Unknown Design Parameters Research Articles

Prototyping of a Machine Learning–Based Burnup Measurement Capability for Pebble Bed Reactor Fuel

The pebble bed reactor is a unique reactor design due to its capacity for continuous multipass circulation of the fuel elements, without causing interruption to reactor operation, with the assistance of the burnup measurement system. Such a system necessarily requires an accurate knowledge of the burnup of each fuel pebble upon ejection from the core so as to inform the reloading decision and to ensure that no pebble exceeds the regulated discharge burnup limit at any point following reinsertion into the reactor core. In this work, we conceptualize, develop, and demonstrate a machine learning–based fuel burnup prediction framework leveraging advanced modeling and simulation capabilities. At its core, machine learning regression models are learned from simulated data to establish the correlation among the irradiated fuel composition (hence burnup), the gamma leakage spectrum, and the gamma spectroscopy results. Sensitivity analysis is conducted to quantify the impact of unknown design parameters, such as fuel enrichment, and irradiation environment, including power density, temperature, and neighboring materials, on the prediction accuracy of various supervised regression algorithms. The effects of a short cooldown period on machine learning prediction accuracy are also investigated. A test data set is used to validate that the data generation methodology proposed in this work successfully results in a machine learning model capable of interpolating its prediction of burnup onto a much wider range of irradiation conditions than were explicitly represented in the training database. The inclusion of a cooldown period of just 2 h leads to a prediction root-mean-square error of <5 MWd/kgU when the fuel enrichment is known and <9 MWd/kgU otherwise.

Robust reliability‐based design approach by inverse FORM with adaptive conjugate search algorithm

AbstractThe concept of reliability‐based design (RBD) of geotechnical works has seen great success in recent years, nevertheless, its application in real‐world engineering practice is still limited. This phenomenon could be mainly attributed to two aspects: the engineer's unfamiliarity of reliability algorithms even in their simplest forms, and the lack of direct connection between reliability algorithms and deterministic design tools. To promote the geotechnical RBD application, this study proposes an inverse first‐order reliability method (FORM) integrating adaptive conjugate direction search technique (RBD via conjugate FORM) to solve classical RBD problems where the number of (multiple) limit state functions is usually equal to the number of unknown design parameters. Notably, the RBD via conjugate FORM can readily back‐calculate those unknown design parameters subject to prescribed reliability index or probability of failure, by means of iterative evaluation of the parametric sensitivities. The proposed algorithm is first verified by several well‐documented examples. Comparative studies show that when the reliability problem to solve is highly nonlinear, the RBD results based on steepest descent search direction may fall in oscillation and non‐convergence, while the proposed RBD via conjugate FORM algorithm performs robustly and efficiently. It is then illustrated in combination with standalone numerical codes to conduct risk‐based optimization of unknown design parameters for two soil slope stability models. The first slope RBD model achieves designing the slope angle under specified reliability index, and the second slope RBD model is to optimize multiple design parameters of the stabilization pile, which can meet different slope reliability requirements under static and seismic loading conditions simultaneously.

Standard and Quasi Oppositional bonobo optimizers for parameter extraction of PEM fuel cell stacks

The accuracy of the mathematical model used to simulate the electrochemical characteristics of fuel cell (FC) stacks has a significant impact on the quality of the electrochemical simulation. Due to the lack of manufacturers’ information, a set of unidentified parameters must be properly estimated for establishing an accurate representation of proton exchange membrane FCs (PEMFCs). In this research, the Bonobo Optimizer (BO) and the modified Quasi Oppositional BO (QOBO) are applied for determining unknown design parameters of various typical PEMFCs. The obtained results in the form of polarization curves are compared with the corresponding experimental ones to validate the appropriateness and adequacy of the used algorithms. The findings obtained using the original BO and modified QOBO algorithms are compared to those acquired using other current optimization approaches, revealing that the QOBO algorithm outperformed the conventional BO algorithm in solving the optimization problem. Further, a statistical analysis is carried out that guaranteed the robustness and reliability of the developed QOBO.

