Generic Design Methodology Research Articles

A Generic Multidimensional Design Methodology for Highly Efficient RF Power Amplifier with Improved Linearity

A Generic Multidimensional Design Methodology for Highly Efficient RF Power Amplifier with Improved Linearity

Towards Area Efficient Logic Circuit: Exploring Potential of Reconfigurable Gate by Generic Exact Synthesis

In this paper, we propose a generic design methodology to achieve area-efficient reconfigurable logic circuits by using exact synthesis based on Boolean satisfiability (SAT) solver. The proposed methodology better leverages the high representation ability of emerging reconfigurable logic gates (RLGs) to achieve reconfigurable circuits with fewer gates. In addition, we propose a fence-based acceleration method to provide >10× speed up for the synthesis without an observable loss of optimality. Furthermore, four sets of RLGs are developed based on a recently proposed valley-spin device as a case study to demonstrate the advantage of the proposed circuit. Simulations have been performed to analyze the impact of the fence searching algorithm and combination of operators. Based on disjoint-support decomposable (DSD) benchmarks, up to 38% and 73% reductions are observed in the area and energy-delay-area product (EDAP), respectively, compared to CMOS counterparts. Compared to the two existing synthesis methods, the proposed scheme provides 40% and 26.3% reduction in EDAP based on MCNC benchmark.

Generic and scalable DNA-based logic design methodology for massive parallel computation

Generic and scalable DNA-based logic design methodology for massive parallel computation

AppAxO : Designing App lication-specific A ppro x imate O perators for FPGA-based Embedded Systems

Approximate arithmetic operators, such as adders and multipliers, are increasingly used to satisfy the energy and performance requirements of resource-constrained embedded systems. However, most of the available approximate operators have an application-agnostic design methodology, and the efficacy of these operators can only be evaluated by employing them in the applications. Furthermore, the various available libraries of approximate operators do not share any standard approximation-induction policy to design new operators according to an application’s accuracy and performance constraints. These limitations also hinder the utilization of machine learning models to explore and determine approximate operators according to an application’s requirements. In this work, we present a generic design methodology for implementing FPGA-based application-specific approximate arithmetic operators. Our proposed technique utilizes lookup tables and carry-chains of FPGAs to implement approximate operators according to the input configurations. For instance, for an \( \text{M}\times \text{N} \) accurate multiplier utilizing K lookup tables, our methodology utilizes K -bit configurations to design \( 2^K \) approximate multipliers. We then utilize various machine learning models to evaluate and select configurations satisfying application accuracy and performance constraints. We have evaluated our proposed methodology for three benchmark applications, i.e., biomedical signal processing, image processing, and ANNs. We report more non-dominated approximate multipliers with better hypervolume contribution than state-of-the-art designs for these benchmark applications with the proposed design methodology.

Cross-Blockchain Technology: Integration Framework and Security Assumptions

Interoperability is identified as one of the major design constraints for blockchain technology. Cross-blockchain technology is fast evolving as the demand for value transfer among different blockchain systems is growing. A generic cross-blockchain design methodology for interoperability requires a set of suitable components to facilitate the integration process. One of the main challenges in the integration is to protect the integrity of the shared data. Various integration systems and their components may operate with different underlying security assumptions and this may lead to compromise of the overall security. In this paper we review the latest advancement in blockchain integration systems and provide a comprehensive analysis of integration characteristics for cross-blockchain technology and then define the essential components and modes of integration. Based on the outcome of our review, we propose a novel integration design decision framework that identifies key assumptions and critical characteristics of the cross-blockchain technology. The proposed framework facilitates the best-practice decision-making process. This reduces the potential for design errors and the associated security risks and performance degradation of the overall system.

Aerodynamic design and experimental validation of high pressure ratio partial admission axial impulse turbines for unmanned underwater vehicles

High pressure ratio partial admission axial impulse turbines are typically employed for underwater vehicles due to small mass flow rate and machining constraints. However, little work has been focused on the generic design methodology for high pressure ratio partial admission axial impulse turbines. The experimental results and empirical correlations are mostly established upon low pressure ratio axial turbines. In this paper, the meanline turbine design method is first documented together with the performance prediction method at off-design points. The numerical loss breakdown method is then described to thoroughly verify empirical correlations. Both the meanline and numerical methods are thoroughly validated against experimental results and the difference among meanline, numerical and experimental results is less than 10%. A case study is then performed and a 13 kW partial admission axial impulse turbine is designed. The suitability of empirical correlations is confirmed by comparing the losses between the meanline and numerical methods, and the difference less than 5% at design and off-design points is attained. This paper provides the insight into the design methodology for high pressure ratio partial admission axial impulse turbines.

