System-level Design Research Articles

Novel methodology for functional design chain analysis of a nuclear power plant: A new built Finnish power plant case study

Energized by the fact that nuclear power plants (NPP) have a multidisciplinary character as well as a high degree of coupling and interaction between their elements, regulatory bodies worldwide tend to induce a massive number of requirements to ensure the safety of the plants. Mainly, these requirements aim to apply the defence-in-depth (DiD) safety approach. In Fennovoima Hanhikivi 1 (FH1) NPP, the DiD approach is applied through formalizing functional entities that are responsible for defending various DiD levels and preventing them from failure. Each functional entity consists of safety functions that share common non-functional requirements such as safety classification, failure criterion, diversity, and separation requirements.When investigating the safety functions of any NPP, one concludes that they are significantly interconnected and their processing logics exchange a huge number of signals. Therefore, verifying each safety function independently is not effective because a function may seem to perform its function when it is considered alone, but challenges are found when also the interconnected functions are considered. Thus, it becomes essential to verify the whole functional chain. A functional chain can be defined as a set of safety functions that interact together to guarantee the success of plant systems in case of an initiating event. It should be noted that the functional chain term, defined in this study, is different from the safety group term defined in the IAEA safety glossary. The latter is an assembly of equipment, whereas a functional chain is a group of safety functions. In other words, considering the safety engineering process of the plant life cycle, a functional chain is constructed and defined based on plant-level design down to system-level design, while a safety group is constructed based on system-level design.In this study, a methodology for analysing the functional chain is developed. The methodology mainly targets verifying five aspects of the functional chain which are: measurements, actuators, decision-making processing logic, human–machine interface (HMI), and cabling between various components. The methodology utilizes system engineering approach integrated with a relational database that can be utilized to query the plant design data in order to verify the application of non-functional requirements to the plant design components. Additionally, the study also develops a tool that can assist its users to systematically apply the developed methodology and record its results.Finally, a case study for practically applying the methodology to a NPP design is presented. The application of the methodology succeeded in revealing issues that need to be further clarified with the plant supplier. The main usefulness of applying the developed “functional chain analysis” methodology to a NPP is that it provides an additional as-designed verification approach besides the existing verification methods.

Cyberassured Systems Engineering at Scale

Our team has developed a model-based systems engineering environment that integrates formal methods at all levels of system design. Our methodology and tools enable systems engineers to address cybersecurity concerns early in the development of complex high-assurance systems.

On Diverse System-Level Design Using Manifold Learning and Partial Simulated Annealing

AbstractThe goal in system-level design is to generate a diverse set of high-performing design configurations that allow trade-offs across different objectives and avoid early concretization. We use deep generative models to learn a manifold of the valid design space, followed by Monte Carlo sampling to explore and optimize design over the learned manifold, producing a diverse set of optimal designs. We demonstrate the efficacy of our proposed approach on the design of an SAE race vehicle and propeller.

Design optimization of engine mounting system for a modular electric vehicle platform with different powertrain characteristics

In recent days, there is much focus on the modularity of the platform to reduce the cost and improve the system-level design efficiency. In this paper, a study has been done for various non-linear input torque (different powertrain) and optimization with a modular powertrain mounting system where only stiffness is varied to achieve the optimization goal. Here, the optimization goals are maximization of kinetic energy distribution to improve the decoupling rate and minimization of powertrain CoG movement under torque. HEEDS and MATLAB optimization tools are used for analysis. First, three different powertrains are considered individually for optimization. Then, all three powertrains for a particular location on the modular platform are optimized with different mount stiffness. Different mount stiffnesses are chosen in such a way that the stiffness can be achieved only by hardness adjustment or minimum design modification without any change in complete mount design. Proposed results show that a properly chosen mounting system can address the platform modularity quite quickly.

Exploring the form of big data products and the supporting systems

There have been studies and practices about forms and supporting systems of single-type data products. However, little literature is dedicated to these factors of big data products, which are huge in volume and contain a variety of different types of data. For example, we do not know the form of data composed of a relation table and a digital video file at the same time. The lack of certain forms of big data products makes it impossible to design its base unit for measuring. It also causes difficulties in pricing and valuation of big data products, and further causes issues of circulation and supporting services. In this paper, we analyze the challenges of productizing big data. By referring to information media such as books, a product form and supporting systems of big data products based on Data Box are proposed. Different from previous studies, we focus on the system-level design and systematically study the pipeline including containerization, circulation, pricing, protection and etc. The proposed Data Box-based big data products and supporting systems can have a positive influence on the research, exploitation, exchange, and circulation of big data products.

