Set Of Design Specifications Research Articles

A statistical model for the design of rotary HTS flux pumps based on deep-learning neuron network

Rotory high temperature superconducting (HTS) flux pumps can consistently generate a DC voltage by rotating magnets over superconducting tapes, and thus energize the circuit if a closed loop is formed. The voltage output is a crucial factor to reflect the performance of such an HTS flux pump, which is determined by a set of design specifications, and some of them have been investigated extensively in the current literature. However, no work has been done yet to study the HTS dynamo output voltage by efficiently integrating all the design parameters together. In this paper, a well-trained deep-learning neuron network (DNN) with back-propagation algorithms has been put forward and validated. The proposed DNN is capable of quantifying the output voltage of an HTS dynamo instantly with an overall accuracy of approximately 98% with respect to the simulated values with all design parameters explicitly specified. The model possesses a powerful ability to characterize the output behavior of HTS dynamos by considering multiple design parameters, e.g., airgap, superconductor tape width, operating frequency, remanent flux density, rotor radius, and permanent magnet width, which have covered all the typical design considerations. The output characteristics of an HTS dynamo against each of the design parameters have been successfully demonstrated using this model. Compared to conventional time-consuming finite element method (FEM) based numerical models, the proposed DNN model has the advantages of automatic learning, fast computation, as well as strong programmability. Therefore the DNN model can greatly facilitate the design and optimization process for HTS dynamos. An executable application has been developed accordingly based on the DNN model, which is believed to provide a useful tool for learners and designers of HTS dynamos.

From Fiction to Function: Imagining New Instruments through Design Workshops

Abstract This article introduces a series of workshop activities carried out with expert musicians to imagine new musical instruments through design fiction. At the workshop, participants crafted nonfunctional prototypes of instruments they would want to use in their own performance practice. Through analysis of the workshop activities, a set of design specifications was developed that can be applied to the design of new digital musical instruments intended for use in real-world artistic practice. In addition to generating tangible elements for instrument design, the theories and models utilized, drawn from human–computer interaction and human-centered design, are offered as a possible model for merging the generation of creative ideas with functional design outputs in a variety of applications within and beyond music and the arts.

Design Study of Small Modular Reactor Class Super Fast Reactor Core for In-Vessel Retention

Abstract The small modular reactor (SMR) class core design concept of once-through supercritical light water-cooled reactor (SCWR) with fast neutron spectrum (super fast reactor (FR)) is being developed at Waseda University. For the 300 MWel class design, complete core meltdown may need to be considered in case of a severe accident. This study proposes the new in-vessel retention (IVR) concept of the SMR class super FR (super FR-IVR), which can avoid recriticality even if the whole core relocates to the lower plenum of the reactor pressure vessel (RPV). The core characteristics with a given set of design specifications and criteria are evaluated based on fully coupled neutronics and thermal-hydraulics core burnup calculations. The debris criticality is evaluated based on Monte Carlo based method to consider the RPV lower plenum and debris configurations. The relationships between the 300 MWel class core design with the inner vessel diameter of 2.32 m and the IVR design are revealed. By reducing the operation cycle length from 720 days to 360 days and increasing the core inlet temperature from 280 °C to 370 °C, the required IVR submergence level could be reduced from 2.18 m to 1.38 m, assuming that the debris bed (melt pool) is homogeneous. However, when fully stratified debris configurations are assumed, the required disperser height and the corresponding IVR submergence level may increase to about 2.70 m.

An extended form of the reciprocal-power function for tolerance allocation

The optimization of dimensional tolerances requires that a cost-tolerance function is evaluated consistently for all the part features involved in a given functional requirement. This is difficult because the parameters of commonly used functions are set using cost data from various sources and on possibly different scales. As an alternative, the paper proposes a revised form of one of the available cost-tolerance functions (reciprocal power), which expresses its parameters in empirical relationship with a set of design specifications on the toleranced features. These include the nominal dimension, the shape, the surface area, and the material. Following a previous study based on cost data available in literature, the values and expressions of the parameters are validated and refined using a feature-based method for the estimation of machining cost. The properties of the extended function allow to develop a simplified method for tolerance allocation that avoids the task of solving the optimization problem; it is a modified version of proportional scaling where the initial solution satisfies optimal ratios between tolerances. The discussion of the results and an application example help to justify the proposed function on grounds of correctness, convenience, and reference value.

