abstract: An orthotropic elasto-plastic damage material model (OEPDMM) suitable for impact analysis of composite materials has been developed through a joint research project funded by the Federal Aviation Administration (FAA) and the National Aeronautics and Space Administration (NASA). The developed material model has been implemented into LS-DYNA®, a commercial finite element program. The material model is modular comprising of deformation, damage and failure sub-models. The deformation sub-model captures the rate and/or temperature dependent elastic and inelastic behavior via a visco-elastic-plastic formulation. The damage sub-model predicts the reduction in the elastic stiffness of the material. The failure sub-model predicts when there is no more load carrying capacity in the finite element and erosion of the element from the finite element model. Most of the input parameters required to drive OEPDMM are in the form of tabulated data. The deformation sub-model is driven by a set of tabulated stress-strain data for a given strain-rate and temperature combination. The damage sub-model is driven by tabulated damage parameter-strain data. Two failure sub-models have been implemented – Puck Failure Model and Generalized Tabulated Failure Model. Puck Failure Model requires scalar parameters as input whereas, the Generalized Tabulated Failure Model is driven by a set of equivalent failure strain tabulated data. The work presented here focuses on the enhancements made to OEPDMM with emphasis on the background, development, and implementation of the failure sub-models. OEPDMM is verified and validated using a carbon/epoxy fiber reinforced composite. Two validation tests are used to evaluate the failure sub-model implementation - a stacked-ply test carried out at room temperature under quasi-static tensile and compressive loadings, and several high-speed impact tests where there is significant damage and material failure of the impacted panel. Results indicate that developed procedures provide the analyst with a reasonable and systematic approach to building predictive impact simulation models. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2020

The lighting retrofit method is adopted as one of the solutions to reduce lighting energy consumption and improve lighting quality in existing buildings. Lighting controls and energy-efficient light sources are used to achieve the goals of the lighting retrofit. Nowadays, Light-Emitting Diodes (LEDs) are replacing traditional lighting technology owing to their high efficiency and longevity. One of the advantages of LEDs is the controllability function, which allows users to set the light level according to their preferences. This saves more energy and satisfies users’ lighting needs. However, over time, the performance of lighting retrofit projects deteriorates subject to failure of the retrofitted lights. Therefore, to maintain the performance of lighting retrofit projects, maintenance must be planned and performed. The impacts of the users’ lighting level requirements on LEDs’ life characteristics and lighting system performance are investigated by using lighting controls. Light and occupancy sensors adjust artificial light to the light level required by users and detect the presence of users in the zones, respectively. Light sensors measure the average illuminance in the zones. The measured illuminance is compared to the users’ set illuminance; if the measured illuminance is higher than the users’ set illuminance, lamps are dimmed to meet users’ lighting preference, when the measured illuminance is less than the users’ set illuminance, lamps in the zone are replaced by new ones. The dimming level in each zone at each sampling interval is used to estimate the operating junction temperature, thereafter the degradation rate and luminous flux are calculated. Light levels at workspace are modelled using the lumen method. This model helps to quantify energy savings and predict when lamps will fail to deliver the required light levels. In existing studies, users’ lighting level requirements are neglected when investigating the lifetime of the lighting system; however, users’ profile and driving schemes affect the operating conditions of a