A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop models for large scale analysis of the stock. This research proposes a probabilistic, engineering-based, bottom-up model to address these issues. In a recent study we classified London's non-domestic buildings based on the service they provide, such as offices, retail premise, and schools, and proposed the creation of one probabilistic representational model per building type. This paper investigates techniques for the development of such models. The representational model is a statistical surrogate of a dynamic energy simulation (ES) model. We first identify the main parameters affecting energy consumption in a particular building sector/type by using sampling-based global sensitivity analysis methods, and then generate statistical surrogate models of the dynamic ES model within the dominant model parameters. Given a sample of actual energy consumption for that sector, we use the surrogate model to infer the distribution of model parameters by inverse analysis. The inferred distributions of input parameters are able to quantify the relative benefits of alternative energy saving measures on an entire building sector with requisite quantification of uncertainties. Secondary school buildings are used for illustrating the application of this probabilistic method. © 2012 Elsevier B.V. All rights reserved.

Unified modelling in support of organization design and change

The aim of this research is to provide a unified modelling-based method to help with the evaluation of organization design and change decisions. Relevant literature regarding model-driven organization design and change is described. This helps identify the requirements for a new modelling methodology. Such a methodology is developed and described. The three phases of the developed method include the following. First, the use of CIMOSA-based multi-perspective enterprise modelling to understand and capture the most enduring characteristics of process-oriented organizations and externalize various types of requirement knowledge about any target organization. Second, the use of causal loop diagrams to identify dynamic causal impacts and effects related to the issues and constraints on the organization under study. Third, the use of simulation modelling to quantify the effects of each issue in terms of organizational performance. The design and case study application of a unified modelling method based on CIMOSA (computer integrated manufacturing open systems architecture) enterprise modelling, causal loop diagrams, and simulation modelling, is explored to illustrate its potential to support systematic organization design and change. Further application of the proposed methodology in various company and industry sectors, especially in manufacturing sectors, would be helpful to illustrate complementary uses and relative benefits and drawbacks of the methodology in different types of organization. The proposed unified modelling-based method provides a systematic way of enabling key aspects of organization design and change. The case company, its relevant data, and developed models help to explore and validate the proposed method. The application of CIMOSA-based unified modelling method and integrated application of these three modelling techniques within a single solution space constitutes an advance on previous best practice. Also, the purpose and application domain of the proposed method offers an addition to knowledge. © IMechE 2009.

Self-similar organization of arrays of individual carbon nanotubes and carbon nanotube micropillars

It is well-known that carbon nanotube (CNT) growth from a dense arrangement of catalyst nanoparticles creates a vertically aligned CNT forest. CNT forests offer attractive anisotropic mechanical, thermal, and electrical properties, and their anisotropic structure is enabled by the self-organization of a large number of CNTs. This process is governed by individual CNT diameter, spacing, and the CNT-to-CNT interaction. However, little information is known about the self-organization of CNTs within a forest. Insight into the self-organization is, however, essential for tailoring the properties of the CNT forests for applications such as electrical interconnects, thermal interfaces, dry adhesives and energy storage. We demonstrate that arrays of CNT micropillars having micron-scale diameters organize in a similar manner as individual CNTs within a forest. For example, as previously demonstrated for individual CNTs within a forest, entanglement of small-diameter CNT micropillars during the initial stage of growth creates a film of entwined pillars. This layer enables coordinated subsequent growth of the pillars in the vertical direction, in a case where isolated pillars would not grow in a self-supporting fashion. Finally, we provide a detailed overview of the self-organization as a function of the diameter, length and spacing of the CNT pillars. This study, which is applicable to many one-dimensional nanostructured films, demonstrates guidelines for tailoring the self-organization which can enable control of the collective mechanical, electrical and interfacial properties of the films. © 2009 Elsevier B.V. All rights reserved.

Investigating the link between well-being and energy use; an explorative case study between passive and active domestic energy management systems

The aim of this study was to explore how the remote control of appliances/lights (active energy management system) affected household well-being, compared to in-home displays (passive energy management system). A six-week exploratory study was conducted with 14 participants divided into the following three groups: active; passive; and no equipment. The effect on well-being was measured through thematic analysis of two semi-structured interviews for each participant, administered at the start and end of the study. The well-being themes were based on existing measures of Satisfaction and Affect. The energy demand for each participant was also measured for two weeks without intervention, and then compared after four weeks with either the passive or active energy management systems. These measurements were used to complement the well-being analysis. Overall, the measure of Affect increased in the passive group but Satisfaction decreased; however, all three measures on average decreased in the active group. The measured energy demand also highlighted a disconnect between well-being and domestic energy consumption. The results point to a need for further investigation in this field; otherwise, there is a risk that nationally implemented energy management solutions may negatively affect our happiness and well-being. © 2013 Elsevier Ltd.

Self-organization, embodiment, and biologically inspired robotics.

Robotics researchers increasingly agree that ideas from biology and self-organization can strongly benefit the design of autonomous robots. Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, indefinite richness, and limited availability of information. Industrial robots, in contrast, operate in highly controlled environments with no or very little uncertainty. Although many challenges remain, concepts from biologically inspired (bio-inspired) robotics will eventually enable researchers to engineer machines for the real world that possess at least some of the desirable properties of biological organisms, such as adaptivity, robustness, versatility, and agility.

Self-organization of reflexive behavior from spontaneous motor activity.

In mammals, the development of reflexes is often regarded as an innate process. However, recent findings show that fetuses are endowed with favorable conditions for ontogenetic development. In this article, we hypothesize that the circuitry of at least some mammalian reflexes can be self-organized from the sensory and motor interactions brought forth in a musculoskeletal system. We focus mainly on three reflexes: the myotatic reflex, the reciprocal inhibition reflex, and the reverse myotatic reflex. To test our hypothesis, we conducted a set of experiments on a simulated musculoskeletal system using pairs of agonist and antagonist muscles. The reflex connectivity is obtained by producing spontaneous motor activity in each muscle and by correlating the resulting sensor and motor signals. Our results show that, under biologically plausible conditions, the reflex circuitry thus obtained is consistent with that identified in relation to the analogous mammalian reflexes. In addition, they show that the reflex connectivity obtained depends on the morphology of the musculoskeletal system as well as on the environment that it is embedded in.

Self-organization of spinal reflexes involving homonymous, antagonist and synergistic interactions

Recent results in spinal research are challenging the historical view that the spinal reflexes are mostly hardwired and fixed behaviours. In previous work we have shown that three of the simplest spinal reflexes could be self-organised in an agonist-antagonist pair of muscles. The simplicity of these reflexes is given from the fact that they entail at most one interneuron mediating the connectivity between afferent inputs and efferent outputs. These reflexes are: the Myotatic, the Reciprocal Inibition and the Reverse Myotatic reflexes. In this paper we apply our framework to a simulated 2D leg model actuated by six muscles (mono- and bi-articular). Our results show that the framework is successful in learning most of the spinal reflex circuitry as well as the corresponding behaviour in the more complicated muscle arrangement. © 2012 Springer-Verlag.

Guided self-organization in a dynamic embodied system based on attractor selection mechanism

Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics. © 2014 by the authors; licensee MDPI, Basel, Switzerland.