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Aircraft survivability is a classical consideration of combat aircraft design and tactical development, but the fundamental model of aircraft survivability must be updated to be able to consider modern tactical scenarios that are applicable to unmanned aircraft. This paper seeks therefore to define the set of design tradeoffs and an evaluation of the tactical effectiveness for unmanned aircraft survivability. Traditional and modern survivability evaluation methods are presented and integrated into a computational simulation to create a probabilistic evaluation of unmanned aircraft survivability. The results demonstrate the development of design tradeoffs for a hypothetical unmanned C-130J Hercules against a single man-portable air defense system. The discussion focuses on the demonstration of the utility of this survivability evaluation framework for consideration of survivability in unmanned aerial vehicle (UAV) design, the utility of considering survivability in the design of multi-UAV configurations (including the loyal wingman and swarms), and the value of the probabilistic survivability model for multi-aircraft simulations.

There is a growing interest among scientists to coordinate diverse and temporally responsive measurements from multiple space and ground-based observatories in order to obtain greater insights into...

This study seeks to compare the quantity of information that is automatically transferred through the associations generated using a model-based systems engineering (MBSE) approach versus a traditional systems engineering approach to measure the benefits of MBSE in architecting a robotic space system. Both an MBSE approach and non-MBSE approach were applied to architecting an orbiting sample Capture and Orient Module (COM) system concept for a Capture, Containment, and Return System (CCRS) payload concept for Mars Sample Return (MSR). These approaches were applied in parallel to provide a side-by-side comparison of the approaches. The approaches were analyzed using design structure matrices (DSM) and evaluated based on the amount of information transferred between process tasks manually (e.g., elements physically typed into text boxes in a presentation slide) vs automatically (e.g., elements automatically filled out within a block in a model view due to explicitly defined element associations). A total of 4,819 information element transfers were traced in DSMs and used to quantitatively compare the two approaches. The non-MBSE approach required manual transfer for all 4,819 information elements. The MBSE approach required manual transfer for 4,189 information elements and automatic transfer for 630 information elements, providing a minor increase in the automation of information transfer relative to the non-MBSE approach. By incorporating the use of additional MBSE artifacts into the trade study and peer review tasks, manual transfer could potentially be reduced to 931 information elements, and automatic transfer increased to 3,888 information elements.

The increasing number of Electronic Control Units (ECUs) in today's vehicles and their greater connectivity with the outside environment has made vehicles more vulnerable to security attacks. Integrating security mechanisms in ECUs has become essential, but incurs overheads, which can delay safety-critical task execution and message transfers. In this work, we introduce a methodology to derive security requirements for tasks and messages in automotive systems based on the ISO 26262 standard. We then propose a framework ( SEDAN ) to increase the security of the system without violating the real-time constraints and security requirements of messages, or ECU utilization limits.

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

Time-triggered automotive networks use time-triggered protocols (FlexRay, TTEthernet, etc.) for periodic message transmissions that often originate from safety and time-critical applications. One of the major challenges with time-triggered transmissions is jitter, which is the unpredictable delay-induced deviation from the actual periodicity of a message. Failure to account for jitter can be catastrophic in time-sensitive systems, such as automotive platforms. In this article, we propose a novel scheduling framework (JAMS-SG) that satisfies timing constraints during message delivery for both jitter-affected time-triggered messages and high-priority event-triggered messages in automotive networks. At design time, JAMS-SG performs jitter-aware frame packing (packing of multiple signals from Electronic Control Units (ECUs) into messages) and schedules synthesis with a hybrid heuristic. At runtime, a Multi-Level Feedback Queue (MLFQ) handles jitter-affected time-triggered messages and high-priority event-triggered messages that are scheduled using a runtime scheduler. Our simulation results, based on messages and network traffic data from a real vehicle, indicate that JAMS-SG is highly scalable and outperforms the best-known prior work in the area in the presence of jitter.

Hydrogen infrastructure for fueling vehicles has progressed in the last decade from stations with restricted access and limited operating hours to customer-friendly retail stations open to the public. There are now 121 retail hydrogen stations around the world. In California, the number of public retail hydrogen stations has increased from zero to more than 30 in less than two years, and the annual amount of hydrogen dispensed by retail stations has grown from 27,400 kg in 2015 to nearly 105,000 kg in 2016 and more than 440,000 kg in 2017—an increase of about four times year over year. For more than a decade, government, industry, and academia have studied many aspects of hydrogen infrastructure, from renewable hydrogen production to retail hydrogen station performance. This paper reviews the engineering and deployment of modern hydrogen infrastructure, including the costs, benefits, and operational considerations (including safety, reliability, availability), as well as challenges to the scale-up of hydrogen infrastructure. The results identify hydrogen station reliability as a key factor in the expense of operating hydrogen systems, placing it in the context of the larger reliability engineering field.

