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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Optimal Control and Architecture Design Optimization for Hydraulic Drive Train of a Compact Track Loader

Hydrostatic transmission (HST) is a common hydraulic drive train transmission used in track loaders and other high power off-road vehicles. While reliable, traditional HST with fixed-displacement motors has relatively poor efficiencies. In this paper, alternative HST and series hybrid drive train architectures are proposed to replace the traditional HST to allow the implementation of optimal control and optimization methods for better vehicle fuel efficiency. Besides the optimal control methods applied to minimize fuel consumption, the proposed architectures are also optimized for the best configuration in terms of component sizing. Backward-facing (acausal) simulation models were developed to estimate the fuel efficiency of each architecture. Finally, each architecture is compared for optimized performance and fuel saving relative to the traditional HST as baseline.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1986

A hybrid nonlinear programming method for design optimization

Solutions to engineering design problems formulated as nonlinear programming (NLP) problems usually require the use of more than one optimization technique. Moreover, the interaction between the user (analysis/synthesis) program and the NLP system can lead to interface, scaling, or convergence problems. An NLP solution system is presented that seeks to solve these problems by providing a programming system to ease the user-system interface. A simple set of rules is used to select an optimization technique or to switch from one technique to another in an attempt to detect, diagnose, and solve some potential problems. Numerical examples involving finite element based optimal design of space trusses and rotor bearing systems are used to illustrate the applicability of the proposed methodology.

Rajan, S. D.↗
NASA NTRSDate not supplied

Coupled Aeropropulsive Design Optimization of a Podded Electric Propulsor

New aircraft concepts are increasingly relying on non-traditional propulsion systems to achieve lower energy consumption. These non-traditional technologies, such as boundary layer ingestion or distributed electric propulsion, require a tight integration of the propulsion sys- tem to the airframe, and therefore, a traditional design approach where the aerodynamics and propulsion disciplines are considered separately is likely to result in sub-optimal designs. As a result, we have to rely on coupled aeropropulsive design optimization, in which fully coupled aeropropulsive models are optimized to maximize the advantages of these tightly integrated propulsion systems. Despite its advantages, aeropropulsive design optimization is a relatively new field, and significant advancements are required for wide adoption of this approach, especially in the context of robustness. In this work, we introduce two fully coupled aeropropulsive design optimization approaches that are compatible with CFD models with body-force terms and boundary conditions to model the effects of the propulsion system in the flow field. To test these new approaches, we developed a podded electric fan model based on the aft-propulsor of NASA’s STARC-ABL concept. This simple design problem enables us to rapidly develop and test different aeropropulsive coupling approaches, while including the challenging physical interactions that arise from coupling CFD and propulsion models. The coupled aeropropulsive model is implemented using the MPhys library, which is built with NASA’s OpenMDAO frame- work. Using this benchmark design problem, we demonstrate the robustness of the approaches and our aeropropulsive design optimization framework by performing a sweep of optimizations for a total of 50 CFD-based aeropropulsive design optimizations. Furthermore, the new aero- propulsive coupling approaches enable multi-point design optimizations. We demonstrate this capability in a multi-point design optimization problem where we optimize cruise performance subject to a fan-face distortion constraint at rolling take-off conditions. The aeropropulsive modeling approaches we present in this work represent a significant milestone in the field of aeropropulsive design optimization. The framework we developed is extremely robust and flexible, and these developments will be crucial for future aeropropulsive design optimizations of complete turbofan engines.

Optimization propulsion aerodynamics↗
NASA NTRS1993

Progress in multidisciplinary design optimization at NASA Langley

Multidisciplinary Design Optimization refers to some combination of disciplinary analyses, sensitivity analysis, and optimization techniques used to design complex engineering systems. The ultimate objective of this research at NASA Langley Research Center is to help the US industry reduce the costs associated with development, manufacturing, and maintenance of aerospace vehicles while improving system performance. This report reviews progress towards this objective and highlights topics for future research. Aerospace design problems selected from the author's research illustrate strengths and weaknesses in existing multidisciplinary optimization techniques. The techniques discussed include multiobjective optimization, global sensitivity equations and sequential linear programming.

Padula, Sharon L.↗
NASA NTRS1997

Rapid Modeling, Assembly and Simulation in Design Optimization

A new capability for design is reviewed. This capability provides for rapid assembly of detail finite element models early in the design process where costs are most effectively impacted. This creates an engineering environment which enables comprehensive analysis and design optimization early in the design process. Graphical interactive computing makes it possible for the engineer to interact with the design while performing comprehensive design studies. This rapid assembly capability is enabled by the use of Interface Technology, to couple independently created models which can be archived and made accessible to the designer. Results are presented to demonstrate the capability.