Optimization design of fractional‐order Chebyshev lowpass filters based on genetic algorithm

AbstractFractional‐order filters have received extensive attention from international scholars because of their greater design freedom and continuously stepped stopband attenuation rate. Based on genetic algorithm (GA), this paper proposes an optimum design approach for fractional‐order Chebyshev lowpass filters that meet design specifications. The fractional order and ripples attenuation of the normalized Chebyshev lowpass filter are calculated according to specifications, the integer order in the Chebyshev polynomial is replaced with the calculated fractional order, and then the fractional‐order Chebyshev polynomial is substituted into the magnitude response of the normalized Chebyshev lowpass filter along with the ripples attenuation to achieve the ideal response. The transfer function parameters of a fractional‐order filter are optimized using GA to make the magnitude response approximate the ideal response described above. This completes the optimized design of a fractional‐order Chebyshev filter that meets specifications. Given three different sets of design specifications, the fractional‐order Chebyshev filter designed using the proposed method is compared with one designed by another method in the literature. Finally, design examples are presented, stability analysis and Pspice simulations are performed, and an actual circuit is constructed to illustrate the effectiveness of the proposed method.

Systems Approach to Pathogenic Mechanism of Type 2 Diabetes and Drug Discovery Design Based on Deep Learning and Drug Design Specifications.

In this study, we proposed a systems biology approach to investigate the pathogenic mechanism for identifying significant biomarkers as drug targets and a systematic drug discovery strategy to design a potential multiple-molecule targeting drug for type 2 diabetes (T2D) treatment. We first integrated databases to construct the genome-wide genetic and epigenetic networks (GWGENs), which consist of protein–protein interaction networks (PPINs) and gene regulatory networks (GRNs) for T2D and non-T2D (health), respectively. Second, the relevant “real GWGENs” are identified by system identification and system order detection methods performed on the T2D and non-T2D RNA-seq data. To simplify network analysis, principal network projection (PNP) was thereby exploited to extract core GWGENs from real GWGENs. Then, with the help of KEGG pathway annotation, core signaling pathways were constructed to identify significant biomarkers. Furthermore, in order to discover potential drugs for the selected pathogenic biomarkers (i.e., drug targets) from the core signaling pathways, not only did we train a deep neural network (DNN)-based drug–target interaction (DTI) model to predict candidate drug’s binding with the identified biomarkers but also considered a set of design specifications, including drug regulation ability, toxicity, sensitivity, and side effects to sieve out promising drugs suitable for T2D.

Low Noise Amplifier Synthesis Using Multidimensional MLP Neural Network

ABSTRACTIn this paper, a new method is proposed for determining the design parameters of radio frequency (RF) low noise amplifier (LNA) to obtain a set of desired design specifications. The proposed method is a simulation-based approach in which the multidimensional multilayer perceptron (M-MLP) neural network (NN) is utilized as the synthesis tool. The outputs of the M-MLP NN constitute the design variables, i.e., the numerical values of the passive components, transistors’ sizing, and the biasing conditions. The algorithm is implemented in MATLAB and the HSPICE simulator is utilized to verify the synthesis results. Strictly speaking, in each feed-forward process of the NN, a set of design parameters are produced at the output of the M-MLP NN, corresponding to which a set of design specifications are derived using HSPICE. These specifications include the input and output return loss, power gain, reverse isolation, noise figure, K stability factor, 3-dB bandwidth, and centre frequency. Considering the differences between the derived specifications and the desired ones, the connection weights of the M-MLP NN are altered in a way that it improves the overall behaviour of the algorithm toward satisfying the competing design measures. In other words, having the desired specifications, our method yields a completely automated design procedure. The formulation of the approach is described in detail. The dynamic learning rate is utilized to further improve the performance of the algorithm to search the solution space efficiently. The TSMC model for the 0.18 µm CMOS process is used in the LNA design.

Performance Enhanced Butt Coupling for Effective Interconnection Between Fiber and Silicon Nanowire

A performance enhanced butt coupling structure incorporating a vertically and laterally tapered SiON polymer core on silicon (Si) to tunnel and widen propagating optical modes of adiabatic taper is proposed. The proposed structure can effectively overcome the strong back reflections caused at the interface between fiber and Si nanowire to enable higher coupling efficiency. The placement of the SiON polymer core is chosen to restrict the passage of light through the bottom cladding to avoid unnecessary leakage. The performance of the structure is characterized for distinct features, including taping length, coupling efficiency, and coupling bandwidth for both single as well as cacaded configurations, compared with the state-of-the-art grating coupler. It is found that, with the right set of design specifications, butt couplers can outperform grating couplers in both coupling efficiency and bandwidth, although grating couplers are considered friendlier for wafer-level testing.