lighting system. From the simulation results, it is noted that lumen output degradation increases when the user’s set illuminance is above the illuminance required under normal operating conditions and decreases when the user’s set illuminance is below the illuminance required under normal operating conditions. Increased lumen output degradation shortens the lifetime of LEDs and reduces energy savings, while decreased lumen output degradation extends the lifetime and increases energy savings. Generally, lighting retrofit projects contain a large lighting population; investigating when each lamp will fail can be time-consuming and costly. In this research, a mathematical model is formulated to model LEDs’ failure by analysing the statistical properties of the lumen degradation rates. Based on the statistical properties of the degradation rates, the cumulative probability of failure distribution and the survival function are modelled. The formulated survival function is incorporated into the lighting maintenance optimization problem to balance energy savings and maintenance costs. A case study carried out shows that, in 10 years, the optimal lighting maintenance plan would save up to 59% of lighting energy consumption with acceptable maintenance costs. It is found that the proposed maintenance plan is more cost-effective than full maintenance. It is concluded that lumen degradation failure should be considered when investigating the performance of lighting retrofit projects, as this may not only affect energy savings but also reduce the level of illumination, which can cause visual discomfort. The initial investment costs of LEDs are still a barrier to the implementation of LED lighting systems in residential buildings. Energy-efficiency projects often face hurdles to access capital investments because decision-makers and funders do not have enough information about operational savings the project can provide and specific financial requirements applied to efficiency investment. In this research, an optimization model is formulated to give decision-makers and funders detailed information about the performance and operational savings that a LED lighting retrofit project can offer and its economic viability. The lumen degradation failure model developed is used to monitor and estimate the energy savings, and the optimal maintenance plan is scheduled to replace failed lamps. In the existing studies, the economic analysis of the lighting retrofit projects is assessed based on lighting population decay due to burnout failure while in this research economic analysis is assessed by considering the lumen degradation failure. The case study results show that the substitution of halogen light bulbs with LED light bulbs could save up to 291.4 GWh of energy consumption, and reduce 273:92 103 tons of CO2 emissions over 10-year period. The optimization model formulated is effective to help the decision-makers and funders to quantify the savings and assess the economic viability of the LED lighting retroïnˇA˛t project. This optimization model can help the decision-makers and funders to make an informed decision. / Thesis (PhD (Electrical Engineering))--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / PhD (Electrical Engineering) / Unrestricted

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Prognostiziert durch Konzepte der Industrie 4.0 und den Cyber-Physischen-Systemen, können autonome Systeme zukünftig dynamisch auf Datenquellen in ihrer Umgebung zugreifen. Während die gemeinsame Nutzung solcher Datenquellen ein enormes Performanzpotenzial bietet, stellt die benötigte Systemarchitektur vorherrschende Sicherheitsprozesse vor neue Herausforderungen. Die vorliegende Arbeit motiviert zunächst, dass diese nur zur Laufzeit des Systems adressiert werden könne, bevor sie daraus zwei zentrale Ziele ableitet und verfolgt. Zum einen wird ein Beschreibungsmodel für die Darstellung von Fehlercharakteristika gemeinsam genutzter Daten vorgestellt. Dieses generische Fehlermodell erlaubt es zum anderen eine Sicherheitsanalyse zu definieren, die eine spezifische, dynamische Systemkomposition zur Laufzeit mit Hinblick auf die zu erwartenden Unsicherheiten bewerten kann. Die als Region of Safety betitelte