Infeasible Design Research Articles

Design of Chatter-Resistant Damped Boring Bars Using a Receptance Coupling Approach

Deep hole boring using slender bars that have tuned mass dampers integrated within them make the boring process chatter vibration resistant. Dampers are usually designed using classical analytical solutions that presume the (un)damped boring bar which can be approximated by a single degree of freedom system, and the damper is placed at the free end. Since the free end is also the cutting end, analytical models may result in infeasible design solutions. To place optimally tuned dampers within boring bars, but away from the free end, this paper presents a receptance coupling approach in which the substructural receptances of the boring bar modelled as a cantilevered Euler–Bernoulli beam are combined with the substructural receptances of a damper modelled as a rigid mass integrated anywhere within the bar. The assembled and damped system response thus obtained is used to predict the chatter-free machining stability limit. Maximization of this limit is treated as the objective function to find the optimal mass, stiffness and damping of the absorber. Proposed solutions are first verified against other classical solutions for assumed placement of the absorber at the free end. Verified models then guide prototyping of a boring bar integrated with a damper placed away from its free end. Experiments demonstrate a ~100-fold improvement in chatter vibration free machining capability. The generalized methods presented herein can be easily extended to design and develop other damped and chatter-resistant tooling systems.

Using Recurrent Neural Networks to Model Spatial Grammars for Design Creation

Abstract The authors present preliminary results on successfully training a recurrent neural network to learn a spatial grammar embodied in a data set, and then generate new designs that comply with the grammar but are not from the data set, demonstrating generalized learning. For the test case, the data were created by first exercising generative context-free spatial grammar representing physical layouts that included infeasible designs due to geometric interferences and then removing the designs that violated geometric constraints, resulting in a data set from a design grammar that is of a higher complexity context-sensitive grammar. A character recurrent neural network (char-RNN) was trained on the positive remaining results. Analysis shows that the char-RNN was able to effectively learn the spatial grammar with high reliability, and for the given problem with tuned hyperparameters, having up to 98% success rate compared to a 62% success rate when randomly sampling the generative grammar. For a more complex problem where random sampling results in only 18% success, a trained char-RNN generated feasible solutions with an 89% success rate. Further, the char-RNN also generated designs differing from the training set at a rate of over 99%, showing generalized learning.

A Framework for Flexible and Cost-Efficient Retrofit Measures of Heat Exchanger Networks

Retrofitting of industrial heat recovery systems can contribute significantly to meeting energy efficiency targets for industrial plants. One issue to consider when screening retrofit design proposals is that industrial heat recovery systems must be able to handle variations, e.g., in inlet temperatures or heat capacity flow rates, in such a way that operational targets are reached. Consequently, there is a need for systematic retrofitting methodologies that are applicable to multi-period heat exchanger networks (HENs). In this study, a framework was developed to achieve flexible and cost-efficient retrofit measures of (industrial) HENs. The main idea is to split the retrofitting processes into several sub-steps. This splitting allows well-proven (single period) retrofit methodologies to be used to generate different design proposals, which are collected in a superstructure. By means of structural feasibility assessment, structurally infeasible design proposals can be discarded from further analysis, yielding a reduced superstructure. Additionally, critical point analysis is applied to identify those operating points within the uncertainty span that determine necessary overdesign of heat exchangers. In the final step, the most cost-efficient design proposal within the reduced superstructure is identified. The proposed framework was applied to a HEN retrofit case study to illustrate the proposed framework.

Optimal Design of a DC–DC Boost Converter in Load Transient Conditions, Including Control Strategy and Stability Constraint

An optimization method is proposed to design a dc–dc boost converter, including the differential input filter and the associated control strategy, for embedded applications [aerospace, electric vehicle/hybrid electric vehicle (EV/HEV)], where high power density and high efficiency are strongly required. The problem is treated as a multiobjective optimization, whose purpose is to maximize compactness and efficiency of the converter. In order to find a tradeoff between the two objective functions (volume and power losses), a genetic algorithm was employed (NSGA-II) to generate the most convenient design solutions. The operating conditions includes both steady-state and load power transients of a constant power load (CPL). In order to comply with the specification on the output voltage admitted range in both conditions, the control strategy is discussed and included in the design procedure. The interaction of a differential input filter with a CPL may lead to serious instability issues. A constraint function in the algorithm excludes all the unstable and unfeasible design solutions. A high-efficiency converter ( $\eta =98.5\%$ ) was realized, and experimental tests prove that stability and design specifications are respected.

