Correlation between Thermodynamic Efficiency and Ecological Cyclicity for Thermodynamic Power Cycles

Weissburg M (2012) Correlation between Thermodynamic Efficiency and Ecological Cyclicity for Thermodynamic Power Cycles. PLoS ONE 7(12): e51841. doi:10.1371/journal.pone.0051841 Correlation between Thermodynamic Efficiency and Ecological Cyclicity for Thermodynamic Power Cycles Astrid Layton 0 John Reap 0 Bert Bras 0 Marc Weissburg 0 Vishal Shah, Dowling College, United States of America 0 1 George W. Woodruff School of Mechanical Engineering, Sustainable Design and Manufacturing, Georgia Institute of Technology , Atlanta , Georgia , United States of America, 2 School of Business and Engineering, Quinnipiac University , Hamden , Connecticut, United States of America, 3 School of Biology, Georgia Institute of Technology , Atlanta , Georgia , United States of America, 4 Center for Biologically Inspired Design, Georgia Institute of Technology , Atlanta, Georgia , United States of America A sustainable global community requires the successful integration of environment and engineering. In the public and private sectors, designing cyclical (''closed loop'') resource networks increasingly appears as a strategy employed to improve resource efficiency and reduce environmental impacts. Patterning industrial networks on ecological ones has been shown to provide significant improvements at multiple levels. Here, we apply the biological metric cyclicity to 28 familiar thermodynamic power cycles of increasing complexity. These cycles, composed of turbines and the like, are scientifically very different from natural ecosystems. Despite this difference, the application results in a positive correlation between the maximum thermal efficiency and the cyclic structure of the cycles. The immediate impact of these findings results in a simple method for comparing cycles to one another, higher cyclicity values pointing to those cycles which have the potential for a higher maximum thermal efficiency. Such a strong correlation has the promise of impacting both natural ecology and engineering thermodynamics and provides a clear motivation to look for more fundamental scientific connections between natural and engineered systems. 1.1 Motivation: Ecology and Industrial Networks A sustainable global community, one that meets the needs of the current generation without sacrificing those of future generations [1] requires the successful integration of environment and engineering. In the public and private sectors, designing cyclical (closed loop) resource networks increasingly appears as a strategy employed to improve resource efficiency and reduce environmental impacts [2,3]. Multiple structural and material flow metrics that one might use to aid in network design exist [4]. These metrics quantify commonsense imperatives to reduce and reuse, but they contain limited, if any, information about sustainable thresholds. Some metrics even hold the potential to mislead [5]. One approach that can improve the efficient use of resources at multiple levels and simultaneously meet sustainable thresholds involves patterning industrial networks on ecological ones [4,6,7]. Decades ago, the potential for transferring ecological principles to human systems was recognized as a way to increase the efficient use of energy and resources and reduce waste [8]. In 1989 Frosch and Gallopoulos proposed to convert the traditional manufacturing model, one composed of linear industrial chains of activities, to an integrated model they deemed an Industrial Ecosystem [9]. Such a system would use lessons learned from biology to optimize the use of raw materials and energy while minimizing waste through the redefining of effluents as raw material for neighboring processes. Since then, ecological systems have provided analogies for sustainable engineering and industrial systems [4,7], but there have been few attempts to translate core ecological principles into industrial practice (but cf. [10]). Attempts to organize human systems into more ecologically-realistic patterns continue to be based on the waste equals food concept (but cf. [11]) where the output of a given system component (e.g. industry) provides the input for another. While better than previous models, this type of organization does not accurately reproduce the connections patterns of ecosystems where full benefits from the analogy could be realized [6]. In this paper we explore if there are similar advantages for thermodynamic networks. To be ultimately sustainable, biological ecosystems have evolved over the long term to be almost completely cyclical in nature, with resources and waste being undefined, since waste to one component of the system represents resources to another. Jelinski, et al. [12] In 1969, Odum recognized that ecological systems, particularly mature ones, are associated with a high degree of internal recycling of energy and materials, such that the amount of new inputs into the system is small compared to what is transformed among the system components [8]. Human systems in contrast (e.g. agricultural ones) are geared for production rather than efficiency, resembling young rather than mature natural systems. Odum has suggested mimicking mature systems would help shift the focus of human systems from production to efficiency. One desirable property of mature systems is a complex food-web structure; a proliferation of connections between species that exchange material and energy by consuming one another [13]. The extent to which principles derived from ecological systems may be applied in other contexts is unclear. If we can connect the structural properties of ecological networks to well understood physical principles, such as the Laws of Thermodynamics, we might gain sufficient insight to apply ecological lessons to the engineering and development of resource networks [9]. 1.2 Cyclicity and Thermodynamic Cycles In this paper we use 28 familiar thermodynamic power cycles of increasing complexity to explore trends in network structure defined by the ecological metric cyclicity [13,14]. Cyclicity is an older metric reintroduced by Fath and Halnes that measures the presence of cyclic (closed loops as opposed to linear) pathways in a system [13]. Unlike the cycling index (CI), a similar metric which also quantifies the amount of cycling in the system, cyclicity needs no knowledge of flow magnitude, only flow path [8,15]. Flow magnitude information can be quite complex, if not impossible, to acquire thus cyclicity greatly increases the usefulness and simplicity of the metric as. Cyclicity, which represents what is also known as strongly connected components in ecology and graph theory, refers to the subset of species for which energy can flow from one another and back [16]. The connections in a system between species, or actors, are organized in a matrix form, from which the systems cyclicity is calculated. The higher the cyclicity of the system the more interconnected its components. High cyclicity values relate strongly to the overall prop (...truncated)