Multidimensional Design Space Research Articles

Holistic Design Explorations of Building Envelopes Supported by Machine Learning

The design of building envelopes requires a negotiation between qualitative and quantitative aspectsbelonging to different disciplines, such as architecture, structural design, and building physics.In contrast to hierarchical linear approaches in which various design aspects are considered andconceived sequentially, holistic frameworks allow such aspects to be taken into considerationsimultaneously. However, these multi-disciplinary approaches often lead to the formulation ofcomplex high-dimensional design spaces of solutions that are generally not easy to handle manually.Computational optimisation techniques may offer a solution to this problem; however, they mainlyfocus on quantitative aspects, not always guaranteeing the flexibility and interactive responsivenessdesigners need in the early design stage. The use of intuitive geometry-based generative tools, incombination with machine learning algorithms, is a way to overcome the issues that arise when dealingwith multi-dimensional design spaces without necessarily replacing the designer with the machine.The presented research follows a human-centred design framework in which the machine assists thehuman designer in generating, evaluating, and clustering large sets of design options. Through a casestudy, this paper suggests ways of making use of interactive tools that do not overlook the performancecriteria or personal prefer

Tensile deformation behavior of twist grain boundaries in CoCrFeMnNi high entropy alloy bicrystals

High entropy alloys (HEA) are a class of materials that consist of multiple elemental species in similar concentrations. The use of elements in far from dilute concentrations introduces a multi-dimensional composition design space by which the properties of metallic systems can be tailored. While the mechanical behavior of HEAs has been the subject of active research recently, the role of grain boundaries (GBs) in their deformation behavior remains poorly understood. Motivated by recent experiments on HEAs demonstrating that GBs act as nucleation sites for deformation twins, herein, we leverage atomistic simulations to construct a series of equiatomic CoCrFeMnNi HEA bicrystals with langle 110 rangle and langle 111 rangle symmetric twist GBs and examine their tensile behavior and underlying deformation mechanisms at 77 K. Simulation results reveal that plastic deformation proceeds by the nucleation of partial dislocations from GBs, which then grow with further loading by bowing into the bulk crystals leaving behind stacking faults. Variations in the nucleation stress exist as function of GB character, defined in this work by the twist angle. Our results provide future avenues to explore GBs as a microstructure design tool to develop HEAs with tailored properties.

Using a Design of Experiments Approach to Inform the Design of Hybrid Synthetic Yeast Promoters.

Hybrid promoter engineering takes advantage of the modular nature of eukaryotic promoters by combining discrete promoter motifs to confer novel regulatory function. By combinatorially screeningsequence libraries for trans-acting transcriptional operators, activators, repressors and core promoter sequences, it is possible to derive constitutive or inducible promotercollections covering a broad range of expression strengths. However, combinatorial approaches to promoter design can result in highly complex, multidimensional design spaces, which can be experimentally costly to thoroughly explore in vivo. Here, we describe an in silico pipeline for the design of hybrid promoter libraries that employs a Design of Experiments (DoE) approach to reduce experimental burden and efficiently explore the promoter fitness landscape. We also describe a software pipeline to ensure that the designed promoter sequences are compatible with the YTK assembly standard.

Application of evolution strategy to determine parameters of the multi-branch Foster and Cauer equivalent circuit of system with eddy-currents

The paper discusses the strategy of searching for the extreme of multi-modal functions with the number of decision variables n ≥ 6, in a multidimensional design space. The optimization algorithm combining an evolution strategy (ES) with operators of

Techno-Economic Optimisation for a Wave Energy Converter via Genetic Algorithm

Although sea and ocean waves have been widely acknowledged to have the potential of providing sustainable and renewable energy, the emergence of a self-sufficient and mature industry is still lacking. An essential condition for reaching economic viability is to minimise the cost of electricity, as opposed to simply maximising the converted energy at the early design stages. One of the tools empowering developers to follow such a virtuous design pathway is the techno-economic optimisation. The purpose of this paper is to perform a holistic optimisation of the PeWEC (pendulum wave energy converter), which is a pitching platform converting energy from the oscillation of a pendulum contained in a sealed hull. Optimised parameters comprise shape; dimensions; mass properties and ballast; power take-off control torque and constraints; number and characteristics of the pendulum; and other subcomponents. Cost functions are included and the objective function is the ratio between the delivered power and the capital expenditure. Due to its ability to effectively deal with a large multi-dimensional design space, a genetic algorithm is implemented, with a specific modification to handle unfeasible design candidate and improve convergence. Results show that the device minimising the cost of energy and the one maximising the capture width ratio are substantially different, so the economically-oriented metric should be preferred.

