRMM in that it carries both a multi-channel polarized passive microwave radiometer (PMW) and a radar system, but in this case it will be a dual frequency Ku-Ka band radar system enabling explicit measurements of microphysical DSD properties. The remainder of fleet members are eight orbit-synchronized, sun-synchronous "constellation" spacecraft each carrying some type of multi-channel PMW radiometer, enabling no worse than 3-hour diurnal sampling over the entire globe. In this configuration the "core" spacecraft serves as a high quality reference platform for training and calibrating the PMW rain retrieval algorithms used with the "constellation" radiometers. Within NASA, GPM has advanced to the pre-formulation phase which has enabled the initiation of a set of science and technology studies which will help lead to the final mission design some time in the 2003 period. This presentation first provides an overview of the notional GPM program and mission design, including its organizational and programmatic concepts, scientific agenda, expected instrument package, and basic flight architecture. Following this introduction, we focus specifically on the last topic, that being an analysis which leads to an optimal flight architecture dictated in part by science requirements but constrained by allowable orbital mechanics, instrument scan patterns, and antenna aperture properties. Because the optimal architecture involves an interplay between orbit mechanics and instrument specifications, it is important to recognize that in attempting to serve various scientific themes, the final optimal architecture will represent a compromise concerning dynamic range, spatial resolution, sampling interval, pointing, beam coincidence, and measurement uncertainty. Moreover, cost becomes a major factor in seeking the optimal architecture through the pathways of antenna and instrument scan designs, as well as propulsion requirements associated with the orbit heights of various "constellation" members. Although the results presented at the IGARSS-2001 meeting will likely not be the fully refined flight architecture specifications, they are expected to be nearly complete.

Smith, Eric A.↗
NASA NTRS1984

Dual structural-control optimization of large space structures

A new approach is proposed for solving dual structural-control optimization problems for high-order flexible space structures where reduced-order structural models are employed. For a given initial structural dessign, a quadratic control cost is minimized subject to a constant-mass constraint. The sensitivity of the optimal control cost with respect to the stuctural design variables is then determined and used to obtain successive structural redesigns using a contrained gradient optimization algorithm. This process is repeated until the constrained control cost sensitivity becomes negligible. A numerical example is presented which demonstrates that this new approach effectively addresses the problem of dual optimization for potentially very high-order structures.

Messac, A.↗
NASA NTRS1982

A dynamic measure of controllability and observability for the placement of actuators and sensors on large space structures

The degree of controllability of a large space structure is found by a four step procedure: (1) finding the minimum control energy for driving the system from a given initial state to the origin in the prescribed time; (2) finding the region of initial state which can be driven to the origin with constrained control energy and time using optimal control strategy; (3) scaling the axes so that a unit displacement in every direction is equally important to control; and (4) finding the linear measurement of the weighted "volume" of the ellipsoid in the equicontrol space. For observability, the error covariance must be reduced toward zero using measurements optimally, and the criterion must be standardized by the magnitude of tolerable errors. The results obtained using these methods are applied to the vibration modes of a free-free beam.

Vandervelde, W. E.↗
NASA NTRS1991

l1-optimal control of multivariable systems with output norm constraints

This paper considers the l1-optimal control problem for general rational plants. It is shown that, for plants with no poles or zeros on the unit circle, an optimal compensator exists and that the resulting closed loop transfer function is polynomial whenever there are at least as many controls as regulated outputs and at least as many measurements as exogeneous inputs. Exactly or approximately, optimal rational compensators can be obtained by solving a sequence of finite linear programs for the coefficients of a polynomial closed-loop transfer function. No assumptions on plant poles or zeros are required to obtain at least approximately optimal compensators. It is shown that constrained problems in which a set of outputs is regulated subject to l(infinity)-norm constraints on another set of outputs can be solved using a slight modification of the same algorithm.

Mcdonald, J. S.↗
NASA NTRS2004

Fleet Assignment Using Collective Intelligence

Airline fleet assignment involves the allocation of aircraft to a set of flights legs in order to meet passenger demand, while satisfying a variety of constraints. Over the course of the day, the routing of each aircraft is determined in order to minimize the number of required flights for a given fleet. The associated flow continuity and aircraft count constraints have led researchers to focus on obtaining quasi-optimal solutions, especially at larger scales. In this paper, the authors propose the application of an agent-based integer optimization algorithm to a "cold start" fleet assignment problem. Results show that the optimizer can successfully solve such highly- constrained problems (129 variables, 184 constraints).

Antoine, Nicolas E.↗
NASA NTRS2010

Reducing the Volume of NASA Earth-Science Data

A computer program reduces data generated by NASA Earth-science missions into representative clusters characterized by centroids and membership information, thereby reducing the large volume of data to a level more amenable to analysis. The program effects an autonomous data-reduction/clustering process to produce a representative distribution and joint relationships of the data, without assuming a specific type of distribution and relationship and without resorting to domain-specific knowledge about the data. The program implements a combination of a data-reduction algorithm known as the entropy-constrained vector quantization (ECVQ) and an optimization algorithm known as the differential evolution (DE). The combination of algorithms generates the Pareto front of clustering solutions that presents the compromise between the quality of the reduced data and the degree of reduction. Similar prior data-reduction computer programs utilize only a clustering algorithm, the parameters of which are tuned manually by users. In the present program, autonomous optimization of the parameters by means of the DE supplants the manual tuning of the parameters. Thus, the program determines the best set of clustering solutions without human intervention.

Lee, Seungwon↗