Gunga Human Physiology in Extreme Environments

1. Auflage 2014
ISBN: 978-0-12-386998-2
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 336 Seiten

Human Physiology in Extreme Environments is the one publication that offers how human biology and physiology is affected by extreme environments while highlighting technological innovations that allow us to adapt and regulate environments. Covering a broad range of extreme environments, including high altitude, underwater, tropical climates, and desert and arctic climates as well as space travel, this book will include case studies for practical application. Graduate students, medical students and researchers will find Human Physiology in Extreme Environments an interesting, informative and useful resource for human physiology, environmental physiology and medical studies. - Presents human physiological challenges in Extreme Environments combined in one single resource - Provides an excellent source of information regarding paleontological and anthropological aspects - Offers practical medical and scientific use of current concepts

Dr. Gunga has been working in the field of integrated research on humans in extreme environments for more than 25 years. He has garnered financial support from the German Government (BMBF/BMWI/DLR) and established public private partnerships with the Center of Space Medicine and Extreme Environments at the Charit‚ University Medicine Berlin. As a PI, he has conducted several national and international research studies in different laboratories and under field conditions around the world and in space. (e.g. MIR, Shuttle, International Space Station). His team combines scientific research at the academic forefront in different extreme environments with teaching duties at one of the largest medical clinics in Europe. In addition, he has been invited to give lecture courses on human in extreme environments at the Northwestern Polytechnic University in Xi'an (China) in the frame of the 'High End Foreign Expert Program' of the Chinese Government from 2012-2016 and recently renewed this contract for an additional three years (2017-2019). Furthermore, in October 2016 he was invited by the Universidad de Antofagasta to give an internet-based lecture, which was officially announced in the frame of the 'Latin American Network of High Altitude Medicine and Physiology.' The Chilean Government and the German Academic Exchange Program (DAAD, Bonn) financed this guest professorship. In April 2017, the guest professorship was renewed and will be conducted in October and November this year, again at the University Antofagasta.

Autoren/Hrsg.

Gunga, Hanns-Christian

Inhaltsverzeichnis

1;Front Cover;1
2;Advances in Applied Mechanics, Volume 33;4
3;Copyright Page;5
4;Contents;6
5;Contributors;8
6;Preface;10
7;Chapter 1. Robust Reliability of Structures;12
7.1;I. Introduction;12
7.2;II. Convexity and Uncertainty;15
7.3;III. Truss with Uncertain Static Load;17
7.4;IV. Geometric Imperfections: Axially Loaded Shell;21
7.5;V. Dynamic System: Lifting Devices;26
7.6;VI. Modal Reliability;32
7.7;VII. Fatigue Failure and Reliability with Uncertain Loading;36
7.8;VIII. Reliability of Mathematical Models;45
7.9;IX. Summary;50
7.10;References;51
8;Chapter 2. Compresssive Failure of Fiber Composites;54
8.1;I. Introduction;54
8.2;II. Competing Failure Mechanisms in Composites;55
8.3;III. Compressive Strength of Unidirectional Composites Due to Microbuckling;73
8.4;IV. Propagation of a Microbuckle in a Unidirectional Composite;105
8.5;V. The Notched Strength of Multi-axial Composites;114
8.6;VI. Directions for Future Research;121
8.7;Acknowledgments;124
8.8;References;124
9;Chapter 3. Delamination of Compressed Thin Films;130
9.1;I. Introduction;131
9.2;II. Experimental Background;133
9.3;III. Folding Patterns as Energy Minimizers;143
9.4;IV. Film Morphologies;174
9.5;V. Conclusion;198
9.6;Acknowledgments;199
9.7;References;199
10;Chapter 4. Motions of Microscopic Surfaces in Materials;204
10.1;I. Introduction;205
10.2;II. Interface Migration: Formulation;207
10.3;III. Interface Migration Driven by Surface Tension and Phase Difference;220
10.4;IV. Interface Migration in the Presence of Stress and Electric Fields;233
10.5;V. Diffusion on: Interface Formulation;246
10.6;VI. Shape Change Due to Surface Diffusion under Surface Tension;256
10.7;VII. Diffusion on an Interface between Two Materials;272
10.8;VIII. Surface Diffusion Driven by Surface- and Elastic-Energy Variation;278
10.9;IX. Electromigration on Surfaces;290
10.10;Acknowledgments;300
10.11;References;300
11;Chapter 5. Strain Gradient Plasticity;306
11.1;I. Introduction;307
11.2;II. Survey of Strain Gradient Plasticity: Formulations and Phenomena;312
11.3;III. The Framework for Strain Gradient Theory;344
11.4;IV. Flow Theory;352
11.5;V. Single-Crystal Plasticity Theory;360
11.6;Appendix: J2 Deformation Theory and Associated Minimum Principles;366
11.7;Acknowledgments;369
11.8;References;369
12;Author Index;373
13;Subject Index;380

