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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Integrated design and manufacturing for the high speed civil transport (a combined aerodynamics/propulsion optimization study)

This report documents the efforts of a Georgia Tech High Speed Civil Transport (HSCT) aerospace student design team in completing a design methodology demonstration under NASA's Advanced Design Program (ADP). Aerodynamic and propulsion analyses are integrated into the synthesis code FLOPS in order to improve its prediction accuracy. Executing the integrated product and process development (IPPD) methodology proposed at the Aerospace Systems Design Laboratory (ASDL), an improved sizing process is described followed by a combined aero-propulsion optimization, where the objective function, average yield per revenue passenger mile ($/RPM), is constrained by flight stability, noise, approach speed, and field length restrictions. Primary goals include successful demonstration of the application of the response surface methodolgy (RSM) to parameter design, introduction to higher fidelity disciplinary analysis than normally feasible at the conceptual and early preliminary level, and investigations of relationships between aerodynamic and propulsion design parameters and their effect on the objective function, $/RPM. A unique approach to aircraft synthesis is developed in which statistical methods, specifically design of experiments and the RSM, are used to more efficiently search the design space for optimum configurations. In particular, two uses of these techniques are demonstrated. First, response model equations are formed which represent complex analysis in the form of a regression polynomial. Next, a second regression equation is constructed, not for modeling purposes, but instead for the purpose of optimization at the system level. Such an optimization problem with the given tools normally would be difficult due to the need for hard connections between the various complex codes involved. The statistical methodology presents an alternative and is demonstrated via an example of aerodynamic modeling and planform optimization for a HSCT.

Baecher, Juergen↗
NASA NTRS1999

Development of Response Surface Models for Rapid Analysis and Multidisciplinary Optimization of Launch Vehicle Design Concepts

Multidisciplinary design optimization (MDO) is an important step in the design and evaluation of launch vehicles, since it has a significant impact on performance and lifecycle cost. The objective in MDO is to search the design space to determine the values of design parameters that optimize the performance characteristics subject to system constraints. Vehicle Analysis Branch (VAB) at NASA Langley Research Center has computerized analysis tools in many of the disciplines required for the design and analysis of launch vehicles. Vehicle performance characteristics can be determined by the use of these computerized analysis tools. The next step is to optimize the system performance characteristics subject to multidisciplinary constraints. However, most of the complex sizing and performance evaluation codes used for launch vehicle design are stand-alone tools, operated by disciplinary experts. They are, in general, difficult to integrate and use directly for MDO.

Unal, Resit↗
NASA NTRS1999

Design of Composite Structures for Reliability and Damage Tolerance

A summary of research conducted during the first year is presented. The research objectives were sought by conducting two tasks: (1) investigation of probabilistic design techniques for reliability-based design of composite sandwich panels, and (2) examination of strain energy density failure criterion in conjunction with response surface methodology for global-local design of damage tolerant helicopter fuselage structures. This report primarily discusses the efforts surrounding the first task and provides a discussion of some preliminary work involving the second task.

Rais-Rohani, Masoud↗
NASA NTRS2017

Trajectory Dispersed Vehicle Process for Space Launch System

The Space Launch System (SLS) vehicle is part of NASA's deep space exploration plans that includes manned missions to Mars. Manufacturing uncertainties in design parameters are key considerations throughout SLS development as they have significant effects on focus parameters such as lift-off-thrust-to-weight, vehicle payload, maximum dynamic pressure, and compression loads. This presentation discusses how the SLS program captures these uncertainties by utilizing a 3 degree of freedom (DOF) process called Trajectory Dispersed (TD) analysis. This analysis biases nominal trajectories to identify extremes in the design parameters for various potential SLS configurations and missions. This process utilizes a Design of Experiments (DOE) and response surface methodologies (RSM) to statistically sample uncertainties, and develop resulting vehicles using a Maximum Likelihood Estimate (MLE) process for targeting uncertainties bias. These vehicles represent various missions and configurations which are used as key inputs into a variety of analyses in the SLS design process, including 6 DOF dispersions, separation clearances, and engine out failure studies.