Analysestrategie erlaubt, in Kombination mit dem generischen Fehlermodell, die Sicherheit der auf gemeinsam genutzten Daten basierenden Kollisionsvermeidungsstrategie zweier Roboter noch zur Designzeit zu garantieren, obwohl die spezifischen Fehlercharakteristika der Daten erst zur Laufzeit bekannt werden.:List of Acronyms List of Theorems List of Definitions List of Figures List of Tables 1. Introduction – Safety in Future Smart Industries 1.1. The Example of Smart Warehouses 1.2. Functional Safety Standards 1.2.1. Overview of Functional Safety Standards 1.2.2. IEC 61508 1.3. Scope of this Thesis 1.3.1. Objectives 1.3.2. Contributions 1.3.3. Outline 1.4. Related Publications by the Author 1.5. Mathematical Notation 2. State of the Art 2.1. State of the Art in Run-Time Safety Assessment 2.1.1. Approaches at the Functional Level 2.1.2. Approaches at the Technical Level 2.1.3. Conclusions 2.2. State of the Art in Failure Modeling 2.2.1. The Definition of (Sensor) Failure Model 2.2.2. Interval-Based Failure Modeling 2.2.3. Distribution-Based Failure Modeling 2.2.4. Failure-Type-Based Failure Modeling 2.2.5. Conclusions 2.3. Conclusions from the State of the Art 3. Generic Failure Model 3.1. Defining the Generic Failure Model 3.1.1. Time- and Value-Correlated Random Distribution 3.1.2. A Failure Type’s Failure Amplitudes 3.1.3. A Failure Type’s State Function 3.1.4. Polynomial Representation of a Failure Type 3.1.5. Discussion on the Fulfillment of the Predefined Criteria 3.2. Converting a Generic Failure Model to an Interval 3.2.1. Converting a Time- and Value-Correlated Random Distribution 3.2.2. A Failure Type’s Interval 3.3. Processing Chain for Generating Generic Failure Models 3.3.1. Identifying Failure Types 3.3.2. Parameterizing Failure Types 3.3.3. Confidence Calculation 3.4. Exemplary Application to Artificial Failure Characteristics 3.4.1. Generating the Artificial Data Set – Manually Designing GFMs 3.4.2. Identifying Failure Types 3.4.3. Parameterizing Failure Types 3.4.4. Confidence Calculation 3.4.5. Comparison to State-of-the-Art Models 3.5. Summary 4. Region of Safety 4.1. Explicitly Modeling Uncertainties for Dynamically Composed Systems 4.2. Regions of Safety for Dynamically Composed Systems 4.2.1. Estimating Regions of Attraction in Presence of Uncertainty 4.2.2. Introducing the Concept of Region of Safety 4.2.3. Discussion on the Fulfillment of the Predefined Criteria 4.3. Evaluating the Concept of Region of Safety 4.3.1. Defining the Scenario and Considered Uncertainties 4.3.2. Designing a Control Lyapunov Function 4.3.3. Determining an Appropriate Value for λc 4.3.4. The Effect of Varying Sensor Failures on Regions of Safety 4.4. Summary 5. Evaluation and Integration 5.1. Multi-Robot Collision Avoidance 5.1.1. Assumptions 5.1.2. Design of the Circle and Navigation Scenarios 5.1.3. Kinematics 5.1.4. Control Policy 5.1.5. Intention Modeling by Model Uncertainty 5.1.6. Fusing Regions of Safety of Multiple Stability Points 5.2. Failure Modeling for Shared Data – A Marker Detection Failure Model 5.2.1. Data Acquisition 5.2.2. Failure Model Generation 5.2.3. Evaluating the Quality of the Failure Model 5.3. Safe Handling of Shared Data in a Collision Avoidance Strategy 5.3.1. Configuration for Region of Safety Estimation 5.3.2. Estimating Regions of Safety 5.3.3. Evaluation Using the Circle Scenario 5.3.4. Evaluation Using the Navigation Scenario 5.4. Summary 6. Conclusions and Future Work 6.1. Summary 6.2. Limitations and Future Work 6.2.1. Limitations and Future Work on the Generic Failure Model 6.2.2. Limitations and Future Work on Region of Safety 6.2.3. Future Work on Safety in Dynamically Composed Systems Appendices A. Defining Factors of Risk According to IEC 61508 B. Evaluation Results for the Identification Stage C. Overview of Failure Amplitudes of Marker Detection Results Bibliography / The concepts of Cyber-Physical-Systems and Industry 4.0 prognosticate autonomous systems to integrate sources of shared data dynamically at their run-time. While this promises substantial increases in their performance, the openness of the required system architecture poses new challenges to processes guaranteeing their safety. This thesis firstly motivates that these can be