RodSteward: A Design‐to‐Assembly System for Fabrication using 3D‐Printed Joints and Precision‐Cut Rods

AbstractWe present RodSteward, a design‐to‐assembly system for creating furniture‐scale structures composed of 3D‐printed joints and precision‐cut rods. The RodSteward systems consists of: RSDesigner, a fabrication‐aware design interface that visualizes accurate geometries during edits and identifies infeasible designs; physical fabrication of parts automatically generated 3D‐printable joint geometries and cutting plans for rods; and RSAssembler, a guided‐assembly interface that prompts the user to place parts in order while showing a focus+context visualization of the assembly in progress. We demonstrate the effectiveness of our tools with a number of example constructions of varying complexity, style and parameter choices.

Optimization of a DI diesel engine to reduce emission and boost power by exergy and NLPQL method

AbstractThe design and manufacture of efficient diesel engines, which can convert the majority of fuel combustion into a mechanical rotation movement is still growing. Simultaneous investigation of optimum engine configuration and the second law of thermodynamics consideration can best achieve this goal. A baseline 1.8 Ford diesel engine is simulated via AVL Fire software and then the multiobjective nonlinear programming by quadratic Lagrangian method have been applied to find the global optimal design. Then for exergy analysis of the baseline and optimized engine characteristics, a FORTRAN developed code is used. Two basic optimization cases are detected for exergy assessment that are RunID41 (feasible design) and RunID35 (infeasible design) among 55 run points. It was determined that the optimum feasible solution should characterize with low inner wall diameter (Di) with wider half spray cone angle to avoid wall collision in order to stay in the constraint zone and feasible area. The results of exergy also indicates that RunID41 is fuel efficient since the fuel burnt exergy of three design of ID41, ID35, and baseline are 719.52, 694.77, and 643.07 J, respectively. It has also been pointed out that the feasible optimum gives the higher thermo‐mechanical exergy, work exergy, and total exergy, while the infeasible optimum yields a higher heat release rate exergy and chemical exergy as there are no restrictions of injection and combustion chamber geometry set by the designer.

Inverse regression for ridge recovery: a data-driven approach for parameter reduction in computer experiments

Parameter reduction can enable otherwise infeasible design and uncertainty studies with modern computational science models that contain several input parameters. In statistical regression, techniques for sufficient dimension reduction (SDR) use data to reduce the predictor dimension of a regression problem. A computational scientist hoping to use SDR for parameter reduction encounters a problem: a computer prediction is best represented by a deterministic function of the inputs, so data comprised of computer simulation queries fail to satisfy the SDR assumptions. To address this problem, we interpret SDR methods sliced inverse regression (SIR) and sliced average variance estimation (SAVE) as estimating the directions of a ridge function, which is a composition of a low-dimensional linear transformation with a nonlinear function. Within this interpretation, SIR and SAVE estimate matrices of integrals whose column spaces are contained in the ridge directions’ span; we analyze and numerically verify convergence of these column spaces as the number of computer model queries increases. Moreover, we show example functions that are not ridge functions but whose inverse conditional moment matrices are low-rank. Consequently, the computational scientist should beware when using SIR and SAVE for parameter reduction, since SIR and SAVE may mistakenly suggest that truly important directions are unimportant.

A Spatial Grammar Method for the Computational Design Synthesis of Virtual Soft Locomotion Robots

Soft locomotion robots are intrinsically compliant and have a large number of degrees of freedom. They lack rigid components that provide them with higher flexibility, and they have no joints that need protection from liquids or dirt. However, the hand-design of soft robots is often a lengthy trail-and-error process. This work presents the computational design of virtual, soft locomotion robots using an approach that integrates simulation feedback. The computational approach consists of three stages: (1) generation, (2) evaluation through simulation, and (3) optimization. Here, designs are generated using a spatial grammar to explicitly guide the type of solutions generated and exclude infeasible designs. The soft material simulation method developed and integrated is stable and sufficiently fast for use in a highly iterative simulated annealing search process. The resulting virtual designs exhibit a large variety of expected and unexpected gaits, thus demonstrating the method capabilities. Finally, the optimization results and the spatial grammar are analyzed to understand and map the challenges of the problem and the search space.