Structural reliability analysis of offshore jackets for system-level fatigue design

Lattice systems such as offshore jackets and towers are weight-efficient support structures for wind energy and oil and gas units. Nevertheless, their light-weight is achieved at the expense of increasing their proneness to fatigue failure, due to their many welded connections. Consequently, special attention is to be dedicated to the design of the fatigue hot spots. System-level fatigue design methods aim at calibrating the reliability of the fatigue components to achieve a desired target system reliability. These methods rely on the accurate assessment of the system probability of failure, which is computationally demanding due to the statistical dependence among fatigue limit states and the large number of possible deterioration states that need to be taken into account. In the present paper, we develop a novel approach, called the truncation algorithm, to estimate lower and upper bounds of the system reliability due to extreme environmental and fatigue limit states within feasible computational time. The proposed approach is applied to assessing existing system-level fatigue design methods and to study system effects, such as the effects of redundancy and the correlation among fatigue limit states on the system reliability.

Systematic Design of a 100-W 6.78-MHz Wireless Charging Station Covering Multiple Devices and a Large Charging Area

This article details the systematic design of a 100-W multiload wireless charging station. The station provides positional flexibility for the receivers, exhibits limited cross-channel disturbance, and has high efficiency. The positional flexibility is achieved using a transmitter coil design approach, which uses interleaved windings and controlled coil geometry to shape the magnetic field. The limited cross-channel disturbance is realized with an immittance network, which maintains a constant transmitter coil current. A holistic system-level modeling and design method is proposed to design the entire system for maximum efficiency. The first step is decoupling each stages using parameterized source and/or load of adjacent stages, and optimize internally for minimum loss, which leads to a reduced design space. The second step is system-wide optimization, which addresses power loss interdependence. A 100-W, 6.78-MHz experimental system with a 0.5 × 0.5 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> transmitter coil is demonstrated. The measured magnetic field variation on the charging surface is 15.9%. And the system shows no cross-channel disturbance when charging two 50-W receivers. The measured dc-to-dc efficiency is 92.8%.

Early Area and Power Estimation Model for Rapid System Level Design and Design Space Exploration

Power and area estimation in the early stage of designing is very critical for a system. This paper presents the neural network-based early area and power estimation model. The flow starts with the training of the neural network model from the selected behavioral level parameters, which imposes to provide accurate estimations. The model accuracy is validated against ITC99 benchmark programs. The run-times are faster than the synthesis run-times. For the ASIC-based designs, the proposed model took 5 seconds, while Synopsys Design Compiler took 5 minutes. In terms of timing, the estimation speed is more than the order of magnitude faster than the conventional synthesis-based approach. The modeling methodology provides a better, accurate, and fast area and power estimations, at an early stage of the Very-Large-Scale Integration (VLSI) design. In addition, the model eliminates the need for synthesis-based exploration and provides the design picking before synthesis.

Harvesting Systems for RF Energy: Trends, Challenges, Techniques, and Tradeoffs

The RFEH design challenges can be broadly classified into overall radio frequency direct current (RF-to-DC) power conversion efficiency (PCE), form factor, operational bandwidth (BW), and compactness. A detailed overview of the essential components of an RFEH system is presented in this paper. Various design approaches have been proposed for the realization of compact RFEH circuits that contribute immensely to mm-wave rectenna design. Effective mechanisms for configuring the rectenna modules based on the recommended spectrums for the RFEH system were also outlined. This study featured a conceptual viewpoint on design tradeoffs, which were accompanied by profound EH solutions perspectives for wireless power communications. The work covers some challenges attributed to 5G EH in mm-wave rectenna: from a controlled source of communication signals to distributed ambient EH and system level design. Conversely, the primary targets of this work are to: (I) examine a wide range of ambient RF sources and their performance with various antennae and RF-rectifier layouts; (II) propose unique rectenna design techniques suitable for current trends in wireless technology; (III) explore numerous approaches for enhancing the rectenna or RF-rectifier efficiency in a low-power ambient environment; and (IV) present the findings of a comprehensive review of the exemplary research that has been investigated. These are aimed toward addressing the autonomous system’s energy challenges. Therefore, with the careful management of the reported designs, the rectenna systems described in this study would influence the upcoming advancement of the low-power RFEH module.

