Conceptual Design Evaluation Research Articles

Comparison of gasoline and hydrogen pathways in order to reduce the environmental hazards of a solar-hydrogen refueling station: Evaluation based on life cycle cost and Well-To-Wheel models

Today, due to increasing concerns about the use of fossil fuels, renewables have been introduced as a popular, efficient and green alternative. Since the transportation sector is one of the most important energy consumers, attention has been directed to the management of this energy consuming sector. It was proven that the development of electric-hydrogen vehicles can significantly reduce environmental concerns and dependence on fossil fuels in the transportation sector. However, improving the infrastructure of hydrogen refueling stations (HRSs) due to the high cost of hydrogen requires more and detailed works. In this article, the conceptual design and economic evaluation of an on-site HRS based on a photovoltaic (PV)-based solar unit coupled with the power grid has been developed and evaluated under various parameters and scenarios. The planned HRS is also comprised of a water electrolysis process (employing a polymer electrolyte membrane (PEM) electrolyzer unit), and hydrogen storage and dispenser. In the developed economic assessment, the levelized cost of hydrogen (LCOH) is calculated and discussed for different scenarios and plans based on the different size of the HRS and energy management approaches. The development of such refueling stations in cities can be an effective direction to achieve sustainable development. In addition, in order to increase believability, globalization and improve economic sustainability, an economic incentive approach based on Well-To-Wheel assessment (WTWA) and life cycle cost assessment (LCCA) was developed. The developed incentive strategy can lead to a decrease in the value of LCOH. A comprehensive comparison between hydrogen and gasoline refueling pathways is also provided. The outcomes indicated that, by applying the proposed economic incentives, the values of LCOH can be reduced by almost 15.5% and 2.98%, respectively, for discounted payback period s of 8 and 10 years. It was also found that, the WTWA index for the gasoline pathway is almost 33.8% higher compared to the hydrogen pathway. In addition, when the participation percentage of the PV-based solar unit is assumed to be 50% and 75%, the value of the WTWA index of the hydrogen pathway is reduced by approximately 25.3% and 62.8% compared to the gasoline pathway.

An integrated design concept evaluation method based on best–worst entropy and generalized TODIM considering multiple factors of uncertainty

Design concept evaluation (DCE) plays a vital role in the early design stage of new product development because it directly affects the direction of subsequent design and manufacturing activities. However, the previous DCE methods have some limitations, such as (i) they usually only consider one or two factors of uncertainty, while multiple factors of uncertainty are always involved in the group decision-making problems; (ii) the criteria weight calculations usually only consider the subjective expert experience or the objective statistical analysis, which is not complete; and (iii) the psychological behaviors of experts are ignored when considering gains or losses, all of which affect their precision. Hence, this study develops an integrated DCE model to address the above limitations. First, a new uncertain information manipulation method, namely interval-valued intuitionistic fuzzy rough clouds (IVIFRCs), is developed to handle the multiple factors of uncertainty in the group decision-making problems by integrating the interval-valued intuitionistic fuzzy sets (IVIFS)-based trapezium cloud model and the interval rough number (IRN) theory. Then, the arithmetic operations, distance measure, and aggregation operators for IVIFRCs are introduced and discussed. Furthermore, the best–worst and entropy methods are integrated as the best–worst entropy (BWE) to identify the hybrid criteria weights with IVIFRC information. Finally, the generalized TODIM (TOmada de Decisão Iterativa Multicritério) is applied to handle experts’ psychological behaviors as well as rank design concepts. Additionally, a case study, sensitivity analysis, and several comparisons are conducted. Results demonstrate that the developed model is superior to the existing DCE approaches.