Statham, Tamara↗
NASA NTRS2019

Charactarization of Thermal Protection Systems

In 2012, NASA's mission of landing the Mars Science Laboratory (MSL) on Mars was successful. MSL was protected by an ablative heatshield of tiled low density material known as PICA (phenolic impregnated carbon ablator). The heatshield was instrumented with MEDLI (MSL Entry Descent Landing Instrument) a suite of sensors & thermocouples at discrete locations in order to monitor the in-depth ablator temperature response and surface pressure. MEDLI was designed and developed by NASA Langley, in partnership with NASA Ames Research Center for the purposes of probing Mars and evaluating the performance of the spacecraft upon entry into the Martian atmosphere. The flight data reduces the uncertainty in engineer models for predicting the response of a spacecraft towards the extreme heating environment of an entry into the Martian atmosphere. MEDLI2 is a part of the Mars 2020 mission and is the next-generation sensor suite for entry, descent, and landing. This data will again help engineers validate their flight models. Additionally, the atmospheric data, can help us understand atmospheric density and winds. This is a critical study for reducing risks to both robotic and future human missions to Mars. Engineered models for Mars 2020 are dependent upon parameters related to the materials response to heating and radiation. In this work, the thermal properties and other measurements of various ablative materials are analyzed to achieve greater utility of the 2012 MEDLI flight data and more accurately determine the parameters being used in Mars 2020. The purpose of this study was to measure specific heat, thermal conductivity, char yield, reflectance as a function of wavelength, and other thermal parameters and optical properties of various ablative materials. CO2(g) at extreme temperatures emits radiation impacting MEDLI2 flight predictions. Emissivity & absorptivity as a function of temperature were calculated from FTIR and UV-Vis data as a means of investigating whether these materials might absorb CO2 radiation upon entry into the Mars atmosphere.

HEEET↗
NASA NTRS1999

Improving the Unsteady Aerodynamic Performance of Transonic Turbines using Neural Networks

A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The procedure yielded a modified design that improves the aerodynamic performance through small changes to the reference design geometry. These results demonstrate the capabilities of the neural net-based design procedure, and also show the advantages of including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

Rai, Man Mohan↗
NASA NTRS2000

Optimization of Car Body under Constraints of Noise, Vibration, and Harshness (NVH), and Crash

To be competitive on the today's market, cars have to be as light as possible while meeting the Noise, Vibration, and Harshness (NVH) requirements and conforming to Government-man dated crash survival regulations. The latter are difficult to meet because they involve very compute-intensive, nonlinear analysis, e.g., the code RADIOSS capable of simulation of the dynamics, and the geometrical and material nonlinearities of a thin-walled car structure in crash, would require over 12 days of elapsed time for a single design of a 390K elastic degrees of freedom model, if executed on a single processor of the state-of-the-art SGI Origin2000 computer. Of course, in optimization that crash analysis would have to be invoked many times. Needless to say, that has rendered such optimization intractable until now. The car finite element model is shown. The advent of computers that comprise large numbers of concurrently operating processors has created a new environment wherein the above optimization, and other engineering problems heretofore regarded as intractable may be solved. The procedure, shown, is a piecewise approximation based method and involves using a sensitivity based Taylor series approximation model for NVH and a polynomial response surface model for Crash. In that method the NVH constraints are evaluated using a finite element code (MSC/NASTRAN) that yields the constraint values and their derivatives with respect to design variables. The crash constraints are evaluated using the explicit code RADIOSS on the Origin 2000 operating on 256 processors simultaneously to generate data for a polynomial response surface in the design variable domain. The NVH constraints and their derivatives combined with the response surface for the crash constraints form an approximation to the system analysis (surrogate analysis) that enables a cycle of multidisciplinary optimization within move limits. In the inner loop, the NVH sensitivities are recomputed to update the NVH approximation model while keeping the Crash response surface constant. In every outer loop, the Crash response surface approximation is updated, including a gradual increase in the order of the response surface and the response surface extension in the direction of the search. In this optimization task, the NVH discipline has 30 design variables while the crash discipline has 20 design variables. A subset of these design variables (10) are common to both the NVH and crash disciplines. In order to construct a linear response surface for the Crash discipline constraints, a minimum of 21 design points would have to be analyzed using the RADIOSS code. On a single processor in Origin 2000 that amount of computing would require over 9 months! In this work, these runs were carried out concurrently on the Origin 2000 using multiple processors, ranging from 8 to 16, for each crash (RADIOSS) analysis. Another figure shows the wall time required for a single RADIOSS analysis using varying number of processors, as well as provides a comparison of 2 different common data placement procedures within the allotted memories for each analysis. The initial design is an infeasible design with NVH discipline Static Torsion constraint violations of over 10%. The final optimized design is a feasible design with a weight reduction of 15 kg compared to the initial design. This work demonstrates how advanced methodology for optimization combined with the technology of concurrent processing enables applications that until now were out of reach because of very long time-to-solution.