In-motion charging of electric vehicles (EVs) using wireless power transfer (WPT) represents an alternative to both traditional internal combustion engine (ICE) transportation and long-range EVs. This paper focuses on understanding the economics, environmental impact, and infrastructure rollout of in-motion WPT applied to the U.S. transportation fleet. This paper represents a novel, large-scale integration of numerous research methodologies previously presented by our research group into a comprehensive study to thoroughly address potential in-motion WPT implementation scenarios using geographically diverse data sets, validated vehicle models, real-world drive cycles, variable vehicle adoption rates, and variable infrastructure deployment rates. By using both in-motion WPT and conventional charging infrastructure, the proposed vehicle and roadway architectures satisfy 97.7% of the sampled 24-h drive cycles, a 22.4% increase over a baseline short-range EV without in-motion charging. Economic results show a national return on investment but economic viability is dramatically impacted by upfront capital costs and technology adoption. An environmental impact assessment shows that total greenhouse gas emissions from light-duty vehicles and Class 8 trucks would be reduced by 29.3 trillion kg CO2-eq. (30.6%) when compared to a business as usual scenario (i.e., a scenario were current overall emissions trends continue) for the first 50 years of technology deployment. These results demonstrate that in-motion charging using WPT presents both economic and environmental benefits when compared to conventional ICE transportation and a long-range EV fleet.

Electric vehicle (EV) charging infrastructure is present all over the United States, but charging prices vary greatly, both in amount and in the methods by which they are assessed. For this paper, we interpret and analyze charging price information from PlugShare, a crowd-sourced EV charging data platform. Because prices in these data exist in a semi-structured textual format, an ad hoc text mining approach is used to extract quantitative price information. Descriptive analytics of the processed dataset demonstrate how the prices of EV charging vary with charging level (Direct Current Fast Charging versus Level 2), geographic location, network provider, and location type. Our research indicates that a great deal of diversity and flexibility exists in structuring the prices of EV charging to enable incentives for shaping charging behaviors, but that it has yet to be widely standardized or utilized. Comparisons with estimates of the levelized cost of EV charging illustrate some of the challenges associated with operating and using these stations.

The use of new vehicle technologies such as fuel cell hybrid electric and fully electric powertrains for biomass feedstock supply is an unexplored solution to reducing biofuel production cost, greenhouse gas emissions, and health impacts. These technologies have found success in light-duty vehicle applications and are in development for heavy-duty trucks. This study presents the first detailed stochastic techno-economic analysis and life-cycle assessment of biomass feedstock supply systems with diesel, fuel cell hybrid electric, and fully electric trucks and determines their impacts on biofuel production considering butanol as a representative biofuel. This study finds that fuel cell hybrid electric and fully electric trucks consume less energy relative to the diesel-powered truck regardless of the evaluated circumstances, including payloads of truck (loaded and empty), pavement types (gravel and paved), road conditions (normal and damaged), and road networks (local and highways). The use of fuel cell hybrid and fully electric trucks powered by H2-fuel and renewable sources of electricity, respectively, results in a large reduction in cost and carbon footprint, specifically for a long-distance hauling, and minimize other economic and environmental impacts. While the economic advantage of fuel cell hybrid electric vehicle is dependent on the price of H2-fuel and road conditions, use reduces the GHG emissions of biobutanol per 100 km-trucking-distance by 0.98–10.9 gCO2e/MJ. Results show that converting to fully electric truck transport decreases the biobutanol production cost and GHG emissions per 100 km-trucking-distance by 0.4–7.3 cents/L and 0.78 to 9.1 gCO2e/MJ, respectively. This study establishes the foundation for future investigations that will guide the development of economically, socially, and environmentally sustainable biomass feedstock supply system for cellulosic biorefineries or other goods transportation systems.

