Dynamics and control research applicable to Apollo extension systems
Disturbance envelope and experiment requirements for Apollo missions used to determine optimum control-moment gyro-jet system
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Disturbance envelope and experiment requirements for Apollo missions used to determine optimum control-moment gyro-jet system
This report describes the principal activities of a technical review team formed to address National Full Scale Aerodynamic Complex (NFAC) blade repair problems. In particular, the problem of lack of good adhesive bonding of the composite overwrap to the Hyduliginum wood blade material was studied extensively. Description of action plans and technical elements of the plans are provided. Results of experiments designed to optimize the bonding process and bonding strengths obtained on a full scale blade using a two-step cure process with adhesive primers are presented. Consensus recommendations developed by the review team in conjunction with the NASA Ames Fan Blade Repair Project Team are provided along with lessons learned on this program. Implementation of recommendations resulted in achieving good adhesive bonds between the composite materials and wooden blades, thereby providing assurance that the repaired fan blades will meet or exceed operational life requirements.
The ROSE framework was designed to facilitate complex system analyses. It completely divorces the model execution process from the model itself. By doing so ROSE frees the modeler to develop a library of standard modeling processes such as Design of Experiments, optimizers, parameter studies, and sensitivity studies which can then be applied to any of their available models. The ROSE framework accomplishes this by means of a well defined API and object structure. Both the API and object structure are presented here with enough detail to implement ROSE in any object-oriented language or modeling tool.
Abstract The Columbia Stellarator eXperiment (CSX), currently being designed at Columbia University, aims to test theoretical predictions related to QA plasma behavior, and to pioneer the construction of an optimized stellarator using three-dimensional, non-insulated high-temperature superconducting (NI-HTS) coils. The magnetic configuration is generated by a combination of two circular planar poloidal field (PF) coils and two 3D-shaped interlinked (IL) coils, with the possibility to add windowpane coils to enhance shaping and experimental flexibility. The PF coils and vacuum vessel are repurposed from the former Columbia Non-Neutral Torus experiment, while the IL coils will be custom-wound in-house using NI-HTS tapes. To obtain a plasma shape that meets the physics objectives with a limited number of coils, novel single-stage optimization techniques are employed, optimizing both the plasma and coils concurrently, in particular targeting a tight aspect ratio QA plasma and minimized strain on the HTS tape. Despite the increased complexity due to the expanded degrees of freedom, these methods successfully identify optimized plasma geometries that can be realized by coils meeting engineering specifications. This paper discusses the derivation of the constraints and objectives specific to CSX, and describe how two recently developed single-stage optimization methodologies are applied to the design of CSX. A set of selected configurations for CSX is then described in detail.
A numerical minimization scheme is used in conjunction with two-dimensional and three-dimensional inviscid transonic flow analysis codes to provide procedures for wing leading edge aerodynamic design. The procedures are demonstrated in the design of a new leading edge to improve C-141 cruise performance. For the high aspect ratio moderately swept C-141 wing, the 2-D procedure is shown to yield results which are in close agreement with those obtained using the 3-D technique. Although the 2-D approach uses much less computation time than the 3-D technique, the latter requires fewer manhours than the former. Comparisons of predicted and wind tunnel measured performance improvements are presented which verify the design procedures.
Under certain specified conditions, the Bayes procedure for designing two-level fractional factorial experiments is that which maximizes the expected utility over all possible choices of parameter-estimator matchings, physical-design variable matchings, defining parameter groups, and sequences of telescoping groups. NAMER computes the utility of all possible matchings of physical variables to design variables and parameters to estimators for a specified choice of defining parameter group or groups. The matching yielding the maximum expected utility is indicated, and detailed information is provided about the optimal matchings and utilities. Complete documentation is given; and an example illustrates input, output, and usage.
A four-parameter, three-level, central composite experiment design has been used to optimize the configuration of an annular scramjet injector geometry using computational fluid dynamics. The computational fluid dynamic solutions played the role of computer experiments, and response surface methodology was used to capture the simulation results for mixing efficiency and total pressure recovery within the scramjet flowpath. An optimization procedure, based upon the response surface results of mixing efficiency, was used to compare the optimal design configuration against the target efficiency value of 92.5%. The results of three different optimization procedures are presented and all point to the need to look outside the current design space for different injector geometries that can meet or exceed the stated mixing efficiency target.
NAMER is used to find Bayes procedure for designing two level, fractional factorial experiments when given partial prior information. Program is written in FORTRAN 4 for IBM 7094 computer.