Kodiyalam, Srinivas↗
NASA NTRS1998

Neural Net-Based Redesign of Transonic Turbines for Improved Unsteady Aerodynamic Performance

A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology (RSM) and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The optimization procedure yields a modified design that improves the aerodynamic performance through small changes to the reference design geometry. The computed results demonstrate the capabilities of the neural net-based design procedure, and also show the tremendous advantages that can be gained by including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

Madavan, Nateri K.↗
NASA NTRS2006

Aircraft Conceptual Design and Risk Analysis Using Physics-Based Noise Prediction

An approach was developed which allows for design studies of commercial aircraft using physics-based noise analysis methods while retaining the ability to perform the rapid trade-off and risk analysis studies needed at the conceptual design stage. A prototype integrated analysis process was created for computing the total aircraft EPNL at the Federal Aviation Regulations Part 36 certification measurement locations using physics-based methods for fan rotor-stator interaction tones and jet mixing noise. The methodology was then used in combination with design of experiments to create response surface equations (RSEs) for the engine and aircraft performance metrics, geometric constraints and take-off and landing noise levels. In addition, Monte Carlo analysis was used to assess the expected variability of the metrics under the influence of uncertainty, and to determine how the variability is affected by the choice of engine cycle. Finally, the RSEs were used to conduct a series of proof-of-concept conceptual-level design studies demonstrating the utility of the approach. The study found that a key advantage to using physics-based analysis during conceptual design lies in the ability to assess the benefits of new technologies as a function of the design to which they are applied. The greatest difficulty in implementing physics-based analysis proved to be the generation of design geometry at a sufficient level of detail for high-fidelity analysis.

Olson, Erik D.↗
DOE OSTI2025

Design and synthesis of biobased superhydrophobic biochar catalyst derived from Citrus sinensis for biodiesel production using inedible oil feedstocks

In an one-pot in situ trans/esterification of low-grade feedstocks, the simultaneous presence of triglycerides (TAGs) and free fatty acids (FFAs) presents a dual catalytic challenge. While TAGs undergo transesterification to form biodiesel and glycerol, FFAs present in the feedstocks are esterified simultaneously, producing water as a by-product. This water can deactivate acid catalysts by interacting with their active sites, reducing catalytic efficiency and reusability. To overcome this, we developed a hydrophobic sulfonic acid-functionalized biochar catalyst derived from Citrus sinensis (orange peel) via sulfonation with H 2 SO 4 and subsequent silylation using hexamethyldisilazane (HMDS). The optimized catalyst exhibited a high surface area (355.09 m 2 g −1 ), sulfur content (5.33 wt%), and strong hydrophobicity (water contact angle: 154°). Compared to non-hydrophobic analogs, it showed enhanced activity and reusability (up to 10 cycles). Using response surface methodology with central composite design, a biodiesel yield of 99.1 ± 0.4% was achieved under optimal conditions. Life cycle assessment was performed to evaluate the environmental impacts of biodiesel production utilising the synthesized catalyst, considering 1000 kg of biodiesel produced as 1 functional unit. In conclusion, the recorded results showed the cumulative abiotic depletion of fossil resources over the entire biodiesel production process as 87 243.423 MJ and global warming potential as 4103.494 kg CO 2 equivalent.

Biochar↗
DOE OSTI2023

Sustainable bioleaching of lithium-ion batteries for critical metal recovery: Process optimization through design of experiments and thermodynamic modeling

Recycling spent lithium-ion batteries (LIBs) could alleviate supply risks for critical metals and be less harmful to the environment compared to new production of metals from mining. Developing a cost-effective LIB bioleaching process could be a promising alternative to traditional energy-intensive recycling technologies. Here, this study aimed to optimize bioleaching conditions for maximum economic competitiveness through design of experiments using iterative response surface methodology (RSM), assisted by thermodynamic modeling. The optimal condition was identified as 2.5% pulp density in 75 mM gluconic acid biolixiviant at 55°C for 30 h which could recover 57%–84% of nickel, 71%–86% of cobalt, and 100% of lithium and manganese, yielding a 17%–26% net profit margin. The recommended pulp density and acid concentrations, together with the observed metal solubilization, were supported by thermodynamic modeling predictions. Our study demonstrated that combining RSM with thermodynamic simulations could be a powerful tool for optimizing bioleaching conditions.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1993