This study proposes a novel integration of a municipal wastewater treatment facility (WWTF) with a cyanobacterial nutrient removal process for sidestream wastewater treatment. A life cycle assessment (LCA) approach was used to determine the effectiveness and environmental performances of the integrated system. The LCA is populated by models of wastewater process engineering, material balance, cyanobacterial growth, and kinetics of anaerobic digestion. The cyanobacteria growth model incorporates chlorophyll synthesis, nitrogen uptake, photosynthesis, centrate inhibition, and competition for nitrogen between cyanobacteria and nitrifiers. Modeling results are validated against experiments with Synechocystis sp. PCC6803 grown in sludge centrate. With a maximum specific growth rate of 1.09 day−1, the nitrogen removal rate of the proposed WWTF would be increased by 15% when compared to the baseline wastewater treatment facility with a biological nutrient removal process. Incorporating the cyanobacterial nutrient removal process as the sidestream wastewater treatment of a conventional activated sludge process reduces the total nitrogen concentrations discharged from the WWTF from 25.9 to 15.2 mg 1−1. Methane yield was found to be increased by 4% of the baseline value when cyanobacterial biomass was co-digested with the activated sludge. Life cycle energy use and greenhouse gas emissions were found to be reduced by 8% and 17%, respectively, relative to a baseline wastewater treatment facility. Overall, a cyanobacteria-based sidestream municipal wastewater treatment process could be an effective and environmentally sustainable biological nutrient removal process in the future addressing the water-energy-food nexus.

Widely published research shows that significant fuel economy improvements through optimal control of a vehicle powertrain are possible if the future vehicle velocity is known and real-time optimization calculations can be performed. In this research, however, we seek to advance the field of optimal powertrain control by limiting future vehicle operation knowledge and using no real-time optimization calculations. We have realized optimal control of acceleration events (AEs) in real-time by studying optimal control trends across 384 real world drive cycles and deriving an optimal control strategy for specific acceleration event categories using dynamic programming (DP). This optimal control strategy is then applied to all other acceleration events in its category, as well as separate standard and custom drive cycles using a look-up table. Fuel economy improvements of 2% average for acceleration events and 3.9% for an independent drive cycle were observed when compared to our rigorously validated 2010 Toyota Prius model. Our conclusion is that optimal control can be implemented in real-time using standard vehicle controllers assuming extremely limited information about future vehicle operation is known such as an approximate starting and ending velocity for an acceleration event.

Fuel economy (FE) improvements for hybrid electric vehicles using a predictive Optimal Energy Management Strategy (Optimal EMS) is an active subject of research. Recent developments have focused on real-time prediction-based control strategies despite the lack of research demonstrating the aspects of prediction that are most important for FE improvements. In this paper, driving-derived nonstochastic prediction errors are applied to a globally optimal control strategy implemented on a validated model of a 2010 Toyota Prius, and the FE results are reported for each type of prediction error. This paper first outlines the real-world drive cycle development, then the baseline model development that simulates a 2010 Toyota Prius, followed by an implementation of dynamic programming (DP) to derive the globally optimal control, and finally the use of the DP solution to evaluate prediction errors. FE comparisons are reported for perfect prediction, prediction errors from 14 alternate drive cycles, and prediction errors from 6 alternate vehicle parameters. The results show that FE improvements from the Optimal EMS are maintained under mispredicted stops, traffic, and vehicle parameters, while route changes and compounded drive cycle mispredictions may result in FE improvements being lost. Taken together, these results demonstrate that implementation of an Optimal EMS can result in a reliable FE improvement.

Advanced driver-assistance systems (ADASs) have become a salient feature for safety in modern vehicles. They are also a key underlying technology in emerging autonomous vehicles. State-of-the-art ADASs are primarily vision based, but light detection and ranging (lidar), radio detection and ranging (radar), and other advanced-sensing technologies are also becoming popular. In this article, we present a survey of different hardware and software ADAS technologies and their capabilities and limitations. We discuss approaches used for vision-based recognition and sensor fusion in ADAS solutions. We also highlight challenges for the next generation of ADASs.

The fuel cell plug in hybrid electric vehicle (FCPHEV) is a near-term realizable concept to commercialize hydrogen fuel cell vehicles (FCV). Relative to conventional FCVs, FCPHEVs seek to achieve fuel economy benefits through the displacement of hydrogen energy with grid-sourced electrical energy, and they may have less dependence on a sparse hydrogen fueling infrastructure. Through the simulation of almost 690,000 FCPHEV trips using geographic information system (GIS) data surveyed from a fleet of private vehicles in the Puget Sound area of Washington State, USA, this study derives the electrical and hydrogen energy consumption of various design and control variants of FCPHEVs. Results demonstrate that FCPHEVs can realize hydrogen fuel consumption reductions relative to conventional FCV technologies, and that the fuel consumption reductions increase with increased charge depleting range. In addition, this study quantifies the degree to which FCPHEVs are less dependent on hydrogen fueling infrastructure, as FCPHEVs can refuel with hydrogen at a lower rate than FCVs. Reductions in hydrogen refueling infrastructure dependence vary with control strategies and vehicle charge depleting range, but reductions in fleet-level refueling events of 93% can be realized for FCPHEVs with 40 miles (60 km) of charge depleting range. These fueling events occur on or near the network of highways at approximately 4% of the rate (refuelings per year) of that for conventional FCVs. These results demonstrate that FCPHEVs are a type of FCV that can enable an effective and concentrated hydrogen refueling network.