Wastewater treatment to improve energy and water nexus with hydrogen fuel production option: Techno-economic and process analysis

The use of fresh water to produce green hydrogen fuel through the water electrolysis process can exacerbate the challenges of water scarcity. Using non-potable water for this purpose can lead to the design of a process with high security, reliability and sustainability. This paper develops the conceptual design and techno-economic evaluation of an innovative hydrogen energy production process from a solid oxide electrolyzer (SOE) integrated with a water treatment and recovery process. Accordingly, purified wastewater and waste heat of flue gases of the power plants have been utilized as feedstocks for the electrolysis process. Two different scenarios were assumed to supply the required thermal energy of the electrolyzer: the first scenario is the use of a preheater based on fossil energies, while in the latter scenario the required thermal energy is supplied through parabolic trough collectors, PTCs,-based solar farm. The integrating fossil fuels-driven power plants with evolving green technologies can mitigate the greenhouse gas emission crisis in addition to reducing the limitations of fossil energies. The outcomes indicated that the Levelized cost of hydrogen (LCH) for the second scenario increases by almost 38.7% compared to the first scenario. The reason for the high value of LCH for the second scenario is the high capital cost of solar collectors. Furthermore, the overall conversion efficiency for the proposed hydrogen production process was calculated as 53.26%. The introduced system can be competitive and reliable from the point of view of saving water consumption. The net potential energy saving and carbon emissions of the process was also determined.

Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations

Abstract Conceptual design evaluation is an indispensable component of innovation in the early stage of engineering design. Properly assessing the effectiveness of conceptual design requires a rigorous evaluation of the outputs. Traditional methods to evaluate conceptual designs are slow, expensive, and difficult to scale because they rely on human expert input. An alternative approach is to use computational methods to evaluate design concepts. However, most existing methods have limited utility because they are constrained to unimodal design representations (e.g., texts or sketches). To overcome these limitations, we propose an attention-enhanced multimodal learning (AEMML)-based machine learning (ML) model to predict five design metrics: drawing quality, uniqueness, elegance, usefulness, and creativity. The proposed model utilizes knowledge from large external datasets through transfer learning (TL), simultaneously processes text and sketch data from early-phase concepts, and effectively fuses the multimodal information through a mutual cross-attention mechanism. To study the efficacy of multimodal learning (MML) and attention-based information fusion, we compare (1) a baseline MML model and the unimodal models and (2) the attention-enhanced models with baseline models in terms of their explanatory power for the variability of the design metrics. The results show that MML improves the model explanatory power by 0.05–0.12 and the mutual cross-attention mechanism further increases the explanatory power of the approach by 0.05–0.09, leading to the highest explanatory power of 0.44 for drawing quality, 0.60 for uniqueness, 0.45 for elegance, 0.43 for usefulness, and 0.32 for creativity. Our findings highlight the benefit of using multimodal representations for design metric assessment.

Developing Students’ Worksheet for English Language Teaching to Cope with the 4C’s Skill Through Project Based Learning

This research aims to implement and evaluate design concepts and develop a PjBL-based students worksheet adapted to teaching materials to improve the 4C’s skill of Senior High School students in English subjects. Development research was used in this research. The PjBL-based students' worksheet was developed by matching the criteria for the components of the students' worksheet model: model syntax, social systems, management reaction principles, support systems, instructional impacts, and accompaniments, as well as other necessary instruments. The instruments developed were students' worksheet model validation sheets and learning tools validated by experts and practitioners. The data were analyzed by using descriptive statistical analysis. The results of this research indicated that the Project-based Learning (PjBL)-based students' worksheet model met the level of validity, practicality, and effectiveness to improve the 4C's skills of Senior High School students which contained: (a) syntax, (b) social system built on the principle of collaboration, (c) the principle of management reaction in which teachers act as facilitators, motivators, moderators, and consultants, (d) support systems in the form of Learning Implementation Plans, Teacher Books, Student Books, and Participant Worksheets Educate, (e) instructional impact in the form of students' 4C's skills and accompaniment impact in the form of critical thinking, creative thinking, responsibility, openness, and understanding of concepts.

A novel point-of-care diagnostic prototype system for the simultaneous electrochemiluminescent sensing of multiple traumatic brain injury biomarkers.

