Reusable Liquid Rocket Engine Research Articles

Deterioration effect of creep on fatigue properties in QCr0.8 alloy: Damage mechanism and fatigue-creep life evaluation

The QCr0.8 alloy used in the thrust chamber liner wall of the reusable liquid rocket engine is subjected to high-temperature fatigue and high-temperature creep loading. This work investigates the effect of fatigue-creep interaction (FCI) on the fatigue life of QCr0.8 alloy. Experimental results reveal accelerated cyclic softening and decelerated stress relaxation in the alloy under FCI conditions, attributed to creep deformation and stress amplitude growth. The damage evolution mechanism of FCI is revealed by the results of the energy dispersive spectroscopy (EDS) and scanning electron microscopy (SEM). The decrease in fatigue-creep life of the QCr0.8 alloy is attributed to the fracture of the outer surface oxide layer, as well as the coupling development of creep voids and secondary cracks. Finally, a new fatigue-creep life prediction model has been developed, taking into account the increase in plastic strain energy resulting from stress relaxation and creep deformation behaviors. The study results provide substantial theoretical justification for the utilization of QCr0.8 alloy in the thrust chamber wall of reusable liquid rocket engines.

Improved performance of data-driven simulator of liquid rocket engine under varying operating conditions

Integrating a model-based and data-driven approach to generate sufficient number of training data that include time-varying operating conditions is needed to develop a prognostics and health management technique using machine learning for a reusable rocket engine. A data-driven approach was used to predict the engine behavior, which cannot be determined using a model-based approach. This paper proposes a clustered regression model to predict various operating conditions for a reusable rocket engine. The training data were divided in advance according to the operating conditions using the Gaussian mixture model, and multiple regression models were individually trained on each cluster. By switching the regression models according to the operating conditions, the time-series data comprising various operating conditions, such as the combustion and chill-down phases and transition between phases, were accurately predicted when compared with the prediction results of a single regression model. The prediction results of the present hybrid approach agree reasonably well with the static firing test results of a reusable rocket engine if the explanatory variables are adequately included. By integrating model-based and data-driven approaches, adequate training data can be generated for prognostics and health management of the reusable liquid rocket engine.

An investigation on the cyclic deformation and service life of a reusable liquid rocket engine thrust chamber wall

PurposeRegeneratively cooled thrust chamber is a key component of reusable liquid rocket engines. Subjected to cyclic thermal-mechanical loadings, its failure can seriously affect the service life of engines. QCr0.8 copper alloy is widely used in thrust chamber walls due to its excellent thermal conductivity, and its mechanical and fatigue properties are essential for the evaluation of thrust chamber life. This paper contributes to the understanding of the damage mechanism and material selection of regeneratively cooled thrust chambers for reusable liquid rocket engines.Design/methodology/approachIn this paper, tensile and low-cycle fatigue (LCF) tests were conducted for QCr0.8 alloy, and a Chaboche combined hardening model was established to describe the elastic-plastic behavior of QCr0.8 at different temperatures and strain levels. In addition, an LCF life prediction model was established based on the Manson–Coffin formula. The reliability and accuracy of models were then verified by simulations in ABAQUS. Finally, the service life was evaluated for a regenerative cooling thrust chamber, under the condition of cyclic startup and shutdown.FindingsIn this paper, a Chaboche combined hardening model was established to describe the elastoplastic behavior of QCr0.8 alloy at different temperatures and strain levels through LCF experiments. The parameters of the fitted Chaboche model were simulated in ABAQUS, and the simulation results were compared with the experimental results. The results show that the model has high reliability and accuracy in characterizing the viscoplastic behavior of QCr0.8 alloy.Originality/value(1)The parameters of a Chaboche combined hardening constitutive model and LCF life equation were optimized by tensile and strain-controlled fatigue tests of QCr0.8 copper alloy. (2) Based on the Manson–Coffin formula, the reliability and accuracy of constitutive model were then verified by simulations in ABAQUS. (3)Thermal-mechanical analysis was carried out for regeneratively cooled thrust chamber wall of a reusable liquid rocket engine, and the service life considering LCF, creep and ratcheting damage was analyzed.

