Machine Learning for Identification and Optimal Control of Advanced Automotive Engines.

The complexity of automotive engines continues to increase to meet increasing performance requirements such as high fuel economy and low emissions. The increased sensing capabilities associated with such systems generate a large volume of informative data. With advancements in computing technologies, predictive models of complex dynamic systems useful for diagnostics and controls can be developed using data based learning. Such models have a short development time and can serve as alternatives to traditional physics based modeling. In this thesis, the modeling and control problem of an advanced automotive engine, the homogeneous charge compression ignition (HCCI) engine, is addressed using data based learning techniques. Several frameworks including design of experiments for data generation, identification of HCCI combustion variables, modeling the HCCI operating envelope and model predictive control have been developed and analyzed. In addition, stable online learning algorithms for a general class of nonlinear systems have been developed using extreme learning machine (ELM) model structure.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102392/1/vijai_1.pd

Theory and Design of Flight-Vehicle Engines

Papers are presented on such topics as the testing of aircraft engines, errors in the experimental determination of the parameters of scramjet engines, the effect of the nonuniformity of supersonic flow with shocks on friction and heat transfer in the channel of a hypersonic ramjet engine, and the selection of the basic parameters of cooled GTE turbines. Consideration is also given to the choice of optimal total wedge angle for the acceleration of aerospace vehicles, the theory of an electromagnetic-resonator engine, the dynamic characteristics of the pumps and turbines of liquid propellant rocket engines in transition regimes, and a hierarchy of mathematical models for spacecraft control engines

Aeronautical Engineering: A continuing bibliography with indexes, supplement 153, October 1982

This bibliography lists 535 reports, articles and other documents introduced into the NASA Scientific and Technical Information System in September 1982

Adaptive Machine Learning for Modeling and Control of Non-Stationary, Near Chaotic Combustion in Real-Time.

Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine combustion phasing predictions must contend with non-linear chemistry, non-linear physics, near chaotic period doubling bifurcation(s), turbulent mixing, model parameters that can drift day-to-day, and air-fuel mixture state information that cannot typically be resolved on a cycle-to-cycle basis, especially during transients. Unlike many contemporary modeling approaches, this work does not attempt to solve for the myriad of combustion processes that are in practice unobservable in a metal engine. Instead, this work treads closely to physically measurable quantities within the framework of an abstract discrete dynamical system that is explicitly designed to capture many known combustion relationships, without ever explicitly solving for them. This abstract dynamical system is realized with an Extreme Learning Machine (ELM) that is extended to adapt to the combustion process from cycle-to-cycle with a new Weighted Ring-ELM algorithm. Combined, the above techniques are shown to provide unprecedented cycle-to-cycle predictive capability during transients, near chaotic combustion, and at steady-state, right up to complete misfire. These predictions only require adding an in-cylinder pressure sensor to production engines, which could cost as little as $13 per cylinder. By design, the framework is computationally efficient, and the approach is shown to predict combustion in sub-millisecond real-time using only an iPhone generation 1 processor (the$35 Raspberry Pi). This is in stark contrast to supercomputer approaches that model down to the minutiae of individual reactions but have yet to demonstrate such fidelity against cycle-to-cycle experiments. Finally, the feasibility of cycle-to-cycle model predictive control with this real-time framework is demonstrated.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111333/1/vaughana_1.pd

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Research & Technology 2005

This report selectively summarizes NASA Glenn Research Center's research and technology accomplishments for fiscal year 2005. It comprises 126 short articles submitted by the staff scientists and engineers. The report is organized into three major sections: Programs and Projects, Research and Technology, and Engineering and Technical Services. A table of contents and an author index have been developed to assist readers in finding articles of special interest. This report is not intended to be a comprehensive summary of all the research and technology work done over the past fiscal year. Most of the work is reported in Glenn-published technical reports, journal articles, and presentations prepared by Glenn staff and contractors. In addition, university grants have enabled faculty members and graduate students to engage in sponsored research that is reported at technical meetings or in journal articles. For each article in this report, a Glenn contact person has been identified, and where possible, a reference document is listed so that additional information can be easily obtained. The diversity of topics attests to the breadth of research and technology being pursued and to the skill mix of the staff that makes it possible. For more information, visit Glenn's Web site at http://www.nasa.gov/glenn/. This document is available online (http://www.grc.nasa.gov/WWW/RT/). For publicly available reports, visit the Glenn Technical Report Server (http://gltrs.grc.nasa.gov)

Aeronautical engineering: A continuing bibliography with indexes (supplement 319)

This report lists 349 reports, articles and other documents recently announced in the NASA STI Database. The coverage includes documents on the engineering and theoretical aspects of design, construction, evaluation, testing, operation, and performance of aircraft (including aircraft engines) and associated components, equipment, and systems. It also includes research and development in aerodynamics, aeronautics, and ground support equipment for aeronautical vehicles

Algorithm development on the use of feedback signals in the context of gasoline HCCI combustion

Homogeneous Charge Compression Ignition (HCCI) combustion is a promising research subject due to its characteristics of high efficiency and low emissions. These are highly desirable, given the global picture of increased energy requirements coupled with serious environmental implications. However, one of the main considerations of HCCI implementation is its control strategies which are not straightforward as in conventional Spark Ignition (SI) or Compression Ignition (Cl) engines. In order for closed loop control strategies to be successful, appropriate signals must be selected. In this research, experimental in-cylinder signals have been collected for pressure and ion current. These have been processed and evaluated as regards their suitability for HCCI control. During this process, physical based models have been developed both for treating experimental data as well as simulating theoretical cases. Using these tools, the behaviour of unstable HCCI operation has also been explored