Leseproben

Robust Reliability of Structures

Yakov Ben-Haim    Faculty of Mechanical Engineering Technion—Israel Institute of Technology Haifa, Israel

I Introduction

Though the Twentieth century began with the promulgation of the profoundest deterministic theory since the Seventeenth century, Einstein remained an anomaly; scientific thought for the past hundred years has been mainly influenced by concepts of uncertainty. Also, just as the early years of the century witnessed profound revision of the philosophical understanding of mathematical thought, so too has our understanding and treatment of uncertainty broadened and changed enormously. The technological applications of the mechanical sciences have followed suit and uncertainty thinking has won a place in the engineer’s practice. The major application is reliability: its assessment and attainment in mechanical design.

In accord with the diversification of uncertainty models that have emerged in recent decades, are a variety of reliability theories. In this paper we concentrate on one approach: robust reliability based on convex models of uncertainty. The comparison of alternative possibilities is avoided because this tends to be polemical rather than technological. The choice of an uncertainty model is, to some extent, a matter of taste. Also, some comparison is to be found elsewhere [5,6,15,16]. Finally, it is too early for a far-reaching analysis of the merits of alternative theories of reliability because the monopoly of classical probabilistic reliability has only recently been challenged. Some eligible theories, such as reliability based on a nonstandard probability logic like fuzzy theory, have yet to be thoroughly developed for applied mechanics.

To rely on something means to have confidence based on experience. This is a plain English word that has a classical etymology and it has carried this meaning long before engineers started thinking scientifically or scientists starting thinking probabilistically. Reliability rests on two more primitive concepts: performance and uncertainty. Still speaking lexically and not technically, we can rely on something when, despite uncertainties, its performance is acceptable.

We now ask for a quantitative theory that reflects the intuitive idea of reliability. The current standard theory of reliability is based on probability: the reliability of a system is measured by the probability of no failure. This approach is exceedingly useful and has been developed in recent decades by many able authors.

In this paper we describe a different formulation. We measure the reliability of a system by the amount of uncertainty consistent with no failure. A reliable system will preform satisfactorily in the presence of great uncertainty. Such a system is robust with respect to uncertainty, and hence the name robust reliability. On the other hand, a system has low reliability when small fluctuations can lead to failure. Such a system is fragile with respect to uncertainty.

The relevance of noise-robustness to the quality of technological systems is widely recognized. Consider for example the comment by Taguchi et al. [18, p. 3]:

The broad purpose of the overall system is to produce a product that is robust [italics in the original] with respect to all noise factors. Robustness implies that the product’s functional characteristics are not sensitive to variation caused by noise factors.

In robust reliability, a system has high reliability when it is robust with respect to uncertainties. It has low reliability when even small amounts of uncertainty entail the possibility of failure.