Multi-fidelity Bayesian Optimization of SWATH Hull Forms

This study presents a multi-fidelity framework that enables the construction of surrogate models capable of capturing complex correlations between design variables and quantities of interest. Resistance in calm water is investigated for a SWATH hull in a multidimensional design space using a new method to derive high-quality response surfaces through machine learning techniques based on a low number of high-fidelity computations and a larger number of less-expensive low-fidelity computations. First, a verification and validation study is presented with the goal of comparing and ranking numerical methods against experiments performed on a conventional SWATH geometry. Then, the hull geometry of a new family of unconventional SWATH hull forms with twin counter-canted struts is parametrically defined and sequentially refined using multi-fidelity Bayesian optimization. Ship resistance in calm water is finally predicted using observations from two different fidelity levels. We demonstrate that the multi-fidelity optimization framework is successful in obtaining an optimized design using a small number of high-fidelity computations and a larger number of low-fidelity computations. Simulation and optimization costs are reduced by orders of magnitude, providing accurate certificates of fidelity for the performance of the proposed design.

Optimal Parameter Design for a Cryogenic Submerged Arc Welding(SAW) Process by Utilizing Stepwise Experimental Design and Multi-dimensional Design Space Analysis

Optimal Parameter Design for a Cryogenic Submerged Arc Welding(SAW) Process by Utilizing Stepwise Experimental Design and Multi-dimensional Design Space Analysis

Simulation framework for evaluating lightweight spectral cameras in drone-based aquatic sensing applications.

Optical remote sensing of aquatic environments using aerial drones is becoming more feasible as lightweight, low-power, spectral cameras increase in availability. Use of these cameras in such applications involves complex trade-offs in optical design and in deployment strategies, and simulations provide a means to examine this multidimensional design space to identify specific limitations on performance for a given measurement scenario. In this paper, such a simulation framework is developed, and its use in two realistic aquatic remote sensing scenarios is explored. Such a framework can provide insight into not only uses of existing camera systems, but also aspects of optical design or hardware that would lead to improved accuracy when using such cameras aerially over natural water bodies.

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize batterylifetime;however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore,bothlarge parameter spaces and high sampling variability3,6,7 necessitatea large number ofexperiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, whichcanalleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: anearly-predictionmodel5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validatethe accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces.

Design of Experimental Approach to Analytical Robustness Study for UHPLC Method Developed for Separation and Quantification of Spironolactone and its Impurities in Drug Substances

The present work demonstrates the development of an optimal, robust, validated UHPLC method for quantification of related impurities and assay determination of spironolactone. Design of experiment procedure, in combination with statistical evaluation of the data was used to test the robustness of developed method. A stability indicating method was established by forced degradation experiments. Analytical robustness was determined using design of experiment approach. The chromatographic separation was achieved with Agilent SB-C18 RRHD column using gradient elution with mobile phase-A consists of a mixture of 0.1 % each formic acid and ammonia in water and methanol as mobile phase-B respectively. The developed method is exhaustively validated for parameters like precision, accuracy, linearity, LOD, LOQ, ruggedness and robustness. The stability tests were also performed on drug substances as per ICH norms. Base line separation was achieved for all impurities, degradation products and the API. All impurities were eluted within 12 min, there was a remarkable 3.5-fold decrease in runtime and a clear baseline separation between all peaks in comparison with Ph.Eur monograph. A multi-dimensional design space was built to study the robustness of developed method using design expert software. Significant parameters such as effect of flow rate, buffer strength and mobile phase compositions were optimized at three levels. Plackett-Burman design was applied for screening of chromatographic conditions and factorial design was applied for optimization of essential factors in robustness studies.

Quality by Design Based Method Development for Simultaneous Estimation of Montelukast Sodium and Theophylline in Tablet Dosage Form

