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The cost and fuel consumption per nautical mile for two engine jet transports are computed using OPTIM and TRAGEN. The savings in fuel and direct operating costs per nautical mile for each of the different types of optimal trajectories over a standard profile are shown.
Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.
An integrated system for the multidisciplinary analysis and optimization of airframe and propulsion design parameters is being developed. This system is known as IPAS, the Integrated Propulsion/Airframe Analysis System. The traditional method of analysis is one in which the propulsion system analysis is loosely coupled to the overall mission performance analysis. This results in a time consuming iterative process. First, the engine is designed and analyzed. Then, the results from this analysis are used in a mission analysis to determine the overall aircraft performance. The results from the mission analysis are used as a guide as the engine redesigned and the entire process repeated. In IPAS, the propulsion system, airframe, and mission are closely coupled. The propulsion system analysis code is directly integrated into the mission analysis code. This allows the propulsion design parameters to be optimized along with the airframe and mission design parameters, significantly reducing the time required to obtain an optimized solution.
An integrated system for the multidisciplinary analysis and optimization of airframe and propulsion design parameters is being developed. This system is known as IPAS, the Integrated Propulsion/Airframe Analysis System. The traditional method of analysis is one in which the propulsion system analysis is loosely coupled to the overall mission performance analysis. This results in a time consuming iterative process. First, the engine is designed and analyzed. Then, the results from this analysis are used in a mission analysis to determine the overall aircraft performance. The results from the mission analysis are used as a guide as the engine is redesigned and the entire process repeated. In IPAS, the propulsion system, airframe, and mission are closely coupled. The propulsion system analysis code is directly integrated into the mission analysis code. This allows the propulsion design parameters to be optimized along with the airframe and mission design parameters, significantly reducing the time required to obtain an optimized solution.
A parametric optimization study is performed on the nozzle of a laboratory rotating detonation rocket engine (RDRE) using a three-dimensional computational fluid dynamic simulation. The primary optimization objective is maximum nozzle thrust. The basic nozzle configuration is a shrouded, truncated plug. The fluid in the RDRE chamber leading to the nozzle is choked at its exit so that its cyclic behavior is unaffected by any changes to the nozzle design. Optimization is performed for a single operating point. Parameters varied are the overall nozzle area expansion ratio and the fraction of the expansion area that is provided by the shroud. These two parameters indirectly affect the angle of the plug nozzle cone, and the bluff body area associated with its truncation. Nozzle thrust is evaluated as the difference between the thrust of the RDRE chamber-plus-nozzle combination and that of the chamber alone. The nozzle produces approximately 20% of the total engine thrust. The baseline nozzle is found to perform well, yielding 58.1% of the thrust calculated for a notional ideal RDRE nozzle which can instantaneously change shape to allow isentropic expansion of every fluid element. Optimization improves the performance, bringing the nozzle thrust to 70.0% of the notional ideal, and total engine thrust (chamber-plus-nozzle) to 94% of the ideal.
A parametric optimization study is performed on the nozzle of a laboratory rotating detonation rocket engine (RDRE) using a three-dimensional computational fluid dynamic simulation. The primary optimization objective is maximum nozzle thrust. The basic nozzle configuration is a shrouded, truncated plug. The fluid in the RDRE chamber leading to the nozzle is choked at its exit so that its cyclic behavior is unaffected by any changes to the nozzle design. Optimization is performed for a single operating point. Parameters varied are the overall nozzle area expansion ratio and the fraction of the expansion area that is provided by the shroud. These two parameters indirectly affect the angle of the plug nozzle cone, and the bluff body area associated with its truncation. Nozzle thrust is evaluated as the difference between the thrust of the RDRE chamber-plus-nozzle combination and that of the chamber alone. The nozzle produces approximately 20% of the total engine thrust. The baseline nozzle is found to perform well, yielding 58.1% of the thrust calculated for a notional ideal RDRE nozzle which can instantaneously change shape to allow isentropic expansion of every fluid element. Optimization improves the performance, bringing the nozzle thrust to 70.0% of the notional ideal, and total engine thrust (chamber-plus-nozzle) to 94% of the ideal.