Generic Design Methodology for Vertical Takeoff and Landing Aircraft with Hybrid-Electric Propulsion

This paper proposes a conceptual methodology for designing vertical takeoff and landing aircraft with a hybrid-electric propulsion system as an alternative to facilitate intercity missions for urban air mobility. Four new modules are developed to consider the diversity of configurations, flight mechanisms, and hybrid-electric propulsion system architectures. The flight-analysis module is proposed to account for various lift- and thrust-generating devices. The hybrid-electric propulsion system sizing module is modified for the vertical takeoff and landing aircraft by additionally considering transition flight. Based on a new proposed battery charge/discharge criterion, the mission-analysis module is constructed to predict the battery capacity and fuel consumption required for a given mission. The weight-estimation module is also developed considering the weight of the mechanical and electric powertrains. The proposed methodology is demonstrated by designing an urban air mobility vehicle and comparing the results with those of other sizing tools. A systematic comparative study and design optimization are carried out to highlight the improvement in the performance of the hybrid-electric-powered vertical takeoff and landing aircraft compared to its battery-driven counterpart. The extended mission range of the designed aircraft presents the possibility of intercity urban air mobility vehicles.

Generic Design Methodology for Smart Manufacturing Systems from a Practical Perspective. Part II—Systematic Designs of Smart Manufacturing Systems

In a traditional system paradigm, an enterprise reference model provides the guide for practitioners to select manufacturing elements, configure elements into a manufacturing system, and model system options for evaluation and comparison of system solutions against given performance metrics. However, a smart manufacturing system aims to reconfigure different systems in achieving high-level smartness in its system lifecycle; moreover, each smart system is customized in terms of the constraints of manufacturing resources and the prioritized performance metrics to achieve system smartness. Few works were found on the development of systematic methodologies for the design of smart manufacturing systems. The novel contributions of the presented work are at two aspects: (1) unified definitions of digital functional elements and manufacturing systems have been proposed; they are generalized to have all digitized characteristics and they are customizable to any manufacturing system with specified manufacturing resources and goals of smartness and (2) a systematic design methodology has been proposed; it can serve as the guide for designs of smart manufacturing systems in specified applications. The presented work consists of two separated parts. In the first part of paper, a simplified definition of smart manufacturing (SM) is proposed to unify the diversified expectations and a newly developed concept digital triad (DT-II) is adopted to define a generic reference model to represent essential features of smart manufacturing systems. In the second part of the paper, the axiomatic design theory (ADT) is adopted and expanded as the generic design methodology for design, analysis, and assessment of smart manufacturing systems. Three case studies are reviewed to illustrate the applications of the proposed methodology, and the future research directions towards smart manufacturing are discussed as a summary in the second part.

Generic Design Methodology for Smart Manufacturing Systems from a Practical Perspective, Part I—Digital Triad Concept and Its Application as a System Reference Model

Rapidly developed information technologies (IT) have continuously empowered manufacturing systems and accelerated the evolution of manufacturing system paradigms, and smart manufacturing (SM) has become one of the most promising paradigms. The study of SM has attracted a great deal of attention for researchers in academia and practitioners in industry. However, an obvious fact is that people with different backgrounds have different expectations for SM, and this has led to high diversity, ambiguity, and inconsistency in terms of definitions, reference models, performance matrices, and system design methodologies. It has been found that the state of the art SM research is limited in two aspects: (1) the highly diversified understandings of SM may lead to overlapped, missed, and non-systematic research efforts in advancing the theory and methodologies in the field of SM; (2) few works have been found that focus on the development of generic design methodologies for smart manufacturing systems from the practice perspective. The novelty of this paper consists of two main aspects which are reported in two parts respectively. In the first part, a simplified definition of SM is proposed to unify the existing diversified expectations, and a newly developed concept named digital triad (DT-II) is adopted to define a reference model for SM. The common features of smart manufacturing systems in various applications are identified as functional requirements (FRs) in systems design. To model a system that is capable of reconfiguring itself to adapt to changes, the concept of IoDTT is proposed as a reference model for smart manufacturing systems. In the second part, these two concepts are used to formulate a system design problem, and a generic methodology, based on axiomatic design theory (ADT), is proposed for the design of smart manufacturing systems.