Anaerobic digestion is a well-known and potentially beneficial process for rural communities in emerging markets, providing the opportunity to generate usable gaseous fuel from waste resources. With recent developments in low-cost digestion technology, communities across the world are gaining affordable access to the benefits of anaerobic digestion derived biogas. For example, biogas provides a more efficient and cleaner burning alternative to biomass (wood, charcoal, dung), effectively reducing harmful emissions and fuel consumption. This study sought to develop and test a design approach for optimizing flame port geometry for household biogas-fired burners. The approach consists of a multi-component simulation that incorporates three-dimensional CAD designs with simulated chemical kinetics and computational fluid dynamics. The simulated flame port designs included an array of circular and rectangular geometries using a widely available biogas burner. The three highest performing designs identified were manufactured and tested experimentally to validate model outputs and to compare against a baseline port geometry. In the experiment, each of the three designs suggested improved thermal efficiency relative to the baseline. A configuration of four millimeter circular ports resulted in a 7.17% improvement, raising an average thermal efficiency of 53.0% to 56.8%. The results indicated that hydraulic diameter, velocity and mixture density are relevant factors in port geometry design to improve the thermal efficiency of a biogas burner. Conversely, the emissions predictions made by the model were found to be unreliable and incongruent with laboratory experiments.

Unconventional oil and gas (UOG) production requires a vast quantity of freshwater while generating substantial volumes of wastewater. Although numerous studies have focused on technology development, other aspects beyond treatment technology, including regulations, economics, system logistics, and public perception, play equally or more important roles collectively in the selection and deployment of UOG wastewater management practices. In this article, we begin with a critical analysis of the motivations that drive UOG wastewater management towards treatment and reuse. Then we examine four main barriers against such a paradigm shift, pertaining to treatment technology, regulatory compliance, economic feasibility, and social acceptance. Despite the need of further improving technology efficiency for UOG wastewater treatment, the lack of established regulatory framework, the uncertainties of economic viability, as well as public resistance, hinder practical implementation of treatment technologies. We highlight the importance of knowledge and collaborative efforts from engineers, regulators, policy makers, economists, and social scientists to address those barriers, and emphasize that future research efforts should be directed at domains well beyond treatment technology. A systems approach and broader collaboration across multiple disciplines is needed to translate technology innovation into solutions that truly improve water sustainability in the context of rising UOG production.

The implementation of autonomous vehicles involves an increase in the number and depth of system interactions in comparison to user-driven cars. There is a corresponding need to address the system safety implications of autonomy. Traditional hazard analysis techniques are not designed to identify hazardous states caused by system interactions. An emerging technique based on systems theory, Systems Theoretic Process Analysis (STPA), allows for inclusion of system-level causal factors by focusing on component interactions. This study researches the application of STPA to a lane keeping assist system, resulting in identification of design constraints and requirements needed to engineer a safer system.

The net energy ratios (NERs) and greenhouse gas (GHG) emissions of a Photosynthetic Cyanobacterial Biorefinery (PSBR) were evaluated using a Life Cycle Assessment (LCA) approach. This study assessed engineered cyanobacterium cultures in which biosynthetic biofuels are directly secreted. Biofuels researched in this study include bisabolane, a biosynthetic substitute for D2 diesel; heptadecane, a substitute for diesel; and ethanol, a substitute for gasoline. Results demonstrate that cyanobacteria-based ethanol has higher yields, lower NER, and lower GHG emissions than bisabolane and heptadecane products in Synechocystis sp. PCC6803. By performing a sensitivity analyses of the life cycle NER and GHG emissions responses of the system, we derived the improvements in system biology, metabolic engineering, and process engineering that would be required to minimize the environmental impacts at an industrial scale. The NER and GHG emissions of the biofuel products were found to be the most sensitive to organism-level biofuel productivities, the energy consumption of vapor compression and distillation, and the energy consumption of culture mixing. This LCA will serve as a baseline for stakeholders; including policy makers, cyanobacterial refineries, and researchers, to establish the goals to engineer cyanobacteria and sustainable bioprocesses.