Background: Analytical Quality by design (AQbD) is one of the upcoming regulatory requirements in the field of pharmaceutical analysis for the development of reliable and robust analytical methods. Montelukast sodium and theophylline available in tablet dosage forms are used widely for the treatment of airway disorders. Liquid chromatographic methods for estimation of the drugs are reported but those chromatographic methods developed by the AQbD approach are not available. Methods: A systematic design of experiments has been employed to develop a liquid chromatographic method for simultaneous estimation of montelukast sodium and theophylline. Critical process parameters are defined and optimization of methanol content in mobile phase, the concentration of trifluoroacetic acid and flow rate was employed within the framework of the full factorial design. The statistical analysis of the experimental data for critical quality attributes namely tailing factor of both the drugs and resolution was performed. Results: A multi-dimensional design space for the critical process parameters was obtained. The optimal chromatographic separation of the two API was achieved using C18 (250mm×4.6mm, 5µm) column as stationary phase and mobile phase composed of acetonitrile, trifluoroacetic acid (TFA) (0.2%v/v in water) and methanol in the ratio of 25:15:60 (v/v/v) at flow rate of 1.0 ml min-1. The influence of the CPP was evaluated by various data tables for example, analysis of variance and lack of fit. Interpretation of various plots obtained from the experimental data such as predicted vs. actual, predicted vs. run, pareto chart, perturbation plot and contour plot provided the information on the manner in which each factor affects the critical quality attributes. Conclusion: A design space generated by design of experiments is an evidence for the robustness of the method. The assay results show the applicability of the method for analysis of commercially available tablets.

Data-Driven Design Space Exploration and Exploitation for Design for Additive Manufacturing

Recently, design for additive manufacturing has been proposed to maximize product performance through the rational and integrated design of the product, its materials, and their manufacturing processes. Searching design solutions in such a multidimensional design space is a challenging task. Notably, no existing design support method is both rapid and tailored to the design process. In this study, we propose a holistic approach that applies data-driven methods in design search and optimization at successive stages of a design process. More specifically, a two-step surrogate model-based design method is proposed for the embodiment and detailed design stages. The Bayesian network classifier is used as the reasoning framework to explore the design space in the embodiment design stage, while the Gaussian process regression model is used as the evaluation function for an optimization method to exploit the design space in detailed design. These models are constructed based on one dataset that is created by the Latin hypercube sampling method and then refined by the Markov Chain Monte Carlo sampling method. This cost-effective data-driven approach is demonstrated in the design of a customized ankle brace that has a tunable mechanical performance by using a highly stretchable design concept with tailored stiffnesses.

Migrating parallel applications to the cloud: assessing cloud readiness based on parallel design decisions

Parallel applications are the computational backbone of major industry trends and grand challenges in science. Whereas these applications are typically constructed for dedicated High Performance Computing clusters and supercomputers, the cloud emerges as attractive execution environment, which provides on-demand resource provisioning and a pay-per-use model. However, cloud environments require specific application properties that may restrict parallel application design. As a result, design trade-offs are required to simultaneously maximize parallel performance and benefit from cloud-specific characteristics. In this paper, we present a novel approach to assess the cloud readiness of parallel applications based on the design decisions made. By discovering and understanding the implications of these parallel design decisions on an application’s cloud readiness, our approach supports the migration of parallel applications to the cloud. We introduce an assessment procedure, its underlying meta model, and a corresponding instantiation to structure this multi-dimensional design space. For evaluation purposes, we present an extensive case study comprising three parallel applications and discuss their cloud readiness based on our approach.

A full palette: Crystal chemistry, polymorphism, synthetic strategies, and functional applications of lanthanide oxyhalides

Ionic compounds wherein lanthanide cations are arrayed alongside anions adopt a wide range of crystal structures as a result of the variation of ionic radii and electronic configurations across the lanthanide series. Owing to the constricted nature of 4f orbitals, local coordination environments and structural preferences in such compounds are primarily dictated by electrostatic interactions and steric considerations with the primary driving force being the minimization of the crystal lattice energy. In this review, we examine a broad class of dianionic rare-earth compounds, lanthanide oxyhalides that present a multidimensional design space for tuning of functional properties by dint of the possibilities for extensive alloying on cationic and anionic sublattices; the considerable span of ionic radii and hardness across the lanthanide series and down the halide group, respectively; a large tolerance window for point defects such as oxide and halide vacancies; as well as multiple accessible polymorphs. In addition to their structural versatility, control over functional properties is accessible based on alteration of microstructure and surface chemistry. Synthetic strategies for accessing atomistic, nanoscale, and mesoscale control are discussed using illustrative examples placing particular emphasis on non-hydrolytic sol—gel processes that provide access to well-defined solid-solution nanocrystals. The reactivity and post-synthetic modification of these compounds is further delineated. Methods for defining color centers within these compounds, cooperativity of the optical response of the color centers with the host lattice (determined by overlap integrals specific to each structure type and the polarization and ligand field effects of different halide ions) and adjacent color centers (reliant on energy transfer schema), and their practical application in phosphors are further detailed. Mechanisms facilitating down- and up-conversion of energy absorbed from incident electromagnetic radiation or high-energy particles are delineated with particular emphasis on X-ray and electron-beam excitation. The accessible energy conversion mechanisms, efficacious radiative recombination channels, and opportunities for systematically tuning color through compositional modulation have led to the emergence of these materials as potential candidates for phosphors. These materials have potential applications in solid-state lighting, radiation detection, and as the active elements of imaging devices. The tolerance towards large concentrations of anion vacancies and the facile diffusivity of anions in these compounds further underpins the function of lanthanide oxyhalides as ion conductors, gas sensors, and heterogeneous catalysts. Increasing interest has focused on utilization of well-defined color centers for quantum information science and multiplexed sensing in biological systems. Such applications require additional control of the positioning of dopant atoms and understanding of their interactions with the host lattice and other dopant atoms.

