Chemical-Equilibrium Analysis with Adjoint Derivatives for Propulsion Cycle Analysis

Gray, Justin S.; Chin, Jeffrey; Hearn, Tristan A.; Hendricks, Eric S.; Lavelle, Thomas M.; Martins, Joaquim R. R. A.

doi:10.2514/1.b36215

Cited by 29 publications

(21 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first portion of the verification study focused on examining the thermodynamic properties computed by pyCycle throughout the N + 3 engine model. It is important to first note that the core thermodynamic property solver of pyCycle was previously verified by Gray et al [32] via extensive comparisons to the CEA tool. The present study differs from that verification in that it examined the properties determined by a fully converged engine model and therefore verifies the engineering calculations present in each of the cycle elements and balance residual equations.…”

Section: Pycycle Verificationmentioning

confidence: 97%

“…For pyCycle, the thermodynamic properties are computed using a minimization of Gibbs free energy approach very similar to that of Gordon and McBride [30] and consistent with the CEA option in NPSS. Some minor modifications were made to the numerical solution methods of this approach to facilitate computation of analytic derivatives and are documented in prior work by the Gray et al [32]. In comparison to the NPSS implementation of this method, however, pyCycle uses a flattened formulation that exposes all of the chemical equilibrium and thermodynamic residuals to the top-level solver.…”

Section: Physical Equations Of Cycle Analysismentioning

confidence: 99%

“…However, OpenMDAO framework has enabled the usage of analytic derivatives even for modular analyses [31]. In 2017, Gray et al leveraged OpenMDAO to demonstrate the use of analytic derivatives for the efficient optimization of chemical equilibrium thermodynamic models, demonstrating significant computational savings compared to finite-difference approaches [32].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

pyCycle: A Tool for Efficient Optimization of Gas Turbine Engine Cycles

Hendricks

Gray

2019

Aerospace

View full text Add to dashboard Cite

Aviation researchers are increasingly focusing on unconventional vehicle designs with tightly integrated propulsion systems to improve overall aircraft performance and reduce environmental impact. Properly analyzing these types of vehicle and propulsion systems requires multidisciplinary models that include many design variables and physics-based analysis tools. This need poses a challenge from a propulsion modeling standpoint because current state-of-the-art thermodynamic cycle analysis tools are not well suited to integration into vehicles level models or to the application of efficient gradient-based optimization techniques that help to counteract the increased computational costs. Therefore, the objective of this research effort was to investigate the development a new thermodynamic cycle analysis code, called pyCycle, to address this limitation and enable design optimization of these new vehicle concepts. This paper documents the development, verification, and application of this code to the design optimization of an advanced turbofan engine. The results of this study show that pyCycle models compute thermodynamic cycle data within 0.03% of an identical Numerical Propulsion System Simulation (NPSS) model. pyCycle also provides more accurate gradient information in three orders of magnitude less computational time by using analytic derivatives. The ability of pyCycle to accurately and efficiently provide this derivative information for gradient-based optimization was found to have a significant benefit on the overall optimization process with wall times at least seven times faster than using finite difference methods around existing tools. The results of this study demonstrate the value of using analytic derivatives for optimization of cycle models, and provide a strong justification for integrating derivatives into other important engineering analyses.Aerospace 2019, 6, 87 2 of 36 design process with cycle analysis serving as the key analysis method for initializing, guiding the development of, and then ultimately confirming the final engine design [6].Cycle analysis techniques have been studied since the conception of the gas turbine engine in the 1930s. While the fundamental equations governing cycle analysis are rooted in the laws of thermodynamics for a steady one-dimensional flow, the implementation of these equations has changed substantially over the years. These different implementations, predominantly in the form of various computer codes, can be organized into several major eras as shown in Figure 1. This history was pieced together from several sources [6][7][8] with incomplete or conflicting information, so it should be noted that the dates defining the start and end of these eras are approximate.

show abstract

Section: Pycycle Verificationmentioning

confidence: 97%

Section: Physical Equations Of Cycle Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

pyCycle: A Tool for Efficient Optimization of Gas Turbine Engine Cycles

Hendricks

Gray

2019

Aerospace

View full text Add to dashboard Cite

show abstract

“…The gas turbine portion of the propulsion system was modeled using a new analysis tool for thermodynamic cycle analysis. The code, called pyCycle [24,25], was selected for this study as it is also built on top of the OpenMDAO framework. pyCycle differs from other thermodynamic cycle analysis tools such as NPSS in that it provides analytic derivatives to better support gradient-based optimization of the gas turbine as part of larger multidisciplinary optimization studies.…”

Section: Thermodynamic Cycle Analysismentioning

confidence: 99%

Multidisciplinary Optimization of a Turboelectric Tiltwing Urban Air Mobility Aircraft

Hendricks

Falck

Gray

et al. 2019

AIAA Aviation 2019 Forum

View full text Add to dashboard Cite

Urban air taxis, also known as urban air mobility (UAM) vehicles, are anticipated to be an area of significant market growth in the near future. These vehicles are typically vertical takeoff and landing (VTOL) designs which are capable of carrying 1 to 30 passengers in an intra-urban environment with flights of less than 50 nautical miles. Development of UAM vehicles and their integration into the airspace will be enabled by advancements in a number of areas including electrified propulsion systems, structures, acoustics, automation, and controls. However, the strong multidisciplinary interactions for these unique vehicles presents a significant new design challenge. This work describes the development of a multidisciplinary analysis and optimization environment which can be used to support the conceptual design of these UAM vehicles, using efficient gradient based optimization with analytic derivatives. The tools included in this multidisciplinary analysis model the aircraft trajectory, vehicle aerodynamics, structures, and electrified propulsion system. The multidisciplinary environment created in this research is unique in that all the physics tools are tightly integrated together, with the trajectory model directly calling the aerodynamics, structures, and propulsion models. This multidisciplinary analysis environment is then demonstrated in the design optimization of a turboelectric tiltwing UAM vehicle concept.

show abstract

“…Thermodynamic properties of the flow at various points are determined based on the chemical equilibrium of a fuel air mixture calculated using equations of Gibbs free energy. 13 Mass flow through components is calculated within certain elements and a Newton solver is then used to converge the total system.…”

Section: Turboshaft Descriptionmentioning

confidence: 99%

Multi-point Design and Optimization of a Turboshaft Engine for Tiltwing Turboelectric VTOL Air Taxi

Chapman

2019

AIAA Scitech 2019 Forum

View full text Add to dashboard Cite

Chemical-Equilibrium Analysis with Adjoint Derivatives for Propulsion Cycle Analysis

Cited by 29 publications

References 42 publications

pyCycle: A Tool for Efficient Optimization of Gas Turbine Engine Cycles

pyCycle: A Tool for Efficient Optimization of Gas Turbine Engine Cycles

Multidisciplinary Optimization of a Turboelectric Tiltwing Urban Air Mobility Aircraft

Multi-point Design and Optimization of a Turboshaft Engine for Tiltwing Turboelectric VTOL Air Taxi

Contact Info

Product

Resources

About