Traumatic brain injuries (TBI) are typically acquired when a sudden violent event causes damage to the brain tissue. A high percentage (70-85%) of all TBI patients are suffering from mild TBI (mTBI), which is often difficult to detect and diagnose with standard imaging tools (MRI, CT scan) due to the absence of significant lesions and specific symptoms. Recent studies suggest that a screening test based on the measurement of a protein biomarker panel directly from a patient's blood can facilitate mTBI diagnosis. Herein, we report a novel prototype system designed as a precursor of a future hand-held point-of-care (POC) diagnostic device for the simultaneous multi-biomarker sensing, employing a microarray-type spatially resolved electrochemiluminescence immunoassay (SR-ECLIA). The small tabletop prototype consists of a screen-printed electrode compartment to conduct multi-analyte ECL sandwich assays, a potentiostat module and a light collection module, all integrated into a compact 3D-printed housing (18.2 × 16.5 × 5.0 cm), as well as an sCMOS detector. Based on this design concept, further miniaturization, system integration, performance optimization and clinical evaluation shall pave the way towards the development of a portable instrument for use at the site of accident and healthcare. To demonstrate the system's feasibility, current performance and efficiency, the simultaneous detection of three mTBI biomarkers (GFAP, h-FABP, S100β) in 50% serum was achieved in the upper pg mL-1 range. The proposed device is amenable to the detection of other biomarker panels and thus could open new medical diagnostic avenues for sensitive multi-analyte measurements with low-volume biological sample requirements.

Evaluation of product conceptual design based on Pythagorean fuzzy set under big data environment

The concept design evaluation phase of the new product launch is extremely important. However, current evaluation information relies mainly on the a priori knowledge of decision makers and is subjective and ambiguous. For this reason, a conceptual design solution decision model based on Pythagorean fuzzy sets in a big data environment is proposed. Firstly, we use the ability of big data to mine and analyze information to construct a new standard for product concept design evaluation in the big data environment. Secondly, the Pythagorean fuzzy set (PFS), Analytic Hierarchy Process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are integrated into a decision model. AHP, extended by the Pythagorean fuzzy set, is used to determine the weights of new conceptual design criteria in a big data environment. The Pythagorean fuzzy TOPSIS is used to prioritize alternative conceptual design solutions. The feasibility of the approach is proven with a practical case, the generalizability of the method is confirmed with two descriptive digital cases, and the reliability, validity, and superiority of the process are demonstrated with sensitivity analysis, comparative analysis, and computational complexity analysis.

An MAGDM method for design concept evaluation based on incomplete information.

Design concept evaluation is a huge challenge in the R&D stage of new product development. The information in the assessments often depends on the decision-makers' individual preferences. However, sometimes the decision-makers cannot give precise and complete information because it is very difficult for them to be familiar with all the criteria. In this situation, an incomplete information decision-making matrix is established. In this paper, decision-making methods based on incomplete information are compared in the literature review. Incomplete information determination method using trust mechanism is proved as a proper way to solve this problem, and the missing information are computed based on the alternatives However, in design concept evaluation, experts commonly provide their preferences using linguistic words according to the different attributes. Hence, we propose a three-step Multiple Attributes Group Decision-making (MAGDM) method where the missing value are determined by attributes. In step one, a data repairing method is proposed based on trust theory. After that, in step two, a comprehensive weight determination method combining AHP and entropy is proposed to obtain the weight of index attributes. Finally, the Rough-TOPSIS method is applied in the design scheme ranking step. In the case study, the proposed method is implemented in a tourism product design process to show its effectiveness.

Polymeric Orthosis with Electromagnetic Stimulator Controlled by Mobile Application for Bone Fracture Healing: Evaluation of Design Concepts for Medical Use.