Benchmark tests on high strength steel frames

Physical experiments are widely used in structural engineering for the validation of finite element models and the establishment and assessment of design provisions. However, while there exists an abundance of member-level test data, there is a clear need for further benchmark frame-level test data, particularly given the growing trend for advanced system-level simulation and design. Towards meeting this need, and exploiting state-of-the-art measurement techniques, tests on eight high strength steel frames are presented herein. The tested frames were fixed-base, single storey, unbraced in-plane and made up of welded S690 steel I-section members. Two section sizes, of different local slenderness, were employed to assess the influence of local buckling on cross-section strength and the potential for plastic redistribution of forces within the frames. All the frames were laterally restrained out-of-plane using a bespoke lateral bracing system, but free to deform in-plane. The frames were subjected to different combinations of horizontal and vertical loading, in order for beam, sway and combined mode mechanisms to form. Extensive use was made of digital image correlation in order to obtain detailed information on the deformation of the frames and their components. The test setup, instrumentation, loading procedures and frame structural response, including load-deformation characteristics, failure modes and connection rotational stiffnesses are fully reported. The experimental data indicated that, despite the lower ductility and strain hardening of HSS relative to normal strength steel (NSS), HSS frames with stocky cross-sections have the ability to form plastic hinges with sufficient rotation capacity to achieve a considerable amount of inelastic moment redistribution; the potential for extending plastic design to HSS structures has therefore been demonstrated. The test results also provide a firm basis for the validation of advanced numerical models and system-level design approaches.

An Atlas for the Inkjet Printing of Large-Area Tactile Sensors.

This review aims to discuss the inkjet printing technique as a fabrication method for the development of large-area tactile sensors. The paper focuses on the manufacturing techniques and various system-level sensor design aspects related to the inkjet manufacturing processes. The goal is to assess how printed electronics simplify the fabrication process of tactile sensors with respect to conventional fabrication methods and how these contribute to overcoming the difficulties arising in the development of tactile sensors for real robot applications. To this aim, a comparative analysis among different inkjet printing technologies and processes is performed, including a quantitative analysis of the design parameters, such as the costs, processing times, sensor layout, and general system-level constraints. The goal of the survey is to provide a complete map of the state of the art of inkjet printing, focusing on the most effective topics for the implementation of large-area tactile sensors and a view of the most relevant open problems that should be addressed to improve the effectiveness of these processes.

Prototyping a novel compact 3-DOF hydraulic robotic actuator via metallic additive manufacturing

ABSTRACT In this study, the development of a novel hydraulic robotic actuator is introduced. With the use of additive manufacturing (AM), lightweight and compact design can be achieved. A high load-to-weight ratio 3-DOF robot actuator with orthogonally connected hydraulic rotary cylinders is designed for AM. To validate the feasibility, a titanium hydraulic robot actuator is prototyped by using the metal powder bed fusion (M-PBF) AM process. A set of loading experiments are conducted on the AM-printed prototype. The study shows that system-level AM design and manufacturing methods can help gain increased output torque but with reduced weight and volume. With the use of hydraulic driving, the robotic actuator is more compact and solid to resist to tough environments as compared with the traditional actuators using electric motors. This study also implies the potential wide application of AM to design and print similar hydraulic link mechanism in heavy-duty robot or mobile humanoid robot.

Machine Learning Enabled Design Automation and Multi-Objective Optimization for Electric Transportation Power Systems

This article presents an automated design and optimization framework for electric transportation power systems (ETPS) enabled by machine learning (ML). The use of physical models, simulations, and optimization methods can greatly aid the engineering design process. However, when considering the optimal co-design of multiple interdependent subsystems that span multiple physical domains, such model-based simulations can be computationally expensive, and traditional metaheuristic optimization methods can be unreliable. Bayesian optimization (BO), an ML framework, paves one feasible pathway to realize an efficient design process practically. However, current state-of-the-art BO algorithms are non-compatible or perform poorly when applied to system-level ETPS design with multiple objectives and constraints. This article proposes a novel BO algorithm referred to as max-value entropy search for multiobjective optimization with constraints (MESMOC) to solve multiobjective optimization (MOO) problems with black-box constraints that can only be evaluated through design simulations. After a full presentation of the algorithm, MESMOC is applied to a realistic ETPS design case using a heavy-duty electric vertical-takeoff-landing (eVTOL) urban aerial vehicle (UAV) power system. Two MOO experimental trials show a drastic reduction in the number of design simulations to discover a high-quality Pareto front. In Trial 1, MESMOC uncovered the entire Pareto front while only requiring to explore ~4% of the design space. With expanded design parameters and larger design space in Trial 2, a near complete but high-quality Pareto front was uncovered. Both trials compared MESMOC to the popular genetic algorithm NSGA-II and another BO algorithm predictive entropy search for multi-objective Bayesian optimization with constraints (PESMOC), showing superior performance.

