How Should Life Support Be Modeled and Simulated?

Why do most space life support research groups build and investigate large models for systems simulation? The need for them seems accepted, but are we asking the right questions and solving the real problems? The modeling results leave many questions unanswered. How then should space life support be modeled and simulated? Life support system research and development uses modeling and simulation to study dynamic behavior as part of systems engineering and analysis. It is used to size material flows and buffers and plan contingent operations. A DoD sponsored study used the systems engineering approach to define a set of best practices for modeling and simulation. These best practices describe a systems engineering process of developing and validating requirements, defining and analyzing the model concept, and designing and testing the model. Other general principles for modeling and simulation are presented. Some specific additional advice includes performing a static analysis before developing a dynamic simulation, applying the mass and energy conservation laws, modeling on the appropriate system level, using simplified subsystem representations, designing the model to solve a specific problem, and testing the model on several different problems. Modeling and simulation is necessary in life support design but many problems are outside its scope

Mechanical Engineering Design: Going over the Analysis-Synthesis Mountain to Seed Creativity

This chapter advocates and exemplifies a change in delivering mechanical engineering design (MED) to undergraduate students. It looks at, and critiques the current delivery mode which treats MED as an extension of Natural and Engineering Science, through its bias for analysis of existing systems. It is argued that even though students’ innovativeness might be getting slightly enhanced, their creativity is stunted by the mode. So, is their understanding of how machines evolve from human needs, and of how non science related issues affect the evolution. A new teaching approach which attempts to align student thinking and learning activities with what exists in industrial MED is suggested. In this approach, human needs drive engineering problem formulation, which in turn, precipitates a synthesis of machines, mechanisms and constituent elements to satisfy the needs in a regulated environment. The regulation obeys laws of science but is mostly, ‘Humanities’—constrained. Creativity and innovation case studies are given, and it is shown how new machines can come into existence in the course of learning MED. This would be difficult in the current delivery mode. The new mode, of synthesis followed by iterative analysis, helps students build self-confidence and prepares them better for industry

The physics behind systems biology

Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’) experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from ist Theoretical Physics foundation

The economics of the space station

Space exploration and development are naturally conducted on the cutting edge of science and technology. Such efforts inevitably involve decisions made in the presence of extensive uncertainty. For some projects, particularly those which involve the creation and maintenance of an infrastructure, the emphasis is switching from specific engineering goals (for example, a man on the moon by 1969) to more diffuse, continuing, multiple-dimensional goals. This is especially true of the space station, which is envisioned as both a vital link in the exploration of the planets and a major facility for the advancement of commercial efforts in space. The combination of uncertainty and diffuse, long-term goals fundamentally alters the viability and validity of traditional economic and engineering approaches to the management of large public research and development projects. It has become popular to call into question the recent management of continuing projects like the space shuttle or major new weapons systems. We must, however, recognize that cost overruns, gold plating and other forms of apparent mismanagement are usually not the result of individual venality and misbehavior but only the natural outcomes of the existing organizational rules of the game. Just as the performance of an engineering design is guided by the laws of physics, the performance of an organizational design is guided by the laws of behavior. This fact means that to improve performance we cannot simply add more or better manpower; rather, we must look for new organizational solutions. There are many ad hoc opinions about how to do this; what I propose is a more systematic, scientific approach. This paper examines some of the economic and management issues which must be addressed if the space station is to effectively and efficiently pursue the myriad goals that have been chosen for it. I characterize and evaluate in a somewhat stylized fashion three possible policies: an "engineering" approach, an "economics" approach, and a systematic custom design approach. I will use the space station as an example to highlight some of the major economic issues facing large-scale multipurpose research and development efforts, the analytical capabilities we now have to address these issues, and the (non-engineering) research that needs to be done to advance the successful long-term development of space

Stochastic System Design and Applications to Stochastically Robust Structural Control

The knowledge about a planned system in engineering design applications is never complete. Often, a probabilistic quantification of the uncertainty arising from this missing information is warranted in order to efficiently incorporate our partial knowledge about the system and its environment into their respective models. In this framework, the design objective is typically related to the expected value of a system performance measure, such as reliability or expected life-cycle cost. This system design process is called stochastic system design and the associated design optimization problem stochastic optimization. In this thesis general stochastic system design problems are discussed. Application of this design approach to the specific field of structural control is considered for developing a robust-to-uncertainties nonlinear controller synthesis methodology. Initially problems that involve relatively simple models are discussed. Analytical approximations, motivated by the simplicity of the models adopted, are discussed for evaluating the system performance and efficiently performing the stochastic optimization. Special focus is given in this setting on the design of control laws for linear structural systems with probabilistic model uncertainty, under stationary stochastic excitation. The analysis then shifts to complex systems, involving nonlinear models with high-dimensional uncertainties. To address this complexity in the model description stochastic simulation is suggested for evaluating the performance objectives. This simulation-based approach addresses adequately all important characteristics of the system but makes the associated design optimization challenging. A novel algorithm, called Stochastic Subset Optimization (SSO), is developed for efficiently exploring the sensitivity of the objective function to the design variables and iteratively identifying a subset of the original design space that has v i high plausibility of containing the optimal design variables. An efficient two-stage framework for the stochastic optimization is then discussed combining SSO with some other stochastic search algorithm. Topics related to the combination of the two different stages for overall enhanced efficiency of the optimization process are discussed. Applications to general structural design problems as well as structural control problems are finally considered. The design objectives in these problems are the reliability of the system and the life-cycle cost. For the latter case, instead of approximating the damages from future earthquakes in terms of the reliability of the structure, as typically performed in past research efforts, an accurate methodology is presented for estimating this cost; this methodology uses the nonlinear response of the structure under a given excitation to estimate the damages in a detailed, component level