This paper describes the design of a turboshaft engine for a tiltwing air taxi application. In this case, the tiltwing air taxi is intended to fly a 400 nm mission with up to fifteen passengers. Engine requirements for the concept engine are taken from aircraft system studies where thrust is produced by four propellers driven by electric motors and powered by a single gas turbine engine. The purpose of this paper is to perform a cycle design optimization that minimizes fuel consumption and weight while respecting current technology limitations to meet mission requirements. To achieve results, the engine overall pressure ratio and maximum temperature at the exit of the combustor are set as the design parameters. Several sensitivity studies are also performed to visualize optimization trends. Results of the optimization study show solutions are heavily dependent on engine cooling flow requirements and exact mission requirements. This engine is intended for use in large system optimization research.
This paper describes the design of a turboshaft engine for a tiltwing air taxi application. In this case, the tiltwing air taxi is intended to fly a 400-nautical mile mission with up to fifteen passengers. Engine requirements for the concept engine are taken from aircraft system studies where thrust is produced by four propellers driven by electric motors and powered by a single gas turbine engine. The purpose of this paper is to perform a cycle design optimization that minimizes fuel consumption and weight while respecting current technology limitations to meet mission requirements. To achieve results, the engine overall pressure ratio and maximum temperature at the exit of the combustor are set as the design parameters. Several sensitivity studies are also performed to visualize optimization trends. Results of the optimization study show solutions are heavily dependent on engine cooling flow requirements and exact mission requirements. This engine is intended for use in large system optimization research.
The design optimization of a reactor-Stirling heat-pipe-cooled radiator is presented. The radiator is a self-deploying concept that uses individual finned heat pipe 'petals' to reject waste heat from a Stirling engine. Radiator optimization methodology is presented, and the results of a parametric analysis of the radiator design variables for a 100-kW(e) system are given. The additional steps of optiminzing the radiator resulted in a net system mass savings of 3 percent.
This report describes lessons learned in the establishment, execution and termination of a large, multi-institutional consortium, derived from the Co-Optimization of Fuels and Engines experience. The decision to form a consortium comes with benefits (in advancing challenging multidisciplinary research) and costs (in time and additional management funds). Once the decision is made, key elements to a strong start include establishing a shared vision and goals; engaging an experienced project manager early; instituting feedback and oversight mechanisms to ensure relevance, strong performance, and situational awareness. Once a consortium is up and running, DOE and leadership should strike the right balance between competition and collaboration; foster an environment that builds trust; and adjust the organizational structure as needed to maintain collaboration. Finally, DOE and the labs can plan effectively for a smooth transition as a consortium winds down. This report provides some additional lessons and details on these lessons that we hope future DOE and lab leaders will find useful as they contemplate standing up new consortia.
This paper covers the development of a model-based engine control (MBEC) methodology featuring a self tuning on-board model applied to an aircraft turbofan engine simulation. Here, the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) serves as the MBEC application engine. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC over a wide range of operating points. The on-board model is a piece-wise linear model derived from CMAPSS40k and updated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. Investigations using the MBEC to provide a stall margin limit for the controller protection logic are presented that could provide benefits over a simple acceleration schedule that is currently used in traditional engine control architectures.
Abstract The design optimization of a diesel exhaust-coupled heat and mass exchanger that drives a 2.71 kW cooling capacity absorption heat pump is presented in this study. Fouling layer thermal resistance and pressure drops from single-tube experiments are used to develop a thermodynamic, heat transfer, and pressure drop model for the exhaust-coupled desorber. A parametric study is performed to select a desorber design that meets system performance while minimizing footprint. Experimental heat duties and pressure drops are within 10% and 3%, respectively, of the model predictions. Thus, large data sets from single-tube experiments with representative geometries are successful in accounting for fouling effects at the component level. Desorber design optimization based on this approach ensures continued heat pump performance after fouling. This study, along with the single-tube experiments, presents a systematic approach to design exhaust-coupled heat exchangers while considering the effects of fouling. These results are applicable for a wide range of waste-heat recovery applications, and this method can be extended to different geometries and operating conditions.