Sparse Gaussian Process Emulators for Surrogate Design Modelling

Efficient surrogate modelling of computer models (herein defined as simulators) becomes of increasing importance as more complex simulators and non-deterministic methods, such as Monte Carlo simulations, are utilised. This is especially true in large multidimensional design spaces. In order for these technologies to be feasible in an early design stage context, the surrogate model (oremulator) must create an accurate prediction of the simulator in the proposed design space. Gaussian Processes (GPs) are a powerful non-parametric Bayesian approach that can be used as emulators. The probabilistic framework means that predictive distributions are inferred, providing an understanding of the uncertainty introduced by replacing the simulator with an emulator, known as code uncertainty. An issue with GPs is that they have a computational complexity of O(N3) (where N is the number of data points), which can be reduced to O(NM2) by using various sparse approximations, calculated from a subset of inducing points (where M is the number of inducing points). This paper explores the use of sparse Gaussian process emulators as a computationally efficient method for creating surrogate models of structural dynamics simulators. Discussions on the performance of these methods are presented along with comments regarding key applications to the early design stage.

Application of definitive screening design to quantify the effects of process parameters on key granule characteristics and optimize operating parameters in pulsed-spray fluid-bed granulation

The pulsed-spray fluid-bed granulation (PSFBG) process was investigated and optimized using definitive screening design, a recently proposed new class of three-level design of experiment method. Such a design enabled quadratic models to be established that described the effect of six input process parameters – inlet air temperature, inlet air humidity, binder spray rate, atomization pressure, pulse period, and pulse width – on the granule quality in a PSFBG process. Mathematical models of the mean particle size, relative size distribution width, production yield, and porosity were developed to quantify the relationships between the influencing factors and critical quality attributes. On the basis of constraints on the desired granule properties, a design space for PSFBG was determined and ranges of the operating parameters were defined. An acceptable degree of prediction was confirmed by validation experiments, demonstrating the reliability and effectiveness of using definitive screening design to study the PSFBG process. This method can accelerate screening and optimization of this process within a large multidimensional design space.

Development of Single-Crystal Ni-Base Superalloys Based on Multi-criteria Numerical Optimization and Efficient Use of Refractory Elements

The development of new Ni-base superalloys with a complex composition consisting of eight or more alloying elements is a challenging task. The experimental state-of-the-art development cycle is based on the adaption of already existing compositions. Although new alloy compositions with potentially improved material properties are expected to be similar to already known superalloys, this procedure impedes efficiently finding these compositions in the large multi-dimensional design-space of all alloying elements. Modern alloy development combines numerical optimization methods with experimental validation to guide the development towards promising compositions. In this work, an improved numerical multi-criteria optimization tool using CALPHAD calculations and semi-empirical models for alloy development is presented. The model improvements to its predecessor are described and the successful application for the development of rhenium-free single-crystal Ni-base superalloys ERBO/13 and ERBO/15 is revisited. The optimization tool is described and the designed alloys are discussed regarding phase stability. Finally, a possible phase stability model extending the optimization tool and improving the alloy composition predictions is presented.

Optimization of a Coupling Process for Insulin Degludec According to a Quality by Design (QbD) Paradigm.

This case study described a successful application of the quality by design (QbD) principles to a coupling process development of insulin degludec. Failure mode effects analysis (FMEA) risk analysis was first used to recognize critical process parameters (CPPs). Five CPPs, including coupling temperature (Temp), pH of desB30 solution (pH), reaction time (Time), desB30 concentration (Conc), and molar equivalent of ester per mole of desB30 insulin (MolE), were then investigated using a fractional factorial design. The curvature effect was found significant, indicating the requirement of second-order models. Afterwards, a central composite design was built with an augmented star and center points study. Regression models were developed for the CPPs to predict the purity and yield of predegludec using above experimental data. The R2 and adjusted R2 were higher than 96 and 93% for the two models respectively. The Q2 values were more than 80% indicating a good predictive ability of models. MolE was found to be the most significant factor affecting both yield and purity of predegludec. Temp, pH, and Conc were also significant for predegludec purity, while Time appeared to remarkably influence the yield model. The multi-dimensional design space and normal operating region (NOR) with a robust setpoint were determined using a probability-based Monte-Carlo simulation method. The verified experimental results showed that the design space was reliable and effective. This study enriches the understanding of acetylation process and is instructional to other complicated operations in biopharmaceutical engineering.

