SEARCH · Engineering Papers

Results for “engine optimization”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Exploitation of a Validation Hierarchy for Modeling and Simulation

Across engineering there is an evolving need to increase reliance on physics-based simulation to develop, design and optimize engineering systems. This increased reliance on modeling and simulation has highlighted a growing need to transform the confidence that modeling and simulation analysts have in their results into credibility for systems engineers to design and field systems more quickly and with less physical testing. For isolated components of a complex system, where a single discipline may drive product design, this is less of a concern as the relationship is often straightforward and easy to explain. However, when these isolated components are integrated, and are expected to operate in a multi-disciplinary context in which safety critical systems are involved, new concepts and model assurance standards are required. In this paper we address this challenge by showing how a model validation hierarchy can be exploited to identify those model validation experiments that will contribute most to increasing confidence and credibility of modeling and simulation predictions. The approach that is adopted contains four main steps. The first step is the construction of a model validation hierarchy that links subsystems, assemblies, and components to a hierarchy of physical experiments that can be used support model validation. This hierarchy connects the concerns of systems engineers to those of the modeling and simulation analyst in a clear and logical way. The structure and content of this hierarchy is then used in a second step to establish which physical phenomena have the greatest impact on overall system performance metrics. A gap analysis technique, based upon modeling and simulation concerns, is then used to prioritize the important physical phenomenon. Unfortunately, a common outcome of such gap analyses is the identification of many important gaps and so, in the final step of our process, we advocate the use of a global sensitivity analysis as a means to complete the prioritization.

Verification and Validation↗

NASA NTRS2019

A Mixed Integer Efficient Global Optimization Algorithm with Multiple Infill Strategy - Applied to a Wing Topology Optimization Problem

With the advancement in high performance computing and numerical optimization techniques,engineering design optimization problems are becoming more complex, larger scale,higher fidelity, and computationally more demanding, requiring longer run times than ever before. There exists methodologies and techniques that can address some of these challenges but very few can address all, and most are limited in the extent that these concerns can be addressed. With the goal of addressing such challenging engineering problems, we developed anew optimization framework, named AMIEGO, that combines concepts from surrogate-based optimization approaches, gradient-based numerical methods, Partial Least Squares, evolutionary algorithms, and Branch-and-Bound, providing newer capabilities that were not previouslyperceived. However, the original version of this framework, in the process of adaptive samplingto explore and exploit the design space, finds only a single sample point per iteration. The efforthere builds upon this previously developed optimization framework to include multiple infillsampling capability that combines the concept of generalized expected improvement function,unsupervised learning, and multi-objective evolutionary technique. To demonstrate, AMIEGOwith the multiple infill capability (called AMIEGO-MIMOS) solves a series of increasingly difficultengineering design optimization problems. The results reveal the performance of the newapproach is problem dependent. When applied to a ten-bar truss problem, the newly proposedmultiple infill strategy consistently leads to a better design solutions when compared to theexisting CPTV method (implemented with the context of the AMIEGO framework). On theother hand, when applied to a mixed-integer high fidelity wing topology optimization problem- MIMOS, despite showing a steeper convergence at the start, eventually leads to an inferiorsolution as compared to CPTV approach. These results also reveal that a small number ofstarting points, in general, are sufficient to lead to a good overall solution.

Mixed-integer optimization↗

NASA NTRS1999

Further Development, Support and Enhancement of CONDUIT

From the first airplanes steered by handles, wheels, and pedals to today's advanced aircraft, there has been a century of revolutionary inventions, all of them contributing to flight quality. The stability and controllability of aircraft as they appear to a pilot are called flying or handling qualities. Many years after the first airplanes flew, flying qualities were identified and ranked from desirable to unsatisfactory. Later on engineers developed design methods to satisfy these practical criteria. CONDUIT, which stands for Control Designer's Unified Interface, is a modern software package that provides a methodology for optimization of flight control systems in order to improve the flying qualities. CONDUIT is dependent on an the optimization engine called CONSOL-OPTCAD (C-O). C-O performs multicriterion parametric optimization. C-O was successfully tested on a variety of control problems. The optimization-based computational system, C-O, requires a particular control system description as a MATLAB file and possesses the ability to modify the vector of design parameters in an attempt to satisfy performance objectives and constraints specified by the designer, in a C-type file. After the first optimization attempts on the UH-60A control system, an early interface system, named GIFCORCODE (Graphical Interface for CONSOL-OPTCAD for Rotorcraft Controller Design) was created.