Housner, Jerry↗
DOE OSTI2016

Optical properties of InAsBi and optimal designs of lattice-matched and strain-balanced III-V semiconductor superlattices

The optical properties of bulk InAs{sub 0.936}Bi{sub 0.064} grown by molecular beam epitaxy on a (100)-oriented GaSb substrate are measured using spectroscopic ellipsometry. The index of refraction and absorption coefficient are measured over photon energies ranging from 44 meV to 4.4 eV and are used to identify the room temperature bandgap energy of bulk InAs{sub 0.936}Bi{sub 0.064} as 60.6 meV. The bandgap of InAsBi is expressed as a function of Bi mole fraction using the band anticrossing model and a characteristic coupling strength of 1.529 eV between the Bi impurity state and the InAs valence band. These results are programmed into a software tool that calculates the miniband structure of semiconductor superlattices and identifies optimal designs in terms of maximizing the electron-hole wavefunction overlap as a function of transition energy. These functionalities are demonstrated by mapping the design spaces of lattice-matched GaSb/InAs{sub 0.911}Sb{sub 0.089} and GaSb/InAs{sub 0.932}Bi{sub 0.068} and strain-balanced InAs/InAsSb, InAs/GaInSb, and InAs/InAsBi superlattices on GaSb. The absorption properties of each of these material systems are directly compared by relating the wavefunction overlap square to the absorption coefficient of each optimized design. Optimal design criteria are provided for key detector wavelengths for each superlattice system. The optimal design mid-wave infrared InAs/InAsSb superlattice is grown using molecular beam epitaxy, and its optical properties are evaluated using spectroscopic ellipsometry and photoluminescence spectroscopy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Blade planform design optimization to enhance turbine wake control

Abstract This study considers optimizing the planform of wind turbine blades to ultimately enhance wind plant controls, namely, wake steering strategies. Adjoint‐enabled unsteady actuator line simulations are carried out to obtain gradients for optimization of several different performance objectives with respect to blade chord length at 10 locations along blade span. We demonstrate different blade design optimizations that can maximize time‐averaged lateral wake deflection, entrainment of kinetic energy, or total power of multiple turbines. Our optimized designs can produce a 4+% increase in wake deflection, a 4× increase in vertical kinetic energy entrainment, or a 3.6% increase in power when compared with the baseline case. While lateral wake deflection is only modestly sensitive to chord changes, we find that increasing the outboard chord length can dramatically increase kinetic energy entrainment, resulting in faster wake recovery and gains in net power. While this work develops only a few case studies emphasizing relative performance improvements and general trends, these results show the promise of a framework that combines mid‐fidelity computation with adjoint‐based optimization for control and design problems.

WIND ENERGY↗
DOE OSTI2025

Comparison of tungsten versus molybdenum for double shell capsules using machine learning design optimization

Double shell targets are an alternative ignition platform for inertial confinement fusion. One design consideration for double shell targets is the choice of inner shell material to help trap radiation emitted by the hot fuel to aid ignition. Materials such as molybdenum and tungsten are of interest for the inner shell layer of the targets. While molybdenum has a lower density that could inhibit instability growth and allow for radiography and code benchmarking, tungsten has a higher density that could provide more compression and confinement. These tradeoffs have been explored using optimized designs for each material. Our previous work [Vazirani et al., “Coupling 1D xRAGE simulations with machine learning for graded inner shell design optimization in double shell capsules,” Phys. Plasmas 28, 122709 (2021); Vazirani et al., “Coupling multi-fidelity xRAGE with machine learning for graded inner shell design optimization in double shell capsules,” Phys. Plasmas 30, 062704 (2023); and Vazirani et al., “Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design,” Stat. Anal. Data Min. 17, e11698 (2024)] resulted in a multi-fidelity Bayesian optimization framework to find yield-optimized double shell target geometries. By leveraging simulations of varying fidelities (one-dimensional and two-dimensional) to inform one another, the multi-fidelity optimization was able to optimize a design in the highest fidelity with significantly fewer simulations than would be used in a systematic parameter scan. In this work, we apply the multi-fidelity Bayesian optimization to explore the optimized designs of double shell targets with molybdenum and tungsten inner shells as well as the physics producing the high performing implosions. A physics exploration of all the simulations used in this study shows trends in designs that contribute to high yields, ion temperatures, and fuel areal densities. Comparison of molybdenum and tungsten simulations shows that they can produce similar implosion conditions with different geometries, which would be important to study in experiments. Graded density layers produce varying performances with the two materials but continue to be of interest for future studies along with studies of doped inner shell materials and applied surface roughness.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Design optimization of an ethanol heavy-duty engine using design of experiments and bayesian optimization