Background: The occurrence of bone fractures is increasing worldwide, mainly due to the health problems that follow the aging population. The use of additive manufacturing and electrical stimulators can be applied for bioactive achievements in bone healing. However, such technologies are difficult to be transferred to medical practice. This work aims to develop an orthosis with a combined magnetic field (CFM) electrostimulator that demonstrates concepts and design aspects that facilitate its use in a real scenario. Methods: A 3D-printed orthosis made of two meshes was manufactured using PLA for outer mechanical stabilization mesh and TPU for inner fixation mesh to avoid mobilization. A CFM stimulator of reduced dimension controlled by a mobile application was coupled onto the orthosis. The design concepts were evaluated by health professionals and their resistance to chemical agents commonly used in daily activities were tested. Their thermal, chemical and electrical properties were also characterized. Results: No degradation was observed after exposure to chemical agents. The CMF achieved proper intensity (20-40 µT). The thermal analysis indicated its appropriate use for being modelled during clinical assessment. Conclusion: An orthosis with a coupled electrostimulator that works with a combined magnetic field and is controlled by mobile application was developed, and it has advantageous characteristics when compared to traditional techniques for application in real medical environments.

A quantitative simulation-based conceptual design evaluation approach integrating bond graph and rough VIKOR under uncertainty

Conceptual design evaluation is a vital stage of new product development, which determines the sustainability, safety, quality of the final design, as well as its impact on the production cycle cost and manufacturability. Notwithstanding, pertinent evaluation methods advocate quantifying the subjective assessments of conceptual schemes (CSs) by decision makers (DMs) based on qualitative evaluation objectives, but the lack of physical performance verification of CSs with respect to quantitative objectives, leading to inconsistency between the optimal CS and the actual design requirements. To fill the issue, a quantitative simulation-based CS evaluation approach integrating bond graph and rough VIKOR is proposed. First, the bond elements that satisfy the physical effects corresponding to the realization of the function is obtained by function-effect mapping, and the functional structure is transformed into a bond graph model. Second, the AMESim components that satisfy the mathematical model of the above bond graph model are sought, the simulation model without detailed structural parameters is constructed to realize the simulation analysis of the scheme performance. Third, the rough VIKOR model is used to integrate the simulation data and the subjective assessments for the optimal ranking of CSs under uncertainty. Besides, rough number is introduced to aggregate the evaluation information of multi-DMs to improve the objectivity of decision process. Eventually, a cutting variable-speed device case study is further employed to verify the proposed simulation approach, and comparison results show that the proposed model can provide design decision support for value product development and reduce the waste of design resources through early design verification.

Decision making model for design concept evaluation based on interval rough integrated cloud VIKOR

Decision making model for design concept evaluation based on interval rough integrated cloud VIKOR

Design concept evaluation considering information reliability, uncertainty, and subjectivity: An integrated rough-Z-number-enhanced MCGDM methodology

Design concept evaluation plays a vital role in new product development due to its critical impact on successive design activities as well as the novelty and competence of the final product. Lots of decision-making techniques have been developed for the design concept evaluation under various contexts. However, existing methods mainly focus on the evaluation under precise environments or fuzzy set scenarios. Whereas in reality, most risk assessments are derived from experts’ subjective judgments. The reliability of the evaluation is rarely taken into consideration. To bridge this gap, this study proposes a rough-Z-number-enhanced MCGDM (multi-criteria group decision-making) to resolve the design concept evaluation considering information reliability, uncertainty, and subjectivity. A rough-Z-number is introduced to incorporate into the MCGDM framework to represent the individual evaluation and aggregate group risk assessments. A rough-Z-number-based AHP (analytic hierarchy process) is presented to determine the criteria weights. A rough-Z-number-based MABAC (multi-attributive border approximation area comparison) is proposed to rank the design concepts. Experiments and comparative analyses demonstrate the advantage of the rough-Z-number extended MCGDM in complex and uncertain decision-making environments. With the rough-Z-number-based enhancement, the information reliability, uncertainty, and subjectivity in design concept evaluation are well characterized.

A novel integrated MADM method for design concept evaluation

Design concept evaluation plays a significant role in new product development. Rough set based methods are regarded as effective evaluation techniques when facing a vague and uncertain environment and are widely used in product research and development. This paper proposed an improved rough-TOPSIS method, which aims to reduce the imprecision of design concept evaluation in two ways. First, the expert group for design concept evaluation is classified into three clusters: designers, manufacturers, and customers. The cluster weight is determined by roles in the assessment using a Multiplicative Analytic Hierarchy Process method. Second, the raw information collection method is improved with a 3-step process, and both design values and expert linguistic preferences are integrated into the rough decision matrix. The alternatives are then ranked with a rough-TOPSIS method with entropy criteria weight. A practical example is shown to demonstrate the method’s viability. The findings suggest that the proposed decision-making process is effective in product concept design evaluation.