Veronica, Moldoveanu↗

DOE OSTI2022

MODELING AND PARAMETRIC STUDY OF END-GAS AUTOIGNITION TO ALLOW THE REALIZATION OF ULTRA-LOW EMISSIONS, HIGH-EFFICIENCY HEAVY-DUTY SPARK-IGNITED NATURAL GAS ENGINES

Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas (NG) engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by the low emission limits and the wide range of chemical reactivity in pipeline-quality natural gas. Depending on the region and the source of the natural gas, its reactivity, described by its Methane Number (MN), which is analogous to the Octane Number for liquid SI fuels, can span from 65 to 95. In order to realize diesel-like efficiencies, SI NG engines must be designed to operate at high Brake Mean Effective Pressures (BMEP), near or beyond knock limits, over a wide range of fuel reactivity. This requires a deep understanding of the combustion-engine interactions pertaining to flame propagation and End-Gas Autoignition (EGAI), i.e., the autoignition of the unburned gas (end gas) ahead of the flame front. However, EGAI, if controlled, provides an opportunity to increase SI NG engine efficiency by increasing the combustion rate and the total fraction of burned fuel, mitigating the effects of the slow flame speeds characteristic of natural gas fuels, which generally reduce BMEP and increase unburned hydrocarbon emissions. For this reason, to realize diesel-like efficiencies and ultra-low emissions on SI NG engines, this work proposes the study of the main parameters influencing the modeling and prediction of NG EGAI to allow for its control. In this work, a novel EGAI detection and onset determination method was developed to reliably quantify EGAI for data analysis and engine control. The new method allowed the prediction of EGAI on SI NG engines without the need to use engine- and operating-condition-dependent thresholds and reduced the error in quantifying the fraction of the total energy released by the EGAI event by up to 40%pts. One- and three-dimensional engine models were then developed to study the engine/fuel interactions that lead to NG EGAI and its performance benefits. These models, although having decent agreement with experimental data, showed the need to account for NOx chemistry when predicting NG EGAI due to a consistently later prediction of the EGAI onset (~1.65 crank-angle degrees) and thus, a new reduced chemical mechanism for real NG fuels was developed containing NOx chemistry. The new reduced mechanism improved the EGAI onset prediction agreement to within ±0.5 crank-angle degrees and decreased simulation time during combustion by nearly 50% when using the further reduced AREIS50NOx chemical mechanism. These models were then used to study the role of NG composition on EGAI, evaluate the engine/fuel interactions leading to NG EGAI, and perform engine optimization while leveraging EGAI to increase thermal efficiency. Piston design optimization combined with a Controlled EGAI (C-EGAI) combustion mode allowed a Heavy-Duty (HD) SI NG engine to operate at diesel-like efficiencies, i.e., Brake Thermal Efficiency (BTE) ≥44%. Experimental and modeling data analysis revealed that earlier and faster heat release increases combustion efficiency by an average of 1%pts, increases work transferred to the piston resulting in a decrease in exhaust losses by 50% depending on the engine operating condition while slightly increasing heat losses. Finally, the simulation results revealed an opportunity to further enhance the BTE (up to 50%) by enabling C-EGAI combustion at leaner conditions, λ=1.4-1.6.