Diesel-fueled engines still hold a large market share in the medium and heavy-duty transportation sector. However, the increase in fossil fuel prices and the strict emission regulations are leading engine manufacturers to seek cleaner alternatives without a compromise in performance. Alcohol-based fuels, such as ethanol, offer a promising alternative to diesel fuel in meeting regulatory demands. Ethanol provides cleaner combustion and lower levels of soot due to its chemical properties, in particular its lower level of carbon content. In addition, the stoichiometric operating conditions of alcohol fueled engines enable the mitigation of NOx emissions in aftertreatment stage. With the promise of retrofitting diesel engines to run on ethanol to reduce emissions, the thermal efficiency of these engines remains the primary optimization target. In order to find the optimal ethanol-fueled engine design that maximizes the thermal efficiency, a large design space needs to be investigated using engineering tools. In this study, previous research by the authors on optimizing the design of a single-cylinder ethanol-fueled engine was extended to explore the design space for a heavy-duty multi-cylinder engine configuration. A heavy-duty engine setup with multiple operating conditions at different engine speeds and loads were considered. A design optimization analysis was performed to identify the potential designs that maximize the indicated thermal efficiency in an ethanol-fueled compression ignition engine. First, a computational fluid dynamics (CFD) model of the engine was validated using experimental data for four drive cycle points. Using a design of experiments (DoE) approach and a parameterized piston bowl geometry, the model was then exercised to explore the relationship among geometric features of the piston bowl and spray targeting angle and indicated thermal efficiency across all tested operating conditions. After evaluating 165~candidate designs, a piston bowl geometry was identified that yielded an increase between 1.3 to 2.2 percentage points in indicated thermal efficiency for all tested conditions, while satisfying the operational design constraints for peak pressure and maximum pressure rise rate. The increased performance was attributed to enhanced mixing that led to the formation of a more homogeneous distribution of in-cylinder temperature and equivalence ratio, higher combustion temperatures, and shorter combustion duration. Finally, a Bayesian optimization (BOpt) analysis was employed to find the optimal piston bowl geometry with a fixed spray injector angle for one of the operating conditions. Using BOpt, a piston candidate was identified that resulted in a 1.9~percentage point increase in thermal efficiency from the baseline design, yet only required 65\% of the design samples investigated using the DoE approach.

Tekgul, Bulut↗
NASA NTRS2020

Multidisciplinary Design Optimization of Low-Boom Supersonic Aircraft with Mission Constraints

Conceptual design of low-boom supersonic aircraft is heavily dictated by aircraft volume and lift distributions. These unique design characteristics make it a challenge to enforce mission requirements (such as static margins and trim requirements) during design optimization. This low-boom design challenge is resolved by using reversed equivalent area targets for low-fidelity low-boom inverse design and a block coordinate optimization (BCO) method for multidisciplinary design optimization (MDO). The corresponding low-boom MDO problem includes aircraft mission constraints on ranges, cruise speeds, trim for low-boom cruise, static margins for takeoff/cruise/landing, takeoff/landing field lengths, approach velocity, and tail rotation angles for trim at takeoff/landing, as well as fuselage volume constraints for passengers and main gear storage. The BCO method is developed to optimally resolve the conflicts between the low-boom inverse design objective and other design constraints. This method is successfully applied to design a low-boom supersonic configuration that carries 40 passengers, flies a low-boom mission with cruise Mach of 1.6 and range of 2,500 nm, and cruises overwater at Mach 1.8 with range of 3,600 nm. The generated configuration satisfies all specified mission constraints and has the potential to match a reversed equivalent area target with ground noise level below 70 PLdB.

Multidisciplinary design optimization↗
NASA NTRS1974

An assessment of separable fluid connector system parameters to perform a connector system design optimization study

The key to optimization of design where there are a large number of variables, all of which may not be known precisely, lies in the mathematical tool of dynamic programming developed by Bellman. This methodology can lead to optimized solutions to the design of critical systems in a minimum amount of time, even when there are a great number of acceptable configurations to be considered. To demonstrate the usefulness of dynamic programming, an analytical method is developed for evaluating the relationship among existing numerous connector designs to find the optimum configuration. The data utilized in the study were generated from 900 flanges designed for six subsystems of the S-1B stage of the Saturn 1B space carrier vehicle.