Life Analysis of Reusable Liquid Rocket Engine Thrust Chamber

The thrust chamber’s inner wall suffers high temperature and pressure differences from the coolant channel, which limits the life of the rocket engine. Life prediction of the thrust chamber really plays an important role in reusable launch vehicle propulsion systems. The Porowski beam model is widely used in the life prediction of reusable liquid rocket engine thrust chambers, which calculates the life caused by fatigue, creep, and thinning after each firing cycle. In order to analyze the life of the thrust chamber, a LOX/Kerosene rocket engine is investigated in this paper. The life analysis consists of pressure and temperature differences and structural parameters. Two kinds of inner wall materials were chosen for comparison in this research: OFHC copper and Narloy-Z alloy. The results are presented to offer a reference for the design and manufacture of reusable rocket engine thrust chambers in the future.

Model-based supervised sensor placement optimization to detect propellant leak in a liquid rocket engine

The sensor selection for leak detection and diagnosis in a reusable liquid rocket engine was studied. Sufficient amounts of normal and leaked training data were generated by Monte Carlo simulations considering variations in system conditions based on past firing test results. A multivariate supervised analysis, in which measurements are linearly projected onto a vector that characterizes the difference between distributions of normal and leaked cases, successfully detected the simulated leaks that could not be detected by conventional univariate red-line judgment and unsupervised principal component analysis. Significant sensors for leak detection were selected using the greedy approach based on the detection performance score. It was found that only 11 of the 27 sensors were sufficient to maintain the detection performance.

Estimating model parameters of liquid rocket engine simulator using data assimilation

To generate training data for fault detection and diagnosis based on machine learning, a System-Level Simulation (SLS) is developed to model the global behavior of a reusable liquid rocket engine employing the expander-bleed cycle. Model parameters are estimated automatically by an Ensemble Kalman Filter. Parameters are estimated starting from the turbopump component model, followed by the whole rocket engine model, to increase accuracy. Sufficient convergence is obtained within one day by parallelizing the SLS computation for the ensemble members by two. Compared to the static-firing test results, reasonable agreement is obtained within error of 2.3% in the steady-state engine condition. Even for the startup and shutdown sequences, the present SLS reasonably agrees with the test results except for the temperature. Comparing the model parameters obtained for different static-firing test results, the effect of the overhaul inspection on the turbopump is captured as a difference in the model parameters.

Application of fault factor method to fault detection and diagnosis for space shuttle main engine

This paper deals with an application of the multiple linear regression algorithm to fault detection and diagnosis for the space shuttle main engine (SSME) during a steady state. In order to develop the algorithm, the energy balance equations, which balances the relation among pressure, mass flow rate and power at various locations within the SSME, are obtained. Then using the measurement data of some important parameters of the engine, fault factors which reflects the deviation of each equation from the normal state are estimated. The probable location of each fault and the levels of severity can be obtained from the estimated fault factors. This process is numerically demonstrated for the SSME at 104% Rated Propulsion Level (RPL) by using the simulated measurement data from the mathematical models of the engine. The result of the current study is particularly important considering that the recently developed reusable Liquid Rocket Engines (LREs) have staged-combustion cycles similarly to the SSME.

Predictive Fault Diagnosis System for Intelligent and Robust Health Monitoring

This paper describes work related to the Predictive Fault Diagnosis System for Intelligent and Robust Health Monitoring, which exists as a solution to complete failure detection, identification, and prognostics (FDI&P) in health monitoring applications. Several advanced FDI analytical redundancy techniques have been applied for such a purpose, with the most notable being a compound method comprised of optimal filtering, statistical analysis, and neuro-fuzzy algorithms that is able to detect and diagnose both known and unknown failures. Although this scheme has been proven to be quite successful for systems that can be well described in a state space representation, current research has shown the viability in extending the process to highly complex systems with considerable nonlinearities while still maintaining the FDI capabilities. This paper highlights the utility of these algorithms for determining failures in (1) a known reusable liquid rocket engine model and (2) an unknown input-output relation in a fluid flow testbed. Other research has focused on prognostic capabilities provided by a neural architecture enhanced with fuzzy logic using rule-based knowledge. An example of using data to construct fuzzy rules for determining the remaining useful life of components is provided to give insight into the process.