Composite Lamina Model Design with the Use of Heuristic Optimization.

In engineering practice, a problem is quite often faced in which the number of unknown parameters exceeds the number of conditions or requirements or, otherwise, there are too many requirements for too few parameters to design. Such under- or over-defined tasks are sometimes not possible to solve using a direct approach. The number of solutions to such problems is multiple, and it is most rational to search for the optimal one by numerical methods since the more unknown design parameters there are to be designed, the more potential solutions there are. This article discusses a way to find an optimal solution to such an underdetermined problem by heuristic optimization methods on the basis of the example of designing a composite wing skin of an aircraft. Several heuristic approaches, specifically gradient descent and Tabu search, are studied to solve the design problem and to locate an optimal solution. They are also compared to a conventional direct approach. The examined composite lamina is optimized by the target function of minimum weight with the constraints of strength and buckling failure criteria. In most of the observed cases, the heuristic method designed structures which were considerably better than the structures that were obtained by conventional direct approaches in terms of the weight to load ratio.

Strongly Detuned Tunable Photonic Oscillators

A semi-analytical dimensionless formula is presented for predicting the period-one frequency of an optically-injected laser over a wide range of injection powers and positive detunings. The result shows that for any given injected optical power, there exists an optimal detuning at which the period-one frequency is maximized. The formula is applied to a tabletop optical injection system with a commercially-available quantum-well distributed feedback laser with previously unknown design parameters. By extrapolating the linearity of the period-one frequencies at zero-detuning and weak-injection regimes, the physical control parameters of the laser are approximated to the analytical formula for wide ranges of injection powers and positive detunings. The accuracy of the model is experimentally verified, achieving period-one frequencies as high as 84.7 GHz.

Program complex for optimization of surface hardening of steel billets

The aim of the paper is to develop program complex in software MATLAB with integrated numerical 2D nonlinear FLUX model, which is used for solving optimal inductor design and control problems for heating stage of surface induction hardening. Considered program complex is based on alternance method, that allows to write systems of transcendental equations, closed with respect to all unknown design and control parameters of the process. The suggestion for implementation of obtained optimal control algorithm is presented.

Design of Multi-Port With Desired Reference Impedances Using Y-Matrix and Matching Networks

Two synthesis algorithms of multiport network with desired unequal complex port-impedances are developed considering the multiport network as an interconnection of two-port networks (TPNs). The first one is direct-synthesis using Y-matrix, and the second one is port-impedance transformation method. The techniques are single-frequency methods, as the scattering parameters of known multiport networks are defined at the design frequency. In direct-synthesis, the desired S- matrix of the multiport network is converted into the desired Y-matrix, considering the reference port-impedance matrix. Then the multiport network structure or topology can be predicted using the non-zero off-diagonal entries of Y-matrix. The Y-matrix of the predicted multiport is calculated in terms of unknown design parameters or sub-network parameters. Finally, by equating the predicted Y-matrix with the desired Y- matrix, the design equations are obtained. In the port impedance transformation method, a core network with intermediate port-impedances is designed using the direct-synthesis method. Then the intermediate port-impedances are converted into desired port impedances using external matching networks. The impedance matching networks are designed using the desired phased impedance matching theory. Two different designs of a four-port network are provided to validate the proposed concepts. Two pseudocodes or algorithms are developed considering lumped Π networks as building blocks.