Analytical Model for Predicting the Discharge Performance of Thick Battery Electrodes

There is an increasing interest in using thick electrodes in Li-ion batteries to improve the energy density and lower the cost by reducing the amount of inactive components. However, increasing electrode thickness usually leads to inferior rate performance. Currently there lacks a quantitative understanding of the trade-off between the power and energy density for thick electrodes, which is essential for optimizing electrode structures for specific applications and developing novel thick electrode designs. Addressing this deficiency, here we present a simple and yet accurate analytical model for predicting the discharge performance of battery cells containing thick electrodes. Compared to the traditional porous electrode simulations, this model offers a more transparent understanding of the kinetic limitations of thick electrode configurations and the effects of various cell-level structural parameters on battery performance. Computation-wise, the model is substantially more efficient than the numerical pseudo-2D discharge simulation, and provides a powerful tool for performing the optimization of thick electrodes within the multi-dimensional design space. Our model is motivated by two important observations from numerical simulations and experiments, namely, the capacity utilization of thick electrodes is limited by electrolyte transport and a pseudo-steady state of electrolyte transport is established during discharge. Accordingly, we derived analytical expressions for the electrolyte distribution across the electrode and the discharge capacity at termination based on the steady state assumption. As shown in Figure 1a, the predicted distribution of Li ion concentration in electrolyte agrees well with the numerical simulation. The depth of discharge (DoD) calculated from the analytical model provides excellent approximations to the simulation results for various electrode thickness and cell configurations (half vs full cells) without any adjustable parameters, Figure 1b. This model is generally applicable to electrodes that display solid-solution intercalation behavior such as the layer oxide cathodes paired with the graphite anode at low to moderately high discharge rates. An important insight we obtained from the analytical model is that the discharge capacity of half and full cells exhibit qualitatively different scaling relation with electrode thickness and discharge current. As such, the half cell configuration with Li metal anode exhibits markedly improved DoD at large electrode thickness over full cells containing the graphite anode. This finding explains the seemingly scattered results from various thick electrode experiments reported in literature. It cautions against the practice of using the half cell testing as an indicator of the rate performance of full cells with thick electrodes. On the other hand, it suggests that the combination of a thick cathode with a high capacity anode such as Li metal, silicon and tin is an effective strategy to enable thick electrode designs with higher capacity utilization. Figure 1: a) Li ion concentration distribution in electrolyte during discharge calculated from the porous electrode simulation (solid lines) and steady-state analytical model (dashed line). Figure 1

Minimum-Cost Crowdsourcing with Coverage Guarantee in Mobile Opportunistic D2D Networks

With the remarkable proliferation of intelligent wireless devices and active mobile users, we have witnessed a rapid increasing trend of mobile crowdsourcing applications. While crowdsourcing does not depend on any specific underlying network, the device-to-device (D2D)-based crowdsourcing is highly desired when the originator of a crowdsourcing task cannot directly reach out to the participants or the conventional approaches for data transportation are costly. The marriage of crowdsourcing and D2D creates new, interesting research problems, mainly due to the unique non-deterministic setting in D2D. In this work, we focus on the problem of how to efficiently distribute a crowdsourcing task and recruit participants based on D2D communications. We formally formulate the minimum-cost crowdsourcing (MCC) problem in D2D networks, which explores a multi-dimensional design space to seek an optimal solution that minimizes the total crowdsourcing cost while satisfying the coverage probability over the field of interest. We introduce an approximation algorithm based on a reduced solution space and formally prove that its cost is bounded by a desired approximation ratio in comparison with the optimal solution. We further propose a lightweight online heuristic that inherits the same design philosophy but implements it in a distributed manner. We prototype the proposed online scheme in Android and carry out experiments in a campus environment, involving 21 Dell Streak tablets carried by students for a period of 15 days. We also extract the algorithm codes from our prototype and perform extensive simulations based on Haggle trace. The results demonstrate the efficiency of the proposed heuristics and reveal empirical insights into the design tradeoffs and practical considerations in D2D-based crowdsourcing.