Turbulent mixing in pilot-scale cultivation systems influences the productivity of photoautotrophic cultures. We studied turbulent mixing by applying particle image velocimetry and acoustic doppler velocimetry to pilot-scale, flat-panel photobioreactor, and open-channel raceway. Mixing energy inputs were varied from 0.1 to 2.1 W·m-3 . The experimental results were used to quantify turbulence and to validate computational fluid dynamics models, from which Lagrangian representations of the fluid motion in these reactors were derived. The results of this investigation demonstrated that differences in mixing energy input do not significantly impact the structure of turbulence and the light/dark cycling frequencies experienced by photoautotrophic cells within the reactors. The experimental and computational results of our research demonstrated that well-mixed conditions exist in pilot-scale, flat-panel photobioreactors and open-channel raceways, even for relatively low mixing energy inputs.

The microalgae biofuels life cycle assessments (LCA) present in the literature have excluded the effects of direct land use change (DLUC) from facility construction under the assumption that DLUC effects are negligible. This study seeks to model the greenhouse gas (GHG) emissions of microalgae biofuels including DLUC by quantifying the CO2 equivalence of carbon released to the atmosphere through the construction of microalgae facilities. The locations and types of biomass and Soil Organic Carbon that are disturbed through microalgae cultivation facility construction are quantified using geographical models of microalgae productivity potential including consideration of land availability. The results of this study demonstrate that previous LCA of microalgae to biofuel processes have overestimated GHG benefits of microalgae-based biofuels production by failing to include the effect of DLUC. Previous estimations of microalgae biofuel production potential have correspondingly overestimated the volume of biofuels that can be produced in compliance with U.S. environmental goals.

This study focuses on the characterization of the technical and economic feasibility of an enclosed photobioreactor microalgae system with annual production of 37.85 million liters (10 million gallons) of biofuel. The analysis characterizes and breaks down the capital investment and operating costs and the production cost of unit of algal diesel. The economic modelling shows total cost of production of algal raw oil and diesel of $3.46 and $3.69 per liter, respectively. Additionally, the effects of co-products' credit and their impact in the economic performance of algal-to-biofuel system are discussed. The Monte Carlo methodology is used to address price and cost projections and to simulate scenarios with probabilities of financial performance and profits of the analyzed model. Different markets for allocation of co-products have shown significant shifts for economic viability of algal biofuel system.

Adapting turbocharger performance maps to a form suitable for dynamic simulations is challenging for the following reasons: (1) the amount of available data is typically limited, (2) data are typically not provided for the entire operating range of the compressor and turbine and (3) the performance data are non-linear. To overcome these challenges, curve fits are typically generated using the performance data individually for each device. The process, however, can take un-economical amounts of effort to implement for a range of compressors and turbines. This article introduces a method to implement non-dimensional performance maps thereby allowing a range of turbochargers to be modeled from the same performance data, reducing the effort required to implement models of different sizes. The non-dimensional maps seek to model the performance of compressor and turbine families in which the geometry of the rotor and housing are similar and allow the turbocharger to be scaled for simulation in much the same way used to design customized sizes of turbochargers. A method to match the non-dimensional compressor map to engine performance targets by selecting the compressor diameter is presented, as well as a method to match the turbine to the selected compressor.

This study describes the makeup of and results from a simulation consisting of a lumped thermal model integrated with a dynamic algae growth model to simulate the microalgae productivity of an open-channel raceway cultivation system. The thermal model considers the dynamic effects of weather, light absorption, convective heat transfer, radiation heat transfer, conductive heat transfer, thermal capacitance, and water control strategies. The dynamic algae growth model solves a set of ordinary differential equations consisting of growth functions dependent on incident radiation, temperature, nutrient availability, basal metabolism, and losses due to dark- and photo-respiration. Relative errors in the predicted ash-free dry weight of Nannochloropsis oceanica are 12.5%, −6.1%, and 4.4% for three separate replicates of cultivation cycles (~4.2 m2) performed at AzCATI during the ATP3 Unified Field Studies in Fall, Spring, and Summer. This research demonstrates that thermal modeling is an essential contributor to the validation of microalgae growth models.