Progressively increasing regulatory demands on fuel economy and future global emission standards have led to a focus on advanced engine development to improve overall engine efficiency and fulfill emission requirements. Low temperature combustion (LTC) and gasoline compression ignition (GCI) are promising technologies to achieve these goals and have the advantage of using existing refinery infrastructure and subsequent economies of scale for a robust energy supply. By applying spark ignition (SI) for cold start, LTC for low load operations, and GCI for medium to high load operations, a multimode GCI engine concept was proposed and the fuel formation was co-optimized to maximize fuel economy improvement potential while maintaining ULEV 70 emissions standards. HATCI has successfully demonstrated the feasibility of this multimode GCI engine concept, and confirmed the fuel economy improvement over the baseline SI engine by simulating the FTP75 vehicle drive cycle. In this report, the technical approaches, multimode engine control, and engine test results of both steady state and transitions, CFD modeling, fuel testing, and FTP75 drive cycle simulation results are summarized, followed with technical challenges observed, and recommendations for possible follow-up studies.
Optimization of metabolically engineered organisms requires good understanding of producing balanced level of pathway proteins. Targeted proteomics via selected-reaction monitoring (SRM) has been increasingly used in metabolic engineering studies to detect and quantify sets of proteins with high selectivity, multiplexity, and reproducibility. In combination with metabolomics and other omics tools, targeted proteomics has helped optimize the production of many bio-based chemicals in various metabolic engineering cell factories. In this review, we present recent applications of targeted proteomics in metabolic engineering studies and highlight several successful cases of targeted proteomics in boosting production of commodity and high value chemicals. Additionally, we also discuss challenges and limitations of current targeted proteomics and map opportunities for future research.
The document presents recent algorithmic and implementation advances of the two-stage robust optimization (RO) solver PyROS, and a benchmarking study which demonstrates the utility of PyROS for two-stage RO problems. The advances include extensions of the scope of PyROS to models with uncertain variable bounds, improvements to the initializations of the subproblems used by the underlying cutting set algorithm, and extensions of the uncertainty set interfaces. The benchmarking study is performed on a library of over 8,500 instances, with variations in the nonlinearities, degree-of-freedom partitioning, uncertainty sets, and polynomial decision rule approximations. An amine-based CO2 capture case study is presented to demonstrate the utility of PyROS for large-scale process models. Overall, the results highlight the effectiveness of PyROS for obtaining robust solutions to optimization problems with uncertain equality constraints.
Optimizing performance on work activities and processes requires metrics of performance for management to monitor and analyze in order to support further improvements in efficiency, effectiveness, safety, reliability and cost. Information systems are therefore required to assist management in making timely, informed decisions regarding these work processes and activities. Currently information systems regarding Space Shuttle maintenance and servicing do not exist to make such timely decisions. The work to be presented details a system which incorporates various automated and intelligent processes and analysis tools to capture organize and analyze work process related data, to make the necessary decisions to meet KSC organizational goals. The advantages and disadvantages of design alternatives to the development of such a system will be discussed including technologies, which would need to bedesigned, prototyped and evaluated.
A procedure for automatically designing a liquid propellant rocket engine combustion chamber in an optimal fashion is outlined. The procedure is contained in a digital computer code, AUTOCOM. The code is applied to an existing engine, and design modifications are generated which provide a substantial potential payload improvement over the existing design. Computer time requirements for this payload improvement were small, approximately four minutes in the CDC 6600 computer.
This paper describes a method for systematic analysis and optimization of large engineering systems, e.g., aircraft, by decomposition of a large task into a set of smaller, self-contained subtasks that can be solved concurrently. The subtasks may be arranged in many hierarchical levels with the assembled system at the top level. Analyses are carried out in each subtask using inputs received from other subtasks, and are followed by optimizations carried out from the bottom up. Each optimization at the lower levels is augmented by analysis of its sensitivity to the inputs received from other subtasks to account for the couplings among the subtasks in a formal manner. The analysis and optimization operations alternate iteratively until they converge to a system design whose performance is maximized with all constraints satisfied. The method, which is still under development, is tentatively validated by test cases in structural applications and an aircraft configuration optimization. It is pointed out that the method is intended to be compatible with the typical engineering organization and the modern technology of distributed computing.