Bestel, Diego Bernardi↗

DOE OSTI2021

beluga

beluga is an open-source optimal control software that uses publicly available, indirect methods from GitHub. beluga's indirect optimization engine has been reworked to follow a lazy functional paradigm. This improves the software's flexibility and enables users to create their own optimization processes without modification of the source code. beluga is general-purpose and therefore very broad in scope and application. SAND2020-12847 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Sparapany, Michael↗

NASA NTRS1991

Thick thermal barrier coatings for diesel components

An engineered thick thermal barrier coating consisting of multiple layers of zirconia and CoCrAlY with a zirconia top layer and having a system thermal conductance less than 410 w/m(exp 2)K exceeded the 100 hour engine durability goals set forth in this program. The thermal barrier coatings were intact at the test conclusion. Back to back single cylinder research engine tests were conducted with watercooled, metal hardware and oil-cooled, thermal barrier coating insulated hardware to determine apparent heat release and fuel economy. Apparent heat release data revealed that the insulated engine had a shorter ignition delay and a longer combustion duration than the metal engine. The insulated engine fuel economy was approximately two percent worse on average for this series of tests. There was no attempt to optimize engine efficiency of the insulated engine by modifying the engine timing, coating, or other techniques.

Yonushonis, T. M.↗

NASA NTRS1972

Asymptotic solution to the problem of optimal low-thrust energy increase.

Consideration of the problem of optimal ascent from an initial circular planetary orbit to some specified final energy level by a spacecraft equipped with a low-thrust engine. Optimal is defined as minimum time. An accurate small parameter perturbation solution is presented, and the optimal trajectory is analyzed and compared with a tangential thrust trajectory.

Jacobson, R. A.↗

NASA NTRS1993

On the estimation algorithm used in adaptive performance optimization of turbofan engines

The performance seeking control algorithm is designed to continuously optimize the performance of propulsion systems. The performance seeking control algorithm uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated engine deviation parameters may not reflect the engine's actual off-nominal condition. This factor has a necessary impact on the overall performance seeking control scheme exacerbated by the open-loop character of the algorithm. The effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the performance seeking control algorithm to an F100 engine. An equivalence relation between the biases and engine deviation parameters stems from an observability study; therefore, it is undecided whether the estimated engine deviation parameters represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.

Espana, Martin D.↗

NASA NTRS1991

Research on inverse, hybrid and optimization problems in engineering sciences with emphasis on turbomachine aerodynamics: Review of Chinese advances

Advances in inverse design and optimization theory in engineering fields in China are presented. Two original approaches, the image-space approach and the variational approach, are discussed in terms of turbomachine aerodynamic inverse design. Other areas of research in turbomachine aerodynamic inverse design include the improved mean-streamline (stream surface) method and optimization theory based on optimal control. Among the additional engineering fields discussed are the following: the inverse problem of heat conduction, free-surface flow, variational cogeneration of optimal grid and flow field, and optimal meshing theory of gears.

Liu, Gao-Lian↗

NASA NTRS1992

A system approach to aircraft optimization

Mutual couplings among the mathematical models of physical phenomenon and parts of a system such as an aircraft complicate the design process because each contemplated design change may have far reaching consequences throughout the system. This paper outlines techniques for computing these influences as system design derivatives useful in both judgmental and formal optimization purposes. The techniques facilitate decomposition of the design process into smaller, more manageable tasks and they form a methodology that can easily fit into existing engineering optimizations and incorporate their design tools.

Sobieszczanski-Sobieski, Jaroslaw↗

NASA NTRS1992

Multidisciplinary design and optimization

Mutual couplings among the mathematical models of physical phenomena and parts of a system such as an aircraft complicate the design process because each contemplated design change may have a far reaching consequence throughout the system. This paper outlines techniques for computing these influences as system design derivatives useful to both judgmental and formal optimization purposes. The techniques facilitate decomposition of the design process into smaller, more manageable tasks and they form a methodology that can easily fit into existing engineering optimizations and incorporate their design tools.