addressed only at their run-time, before it derives and pursues two corresponding goals. Firstly, a model for describing failure characteristics of shared data is presented. Secondly, this Generic Failure Model is built upon to define a run-time safety assessment methodology that enables analyzing dynamic system compositions integrating shared data with respect to the expected uncertainties at run-time. This analysis strategy, entitled Region of Safety, allows in combination with the generic failure model to guarantee the safety of robots sharing position data for collision avoidance already at design-time, although specific failure characteristics become available only at run-time.:List of Acronyms List of Theorems List of Definitions List of Figures List of Tables 1. Introduction – Safety in Future Smart Industries 1.1. The Example of Smart Warehouses 1.2. Functional Safety Standards 1.2.1. Overview of Functional Safety Standards 1.2.2. IEC 61508 1.3. Scope of this Thesis 1.3.1. Objectives 1.3.2. Contributions 1.3.3. Outline 1.4. Related Publications by the Author 1.5. Mathematical Notation 2. State of the Art 2.1. State of the Art in Run-Time Safety Assessment 2.1.1. Approaches at the Functional Level 2.1.2. Approaches at the Technical Level 2.1.3. Conclusions 2.2. State of the Art in Failure Modeling 2.2.1. The Definition of (Sensor) Failure Model 2.2.2. Interval-Based Failure Modeling 2.2.3. Distribution-Based Failure Modeling 2.2.4. Failure-Type-Based Failure Modeling 2.2.5. Conclusions 2.3. Conclusions from the State of the Art 3. Generic Failure Model 3.1. Defining the Generic Failure Model 3.1.1. Time- and Value-Correlated Random Distribution 3.1.2. A Failure Type’s Failure Amplitudes 3.1.3. A Failure Type’s State Function 3.1.4. Polynomial Representation of a Failure Type 3.1.5. Discussion on the Fulfillment of the Predefined Criteria 3.2. Converting a Generic Failure Model to an Interval 3.2.1. Converting a Time- and Value-Correlated Random Distribution 3.2.2. A Failure Type’s Interval 3.3. Processing Chain for Generating Generic Failure Models 3.3.1. Identifying Failure Types 3.3.2. Parameterizing Failure Types 3.3.3. Confidence Calculation 3.4. Exemplary Application to Artificial Failure Characteristics 3.4.1. Generating the Artificial Data Set – Manually Designing GFMs 3.4.2. Identifying Failure Types 3.4.3. Parameterizing Failure Types 3.4.4. Confidence Calculation 3.4.5. Comparison to State-of-the-Art Models 3.5. Summary 4. Region of Safety 4.1. Explicitly Modeling Uncertainties for Dynamically Composed Systems 4.2. Regions of Safety for Dynamically Composed Systems 4.2.1. Estimating Regions of Attraction in Presence of Uncertainty 4.2.2. Introducing the Concept of Region of Safety 4.2.3. Discussion on the Fulfillment of the Predefined Criteria 4.3. Evaluating the Concept of Region of Safety 4.3.1. Defining the Scenario and Considered Uncertainties 4.3.2. Designing a Control Lyapunov Function 4.3.3. Determining an Appropriate Value for λc 4.3.4. The Effect of Varying Sensor Failures on Regions of Safety 4.4. Summary 5. Evaluation and Integration 5.1. Multi-Robot Collision Avoidance 5.1.1. Assumptions 5.1.2. Design of the Circle and Navigation Scenarios 5.1.3. Kinematics 5.1.4. Control Policy 5.1.5. Intention Modeling by Model Uncertainty 5.1.6. Fusing Regions of Safety of Multiple Stability Points 5.2. Failure Modeling for Shared Data – A Marker Detection Failure Model 5.2.1. Data Acquisition 5.2.2. Failure Model Generation 5.2.3. Evaluating the Quality of the Failure Model 5.3. Safe Handling of Shared Data in a Collision Avoidance Strategy 5.3.1. Configuration for Region of Safety Estimation 5.3.2. Estimating Regions of Safety 5.3.3. Evaluation Using the Circle Scenario 5.3.4. Evaluation Using the Navigation Scenario 5.4. Summary 6. Conclusions and Future Work 6.1. Summary 6.2. Limitations and Future Work 6.2.1. Limitations and Future Work on the Generic Failure Model 6.2.2. Limitations and Future Work on Region of Safety 6.2.3. Future Work on Safety in Dynamically Composed Systems Appendices A. Defining Factors of Risk According to IEC 61508 B. Evaluation Results for the Identification Stage C. Overview of Failure Amplitudes of Marker Detection Results Bibliography