Shape-controlled fabrication of cost-effective, scalable and anti-biofouling hydrogel foams for solar-powered clean water production

• Development of closed-cell hydrogel foams via a controlled foaming − gelation technique. • Demonstration of self-contained solar evaporation and desalination. • Realization of a single-stage water purifier for efficient clean water production. Solar-powered interfacial evaporation has emerged as a sustainable technology for clean water production with a minimized carbon footprint. Fabrication of three-dimensional (3D) steam generators via conventional processing techniques (sol–gel and template-assisted methods) remain one of the main roadblocks toward mass production and scalable applications. In this study, 3D hydrogel foams based on biopolymer composites are developed via a controlled foaming − gelation technique and applied to a monolithic interfacial steam generator. The designed closed-cell structures deliver many important form-factors to the hydrogel foams, such as light-weight, low-cost, low thermal insulation and efficient water diffusion. The hydrogel foam with a maximum height of 3.2 cm attains a high water evaporation rate of 2.12 kg m −2 h −1 and a high cost-effectiveness of 2692 g h −1 $ −1 under one sun. A single-stage water purification system is fabricated based on the high-performing hydrogel foam. Because of its self-floating structure and enhanced passive cooling on open water, the system attains a maximum clean water collection rate of 0.71 L m −2 h −1 under real sky and surpasses a majority of single-stage solar stills. This work demonstrates the rational fabrication of hydrogel foams and combines high-performance solar evaporation with other intriguing properties (i.e., cost-efficient, scalable, anti-biofouling, self-floating and anti-overturning). The system-level design enables facile scale-up of clean water production.

Using a leverage points perspective to compare social-ecological systems: a case study on rural landscapes

ABSTRACT A leverage points perspective recognises different levels of systemic depth, ranging from the relatively shallow levels of parameters and feedbacks to the deeper levels of system design and intent. Analysing a given social-ecological system for its characteristics across these four levels of systemic depth provides a useful diagnostic to better understand sustainability problems, and can complement other types of cause-and-effect systems modelling. Moreover, the structured comparison of multiple systems can highlight whether sustainability challenges in different systems have a similar origin (e.g. similar feedbacks or similar design). We used a leverage points perspective to systematically compare findings from three in-depth social-ecological case studies, which investigated rural landscapes in southeastern Australia, central Romania, and southwestern Ethiopia. Inductive coding of key findings documented in over 60 empirical publications was used to generate synthesis statements of key findings in the three case studies. Despite major socioeconomic and ecological differences, many synthesis statements applied to all three case studies. Major sustainability problems occurred at the design and intent levels. For example, at the intent level, all three rural landscapes were driven by goals and paradigms that mirrored a productivist green revolution discourse. Our paper thus highlights that there are underlying challenges for rural sustainability across the world, which appear to apply similarly across strongly contrasting socioeconomic contexts. Sustainability interventions should be mindful of such deep similarities in system characteristics. We conclude that a leverage points perspective could be used to compare many other types of social-ecological systems around the world.

Assessing the Feasibility of a Cold Start Procedure for Solid State Batteries in Automotive Applications

This paper addresses the thermal management of a solid polymer electrolyte battery system, which is currently the only commercialized solid-state battery chemistry. These batteries aim to increase the range of electric vehicles by facilitating a lithium metal anode but are limited by operational temperatures above 60 °C. The feasibility of a cold start procedure is examined, which would enable a solid polymer battery to be used, without preconditioning, in a wide variety of ambient temperatures. The proposed solution involves dividing the solid-state battery into smaller sub-packs, which can be heated and brought online more quickly. Thermal modelling shows a cold start procedure is theoretically feasible when using a small liquid electrolyte lithium battery at the start. The key bottlenecks are the rate at which the solid-state batteries can be heated, and the discharge rates they can provide. After resistive heating is used for the first solid-state module, all subsequent heating can be provided by waste heat from the motor and operating battery modules. Due to the insulation required, the proposed system has lower volumetric, but higher gravimetric energy density than liquid electrolyte systems. This work suggests that with suitable system-level design, solid-state batteries could be widely adopted despite temperature constraints.