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

The world’s increasing population requires the process industry to produce food, fuels, chemicals, and consumer products in a more efficient and sustainable way. Functional process materials lie at the heart of this challenge. Traditionally, new advanced materials are found empirically or through trial-and-error approaches. As theoretical methods and associated tools are being continuously improved and computer power has reached a high level, it is now efficient and popular to use computational methods to guide material selection and design. Due to the strong interaction between material selection and the operation of the process in which the material is used, it is essential to perform material and process design simultaneously. Despite this significant connection, the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required. Hybrid modeling provides a promising option to tackle such complex design problems. In hybrid modeling, the material properties, which are computationally expensive to obtain, are described by data-driven models, while the well-known process-related principles are represented by mechanistic models. This article highlights the significance of hybrid modeling in multiscale material and process design. The generic design methodology is first introduced. Six important application areas are then selected: four from the chemical engineering field and two from the energy systems engineering domain. For each selected area, state-of-the-art work using hybrid modeling for multiscale material and process design is discussed. Concluding remarks are provided at the end, and current limitations and future opportunities are pointed out.

Design of an Integrated DC-Link Structure for Reconfigurable Integrated Modular Motor Drives

In this article, a dc-link structure feasible for integration with the circumscribing polygon modular integrated drives is proposed. The proposed dc-link structure combines both the dc-link capacitors and the bus-bar together and integrates them with the machine and the converter modules without increasing the outer diameter of the integrated machine/converter structure. A generic design methodology for the proposed dc-link structure is provided and applied on a reconfigurable 15 stator coils concentrated winding axial flux machine for all its possible phase configurations. The design methodology presented in this article involves the determination of the required dc-link capacitance and the multiphysics design of the bus-bar. The parasitics of the bus-bar part of the proposed dc-link structure are evaluated using electromagnetic finite element method (FEM) models and their influence on the dc-link waveforms is evaluated. Due to the expected high ambient temperature inside an integrated drive, electromagnetic and computational fluid dynamics (CFD) models are developed for the proposed dc-link structure to evaluate the loss density and the temperature distribution of the bus-bar to ensure reliable operation. An experimental setup is built to validate the design methodology.

A power-efficient and re-configurable analog artificial neural network classifier

This paper presents a power-efficient and re-configurable radial basis function-artificial neural network (RBF-ANN) for real-time pattern classification tasks. We have developed a MATLAB-based behavioral model, which can be employed to facilitate the off-chip learning of the proposed network with a hybrid learning algorithm. Besides, a generic design methodology is presented to ease implementing different scales of neural networks. For the RBF-ANN, an analog tunable Gaussian kernel circuit is used as an activation neuron while a current-mode computation is employed to improve the power efficiency. The proposed network is designed and simulated in 0.18 μm X-FAB CMOS process for a non-linear XOR-pattern classification and voice activity detector (VAD), respectively. It is found that the network's weights obtained using the developed model in MATLAB are well-matched with the Spectre simulation (Virtuoso) results with a maximum relative error of 7.1%. Consequently, the proposed model can be effectively employed for the off-chip training of RBF-ANN. Besides, the preliminary simulation results of the VAD depict that the classification accuracy reaches as large as 95% in a noisy environment of babble with an acoustic signal to noise ratio (SNR) of 10 dB. Meanwhile, the power efficiency of the proposed network gets significantly improved compared to the recent prior arts.