An experiment is presented in which the effects of roll motions on human operator performance were investigated. The motion cues considered were the result of commanded vehicle motion and vehicle disturbances. An optimal control pilot-vehicle model was used in the design of the experiment and to predict system performance prior to executing the experiment. The model predictions and experimental results are compared. Seventy-eight per cent of the model predictions are within one standard deviation of the means of the experimental results. The high correlation between model predictions and system performance indicate the usefulness of the predictive model for experimental design and for prediction of pilot performance influenced by motion cues.
Finding the optimal implosion design on existing experimental facilities for inertial confinement fusion requires an exhaustive search of the vast design parameter space. This is infeasible both with experiments and with simulations. Consequently, a large fraction of the experimentally realizable design space remains unexplored, and new design schemes are challenging to optimize in a reasonable time frame. On the OMEGA laser facility, predictive machine learning models have been developed to accurately forecast the result of an experiment using only inexpensive simulations and the large dataset of prior experimental data. However, the full design space remains vast enough to be unassailable with simple optimization techniques. Here we develop an automated and optimally parallel Bayesian optimization algorithm that can entirely optimize the target and pulse shape of a direct-drive ICF implosion under a given design paradigm. We use this algorithm to find a markedly improved design for the performance implosions on OMEGA that is predicted to hydroequivalently scale to ignition at 2.15 MJ.
This report summarizes the work that was done on the project from 1 Apr. 1992 to 31 Mar. 1993. The main goal of this research is to develop a practical tool for rotorcraft control system design based on interactive optimization tools (CONSOL-OPTCAD) and classical rotorcraft design considerations (ADOCS). This approach enables the designer to combine engineering intuition and experience with parametric optimization. The combination should make it possible to produce a better design faster than would be possible using either pure optimization or pure intuition and experience. We emphasize that the goal of this project is not to develop an algorithm. It is to develop a tool. We want to keep the human designer in the design process to take advantage of his or her experience and creativity. The role of the computer is to perform the calculation necessary to improve and to display the performance of the nominal design. Briefly, during the first year we have connected CONSOL-OPTCAD, an existing software package for optimizing parameters with respect to multiple performance criteria, to a simplified nonlinear simulation of the UH-60 rotorcraft. We have also created mathematical approximations to the Mil-specs for rotorcraft handling qualities and input them into CONSOL-OPTCAD. Finally, we have developed the additional software necessary to use CONSOL-OPTCAD for the design of rotorcraft controllers.
Realization of the full potential of photorefractive materials in device technology is seriously impeded by our inability to achieve controlled formation of critical defects during single crystal growth and by difficulties in meeting the required degree of compositional uniformity on a micro-scale over macroscopic dimensions. The exact nature and origin of the critical defects which control photorefractivity could not as yet be identified because of gravitational interference. There exists, however, strong evidence that the density of defect formation and their spatial distribution are adversely affected by gravitational interference which precludes the establishment of quantifiable and controllable heat and mass transfer conditions during crystal growth. The current, NASA sponsored research at MIT is directed at establishing a basis for the development of a comprehensive approach to the optimization of property control during melt growth of photorefractive materials, making use of the m-g environment, provided in the International Space Station. The objectives to be pursued in m-g research on photorefractive BSO (Bi12SiO20) are: (a) identification of the x-level(s) responsible for photorefractivity in undoped BSO; (b) development of approaches leading to the control of x-level formation at uniform spatial distribution; (c) development of doping and processing procedures for optimization of the critical, application specific parameters, spectral response, sensitivity, response time and matrix stability. The presentation will focus on: the rationale for the justification of the space experiment, ground-based development efforts, design considerations for the space experiments, strategic plan of the space experiments, and approaches to the quantitative analysis of the space experiments.
Structural optimization procedures usually start from a given design topology and vary its proportions or boundary shapes to achieve optimality under various constraints. Two different categories of structural optimization are distinguished in the literature, namely sizing and shape optimization. A major restriction in both cases is that the design topology is considered fixed and given. Questions concerning the general layout of a design (such as whether a truss or a solid structure should be used) as well as more detailed topology features (e.g., the number and connectivities of bars in a truss or the number of holes in a solid) have to be resolved by design experience before formulating the structural optimization model. Design quality of an optimized structure still depends strongly on engineering intuition. This article presents a novel approach for initiating formal structural optimization at an earlier stage, where the design topology is rigorously generated in addition to selecting shape and size dimensions. A three-phase design process is discussed: an optimal initial topology is created by a homogenization method as a gray level image, which is then transformed to a realizable design using computer vision techniques; this design is then parameterized and treated in detail by sizing and shape optimization. A fully automated process is described for trusses. Optimization of two dimensional solid structures is also discussed. Several application-oriented examples illustrate the usefulness of the proposed methodology.