An improved parametric level set method for structural frequency response optimization problems

In conventional parametric level set methods, the compactly supported radial basis functions (CSRBF) are used to approximate the level set function due to their unique properties, such as the sparsity of the interpolation matrix. The CSRBFs only consider the contributions of knots within a narrow sub-region, which sacrifices accuracy for efficiency in the interpolation. However, the accuracy loss in the CSRBF-based method may prolong the iteration and gradually lead the topology optimization towards a worse local optimum or even an unfeasible design, especially when the allowable material usage in the design domain is relatively low. This will significantly affect the performance of the optimization method. This paper proposes an improved parametric level set method (iPLSM), which is more efficient and effective in topology optimization designs. In this method, the Gaussian radial basis function with global support is used to parameterize the level set surface, to ensure a high numerical accuracy due to the consideration of all interpolation knots in the global domain. Then, a discrete wavelet transform scheme is incorporated into the parametric form to compress the full interpolation matrix and save the computational cost. The proposed method is applied to both the global and local frequency response optimization problems under wide excitation frequency ranges, to validate its efficiency and effectiveness.

Computational Fluid Dynamics Investigation of a Core-Mounted Target-Type Thrust Reverser—Part 2: Reverser Deployed Configuration

Core-mounted target-type thrust reverser (CMTTTR) design was proposed by NASA in the second half of the 90 s. NASA carried out several experiments at static conditions, and their acquired results suggested that the performance characteristics of the CMTTTR design fall short to comply with the mandatory thrust reverser (TR) performance criteria, and were therefore regarded as an infeasible design. However, the authors of this paper believe that the results presented by NASA for the CMTTTR design require further exploration to facilitate the complete understanding of its true performance potential. This part 2 paper is a continuation from Part 1 (reverser stowed configuration) and presents a comprehensive three-dimensional (3D) computational fluid dynamics (CFD) analyses of the CMTTTR in deployed configuration. The acquired results are extensively analyzed for aforementioned TR configuration operating under the static operating conditions at sea level, i.e., sea-level static, International Standard Atmosphere (ISA); the analyses at forward flight conditions will be covered in part 3. The key objectives of this paper are: First, to validate the acquired CFD results with the experimental data provided by NASA; this is achieved by measuring the static pressure values on various surfaces of the deployed CMTTTR model. The second objective is to estimate the performance characteristics of the CMTTTR design and corroborate the results with experimental data. The third objective is to estimate the pressure thrust (i.e., axial thrust generated due to the pressure difference across various reverser surfaces) and discuss its significance for formulating the performance of any TR design. The fourth objective is to investigate the influence of kicker plate installation on overall TR performance. The fifth and final objective is to examine and discuss the overall flow physics associated with the thrust reverse under deployed configuration.

Deriving guidelines for the design of plate evaporators in heat pumps using zeotropic mixtures

This paper presents a derivation of design guidelines for plate heat exchangers used for evaporation of zeotropic mixtures in heat pumps. A mapping of combined heat exchanger and cycle calculations for different combinations of geometrical parameters and working fluids allowed estimating the trade-off between heat transfer area and pressure drops on the thermodynamic and economic performance indicators of the cycle. Compressor running costs constituted the largest cost share, and increased due to a steep decrease of the heat pump coefficient of performance at high refrigerant pressure drops. It was found that the pressure drop limit leading to infeasible designs was dependent on the working fluid, thereby making it impossible to define a guideline based on maximum allowable pressure drops. It was found that economically feasible designs could be obtained by correlating the vapour Reynolds number and the Bond number at the evaporator inlet as ReV−0.42Bd0.26≈0.040. The use of the proposed guideline was illustrated for the mixture Propane/Iso-Pentane (0.5/0.5), leading to evaporator designs with net present values deviating maximum −4.4% from the best value found in the mapping. The presented methodology can be applied in different scenarios to develop similar guidelines, thereby decreasing the cost of combined cycle and component optimizations.

