Diesel Modeler S Guide

Diesel locomotives first began appearing on American railroads more than 65 years ago, and it wasn't long before they began to appear on model railroaders' layouts. Crude by today's standards, those early replicas are now prized collectibles. By the 1950s, injection-molding techniques made it possible to offer fairly accurate models at a reasonable price. The quantity and quality of models have continued to improve, allowing accurate re-creations in each modeler's design.Diesel Modeler's Guide, Volume 2 provides the reader with more useful prototype information, as well as the latest tips, including techniques and insights from noted modelers. The articles are from issues of Model Railroading magazine published in 1996 and 1997, and are grouped by railroad to provide a handy reference guide.

Subtitled: Photos, Drawings and Projects for the Diesel Modeler. This handy guide takes a look at the NW2 and the roads that used them. Contents provide useful prototype information as well as tips, techniques and insights from noted modelers on a variety of locomotives. All featured articles were originally published in 1994 and 1995 issues of Model Railroading magazine. Now they_re available once again in this handy reference guide! Sftbd., 8 1/2_x 11_, 112 pgs., 176 b&w ill., 116 color.

Subtitled: Detailed Instructions and Ideas for Prototype Modeling. Noted modelers provide detailed information about specific construction techniques which will improve your diesel modeling skills... whether you build the models they built or apply their

Learn about prototype engine servicing areas and their functions, and how to model them on any layout. Includes tips and techniques for modeling trackage, structures, and other terminal details appropriate for the steam or diesel era.

This book presents a step-by-step guide to the engine control system design, providing case studies and a thorough analysis of the modeling process using machine learning, and model predictive control (MPC). Covering advanced processes alongside the theoretical foundation, MPC enables engineers to improve performance in both hybrid and non-hybrid vehicles. Control system improvement is one of the major priorities for engineers seeking to enhance an engine. Often possible on a low budget, substantial improvements can be made by applying cutting-edge methods, such as artificial intelligence when modeling engine control system designs and using MPC. This book presents approaches to control system improvement at mid, low, and high levels of control. Beginning with the model-in-the-loop hierarchical control design of ported fuel injection SI engines, this book focuses on optimal control of both transient and steady state and also discusses hardware-in-the-loop. The chapter on low-level control discusses adaptive MPC and adaptive variable functioning, as well as designing a fuel injection feed-forward controller. At mid-level control, engine calibration maps are discussed, with consideration of constraints such as limits on pollutant emissions. Finally, the high-level control methodology is discussed in detail in relation to transient torque control of SI engines. This comprehensive yet clear guide to control system improvement is an essential read for any engineer working in automotive engineering and engine control system design.

This book constitutes the refereed proceedings of the 17th International Conference on Model Driven Engineering Languages and Systems, MODELS 2014, held in Valencia, Spain, in September/October 2014. The 41 full papers presented in this volume were carefully reviewed and selected from a total of 126 submissions. The scope of the conference series is broad, encompassing modeling languages, methods, tools, and applications considered from theoretical and practical angles and in academic and industrial settings. The papers report on the use of modeling in a wide range of cloud, mobile, and web computing, model transformation behavioral modeling, MDE: past, present, future, formal semantics, specification, and verification, models at runtime, feature and variability modeling, composition and adaptation, practices and experience, modeling for analysis, pragmatics, model extraction, manipulation and persistence, querying, and reasoning.