Space transportation vehicle design evaluation using saturated designs

An important objective in the preliminary design and evaluation of space transportation vehicles is to find the best values of design variables that optimize the performance characteristic (e.g. dry weight). For a given configuration, the vehicle performance can be determined by the use of complex sizing and performance evaluation computer programs. These complex computer programs utilize iterative algorithms and they are generally too expensive and/or difficult to use directly in multidisciplinary design optimization. An alternative is to use response surface methodology (RSM) and obtain quadratic polynomial approximations to the functional relationships between performance characteristics and design variables. In RSM, these approximation models are then used to determine optimum design parameter values and for rapid sensitivity studies. Constructing a second-order model requires that 'n' design parameters be studied at least at 3 levels (values) so that the coefficients in the model can be estimated. There, 3(n) factorial experiments (point designs or observations) may be necessary. For small values of 'n' such as two or three, this design works well. However, when a large number of design parameters are under study, the number of design points required for a full-factorial design may become excessive. Fortunately, these quadratic polynomial approximations can be obtained by selecting an efficient design matrix using central composite designs (CCD) from design of experiments theory. Each unique point design from the CCD matrix is then conducted using computerized analysis tools (e.g. POST, CONSIZ, etc.). In the next step, least squares regression analysis is used to calculate the quadratic polynomial coefficients from the data. However, in some multidisciplinary applications involving a large number of design variables and several disciplines, the computerized performance synthesis programs may get too time consuming and expensive to run even with the use of efficient central composite designs. In such cases, it may be preferable to keep the number of design points to an absolute minimum and trade some model accuracy with cost. For this purpose, another class of experimental designs, called saturated D-optimal designs may be utilized for generating a matrix of vehicle designs. A design is called saturated when the number of design points is exactly equal to the number of terms in the model to be fitted. As a result, saturated designs require the absolute minimum number of design points ((n+1)(n+2)/s) to estimate the quadratic polynomial model coefficients. Saturated designs can be generated using the D-optimality criterion. A good saturated design should give rise to least squares estimates with minimum generalized variance.

Unal, Resit↗
DOE OSTI2019

A New Protocol for Efficient and High Yield Preparation of Nanocellulose from Elaeis guineensis Biomass: A Response Surface Methodology (RSM) Study

An innovative system for isolating nanocellulose was established that uses Ni(NO{sub 3}){sub 2} transition metal salt without the assistance of mechanical disintegrations or mineral acid and was compared with the classic production by tedious acid hydrolysis. Optimization study on Ni(II)-catalyzed hydrolysis of cellulose isolated from oil palm (Elaeis guineensis) empty fruit bunch (OPEFB) towards nanocellulose yield was investigated. Response surface methodology-central composite design was used to design and optimize the experiments with three operating parameters: pH of Ni(NO{sub 3}){sub 2} (pH 2–4), reaction time (20‒100 min) and reaction temperature (25‒65 °C). The present study indicated that the nanocellulose yield as high as 81.37% was achieved under hydrolysis conditions of pH 3, 58 °C within 58 min. At the optimum conditions, the OPEFB derived nanocellulose rendered high crystallinity of 91.1% and excellent thermal stability of 341 °C. Evidence of the successful isolation of nanocellulose was proven by HRTEM observation revealing fibrils formed the long and interconnected network-like structure with the average width of 41.1 ± 1.6 nm and several micrometers in length, which resulted in high aspect ratio. Thus, the obtained nanocellulose via Ni(II)-catalyzed hydrolysis has numerous potential applications and represent a green alternative for the treatment of OPEFB. This study provided a facile high yield procedure for the production of nanocellulose with similar characteristics to traditional nanocellulose, which was significant to the commercialization of nanocellulose.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Geometry Optimization for Structural Reliability and Performance of a Thermoelectric Generator

The opposing requirements for the performance and reliability of a thermoelectric generator (TEG) are investigated with numerical finite element analysis simulations. The parameters that significantly influence the operational performance and structural reliability of a TEG are considered for simultaneous optimization. The design of experiments (DOE) based response surface optimization methodology in conjunction with results from finite element analysis was employed to identify optimal parameters from the structural reliability and performance (power output and efficiency) perspectives. A high temperature bismuth telluride based TEG couple was used in the studies to demonstrate the general applicability of the methodology. The response surface methodology is found to be a robust technique that can expedite the process of geometry optimization of a TE device during the research and development phase of TEG devices fabricated from new thermoelectric materials.