Quality‐by‐design by skewed spherical structured singular value

This study develops numerical algorithms to compute an ellipsoidal set of input parameters, called a design space, that ensures that the system outputs lie within a set of design specifications in quality-by-design. The algorithm is based on a proposed skewed spherical structured singular value ν s , for which this study derives upper bounds and proves the (scaled) main loop theorems and small-gain theorem for the Frobenius norm. Three examples are included to illustrate applications of the numerical algorithms.

A Survey on Software-Defined Network and OpenFlow: From Concept to Implementation

Software-defined network (SDN) has become one of the most important architectures for the management of largescale complex networks, which may require repolicing or reconfigurations from time to time. SDN achieves easy repolicing by decoupling the control plane from data plane. Thus, the network routers/switches just simply forward packets by following the flow table rules set by the control plane. Currently, OpenFlow is the most popular SDN protocol/standard and has a set of design specifications. Although SDN/OpenFlow is a relatively new area, it has attracted much attention from both academia and industry. In this paper, we will conduct a comprehensive survey of the important topics in SDN/OpenFlow implementation, including the basic concept, applications, language abstraction, controller, virtualization, quality of service, security, and its integration with wireless and optical networks. We will compare the pros and cons of different schemes and discuss the future research trends in this exciting area. This survey can help both industry and academia R&D people to understand the latest progress of SDN/OpenFlow designs.

Determination of ranged sets of design specifications by incorporating design-space heterogeneity

Setting design specifications (targets) is a critical task in the early stages of the design process. Flexible targets can accommodate uncertainty and changes in design by postponing design commitments and preserving design freedom. In this article, a new method is developed for obtaining a ranged set of design specifications that meets design criteria whilst incorporating design-space heterogeneity; meaning some areas in the design attribute space are more achievable than the others. The proposed method has two notable features. First, a quantization algorithm based on rough-set theory is used to decompose a design attribute space into sub-regions on the basis of how well they meet design criteria. Second, a new design-flexibility measure is used as a metric to select the most desired ‘target region’ on the bases of both the size of the region and the influence of potential design alternatives on overall achievability. The proposed approach enhances the capacity of a design system to adapt to evolving design knowledge, as well as to unexpected changes. The proposed method is demonstrated by a numerical example and the design of a domestic blender.

Adaptable compliance or variable stiffness for robotic applications [From the Guest Editors

Exciting new robots are being developed that will operate in a different environment from traditional industrial factories or research laboratories. Researchers are working worldwide to create robots that are integrated into our daily lives. For the advancement of these new robots, compliant, safe, and new actuators are one of the important issues turning energy into safe motion. The biological counterpart is the muscle tendon structure that has functional performance characteristics and a neuromechanical control system that has far more superior capabilities. The superior power to weight ratio, force to weight ratio, and sensing characteristics limit the development of machines that can match motion, safety, and energy efficiency of a human or other animal. One of the key differences of biological systems is their adaptable compliance or variable stiffness compared with the traditional stiff electrical drives used for the standard industrial robotic applications, which require accurate, reference-trajectory tracking.More and more applications such as robots in close human or robot proximity, legged autonomous robots, and rehabilitation devices and prostheses demand a different set of design specifications, for which the use of compliant actuators can be beneficial as compared with the traditional stiff actuation schemes.

Effect of Entrainer Loss on Plant-Wide Design and Control of an Isopropanol Dehydration Process

Azeotropic distillation is an important technology for separating azeotropes or minimizing the energy expenditure of separating close boiling mixtures. In heterogeneous azeotropic distillation, an entrainer is usually added, which is recovered and reused in the subsequent stripping process. Depending on the design, there may be various degrees of entrainer loss. In this work, the plant-wide design and control of an isopropanol dehydration system using cyclohexane as an entrainer via heterogeneous azeotropic distillation is conducted. When there is negligible entrainer loss, a special case of input multiplicity is found, in which a set of design specifications can be achieved by an infinite but bounded set of operating conditions. The optimal condition with the minimum reboiler duty in this bounded set is achieved when the organic recycle flow is the smallest. Due to the existence of these multiple steady states, the entrainer inventory control is self-regulatory over long time operations if a PI control is used for the level of the organic phase in the decanter. If large offsets are allowed in the entrainer inventory control, the system may exhibit a long period of pseudosteady state followed by catastrophic failure. Alternatively, one can allow a small amount of entrainer loss to the bottom of the heterogeneous azeotropic distillation column, and provide a small amount of entrainer makeup. In this case, a control loop is added to guard against process drift due to loss of entrainer. However, because continuous maintenance of entrainer inventory is difficult, an on-off control scheme of entrainer makeup feed is used.

Enhancing general performance of folded cascode amplifier by recycling current

A modification to the conventional folded cascode transconductance amplifier is proposed. The proposed amplifier has the benefit of achieving a given set of design specifications while consuming a fraction of the power compared to the conventional folded cascode. Moreover, the proposed modification is robust even for low voltage applications.