Optimization-based design of crude oil distillation units using surrogate column models and a support vector machine

This paper presents a novel optimization-based approach for the design of heat-integrated crude oil distillation units, which are widely used in refineries. The methodology presented combines, within a unified framework, surrogate distillation column models based on artificial neural networks, feasibility constraints constructed using a support vector machine, and pinch analysis to maximize heat recovery, in order to optimize the distillation column configuration and its operating conditions. The inputs to the surrogate column model are given by the column structure and operating conditions, while the outputs are related to the column performance. The support vector machine classifier filters infeasible design alternatives from the search space, thus reducing computational time, and ultimately improves the quality of the final solution. The overall optimization problem takes the form of a mixed-integer nonlinear program, which is solved by a genetic algorithm that seeks the design and operating variables values that minimize the total annualized cost. The capabilities of the proposed approach are illustrated using an industrially–relevant case study. Numerical results show that promising design alternatives can be obtained using the proposed method. The approach can help engineers to design and operate petroleum refineries optimally, where these are expected to continue to play a major role in the energy mix for some years.

Tool-supported design of data aggregation processes in cloud monitoring systems

Efficient monitoring of a cloud system involves multiple aggregation processes and large amounts of data with various and interdependent requirements. A thorough understanding and analysis of the characteristics of data aggregation processes can help to improve the software quality and reduce development cost. In this paper, we propose a systematic approach for designing data aggregation processes in cloud monitoring systems. Our approach applies a feature-oriented taxonomy called DAGGTAX (Data AGGregation TAXonomy) to systematically specify the features of the designed system, and SAT-based analysis to check the consistency of the specifications. Following our approach, designers first specify the data aggregation processes by selecting and composing the features from DAGGTAX. These specified features, as well as design constraints, are then formalized as propositional formulas, whose consistency is checked by the Z3 SAT solver. To support our approach, we propose a design tool called SAFARE (SAt-based Feature-oriented dAta aggREgation design), which implements DAGGTAX-based specification of data aggregation processes and design constraints, and integrates the state-of-the-art solver Z3 for automated analysis. We also propose a set of general design constraints, which are integrated by default in SAFARE. The effectiveness of our approach is demonstrated via a case study provided by industry, which aims to design a cloud monitoring system for video streaming. The case study shows that DAGGTAX and SAFARE can help designers to identify reusable features, eliminate infeasible design decisions, and derive crucial system parameters.

Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites

The surrogate-based simulation-optimization techniques are frequently used for optimal groundwater remediation design. When this technique is used, surrogate errors caused by surrogate-modeling uncertainty may lead to generation of infeasible designs. In this paper, a conservative strategy that pushes the optimal design into the feasible region was used to address surrogate-modeling uncertainty. In addition, chance-constrained programming (CCP) was adopted to compare with the conservative strategy in addressing this uncertainty. Three methods, multi-gene genetic programming (MGGP), Kriging (KRG) and support vector regression (SVR), were used to construct surrogate models for a time-consuming multi-phase flow model. To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models. The results show that: (1) the surrogate-modeling uncertainty was successfully addressed by the conservative strategy, which means that this method is promising for addressing surrogate-modeling uncertainty. (2) The ensemble surrogate model that combines MGGP with KRG showed the most favorable performance, which indicates that this ensemble surrogate can utilize both stand-alone surrogate models to improve the performance of the surrogate model.

Robust and stepwise optimization design for CO2 pipeline transportation

Carbon capture, utilization, storage (CCUS) technology is an effective means to reduce the CO2 emissions. It has been noted that engineering and economic design of the pipeline transportation are important components for CCUS. However, the uncertainties of the pipeline transportation model may make an infeasible design in practice and cause unnecessary cost. In this paper, a novel robust optimization method is proposed for CO2 pipeline transportation design, which can deal with the multiple uncertainties. A stepwise method is presented to further improve the optimization performance. The proposed optimal algorithm is validated by using numerical studies, which shows that the proposed approach can deal with the multiple uncertainties and improve the design performance in comparison with the existing

Comparing multi-objective non-evolutionary NLPQL and evolutionary genetic algorithm optimization of a DI diesel engine: DoE estimation and creating surrogate model