Sobieszczanski-Sobieski, Jaroslaw↗

DOE OSTI2026

Classical-Quantum Algorithm for Solving Stochastic Programs

Stochastic programming provides a rigorous mathematical framework for making decisions under uncertainty in a risk-aware manner. Two-stage stochastic programming is, perhaps, the simplest form of this framework. Here the first-stage variables represent decisions that must be made "here and now" in the face of uncertainty, while the second-stage variables are decisions made after uncertain events. However, the broad adoption of stochastic programming has been hindered by computational challenges caused by the two-stage stochastic programming formulation which requires solving an ensemble of optimization problems. Using quantum amplitude estimation (QAE), quantum computers have shown the theoretic ability to compute expectations with Monte-Carlo methods with quadratically fewer samples than classical methods. In this work, we present a quantum algorithm for computing the expectation term using QAE for given first-stage decisions. Further, we detail methods of computing gradient information from the quantum calculation enabling the application of classical gradient-based optimization techniques. The result is a classical-quantum hybrid method of solving two-stage stochastic programs. These techniques are demonstrated with computational experiments based an engineering optimization problem.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1982

QCSEE under-the-wing engine acoustic data

Both an over-the-wing (OTW) and an under-the-wing (UTW) experimental engine are discussed. The UTW engine had a variable-geometry fan exhaust nozzle and a variable-pitch fan that provided quick-response reverse thrust capability. An automatic digital control enabled optimal engine operation under all steady-state conditions as well as during forward and reverse thrust transient operation. The engine was tested at the Engine Noise Test Facility alone and with wind and flap segments to simulate an installation on a short-haul transport aircraft. The engine acoustic configuration was varied to give 14 test configurations. All of the acoustic test results from the UTW program at Lewis are presented as 1/3-octave-band sound pressure level (SPL) tabulations for all of the test points and some narrow-band spectra and 1/3-octave-band data plots for selected conditions.

Bloomer, H. E.↗

DOE OSTI2020

ENGINE COMBUSTION SYSTEM OPTIMIZATION USING CFD AND MACHINE LEARNING: A METHODOLOGICAL APPROACH

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Algorithm (ML-GGA) approach was developed to optimize the performance of internal combustion engines. Machine learning (ML) offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine learning Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variables limits extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a Research Octane Number (RON) of 80. The ML-GGA approach yielded > 2% improvements in the merit function, compared to the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared to traditional approaches.

engine optimization↗

DOE OSTI2022

The Evolution of LiNi 0.5 Mn 0.3 Co 0.2 O 2 Particle Damage from Fast Charging in Optimized, Full Li-Ion Cells

Fast charging batteries are critical to the widespread adoption of electric vehicles to compete with refueling times of combustion-based vehicles. In the near term, adapting current commercial battery technologies to perform better under fast charging conditions through engineering optimizations will greatly expedite the process while exploratory fast-charging electrode materials are being pursued. To do so, the degradation modes in optimized Li-ion batteries need to be completely explored to understand fast charging limits while maintaining a high energy density and a long cycle life. While lithium plating on graphite still remains a challenge, cathode degradation also plays a key role in battery performance. In this work, we used nano- and micro-X-ray computed tomography to characterize the mechanical degradation of LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC532) in optimized Li-ion batteries cycled at three rates, 1C, 6C, and 9C, and at different stages of cycle life, 225 and 600 cycles. We report despite using a conservative upper voltage cutoff limit aimed to minimize extensive cathode degradation, higher charging rates and increased cycling caused the polycrystalline NMC532 particles to fracture and pulverize, which likely drives cathode capacity fade and contributes to the decrease in overall cell performance.

25 ENERGY STORAGE↗

NASA NTRS1993

On the estimation algorithm for adaptive performance optimization of turbofan engines

The performance seeking control (PSC) algorithm is designed to continuously optimize the performance of propulsion systems. The PSC algorithm uses a nominal propulsion system model and estimates, in flight, the engine deviation parameters (EDPs) characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated EDPs may not reflect the engine's actual off-nominal condition. This factor has a direct impact on the PSC scheme exacerbated by the open-loop character of the algorithm. In this paper, the effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the PSC algorithm to an F100 engine. An equivalence relation between the biases and EDPs stems from the analysis; therefore, it is undecided whether the estimated EDPs represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.