Generation and evaluation of product concepts by integrating extended axiomatic design, quality function deployment and design structure matrix

Existing product concept generation and evaluation methods are mainly based on designers' experience to determine design schemes in the process of product development, which is time-consuming and ineffective. This paper proposes an approach to generate and evaluate design concepts by integrating the extended axiomatic design, quality function deployment and design structure matrix. Different design domains are mapped for matrix operations to generate feasible concepts based on design criteria. A domain mapping matrix is built to determine technical measures, functional requirements and design parameters based on customer requirements. The proposed approach provides a structured method to quantify, validate and qualify design concepts. A case study of the design of a hand rehabilitation device demonstrates the effectiveness of the proposed method.

Multi-criteria evaluation of smart product-service design concept under hesitant fuzzy linguistic environment: A novel cloud envelopment analysis approach

Design concept evaluation is an essential but challenging task in smart product service system (SPSS) design. It is critical to select the design concept with better smart experience to ensure the success of SPSS design. Meanwhile, SPSS design concept evaluation is an expert knowledge-based decision process with scant data and a short time window under a hesitant, ambiguous, and subjective environment, which would lead to low decision-making efficiency and inaccurate evaluation results. Hence, this paper proposes a novel cloud envelopment analysis method to evaluate SPSS design concept with respect to smart experience criteria. The proposed method integrates hesitant fuzzy linguistic terms (HFLTs), cloud model and data envelopment analysis (DEA) method to accurately evaluate SPSS design concepts with handling the hesitancy, fuzziness and randomness in qualitative and subjective information. A hybrid model of normal cloud and trapezium cloud is used to quantify hybrid-length HFLT variables to avoid decision information loss and distortion. A novel cloud non-linear programming model is constructed to extend the classical DEA into cloud environment. Finally, a case study and some comparison analyses illustrate the effectiveness and advantages of proposed method.

DESIGN OF SAUCER AS A DRINKING TOOL FOR PARKINSON'S PATIENTS USING MORPHHOLOGICAL METHOD

Almost all humansuse glasses to make it easier for them to drink. No wonder there are so many glass products on the market with various designs. People with parkinson’s cannot use glasses properly, because they have movement disorders so that when they hold the glass, the water in it will spill. Seeing from the existing problems, it is necessary to design a saucer as a drinking aid. Which of course will make it easier for them to use glasses. In the design of the saucer using the morphological method. One of the methods commonly used in evaluating design concepts. The results of the selection process for the combination of components that make up this saucer, it can be concluded that from the selection process using the morphological method, the most appropriate saucer is to use concept 3 with score of 12,6, where the design is ranked 1.

New customer-oriented design concept evaluation by using improved Z-number-based multi-criteria decision-making method

The customer-oriented design concept evaluation (CDCE) enables companies to select the best design concept from the perspective of customer to win the customer-centered market. However, previous CDCE studies only focus on the customer’s preference value (PV), but neglect the customer’s confidence attitude on this preference, i.e., the preference reliability (PR), and some design specifications, e.g., the design attribute’s importance (DAI). To address such drawbacks, we propose a new CDCE by using improved Z-number-based multi-criteria decision-making (IZ-MCDM) method to better express and utilize customer’s uncertain opinion. In IZ-MCDM, the Z-number is used to express the customer’s opinion (Z-opinion) that includes PV and its affiliated PR information. Z-opinion is translated into an interval Z-number to form a new type of evaluation value and decision matrix. Based on the evaluation value, a new ideal solution selection (ISS) strategy integrating with PV, PR and DAI information is employed in IZ-MCDM. By comparing with the re-defined ideal solution, the alternative that attracts certain high-preferences for its importance attribute values and uncertain low-preferences for its less importance attribute values is more likely to be recommended as the best one. Hence, IZ-MCDM can get more reasonable design concept than classical PV-only CDCE method. Two empirical experiments from existing CDCE examples have been carried out in this study, and the comparison experimental results further validate the significance of IZ-MCDM, which show that 1) besides PV factor, PR and DAI factors could also significantly impact the evaluation result; 2) these two factors should be acted together to select the ideal solution; 3) IZ-MCDM has suitability as it supports different MCDM models with different deviation measurement metrics to evaluate the alternatives.