This study presents a set of essential parameters specific to geography, climate, soil conditions, and irrigation, which can be used to classify commercially feasible Jatropha farming land, globally. The results are demonstrated considering Nepal as a representative case and including system-level economic feasibility and environmental sustainability analyses for each of the Federal States of Nepal. Probabilistic results describe a distribution of the minimum selling price and greenhouse gas (GHG) emissions of Jatropha-based biodiesel, respectively, with an average of $1.35/L and 46.51 gCO2e/MJ-biodiesel, and the 95% likelihood to be less than $4.57/L and 127.67 gCO2e/MJ-biodiesel. Considering direct, and then both direct and indirect land use changes increase the GHG emissions relative to the baseline by 3.5 and 8 times, respectively. Jatropha oil yield, biodiesel yield and recovery rates, and Jatropha seed production rates were found to be the most influential factors on the selling price of biodiesel and associated GHG emissions. The results imply that at the Jatropha oil yield of 50 wt%, a Jatropha seed yield of at least 3.9 t/ha and 5 t/ha are required to achieve a market-competitive biodiesel selling price of $1/L, and GHG emissions including direct land use change effects below the petroleum baseline of 87.2 gCO2e/MJ. Avoiding the disturbance of forest lands in favor of Jatropha afforestation is one of the identified strategies to minimize the impacts from land use changes. These methods demonstrate a pathway towards addressing current challenges and developing an environmentally and commercially viable Jatropha-based biodiesel industry in many countries including Nepal.

In this chapter, we introduce and summarize the MBSAP methodology that is the core topic of the book. First, we present the basic principles on which the methodology is based, expanding the discussion of MBSE from Chaps. 1 and 2, and we relate our approach to traditional Systems Engineering (SE) constructs such as the “Vee” diagram that is familiar to generations of system engineers. We expand on the key aspects of transitioning from a traditional document-centric approach to one that is model-centric, and we emphasize the fact that MBSE using MBSAP is not a fundamentally different SE process. Next, we give a high-level overview of MBSAP, and we summarize the flow of the methodology from initial requirements analysis and conceptual thinking to a final physical system implementation, following the levels of abstraction defined in the architecture taxonomy in Chap. 1. We address the crucial subject of model structure and present the Viewpoints and Perspectives that together provide the organizing principles of MBSAP. Many such structures are possible, but our experience is that the organization shown here delivers consistently good results across a wide range of system categories. Because of the importance of system stakeholders in determining the success or failure of a system development, we provide a discussion of the essentials of stakeholder interaction, especially using the architecture as a tool for dialog and analysis. We then introduce a hierarchy of modeling and simulation as an integral element of architecture development and refinement. We discuss the reality that architecture development is often constrained by the need to conform to customer policies and mandates, and we lay out a set of preliminary activities that are essential preparation for an effective architecture effort. Finally, we complete this introduction to MBSAP by reviewing a number of alternative methodologies and showing that to a great extent, MBSAP is consistent with them while stressing a rigorous succession of architecting activities with defined artifacts and metrics. Chapter objective: the reader will be able to describe the fundamental motivation for MBSE, its benefits when applied to a general SE process, and the background, goals, scope, products, and overall process of the MBSAP methodology, including stakeholder interactions.

In this chapter, we apply the MBSAP methodology to the special system category where on-time execution is the primary criterion of success or failure. We defined the Real-Time (RT) category in the architecture taxonomy in Chap. 1, and in the following chapters, we have seen how layered architectures are adapted to the unique demands of real-time performance. We now focus on the practical aspects of architecting RT systems. To begin, we expand on the introduction to RT systems from Chap. 1, followed by discussion of fundamental aspects of system timing and RT execution. We use the RT behaviors of the E-X example to illustrate these concepts. We also address the complex issue of developing good requirements for RT systems. The current standard for RT architecture modeling is the Modeling and Analysis of Real-Time and Embedded (MARTE) Profile of UML, which is summarized here and presented in more detail in Appendix D. The key to correct RT system design is assured schedulability, which we discuss and illustrate using the E-X radar. We complete the discussion with multiple practical aspects of implementing RT systems, including selecting of RT components and dealing with safety and security concerns. Chapter Objective: the reader will be able to apply the MBSAP methodology to RT systems, accounting for their unique requirements and constraints.

