Exploring Experience Curves for the Building Envelope: An Investigation for Switzerland for 1970–2020

Energy efficiency potentials slumbering in the envelopes of existing and newly constructed buildings are significant and still largely untapped. Increasing concerns of policy-makers about non-sustainable energy use and its implications especially on climate change currently spur a growing interest in research in this area. The aim of this paper is to fill part of the existing knowledge gap by focusing on experience curve aspects of energy efficiency measures that concern state-of-the-art insulation methods, materials, and windows, and by studying the usefulness of such experience curves for the building envelope for energy policy design and evaluation. The analysis draws on a recent investigation of the situation in Switzerland (Jakob et al. 2002), but also contains a wider perspective especially regarding some more global technological trends and the market diffusion of innovative energy conservation technologies for the building envelope, policy designs, and policy programmes. The results derived from historical data analysis point to significant techno-economic progress over the last 30 years, and demonstrate the basic applicability, merits and limitations of the experience curve concept for energy policy design and impact analyses concerning the building envelope. We conclude from our analysis that building standards and labels can be important drivers for technoeconomic progress, apart from the energy conservation potentials offered, and that experience curves can be a useful tool for targeted and effective policy measures and for the promotion of labels and standards.Experience curve, building envelope, energy efficiency, policy design, energy paradox

A model-based reasoning architecture for system-level fault diagnosis

This dissertation presents a model-based reasoning architecture with a two fold purpose: to detect and classify component faults from observable system behavior, and to generate fault propagation models so as to make a more accurate estimation of current operational risks. It incorporates a novel approach to system level diagnostics by addressing the need to reason about low-level inaccessible components from observable high-level system behavior. In the field of complex system maintenance it can be invaluable as an aid to human operators. The first step is the compilation of the database of functional descriptions and associated fault-specific features for each of the system components. The system is then analyzed to extract structural information, which, in addition to the functional database, is used to create the structural and functional models. A fault-symptom matrix is constructed from the functional model and the features database. The fault threshold levels for these symptoms are founded on the nominal baseline data. Based on the fault-symptom matrix and these thresholds, a diagnostic decision tree is formulated in order to intelligently query about the system health. For each faulty candidate, a fault propagation tree is generated from the structural model. Finally, the overall system health status report includes both the faulty components and the associated at risk components, as predicted by the fault propagation model.Ph.D.Committee Chair: Vachtsevanos, George; Committee Member: Liang, Steven; Committee Member: Michaels, Thomas; Committee Member: Vela, Patricio; Committee Member: Wardi, Yora

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 220, June 1981

Approximately 137 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1981 are recorded, covering a variety of topics in aerospace medicine and biology

Optimal CO2-abatement with socio-economic inertia and induced technological change

The impact of induced technological change (ITC) in energy/climate models on the timing of optimal CO2-abatement depends on whether R&D or learning-by-doing (LBD) is the driving force. Bottom-up energy system models employing LBD suggest strong increases in optimal early abatement. In this paper we extend an existing top-down model supporting this view according to the notion that socio-economic inertia interferes with rapid technological change. We derive analytical results concerning the impact of inertia and ITC on optimal initial abatement and show a wide range of numerical simulations to illustrate magnitudes. Inertia now dominates the timing decision on early abatement, such that LBD might even have a negative effect on early abatement and the impact of R&D is limited. However, ITC still reduces costs of stabilizing atmospheric CO2-concentrations considerably.climate policy; technological change; inertia

Toward an Ontology for Third Generation Systems Thinking

Systems thinking is a way of making sense about the world in terms of multilevel, nested, interacting systems, their environment, and the boundaries between the systems and the environment. In this paper we discuss the evolution of systems thinking and discuss what is needed for an ontology of the current generation of systems thinking

Outlining the distinguishing characteristics of an evolutionary theory of innovation

This paper discusses notions of theory in relation to evolutionary understandings of innovation. It starts by empirically demonstrating the relevance of evolutionary perspectives – broadly defined – for understanding the “basics of what’s going on” in the economic sphere when it comes to innovation. It continues to argue and show that appreciative evolutionary understandings of innovation are connected to the Darwinian processes of variation, selection and retention in the theoretical “high range”. Multilevel theorizing, where researchers move between different levels and degrees of abstraction is therefore a key feature of an evolutionary theory of innovation. The paper ends by identifying puzzles and research challenges that evolutionary reasoning with respect to innovation need to address.Innovation, evolutionary theory.

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Reactive control and reasoning assistance for scientific laboratory instruments

Scientific laboratory instruments that are involved in chemical or physical sample identification frequently require substantial human preparation, attention, and interactive control during their operation. Successful real-time analysis of incoming data that supports such interactive control requires: (1) a clear recognition of variance of the data from expected results; and (2) rapid diagnosis of possible alternative hypotheses which might explain the variance. Such analysis then aids in decisions about modifying the experiment protocol, as well as being a goal itself. This paper reports on a collaborative project at the NASA Ames Research Center between artificial intelligence researchers and planetary microbial ecologists. Our team is currently engaged in developing software that autonomously controls science laboratory instruments and that provides data analysis of the real-time data in support of dynamic refinement of the experiment control. the first two instruments to which this technology has been applied are a differential thermal analyzer (DTA) and a gas chromatograph (GC). coupled together, they form a new geochemicstry and microbial analysis tool that is capable of rapid identification of the organiz and mineralogical constituents in soils. The thermal decomposition of the minerals and organics, and the attendance release of evolved gases, provides data about the structural and molecular chemistry of the soil samples