Biological function following radical photo-polymerization of biomedical polymers and surrounding tissues: Design considerations and cellular risk factors

Radical photo-cross-linking of polymers has been at the forefront in the development of biomedical applications to meet many of the biomaterial design criteria needed to address clinical and healthcare challenges, particularly in relation to regenerative and restorative medicine strategies, to treat damaged or diseased tissues and organs. Exciting new hybrid designs, elegantly expanding the range of properties and applications available to individual polymeric materials, are starting to offer detailed customization with complex and dynamic interactions similar to the events occurring within native tissue microenvironments in vivo. Yet the variety in success reported in the literature highlights the many unknown design criteria and parameters affecting functional restoration of damaged tissues and organs. The applied light curing units and radical initiating system, as well as underlying chemical reactions and resultant network structures, all require detailed consideration as means to modulate biological function while further being assessed as sources of toxicity. This is especially important when cells embedded in the polymeric material (or in surrounding tissues) are directly exposed to photo-irradiation. Ultimately, achieving successful clinical translation necessitates the chemical photo-polymerization platforms to be efficacious, safe, and customizable but also convenient for clinical use and cost-effective production. This review thus aims to summarize current and emerging toolkits to photo-polymerize biomedical polymers requiring the direct irradiation of cells and/or mammalian tissues and its associated impact on biological functionality. This specifically includes (1) in vitro photo-polymerization of cell-laden 3D-hydrogels for tissue engineering and regeneration medicine applications, (2) in vivo transdermal photo-polymerization of injectable hydrogels for cell/drug delivery, and (3) in vivo photo-polymerization of cell-free, injectable resin-based composites for load-bearing restorative surgery, all fast-growing and highly competitive fields of modern medicine. We herein summarize both design considerations and biological risk factors associated with selecting suitable light sources, photo-initiators, functional groups, chemical propagation, as well as how subsequent network properties can modulate biological function and ultimately clinical applicability. As more knowledge is continuously accumulated through materials science, matrix biology, and technology, this review provides recommendations for researchers to extend their chemical, biological, and structural characterizations to systematically enrich the paradigm of photo-polymerizable materials for biomedical applications to help ensure efficient and safe radical photo-processing.

A Theory for the Design of Multi-Stable Morphing Structures

Multi-stable structures can provide desired reconfigurability and require relatively simple actuation. This paper considers general bar and plate structures connected by frictionless hinges that are to be made locally stable in a set of chosen target configurations by attaching extensional and rotational, linear-elastic springs to the structure. The unstressed lengths and angles of the springs, as well as their stiffnesses, are the unknown design parameters to be determined. A set of equilibrium and stability conditions to be satisfied in each of the target configurations of the structure are derived. Solutions of these equations provide specific values of the spring properties that correspond to local energy minima in all of the target configurations. The formulation is fully general and is applicable to structures of any complexity. A simple example is used to illustrate the design process for a bi-stable origami structure and a physical prototype is also presented.

Photosensitive Glass-Ceramics for Heterogeneous Integration

The push for heterogeneous integration requires very unique material properties with respect to processing, material constants, and integration capabilities with other materials (such as copper, II...

Passivity based-control technique incorporating genetic algorithm for design of a free piston Stirling engine

In this paper, a novel design philosophy of free piston Stirling engines based on passivity based-control technique incorporating genetic algorithm is presented. First, the design problem of an FPSE is expressed as a physical nonlinear servo control system with unknown parameters. Accordingly, the desired motions of power piston and displacer (i.e. design objectives) are regarded as the reference trajectories of the control loop. Then, the unknown design parameters of the nonlinear control system including cross sectional-area of the displacer rod, mass and spring stiffness of the power piston and displacer, phase difference, hot gas temperature, and control coefficients are found using a GA along with an error-based objective function. Next, the estimated values of unknown parameters are used to simulate the control system performance. Subsequently, the simulated control system response is compared to the desired trajectories. It is found that the simulated displacements and velocities for both pistons as well as the corresponding limit cycles are close to the desired references of the control system. Finally, simulated performance of the FPSE is verified through experimental results. The outcomes of this research clearly reveal the effectiveness of the proposed nonlinear control-based design technique of the free piston Stirling engine (SUTECH-SR-1).