Design and Control of Entrainer-Added Reactive Distillation for Fatty Ester Production

Substantial energy reduction and high-purity isopropyl palmitate can be obtained by using an entrainer-added reactive distillation process. A special case of input multiplicity, in which a set of design specifications can be achieved by an infinite but bounded set of operating conditions, is found. Plantwide dynamics show that entrainer inventory in the reactive distillation column is self-regulatory, provided that the organic phase liquid level is controlled without offset. A simple plantwide control scheme with three temperature control loops, PI decanter level control, and conventional level control in the reboiler will be sufficient to maintain purities of the fatty ester and water products.

Simultaneous optimal design and operation of multipurpose batch distillation columns

This work presents a robust method for the integrated design and operation of batch distillation whereby optimal column sizing, process flexibility and operating policies are obtained simultaneously based on the complex economic trade-offs between capital investment, production revenue and utility costs. The proposed stochastic framework, which utilises a Genetic Algorithm and a penalty function strategy, is found to be successful in obtaining profitable and feasible column designs for many design scenarios including binary and multicomponent mixtures, single duty and multipurpose columns, as well as for regular and complex column configurations. The method can also be used with column models of different complexity. Given a set of design specifications and separation requirements, the optimal number of stages, reboiler duty, reflux profiles, product recoveries, time interval of each distillation tasks, process allocation and number of batches can be obtained. Several design case studies are presented and a comparison of optimal designs for various design scenarios, such as different production time, capital costs, process allocation and mixture characteristics, are discussed.

H∞ Control for a Flexible Transmission System

The aim of this paper is to synthesise a discretetime H ∞ controller for a flexible transmission system, proposed as a benchmark at the 1995 European Control Conference. A set of design specifications in both time and frequency domains is to be met for the three available plant models. The final H ∞ controller compares favourably with previously proposed controllers: nearly all design specifications are satisfied despite the low complexity of the control law.

COMBINED FINITE ELEMENT AND ANALYTICAL METHODS FOR ROTOR DESIGN OF PERMANENT MAGNET SYNCHRONOUS MOTORS

ABSTRACT While design procedures for conventional induction and synchronous machines are now mature and constrained by frame sizes and standards, there are opportunities to use modem CAD techniques to exploit the unique capabilities and characteristics of permanent magnet synchronous motors (PMSM). This paper presents a design methodology involving the use of an analytical model based on generalized machine theory, complemented by finite element analysis. The synergistic interaction of these two methods allows an engineer to design a PMSM to specific requirements with confidence. The effects of machine airgap and of the span angle and thickness of the permanent magnets are evaluated. A set of design specifications and a goodness function are established to select the preferred rotor geometry for a 30 hp prototype permanent magnet synchronous motor.

Improving control design for nonlinear parametric uncertainty

Models developed from first principles often contain coefficients that are nonlinear functions of design parameters, which themselves are generally only known within some tolerance. Analysis and design of controllers for such models (plants) are often expedited through the use of an overbounding interval plant representation of the original plant. However, such representations are known to introduce conservativeness. As an alternative, we provide an algorithm for synthesizing a frequency-dependent convex hull approximation of an uncertain plant with nonlinear coefficients, which we refer to as a minimized plant. We develop several theorems that demonstrate reduced conservativeness through the use of minimized plants. In particular, the use of these plants to determine robust stability margins results in an improvement over the use of overbounding interval plants. We also illustrate in an example how a constant coefficient compensator and minimized plant meet a given set of design specifications, but the same compensator fails to meet specifications when analysed using an interval plant. As reduced conservativeness simplifies the design process, the convex hull synthesis algorithm and associated theorems provided in this paper facilitate robust controller analysis and design.

Comparison of Three Non-derivative Optimization Methods with a Genetic Algorithm for Component Selection

SUMMARY This paper presents a design method to automate the selection of an optimal set of components for a given layout and set of design specifications. The systems considered in this research are complex, multi-energy domain systems which have transient performance design specifications. Selecting components for such systems requires a wide range of knowledge, including knowledge of system design, modelling, dynamic analysis, computer programming and optimization. The component selection method developed in this work consists of a genetic algorithm and a generalized constraint handling procedure. A genetic algorithm is a flexible, evolutionary, combinatorial optimization technique that lends itself well to problems with discrete solution spaces. This component selection approach is compared with other approaches: a geometric simplex algorithm; an enumerative search; a discrete hill-climbing routine; and a random search. The design of a hydraulic lift system was used as the test problem. This method (th...