A 0.75–2.5-GHz All-Digital RF Transmitter With Integrated Class-E Power Amplifier for Spectrum Sharing Applications in 5G Radios

We propose a digitally intensive, reconfigurable RF transmitter with an integrated, tunable Class-E power amplifier (PA) which can be configured to operate from 0.75 to 2.5 GHz in discrete bands, while delivering an output power of 11.5 dBm up to 1.5 GHz and 6.5 dBm up to 2.5 GHz. Digital signal processing is exploited to suppress sampling spurs up to the harmonics of the carrier, thus eliminating the need for sharp RF filters. The proposed techniques make the transmitter adaptable to signals of different bandwidths and to different carrier frequencies with minimal overhead in power and area when compared to other digital transmitter designs. Digital predistortion is used to linearize the PA. The transmitter also includes clock correction circuitry for correcting duty cycle and quadrature errors. A generic design methodology is mathematically developed for a reconfigurable Class-E PA with a fixed series inductor. This transmitter scales well with technology and can be potentially used for spectrum sharing (SS) applications in fifth generation (5G) radios. Implemented in a 130-nm CMOS technology, the proposed transmitter occupies an area of 1 mm2. A maximum output power of 13.7 dBm with a maximum efficiency of 27% is obtained at 1 GHz.

Generic techno-economic optimization methodology for concurrent design and operation of solvent-based PCC processes

A techno-economic equation-based methodology is developed for optimal design and operation of integrated solvent-based post-combustion carbon capture (PCC) processes using a rate-based model for the interaction of gas and liquid. The algorithm considers a wide range of techno-economic design and operation parameters such as number of absorber/desorber columns, height of columns, diameter of columns, operating conditions (P, T) of columns, pressure drop, packing type, percentage of CO2 mitigated, captured CO2 purity, amount of solvent regeneration, flooding velocities of columns, and number of compression stages. A case study is conducted to showcase two common objective-functions i) minimizing total capital investment, and ii) minimizing levelized capture costs, both for a 300 MW coal-power plant in Australia. The former objective leads to the lowest possible total capital cost of $312.4 M corresponding to levelized carbon capture cost of 58.1 $/tonne−CO2. For objective (ii), however, the lowest levelized carbon capture cost is found to be around ten percent lower (52.8 $/tonne−CO2), though it leads to a higher total capital cost ($325.2 M). The results indicate that the design and operation variables are markedly interactive, and no unique optimal design exists which can deliver all desired outcomes at once. Therefore, decisions on the selection of right variables become dependent on the decision-makers techno-economic objectives.

Synchronization in Quantum-Dot Cellular Automata Circuits and Systems

Signal synchronization of large scale Quantum-dot Cellular Automata (QCA) circuits is one of the most complex QCA design challenges. More specifically, the QCA circuits synchronization problem, especially in the large circuits, is characterized as rather complex due to technology constraints. In this paper, by extensively analyzing the most important properties of the signal synchronization problem in QCA circuits, we propose an efficient design methodology to tackle the problem, based on the well-known from computer science, Firing Squad Synchronization Problem (FSSP). Comparing FSSP with the QCA circuits synchronization problem many similarities can be found. Among the numerous FSSP's algorithmic solutions in literature, the Mazoyer algorithm has proven to be the most efficient one. In this paper, a novel design and implementation in QCA technology of this algorithm is presented. Moreover, by the appropriate modification of the Mazoyer algorithm, we are able to propose a generic synchronization design methodology for QCA circuits and systems. This method is enhanced by a novel freezing technique, that makes it applicable to any QCA circuit and system as manifested by our corresponding simulation results. The proposed synchronization methodology is a universal design tool, that can be applied to exiting designs without increasing the complexity.

A model-based solvent selection and design framework for organic coating formulations

Selection of solvents and calculation of their composition in the final product, is a critical step in coating formulation design. This task can be facilitated by computer-aided methods if the necessary thermodynamic and group contribution property models along with chemical property data are available for the ingredients under consideration. However, these computer-aided procedures must be supplemented and verified by using experimental procedures. Hence, the computer-aided stage can be used to speed-up the solvent selection and design process, efficiently utilize experimental resources and serve as a guide for the formulation chemist.We present in this work an adaption of the generic Computer-Aided Product Design (CAPD) methodology for organic coating formulations. Herein, the paper discusses a model-based framework developed for the design of solvents for such coatings. The applicability of this framework is tested via a case study wherein, solvents for the dispersion of two specific organic pigments are required to be determined, when acrylic polymers are used as dispersing aids.