Espana, Martin D.↗

DOE OSTI2021

Generation of Pseudomonas putida KT2440 Strains with Efficient Utilization of Xylose and Galactose via Adaptive Laboratory Evolution

While Pseudomonas putida KT2440 has great potential for biomass-converting processes, its inability to utilize the biomass abundant sugars xylose and galactose has limited its applications. Here, in this study, we utilized Adaptive Laboratory Evolution (ALE) to optimize engineered KT2440 with heterologous expression of xylD encoding xylonate dehydratase from Caulobacter crescentus and galETKM encoding UDP-glucose 4-epimerase, galactose-1-phosphate uridylyltransferase, galactokinase, and galactose-1-epimerase from Escherichia coli K-12 MG1655. Poor starting strain growth (<0.1 h –1 or none) was evolutionarily optimized to rates of up to 0.25 h –1 on xylose and 0.52 h –1 on galactose. Whole-genome sequencing, transcriptomic analysis, and growth screens revealed significant roles of kguT encoding a 2-ketogluconate operon repressor and 2-ketogluconate transporter, and gtsABCD encoding an ATP-binding cassette (ABC) sugar transporting system in xylose and galactose growth conditions, respectively. Finally, we expressed the heterologous indigoidine production pathway in the evolved and unevolved engineered strains and successfully produced 3.2 g/L and 2.2 g/L from 10 g/L of either xylose or galactose in the evolved strains whereas the unevolved strains did not produce any detectable product. Thus, the generated KT2440 strains have the potential for broad application as optimized platform chassis to develop efficient microorganism-based biomass-utilizing bioprocesses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Triton Initiative: FY22 Communications, Outreach, and Engagement End-of-Year Report

The Department of Energy (DOE) Water Power Technologies Office (WPTO) Triton Initiative supports the advancement of the marine energy (ME) industry through environmental monitoring research and technology development. This report presents the results and analysis of the Triton Initiative’s communications, outreach, and engagement (TCOE) efforts in fiscal year 2022. The primary TCOE goals were to: (1) educate and raise awareness of ME and the role of Triton's environmental monitoring research in supporting the industry; (2) build trust with audiences through transparent communications and outreach; and (3) evaluate and refine TCOE tactics based on feedback and metrics. To support these goals, the TCOE team used multiple platforms and approaches. Notable achievements include: (1) 12 newsletter issues sent to 185 subscribers with an average open rate of 56.4%. (2) 10 Triton Stories, resulting in 2,871 collective views contributing to 46% of all Triton website views. (3) 78 Triton-specific social media posts, which generated a total of 81,896 impressions, 1,037 post-clicks, and 7,814 video views. (4) 6,320 Triton website views with increased search engine optimization ranking in several categories. (5) 3 Triton researchers interviewed as guests on two different podcasts, Water Women and Big Deep: An Ocean Podcast. (6) A Triton special issue of the Journal of Marine Science and Engineering (JMSE) featuring 10 peer reviewed publications. All articles ranked in the top 25%, and two in the top 5%, for digital attention of all research outputs scored by Altmetric. (7) A seven-part webinar series called Triton Talks to share and discuss research and results from the research published in the JMSE special issue. Triton leveraged unique opportunities, particularly the JMSE special issue, to disseminate research results to end users, educate stakeholders, and gain valuable feedback. These concerted communication efforts increased exposure across platforms ultimately resulting in greater reach and access across audiences. Based on audience analyses of webinar attendees and newsletter subscribers, the TCOE team was able to successfully engage with general audiences, research partners, and ME stakeholders, including subject matter experts from government agencies, research organizations, and the regulatory community. The TCOE task established channels to gather input and create opportunities for two-way communication with people engaged with Triton’s outreach efforts. The feedback received will enable the TCOE team to support its goals for ongoing evaluation of outreach and engagement tools while continuing to build trust in the ME community and educate diverse audiences about the impactful research conducted by the Triton Initiative. This report is a Triton Initiative Fiscal Year 2022 Quarter 4 Milestone Deliverable due to the DOE WPTO Sponsor on 9/30/2022. An updated version to include data through 9/30/2022 will be delivered in October 2022.

16 TIDAL AND WAVE POWER↗