A similar idea is found in robust adaptive control, where one seeks a control strategy which will perform acceptably in the presence of considerable uncertainty. Robust control has been motivated in large measure by severely limited information about the uncertainties, usually system-model imperfections, which characterize complex-controlled systems such as aerospace vehicles, power, or chemical plants.1 Robustness-to-uncertainty is a useful guideline in formulating control laws in the absence of detailed probabilistic information [1,13].

Finally, self-learning systems are notable for their ability to adapt themselves to uncertain environments. This is important in on-line learning precisely because this adaptability enhances the reliability of the system despite the uncertainties accompanying its operation.

Robustness-to-uncertainty is a natural starting point for a theory of reliability. It is not the only possibility starting point, but it is a fruitful one, well suited considering the limited information available about the uncertainties of mechanical structures and devices.

We will identify three primary components in our analysis of the robust reliability of mechanical systems: (1) a model of the mechanics, (2) a model of the uncertainties, and (3) a criterion of failure. For items (1) and (3) we will employ standard mechanical and physical theories. For item (2) we will use convex models of uncertainty. The relation between convexity and uncertainty, and the theoretical and practical aspects of convex models, are developed extensively elsewhere [3,9], but a brief description is included here in Section II for convenience.

Following that discussion, we provide a sequence of examples of robust reliability analysis, beginning in Section III with a heuristic example of the reliability of a truss supporting a platform bearing uncertain static loads. In Section IV we analyze the reliability of a cylindrical shell with uncertain geometrical imperfections that is subject to a static axial load. The third example in Section V deals with a dynamic system: a lifting device subject to time-varying and spatially uncertain loadings. These three case studies demonstrate the method of robust reliability analysis. In Section VI we discuss the topic from a more general point of view by developing the idea of modal reliability: the assessment of the relative reliability of dynamic degrees of freedom of a structure. Up to this point, all the examples have dealt with linear, or linearized, mechanical models. In Section VII we analyze the robust reliability of an inherently non-linear phenomenon: structural degradation and failure by material fatigue resulting from uncertain, time-varying loads far below the yield limit of the material. Finally, in Section VIII we modify our subject a bit, and discuss the reliability of mathematical models of structures, rather than of the structures themselves.

II Convexity and Uncertainty

Uncertainty: events occur, one after the other, accumulating into clouds, clusters, and patterns. Fragmentary information about the uncertain phenomena can be used to characterize the clustering of uncertain events. Without specifying anything about the probabilistic frequency-of-recurrence of events, a convex model is a set whose structure is derived from the underlying properties of the event clusters.

A convex model is a set of functions or vectors. Each element of the set represents a possible realization of the uncertain phenomenon. The usual engineering approach to formulating a convex model is to start with what we know about the phenomenon and to define the set of all functions consistent with that information.

An an example, consider transient excursions in the vector f(t) of loads applied to a structure. Suppose that the nominal load history is known, ¯t, and that the cumulative energy of deviation from this nominal load is bounded. The set of all functions consistent with this information is a cumulative energy-bound convex model, defined as:

a=ft:?08ft-f¯tTft-f¯tdt=a2

  (1)

We may not know the value of a, and in robust reliability analysis, we do not need to know it. a is the uncertainty parameter whose value determines the “size” of the set, the latitude of variation of the uncertain phenomenon, or the information gap between the anticipated nominal load and the loads which may occur in practice. The convex model defines a set of functions consistent with particular prior information. It defines a cluster of events.

Because we do not know the value of a, we will often think of a convex model as a family of sets: (a) for a = 0. Thinking in this way, we realize that every L2-integrable function f(t) belongs to the family for sufficiently large values of a. The convex model is not a black-and-white division of events into “completely allowed” and “strictly forbidden.” Rather, the family of convex models (a), a = 0, arranges the space of all events in a particular order. It is not an order in terms of frequency of occurrence as in probability, but an order in terms of clustering. In the convex model of eq. (1) the events cluster around the nominal load profile, ¯t, and the variation is ordered by the cumulative energy of deviation.

We will often define convex models in such a way as...