A High-Resolution Single-Photon Arrival-Time Measurement With Self-Antithetic Variance Reduction in Quantum Applications: Theoretical Analysis and Performance Estimation

An almost all-digital time-to-digital converter (TDC) possessing sub-picosecond resolutions, scalable dynamic ranges, high linearity, high noise-immunity, and moderate conversion-rates can be achieved by a random sampling-and-averaging (RSA) approach with the self-antithetic variance reduction (SAVR) technique for time-correlated single-photon counting (TCSPC) quantum measurements. This paper presents detailed theoretical analysis and behavior-model verifications of the SAVR technique to effectively enhance the conversion-rate of an asynchronous RSA-based TDC by more than 62× with 7% power overhead. In addition, the proposed performance estimation methodology for SAVR can greatly improve the computation efficiency during the system-level design and reduce the read-out circuit complexity in the silicon-photonics RSA-based TCSPC realization.

Small-Signal Stability Modeling for MMC-Based DC Grids With Voltage Slope Control and Influence Analysis of Parameters

In the flexible DC grids, selections of slope coefficients in system level controls and designs of smoothing reactors have a crucial influence on the system stability. In order to obtain their optimal values, a comprehensive small-signal stability model with the DC voltage slope control and smoothing reactors is proposed in this paper. Firstly, the linear state-space model of the entire system based on the small-signal method is deduced according to the coupling equations between the inputs, the outputs, and state variables related to converter stations and the AC sides, controllers, and the DC grid with smoothing reactors. Meanwhile, the influences of the MMC bridge arm circulating current and the capacitor voltage fluctuation of the sub-module are considered in this model. Secondly, the impacts of control parameters, smoothing reactor parameters, and bridge arm inductances are analyzed on the system stability through the Lyapunov stability theory. Finally, the simulation of Zhangbei four-terminal DC grid project in China is established to testify the correctness of the presented small-signal model and the conclusions about the impact of parameters on the system stability. The proposed method will provide some theoretical guidance for the system design and the parameter selection in DC grid applications.

Millimeter-Wave Power Amplifier ICs for High Dynamic Range Signals

The next-generation 5G and beyond- 5G wireless systems have stimulated a substantial growth in research, development, and deployment of mm-Wave electronic systems and antenna arrays at various scales. It is also envisioned that large dynamic range modulation signals with high spectral efficiency will be ubiquitously employed in future communication and sensing systems. As the interface between the antennas and transceiver electronics, power amplifiers (PAs) typically govern the output power, energy efficiency, and reliability of the entire wireless systems. However, the wide use of high dynamic range signals at mm- Wave carrier frequencies substantially complicates the design of PAs and demands an ultimate balance of energy efficiency and linearity as well as other PA performances. In this review paper, we will first introduce the system-level requirements and design challenges on mm-Waves PAs due to high dynamic range signals. We will review advanced active load modulation architectures for mm-Wave PAs and power devices. We will then introduce recent advances in mm-Wave PA technologies and innovations with several design examples. Special design considerations on mm-Wave PAs for phased array MIMOs and high mm-Wave frequencies will be outlined. We will also share our vision on future technology trends and innovation opportunities.

Thermoelectric Generators: A comprehensive review of characteristics and applications

• The open literature on thermoelectric generators is comprehensively reviewed. • Technical aspects of thermoelectric generators is thoroughly discussed. • Device and system-level designs for thermoelectric generators are scrutinized. • Thermoelectric generators applications are broadly elaborated and reviewed. • Recommendations are provided for future studies. Energy crisis and associated environmental issues are the main challenges in today’s changing world, where there is a growing demand for electricity. To address such challenges, modern-day energy conversion systems for which, reliability and scalability are the two most desired features, are in need. Thermoelectric generators are a kind of heat engines capable of offering solutions for these challenges if they reach their expected potentials. The advantages for such technologically advanced devices are manifold; they are environmentally friendly, highly scalable, reliable, and have long lifespan. Additionally, by applying thermoelectric generators, one can improve the existing systems’ efficiency or supply the electricity demand of different systems with high flexibility. Nonetheless, the low conversion efficiency of thermoelectric generators has impeded their wide application, restricting them to an academic subject. Yet, recent advancements in thermoelectric materials and devices are pushing the technology to find its place among state-of-the-art energy conversion systems. The present review explores the recent literature to present a comprehensive and realistic perspective of the state of the technology. Furthermore, this review carefully investigates the feasibility of integrating thermoelectric generators into different systems and applications, and eventually, provides an in-depth analysis with recommendations for future studies.