High dimensional model representation for reliability analyses of complex rock–soil slope stability

The high-dimensional model representation (HDMR) and its modifications, the fractional HDMR (FHDMR) and hybrid HDMR (HHDMR), are new tools for calculating reliability indexes in stability analyses when several variables with large uncertainties are used to describe rock and soil behaviours. Plain HDMR utilises an inverse reliability analysis for the study of unknown design parameters associated with target reliability index values. This approach uses implicit response functions, named limit state functions, according to the response surface method (RSM). In this study, both the FHDMR and HHDMR are applied to the reliability index calculation of safety factors related to the stability analyses of sliding failure mechanisms in complex formations. These two methods improve the computational efficiency of the RSM in reliability index calculations compared to the HDMR. A case study of Carpathian Flysch rock–soil slopes is presented, and the efficiency of the reliability index calculation is estimated by comparing results with ones from neural network application.

Frequency-based design of a free piston Stirling engine using genetic algorithm

This paper focuses on the frequency-based design of a FPSE (free piston Stirling engine) using a GA (genetic algorithm). First, a mathematical description of the FPSE is presented. The engine design parameters including mass and stiffness of power and displacer pistons and cross-sectional area of the displacer rod are considered as unknown variables. Then, based on a desirable operating frequency, positions of closed-loop poles of the engine system are selected. The unknown design parameters are thus found via an optimization scheme using GA. A new objective function based on the eigenvalues of the state matrix of the FPSE is proposed and GA is used to obtain the optimal values of design variables so that the objective function is minimized. Next, the effectiveness of the proposed design is evaluated through numerical simulation. Two mathematical approaches are presented to compute the phase difference between the motions of power and displacer pistons. Furthermore, the generated work and power of the FPSE are found based on the computed phase angle. Finally, the designed FPSE is constructed and primarily tested. It is found that the simulation results are in a good agreement with the experiment through which validity of the presented design technique is affirmed.

Synthesis and Design of Mixed Lumped and Distributed Low-Pass Filters/Low-Passing Impedance Transformers With Taylor Series

In this paper, a synthesis method by applying the Taylor series is proposed to design a variety of low-pass mixed lumped and distributed circuit networks, including the low-pass filters and low-passing impedance transformers. First, the principle of applying the Taylor polynomials for these mixed circuit networks is discussed. Herein, the trigonometric functions, commonly used to characterize distributed circuits, are expressed in the form of polynomials by the application of Taylor polynomials. Therefore, the transfer function of the mixed networks is simplified from a multivariable to a polynomial form. Following this, a generic circuit structure is proposed to design either a low-pass filter or a low-passing impedance transformer with specified port impedances. The desired filtering function uses the similar method to transform the trigonometric function into a format of polynomials. Under the equalization of the transfer function and filtering function, all the unknown design parameters are obtained. As two examples, a low-pass filter and a low-passing impedance transformer are synthesized, designed, and fabricated to experimentally verify the proposed design approach.

Synthesis of Multiport Networks Using Port Decomposition Technique and its Applications

A systematic approach to synthesize multiport ( $N$ -port) network with desired characteristics, for a given network topology, has been proposed. The heart of the algorithm lies with a novel port decomposition technique introduced here, wherein a reduced two-port network (2PN) is obtained by short-circuiting the remaining $N-2$ ports of the original topology. This reduced network is easily analyzed in terms of its unknown design parameters and can be compared with the known characteristics to provide a subset of design equations. Iteratively proceeding with this approach, one can obtain the required synthesis equations for any multiport network, even when there exists no plane of symmetry. Utilizing this algorithm, four theorems pertaining to multiport network synthesis have been proposed. The first and second theorems establish the synthesis guidelines of an arbitrary-phased rat-race and branch-line type coupler having unequal (or equal) power division at the output ports, with a ring type topology consisting of arbitrary building blocks. The third theorem deals with the synthesis of a five-port network, and the final one provides the design equations for arbitrary phase difference coupler involving coupled lines.

A New Perspective of Analysis and Optimization for Absorption Thermal Energy Storage System Based on Entransy Theory

Abstract Absorption thermal energy storage system is a new technology with promising future. This contribution proposes a new perspective to analyze this kind system and introduces the entransy dissipation-based thermal resistance theory to optimize the system. The analogy between heat transfer and electric conductance, integration of the heat transfer and the phase change yield a new physical image and a direct mathematical model to describe the physical relation between the design requirements and the unknown design parameters, i.e. the heat transfer area, the heat capacity of each heat exchanger. Then the optimization problem converts to a conditional extremum problem applying the mathematical model. Finally, two examples are taken to testify the validity of the new method.