Methodology for reconfigurable fixture architecture design

A reconfigurable fixture is an important enabler to cope with increasing product variety and shorter lifecycles, since it helps to change between product variants more efficiently and reduces the time and resource usage for introduction of new product variants. For fixtures to be reconfigurable, they must be designed to accommodate a family in a single fixture that can be efficiently converted to different product variants, while also being designed for rapid introduction of new products since product families are dynamic and evolve over time. This brings some critical issues to the design phase of the reconfigurable fixture, regarding the frequency of reconfigurations, the time spent on reconfigurations, and the time and resources spent on introducing new products. However, reconfigurable fixture architecture design is not addressed in existing literature. Therefore, this paper proposes a generic architecture design methodology for the design of reconfigurable fixtures. A method developed for reconfigurable manufacturing systems design has been adapted for the design of reconfigurable fixtures. The method is validated by applying it on an industrial welding task, allowing the assembly of 14 different sub-components in one single reconfigurable fixture, which previously required six different fixtures for the same tasks. This demonstrates the validity of the proposed methodology by illustrating the changeability of the new, improved, and redesigned modular fixture.

Optimizing legacy building operation: The evolution into data-driven predictive cyber-physical systems

Fossil fuels serve a substantial fraction of global energy demand, and one major energy consumer is the global building stock. In this work, we propose a framework to guide practitioners intending to develop advanced predictive building control strategies. The framework provides the means to enhance legacy and modernized buildings regarding energy efficiency by integrating their available instrumentation into a data-driven predictive cyber-physical system. For this, the framework fuses two highly relevant approaches and embeds these into the building context: the generic model-based design methodology for cyber-physical systems and the cross-industry standard process for data mining. A Spanish school's heating system serves to validate the approach. Two different data-driven approaches to prediction and optimization are used to demonstrate the methodological flexibility: (i) a combination of Bayesian regularized neural networks with genetic algorithm based optimization, and (ii) a reinforcement learning based control logic using fitted Q-iteration are both successfully applied. Experiments lasting a total of 43 school days in winter 2015/2016 achieved positive effects on weather-normalized energy consumption and thermal comfort in day-to-day operation. A first experiment targeting comfort levels comparable to the reference period lowered consumption by one-third. Two additional experiments raised average indoor temperatures by 2K. The better of these two experiments only consumed 5% more energy than the reference period. The prolonged experimentation period demonstrates the cyber-physical system-based approach's suitability for improving building stock energy efficiency by developing and deploying predictive control strategies within routine operation of typical legacy buildings.

A generic methodology for processing route synthesis and design based on superstructure optimization

In this paper, a systematic framework for novel and sustainable synthesis-design of processing routes is presented along with the associated computer-aided methods and tools. In Stage 1, superstructure optimization is used to determine the optimal processing route(s). In Stage 2, the design issues are resolved and targets for improvement are identified through the use of integrated tools. In Stage 3, new alternatives are generated using the selected route and the previously identified targets. In addition to the various computer-aided tools, two special tools are presented: (1) a database employing a specially developed knowledge representation system, and (2) Super-O, a software interface that guides users through the formulation and solution of synthesis problems. Super-O transfers data between the different tools, including a library of generic models, representing a wide range of processing options. Application of the synthesis and design stages is highlighted through two case studies (biorefinery and carbon capture-utilization).

CoSe2 nanoparticles embedded defective carbon nanotubes derived from MOFs as efficient electrocatalyst for hydrogen evolution reaction

Development of electrocatalysts for hydrogen evolution reaction (HER) with low overpotential and robust stability remained as one of the most serious challenges for energy conversion. In this work, CoSe2 nanoparticles embedded in defective carbon nanotubes (CoSe2@DC) was synthesized by a carbonization-oxidation-selenylation procedure of Co-based metal-organic frameworks (MOFs). The pre-oxidation treatment was a crucial step in introducing an increasing number of defects into carbon nanotubes, which promoted the reaction between Co@carbon and selenium and led to the enhanced HER performance. The as-prepared CoSe2@DC exhibited excellent HER catalytic reactivity with a low onset potential of −40mV vs. RHE, a small Tafel slope of 82mV dec−1 as well as a high current density with robust catalytic stability in 0.5M H2SO4. This work may provide a generic methodology for rational design and fabrication of the partially encased core-shell structure derived from MOFs as efficient HER electrocatalysts.