In this chapter, we provide the essential theory and practical details of employing the principles of Object Orientation (OO) in system architecture modeling. We do not attempt to provide an exhaustive OO tutorial, since there exists a very large literature on this subject (see the references to this chapter). Our goal, instead, is to give the reader the level of understanding needed to master the MBSAP methodology and apply it to practical system architecting challenges. First, we present the rationale for OO and define the essential concepts and terminology, stressing the benefits it delivers in dealing with complex entities. Although we use the Systems Modeling Language (SysML) as the basis for this book, we begin by discussing and illustrating the primary diagrams of the Unified Modeling Language (UML), which is the root language standard for OO. This is then followed by a description of the changes to UML introduced by the SysML, which is defined as a Profile of UML. The reality is that a large part of the SysML is taken directly from the UML language specification. We believe understanding of this material is enhanced by first considering the parent UML and then explicitly showing how UML modifies and extends the language. We include generic UML and SysML diagrams that illustrate important principles and syntax and that can serve as format samples in developing real architecture models. Appendices A and B are concise references to UML and SysML, respectively, intended to give readers a quick and easy way to look up the details. Chapter objective: the reader will be able to create and read UML and SysML diagrams and to describe the role of each.

This chapter describes the next phase of the MBSAP process, showing how the top-level architecture from the Operational Viewpoint (OV) is transformed into a functional design of the system in a Logical/Functional Viewpoint (LV). We familiarize the reader with the concepts and techniques involved in carrying the five Perspectives of the OV to the level of a functional architecture, using the E-X example for illustration. We discuss design patterns, making the point that although these arose in the world of software, they have great applicability and payoff when applied to entire systems. For example, we show how Domains are decomposed to the level of actual system components, modeled as Blocks. Similarly, we will take the representation of system behaviors down to individual Blocks or groups of Blocks and begin to look at the timing of system functions. In the Data Perspective, we illustrate the use of Inheritance to specialize Foundation Classes from the Conceptual Data Model into actual system data entities in a Logical Data Model. We introduce the key subject of layered architectures that are organized on the basis of services to create a computing platform on which applications software and user interfaces can be hosted. Next, we show what the LV looks like in the Smart Microgrid. When the LV is complete and fully documented, the system architect can furnish a set of functional specifications and other architecture artifacts to hardware and software developers, procurement and material management specialists, and others to use in implementing, integrating, testing, producing, and sustaining the system. We end the chapter with a discussion of executable architectures and a summary of key points in the LV. Chapter objective: the reader will be able to transform the high-level architecture of an OV into a functional design in an LV with supporting implementation documentation.

In this chapter, we develop the Physical Viewpoint (PV) of the MBSAP methodology, which models the actual implementation of a system. The PV builds on the artifacts of the Operational and Logical/Functional Viewpoints (OV and LV). We discuss underlying concepts and techniques, especially trade studies, involved in transforming function to physical design. Functional specifications from the LV support the development, or selection and procurement, of products. Blocks become Block Instances as design data is added to their specifications and the Structural and Behavioral Perspectives of the earlier Viewpoints combine into a Design Perspective. Concentrated attention on the choice and application of standards yields the Standards Perspective. We describe the key elements of a Design Perspective and illustrate them with the E-X system example. Because integration of physical components from multiple sources is a vital concern in PV development, we provide a detailed discussion of this subject. We then address the remaining Perspectives of the PV, modeling the Standards, Data, Services, and Contextual dimensions of a physical architecture. Next, we expand on a variety of Focused Viewpoints that may be needed by various stakeholders and carry the discussion of executable architecture to the level of physical simulations. Finally, we present some considerations that are important in ensuring that a system can evolve over its lifetime to remain operationally effective and supportable as requirements, environments, and other real-world influences impact it. Chapter objective: the reader will be able to take the functional design resulting from an LV and use specifications and other artifacts as the basis for building, integrating, and evaluating the real system.

A major payoff from systematic and rigorous application of MBSE results from cost and time savings that are achieved through reuse of designs, product information, integration and test materials, data models, timing analyses, and many other artifacts of system architecture. In this chapter, we explore the primary approach to such reuse, which is the development and use of reference architectures (RAs). First, we introduce the RA concept, and we define terms and highlight the role of RAs in MBSAP. We then lay out a systematic approach to the creation, archiving, retrieval, tailoring, and use of RAs in real development programs, stressing the nature of an RA as a system-level design pattern. We illustrate these ideas using the E-X system example. We extend the RA concept using SysML constructs like Profiles and Libraries. We discuss architecture frameworks, which are closely allied to RAs and can facilitate effective architecture reuse, and we discuss a number of recognized frameworks. We then conclude the RA topic by carrying it to the level of product line architectures that are predicated on efficiently tailoring RA content to meet related but distinct customer needs in a well-defined system or product category. Chapter Objective: the reader will be able to employ the MBSAP methodology to develop, model, and apply RAs as a means of reusing proven architecture content in new system developments.