Value Prediction of Design Parameters Based on Weighting Least Squares

Parametric equipments or standard parts usually have many different types of original design parameters. So when designing some new specifications, it requires a lot of estimation or trial and error to determine the value trends and intervals of other unknown design parameters. Based on a finite number of historical examples of design parameter groups, the paper gives an algorithm to fit value trend line using multivariate linear weighted least squares method, whose weights are designed by using distance-proximity coefficient and correlation coefficient. The algorithm uses a small amount of new design parameters, fits value trend lines of other unknown parameters, predicts all other design parameters, finally makes up a design parameter group for a new specification. Two test results of standard parts from home and abroad show that, the accuracy of value prediction is able to meet the requirements of engineering applications.

A global optimization method for regenerative air refrigeration systems

The air refrigeration systems always involve such physical processes as heat transfer processes in heat exchangers, compression processes in compressors and expansion processes in expanders. This contribution proposes a theoretical global optimization method for regenerative air refrigeration systems and introduces the entransy theory to analyze the heat transfer processes in the hot-end heat exchanger, the cold-end heat exchanger and the regenerator. Integration of the heat transfer analyses and the thermodynamic analyses for the compression and expansion processes yields a mathematical model to describe the physical relation between the design requirements and the unknown design parameters, i.e. the heat transfer area of each heat exchanger and the heat capacity rate and the intermediate temperature of the air. Based on this model, the optimization can be converted into a conditional extremum problem. That is, solving the problem via the Lagrange multiplier method offers an optimization equation group, which directly leads to the optimal values of all the unknown parameters. Finally, this optimization method is validated through an optimization case to minimize the total thermal conductance of all the heat exchangers in a typical regenerative air refrigeration system.

Confidence Bounds on Design Variables Using High-Dimensional Model Representation–Based Inverse Reliability Analysis

AbstractThis paper presents a novel solution procedure for inverse reliability problems with implicit response functions without requiring the derivatives of the response functions with respect to uncertain variables. This can be used to determine the unknown design parameters such that the prescribed reliability indexes are attained in the presence of mixed uncertain (both random and fuzzy) variables. The proposed computational procedure involves three steps: (1) probability of failure calculation using high-dimensional model representation (HDMR) for the limit state/performance function approximation, transformation technique to obtain the contribution of the fuzzy variables to the convolution integral, and fast-Fourier transform for solving the convolution integral; (2) reliability index update; and (3) most probable point update. The limit state/performance function approximation is obtained by linear and quadratic approximations of the first-order HDMR component functions at most probable point. This...

Inverse structural reliability analysis under mixed uncertainties using high dimensional model representation and fast Fourier transform

This paper presents a novel solution procedure for inverse reliability problems with implicit response functions without requiring the derivatives of the response functions with respect to the uncertain variables, that can be used to determine the unknown design parameters such that prescribed reliability indices are attained in the presence of mixed uncertain (both random and fuzzy) variables. The proposed computational procedure involves three steps: (i) probability of failure calculation using High Dimensional Model Representation (HDMR) for the limit state/performance function approximation, transformation technique to obtain the contribution of the fuzzy variables to the convolution integral, and fast Fourier transform for solving the convolution integral, (ii) reliability index update, and (iii) most probable point MPP update. The limit state function approximation is obtained by linear and quadratic approximations of the first-order HDMR component functions at most probable point. This is a versatile method that can solve even highly nonlinear problems or the problems with multiple parameters. The methodology developed is applicable for inverse reliability analysis involving any number of fuzzy variables and random variables with any kind of distribution. The accuracy and efficiency of the proposed method is demonstrated through four examples involving explicit/implicit performance functions.