This study is concerned with the application of two major kinds of optimization algorithms on the baseline diesel engine in the class of evolutionary and non-evolutionary algorithms. The multi-objective genetic algorithm and non-linear programming by quadratic Lagrangian (NLPQL) method have completely different functions in optimizing and finding the global optimal design. The design variables are injection angle, half spray cone angle, inner distance of the bowl wall, and the bowl radius, while the objectives include NOx emission, spray droplet diameter, indicated mean effective pressure (IMEP), and indicated specific fuel consumption (ISFC). The restrictions were set on the objectives to distinguish between feasible designs and infeasible designs to sort those cases that cannot fulfill the demands of diesel engine designers and emission control measures. It is found that a design with deeper bowl and more encircled shape (higher swirl motion) is more suitable for NOx emission control, whereas designs with a bigger bowl radius, and closer inner wall distance of the bowl (Di) may lead to higher engine efficiency indices. Moreover, it was revealed that the NLPQL could rapidly search for the best design at Run ID 41 compared to genetic algorithm, which is able to find the global optima at last runs (ID 84). Both techniques introduce almost the same geometrical shape of the combustion chamber with a negligible contrast in the injection system.

Analytical surrogate model for the aberrations of an arbitrary GRIN lens.

Current analytical expressions between Gradient-Index (GRIN) lens parameters and optical aberrations are limited to paraxial approximations, which are not suitable for realizing GRIN lenses with wide fields of view or small f-numbers. Here, an analytical surrogate model of an arbitrary GRIN lens ray-trace evaluation is formulated using multivariate polynomial regressions to correlate input GRIN lens parameters with output Zernike coefficients, without the need for approximations. The time needed to compute the resulting surrogate model is over one order-of-magnitude faster than traditional ray trace simulations with very little losses in accuracy, which can enable previously infeasible design studies to be completed.

Developing products with set-based design: How to set up an idea portfolio and a team organization to establish design feasibility

AbstractPrior research has identified set-based design as a method that accounts for the high level of uncertainty that is associated with the design of innovative products or systems. Rather than precisely specifying a system architecture in the early design stages, set-based design builds on designing a system and its architecture in an evolutionary way. The literature on set-based design has studied how a system's design evolves by moving from a number of optional design ideas to the final system through gradually eliminating unfeasible design ideas and continually developing design ideas for which engineers increasingly establish feasibility. However, little is known about how firms set up the design process and the organization to successfully create new products with set-based design. Our research contributes to closing this gap. First, we study how firms determine the number (i.e., portfolio) of design ideas to pursue, an important step of the early design process. Second, we study how firms organize for set-based design by assigning teams to develop design ideas and eventually design a system's architecture. Our research uses an exploratory case study approach, investigating five cases in three different firms. First, we find that the early design process is characterized by the absence of formal idea evaluation and selection. Instead, firms start to pursue all initially created design ideas, evaluating and selecting them in an evolutionary manner as the design project progresses. Second, we identify two organizational approaches associated with set-based design: assign one team to pursue all ideas or assign one team per design idea.

Towards statistical guarantees in controlling quality tradeoffs for approximate acceleration

Conventionally, an approximate accelerator replaces every invocation of a frequently executed region of code without considering the final quality degradation. However, there is a vast decision space in which each invocation can either be delegated to the accelerator---improving performance and efficiency--or run on the precise core---maintaining quality. In this paper we introduce M ithra , a co-designed hardware-software solution, that navigates these tradeoffs to deliver high performance and efficiency while lowering the final quality loss. M ithra seeks to identify whether each individual accelerator invocation will lead to an undesirable quality loss and, if so, directs the processor to run the original precise code. This identification is cast as a binary classification task that requires a cohesive co-design of hardware and software. The hardware component performs the classification at runtime and exposes a knob to the software mechanism to control quality tradeoffs. The software tunes this knob by solving a statistical optimization problem that maximizes benefits from approximation while providing statistical guarantees that final quality level will be met with high confidence. The software uses this knob to tune and train the hardware classifiers. We devise two distinct hardware classifiers, one table-based and one neural network based. To understand the efficacy of these mechanisms, we compare them with an ideal, but infeasible design, the oracle. Results show that, with 95% confidence the table-based design can restrict the final output quality loss to 5% for 90% of unseen input sets while providing 2.5× speedup and 2.6× energy efficiency. The neural design shows similar speedup however, improves the efficiency by 13%. Compared to the table-based design, the oracle improves speedup by 26% and efficiency by 36%. These results show that M ithra performs within a close range of the oracle and can effectively navigate the quality tradeoffs in approximate acceleration.

Methods to Find Best Designs Among Infeasible Design Data Sets for Highly Constrained Design Optimization Problems

Methods to Find Best Designs Among Infeasible Design Data Sets for Highly Constrained Design Optimization Problems