The AEOLUS Center is dedicated to developing a unified optimization-under-uncertainty framework for (1) learning predictive models from data and (2) optimizing experiments, processes, and designs governed by these models, all driven by complex, uncertain energy systems. AEOLUS addressed the critical need for principled, rigorous, scalable, and structure-exploiting capabilities for exploring parameter and decision spaces of complex forward simulation models---the so-called outer loop. This report summarizes the work done under DE-SC0021077 on (1) nonlocal models for solidification problems, (2) a multifidelity method for a nonlocal diffusion model, and (3) multifidelity Monte Carlo methods.
In this paper we introduce a comprehensive and rigorous robust design procedure to overcome some limitations of the current approaches. A comprehensive approach is general enough to model the two major types of robust design applications, namely, robust design associated with the minimization of the deviation of performance caused by the deviation of noise factors (uncontrollable parameters), and robust design due to the minimization of the deviation of performance caused by the deviation of control factors (design variables). We achieve mathematical rigor by using, as a foundation, principles from the design of experiments and optimization. Specifically, we integrate the Response Surface Method (RSM) with the compromise Decision Support Problem (DSP). Our approach is especially useful for design problems where there are no closed-form solutions and system performance is computationally expensive to evaluate. The design of a solar powered irrigation system is used as an example. Our focus in this paper is on illustrating our approach rather than on the results per se.
Terrestrial Gamma-ray Flashes (TGFs) have now been detected with four different orbiting spacecraft. The latest observations are being made with the scintillation detectors of Gamma-ray Burst Monitor (GBM) on the Fermi Gamma-ray Space Telescope Observatory (Fermi). Although this experiment was designed and optimized for the observation of cosmic gamma-ray bursts (GRBs), it has unprecedented capabilities for TGF observations, surpassing those of the experiment that discovered TGFs, the BATSE experiment on the Compton Gamma-ray Observatory. Launched in June 2008 from the Kennedy Space Center, the Fermi-GBM has been detecting about one TGF every four weeks. The thick bismuth germinate (BGO) scintillation detectors of the GBM have now observed photon energies from TGFs at energies up to approx.40 MeV. Individual photons are detected with an absolute timing accuracy of 2 microsec. Unlike the BATSE instrument, the GBM data system allows higher counting rates to be recorded and deadtime characteristics are well-known and correctable; thus the saturation effects seen with BATSE are avoided. TGF pulses as narrow as approx.0.1ms have been observed with the GBM. Like BATSE (and unlike RHESSI) an on-board trigger is required to detect TGFs. The minimum time window for this trigger is 16ms. A trigger window this wide greatly reduces the number of detected TGFs, since they most often have a much shorter duration than this window, thus reducing the signal-to-background. New on-board trigger algorithms based on detected photon energies are about to be implemented; this should increase the number of TGF triggers. High-energy spectra from TGFs observed with Fermi-GBM will be described.
Efficient recovery of volatile fatty acids (VFAs) from fermentation broth is a challenge due to low VFA titers and thus limits the commercialization of VFA production using biological routes. Liquid–liquid extraction using hydrophobic ionic liquids (ILs) shows great promise for the extraction and esterification of hydrophilic VFAs. In this study, several ILs were evaluated to select a water-immiscible and efficient extraction solvent. The selected IL, trihexyltetradecyl phosphonium dibutylphosphate ([P 666,14 ][DBP]), gave a cumulative VFA extraction of around 842.8 mg per g IL. The predicted excess enthalpy (H E ) and logarithmic activity coefficients ln(γ) using the COSMO-RS model were validated with the experimentally obtained VFA recovery from fermentation broth. To understand the extraction mechanism of VFAs, quantum theory of atoms in molecules (QTAIM) and noncovalent interaction (NCI) were performed. The results suggest that long chain fatty acids exhibit strong van der Waals interaction with the DBP anion leading to higher VFA extraction. The enzymatic esterification of VFAs with ethanol in [P 666,14 ][DBP] was optimized using the Box–Behnken response surface design of experiment. Under the optimized conditions, up to 83.7% of hexanoic acid was converted to ethyl esters, while other shorter chain VFAs have lower conversion efficiency (38.3–63.2%).
The AEOLUS Center is dedicated to developing a unified optimization-under-uncertainty framework for: (1) learning predictive models from data; and (2) optimizing experiments, processes, and designs governed by these models, all driven by complex, uncertain energy systems. AEOLUS addresses the critical need for principled, rigorous, scalable, and structure-exploiting capabilities for exploring parameter and decision spaces of complex forward simulation models. This report summarizes the key highlights of our research during the period of performance.