Prasthofer, W. P.↗
NASA NTRS1992

Optimal design of compact spur gear reductions

The optimal design of compact spur gear reductions includes the selection of bearing and shaft proportions in addition to gear mesh parameters. Designs for single mesh spur gear reductions are based on optimization of system life, system volume, and system weight including gears, support shafts, and the four bearings. The overall optimization allows component properties to interact, yielding the best composite design. A modified feasible directions search algorithm directs the optimization through a continuous design space. Interpolated polynomials expand the discrete bearing properties and proportions into continuous variables for optimization. After finding the continuous optimum, the designer can analyze near optimal designs for comparison and selection. Design examples show the influence of the bearings on the optimal configurations.

Savage, M.↗
NASA NTRSDate not supplied

Multidisciplinary Design Optimization of Low-Boom Supersonic Aircraft with Mission Constraints

Design of low-boom supersonic aircraft is heavily dictated by aircraft volume and lift distributions. These unique design characteristics make it a challenge to enforce mission requirements (such as static margins and trim requirements) during design optimization. This low-boom design challenge is resolved by using reversed equivalent area targets for low-fidelity low-boom design and a decomposition method for multidisciplinary design optimization(MDO).The corresponding low-boom MDO problem includes aircraft mission constraints for cruise ranges, cruise speeds, trim margin for low-boom cruise, static margins for takeoff/cruise/landing, tail rotation angles for trim at takeoff/landing, takeoff/landing field lengths, and approach velocity, as well as volume constraints for cabin and main landing gear packaging. The decomposition method is developed to optimally resolve the conflicts between the low-boom design objective and other design constraints. The decomposition method is successfully applied to design a low-boom supersonic configuration that carries 40 passengers, has low-boom cruise of Mach 1.6 with range ≥ 2,500 nm, and can also cruise at Mach 1.8 with range ≥ 3,600 nm. The generated configuration satisfies all specified mission constraints and has the potential to achieve a low-boom ground noise level below 75 PLdB.

Wu Li↗
NASA NTRS1993

Formulation for Simultaneous Aerodynamic Analysis and Design Optimization

An efficient approach for simultaneous aerodynamic analysis and design optimization is presented. This approach does not require the performance of many flow analyses at each design optimization step, which can be an expensive procedure. Thus, this approach brings us one step closer to meeting the challenge of incorporating computational fluid dynamic codes into gradient-based optimization techniques for aerodynamic design. An adjoint-variable method is introduced to nullify the effect of the increased number of design variables in the problem formulation. The method has been successfully tested on one-dimensional nozzle flow problems, including a sample problem with a normal shock. Implementations of the above algorithm are also presented that incorporate Newton iterations to secure a high-quality flow solution at the end of the design process. Implementations with iterative flow solvers are possible and will be required for large, multidimensional flow problems.

Hou, G. W.↗
NASA NTRS1989

A decomposition-based design optimization method with applications

A two-level design optimization metholology is described. A progress report of its application to Printed Wiring Board (PWB) assembly examples is given. The design of PWB assemblies is a complex task which is generally conducted as a sequential process. Individual PWBs are usually designed first, followed by the composition of the PWBs into an assembly. As a result, optimizing design considerations such as assembly reliability cannot be accomplished. This study showed that a two-level decomposition method can be employed to optimize for reliability at both the PWB- and the assembly-level in a coupled manner. The two-level decomposition method also resolved the mixed-integer nonlinear programming nature of the problem rather easily.

Azarm, Shapour↗
DOE OSTI2019

Secondary Pore Network Design Optimization Analytical Diffusion Model for Lithium Ion Battery

Fast ion transport along the electrode thickness is required for Lithium ion battery (LIB) to sustain continuous high-rate discharge or charge currents, which is a desired feature for the fast charging of electric vehicle batteries. Advance in LIB electrode manufacturing allows to control, to a certain extent, the electrode microstructure architecture in order to improve the macroscopic performances of these devices. For instance, several groups have experimentally tested architectures tailored specifically to increase the through plane ionic diffusion in regards for fast charge application by adding channels or groove-like large pores oriented along the electrode thickness. These voids do not replace the existing pores of the electrode microstructure but consist in a second, coarser, pore network, called the secondary pore network (SPN). The model quantifies the impact of SPN on ionic diffusion with a composite electrode through a simple analytical approach, considering various pore channel geometries and comparing to standard electrodes with identical gravimetric and identical volumetric specific theoretical capacity. The goal is to find optimal SPN design parameters according to three optimization objectives aiming to balance the improved overall through-plane, thanks to the coarse aligned channels, and the degraded in-plane diffusion, due to the porous matrix densification required to maintain gravimetric and volumetric specific theoretical capacity. The analytical model must be considered as a pre-screening tool to quickly investigate large design space, that can be refined later with more complex electrochemical CPU-expensive numerical models.

Usseglio Viretta, Francois↗