Coupling of Component Models with Mismatching Interfaces for an Efficient NVH Vehicle Design

<div class="section abstract"><div class="htmlview paragraph">The NVH optimization of new vehicle models can in principle only be carried out in a relatively late stage of the development process, when the geometrical data (CAD) are available and can be used to generate detailed Finite Element (FE) models of the car body. Unfortunately, in this stage of the development process most of the geometrical data are already fixed and countermeasures are limited and expensive. In order to be able to evaluate design concepts in an earlier conceptual stage of the development process existing models of similar predecessor vehicles must be used leading to techniques such as “mesh-morphing” or “concept modelling” (see for instance [<span class="xref">1</span>, <span class="xref">2</span>]). Here, a different approach is investigated based on a substructuring technique. In principle the coupling of the component-structures coming from different models would require post-processing in order to obtain compatible interface degrees-of-freedom (DOFs), an operation which in most cases must be carried out manually and is very time consuming. This paper presents a novel substructuring approach that tackles a continuous interface by only considering a small set of coupling DoFs. This approach employs the discretisation of interfaces between structural parts or structural-acoustic domains in terms of pivotal points and patches. In this manner, the requirement of spatial continuity between the models of the separate substructures is no longer needed and subsystems with incompatible interfaces can be coupled. Thus, an efficient vibro-acoustic design optimisation procedure for components shared by different vehicle models, such as the car floor, is enabled. It will be shown that a single car floor model can be successfully coupled with different upper-body structures, even when they have a different location of coupling DoFs.</div></div>

The influence of virtual human representations on first-year architecture students’ perceptions of digitally designed spaces: a pilot study

ABSTRACT The use of human figures throughout the design process enables designers to experience, communicate, and evaluate design concepts. Earlier studies of architectural representation sought to identify the relationship between the tools and techniques of representation and how individuals perceive architectural scenes. Nonetheless, the effect of virtual human representation (VHR) on students’ perceptions of architectural space remains underexplored. Therefore, an experimental study was conducted with first-year architecture students, in which they experienced three architectural scenes (non-presence of VHR, presence of idle VHR, and presence of animated VHR). Statistical analyses of the students’ self-assessments examined how the inclusion of VHRs in digitally visualized architectural spaces influenced the perceptions of these spaces. The study revealed that incorporating VHR into representations of architectural spaces positively affected the students’ sense of physical dimensions and of the spatial qualities.

Conceptual design and performance evaluation of a novel cryogenic integrated process for extraction of neon and production of liquid hydrogen

Novel integration of a hydrogen liquefaction process and cryogenic rectification of air is presented. The thermodynamics analysis including the 2nd Law and sensitivity analysis regarding this process are conducted. The proposed cryogenic process can produce 111.3 kg/s of nitrogen and 42 kg/s of oxygen with argon. In addition, this process produces more than 132 × 103 tons of liquid hydrogen and 751 tons of crude neon per year. Moreover, as the number of the used compressor is very fewer than of the conventional process, the required power consumption of the proposed cryogenic plant is almost low. Based on the exergy analysis, using expanders, providing the requested refrigeration condition by solar heat exchangers and proper streams, and integrating two processes with the similar operating condition, the exergetic efficiency of the cycle is more than 70 %. The sensitivity analysis of operating parameters such as pressure, temperature, reflux ratio, and the number of stages of column presents their the importance of them on the recovered neon, oxygen, and nitrogen. Moreover, the production of cryogenic liquids is more sensitive to operating pressure and temperature than the reflux ratio and the number of stages.