This is the first of three chapters spelling out the MBSAP methodology in detail. The Operational Viewpoint (OV) transforms a customer’s requirements, which may be documented in a Capabilities or Requirements Database, into an architectural context by mapping them into a high-level definition of system structure, behavior, information content, services, and other content. We address the creation of a system requirements baseline, including the important distinction between function and nonfunctional requirements. We describe the successive activities involved in creating the model while recognizing that the process is inherently both parallel and interactive in treating the various Perspectives of the OV. We present the two system examples that are used throughout the book, the E-X Airborne sensor Platform and a family of Smart Microgrids. We then define a set of architecture styles that may shed light on the fundamentals of architecture for a system category. We apply the diagrams and techniques from Chap. 2, especially Block, Use Case, and Activity Diagrams, and introduce the use of model element specifications to fully define the architecture. We discuss in detail the process of creating the Perspectives of the OV. We show briefly the equivalent content of an OV for the very different case of the Smart Microgrid. We conclude the chapter with a short discussion of executable architecture, which will be developed in more detail in later chapters. The chapter objective is the reader will be able to create an OV in a SysML model using the sequence of activities in the MBSAP methodology to create a requirements baseline and transform it into a high-level architecture.

Virtually every system today confronts the cybersecurity threat, and the system architect must have the ability to integrate security features and functions as integral elements of a system. In this chapter, we survey this large, complex, and rapidly evolving subject with the goal of giving the reader a level of understanding that will enable incorporation of cybersecurity within an MBSE process and effective interaction with security experts. We begin by introducing the subject and describing the primary aspects of the current cybersecurity environment. We define fundamental terminology and concepts used in the cybersecurity community, and we describe the basic steps to include cybersecurity risk in an overall risk management process, which is a central SE responsibility. We then list some of the primary sources of information, guidance, and standards upon which a systems engineer can and should draw. Next, we summarize the major aspects of incorporating security controls in a system architecture and design to achieve an acceptable level of security risk for a system. We extend this to the increasingly important world of service-oriented, network-based, and distributed systems. We conclude with a brief presentation of the application of MBSAP to the specific issues of cybersecurity and summarize the characteristics of a Secure Software Development Life Cycle aimed at creating software with minimum flaws and vulnerabilities. We illustrate the application of cybersecurity principles and practices using the Smart Microgrid example. Chapter Objective: the reader will be able to apply the MBSAP methodology to systems and enterprises that require protection of sensitive data and processes against the growing cybersecurity threat and to work effectively with cybersecurity specialists to achieve effective secure system solutions.

In this short chapter, we collect a set of topics that may be significant in specific architecting situations but lie outside the main flow of material in the book. Most of these are relatively recent developments, and all of them are experiencing growing levels of interest and use in Systems Engineering and Architecture. We summarize Virtualization, which deals with a number of schemes for emulating or abstracting computing resources to create virtual versions that are more efficient and flexible than the actual hardware and software in a wide range of computing environments. We discuss a related topic, Clustering, which is a particular kind of hardware virtualization that integrates a set of resources, usually servers, to create a single platform. Next, we consider several varieties of Distributed Computing, which share the goal of coordinating and employing dispersed resources to create powerful and resilient computing capability. One version of this is a primary constituent of cloud computing, which is perhaps the most prominent current trend in information technology and which, itself, has multiple variants. We look briefly at agents and proxies and summarize several advanced algorithms, including artificial neural networks and genetic algorithms. Finally, we touch on several important aspects of the mechanical design of systems. Chapter Objective: the reader will acquire a basic understanding of important technologies that impact contemporary system architecture.

In this chapter we explore the application of MBSAP to the organizational level of an enterprise or system-of-systems (SoS), the highest level of the structural hierarchy. Enterprise architecture raises a number of new challenges because the individual systems that make up the enterprise are often developed, managed, and operated independently, making it difficult to harmonize their interactions. A fully satisfactory System-of-Systems Engineering (SoSE) methodology does not yet exist, but a number of useful approaches are available that can be incorporated into MBSAP. We first discuss general considerations of enterprise architectures and then focus on the technical challenges, which we approach by considering the enterprise as a system-of-systems (SOS), including a brief look at adaptive and emergent behavior, which can be especially significant in an enterprise context. With this background established, we introduce the extension of MBSAP for enterprise architecture and further develop this in an SoSE methodology. The remainder of the chapter addresses specific aspects of SoSE, including interoperability among systems within an SoS, modeling and simulation at the enterprise level, enterprise networking, and dynamic enterprises. Chapter Objective: the reader will be able to apply MBSAP, with appropriate extensions, to deal with the challenges of an SoS and create effective enterprise architectures that are open, robust, and evolvable.