Karri, Naveen K.↗
DOE OSTI2016

Additive effects of levonorgestrel and ethinylestradiol on brain aromatase (cyp19a1b) in zebrafish specific in vitro and in vivo bioassays

Estrogens and progestins are widely used in combination in human medicine and both are present in aquatic environment. Despite the joint exposure of aquatic wildlife to estrogens and progestins, very little information is available on their combined effects. In the present study we investigated the effect of ethinylestradiol (EE2) and Levonorgestrel (LNG), alone and in mixtures, on the expression of the brain specific ER-regulated cyp19a1b gene. For that purpose, recently established zebrafish-derived tools were used: (i) an in vitro transient reporter gene assay in a human glial cell line (U251-MG) co-transfected with zebrafish estrogen receptors (zfERs) and the luciferase gene under the control of the zebrafish cyp19a1b gene promoter and (ii) an in vivo bioassay using a transgenic zebrafish expressing GFP under the control of the zebrafish cyp19a1b gene promoter (cyp19a1b-GFP). Concentration-response relationships for single chemicals were modeled and used to design the mixture experiments following a ray design. The results from mixture experiments were analyzed to predict joint effects according to concentration addition and statistical approaches were used to characterize the potential interactions between the components of the mixtures (synergism/antagonism). We confirmed that some progestins could elicit estrogenic effects in fish brain. In mixtures, EE2 and LNG exerted additive estrogenic effects both in vitro and in vivo, suggesting that some environmental progestin could exert effects that will add to those of environmental (xeno-)estrogens. Moreover, our zebrafish specific assays are valuable tools that could be used in risk assessment for both single chemicals and their mixtures. - Highlights: • Combined effects of EE2 and LNG were assessed on ER-dependent cyp19a1b expression. • EE2 and LNG alone induced brain aromatase in zebrafish specific bioassays. • Experimental ray design allowed complete concentration-response surfaces modeling. • EE2 and LNG exerted additive effects on brain aromatase in radial glial cells.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2002

Management of Total Pressure Recovery, Distortion and High Cycle Fatigue in Compact Air Vehicle Inlets

It is the purpose of this study to demonstrate the viability and economy of Response Surface Methods (RSM) and Robustness Design Concepts (RDC) to arrive at micro-secondary flow control installation designs that maintain optimal inlet performance over a range of the mission variables. These statistical design concepts were used to investigate the robustness properties of 'low unit strength' micro-effector installations. 'Low unit strength' micro-effectors are micro-vanes set at very low angles-of-incidence with very long chord lengths. They were designed to influence the near wall inlet flow over an extended streamwise distance, and their advantage lies in low total pressure loss and high effectiveness in managing engine face distortion.

Anderson, Bernhard H.↗
NASA NTRS2016

Improved Test Planning and Analysis Through the Use of Advanced Statistical Methods

The goal of this work is, through computational simulations, to provide statistically-based evidence to convince the testing community that a distributed testing approach is superior to a clustered testing approach for most situations. For clustered testing, numerous, repeated test points are acquired at a limited number of test conditions. For distributed testing, only one or a few test points are requested at many different conditions. The statistical techniques of Analysis of Variance (ANOVA), Design of Experiments (DOE) and Response Surface Methods (RSM) are applied to enable distributed test planning, data analysis and test augmentation. The D-Optimal class of DOE is used to plan an optimally efficient single- and multi-factor test. The resulting simulated test data are analyzed via ANOVA and a parametric model is constructed using RSM. Finally, ANOVA can be used to plan a second round of testing to augment the existing data set with new data points. The use of these techniques is demonstrated through several illustrative examples. To date, many thousands of comparisons have been performed and the results strongly support the conclusion that the distributed testing approach outperforms the clustered testing approach.

Green, Lawrence L.↗