Design Performance Metrics Research Articles

Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. This study aims to provide a general framework for the testing scenario library generation (TSLG) problem with different operational design domains (ODDs), CAV models, and performance metrics. Given an ODD, the testing scenario library is defined as a critical set of scenarios that can be used for CAV test. Each testing scenario is evaluated by a newly proposed measure, scenario criticality, which can be computed as a combination of maneuver challenge and exposure frequency. To search for critical scenarios, an auxiliary objective function is designed, and a multi-start optimization method along with seed-filling is applied. The proposed framework is theoretically proved to obtain accurate evaluation results with much fewer number of tests, if compared with the on-road test method. In part II of the study, three case studies are investigated to demonstrate the proposed methodologies. Reinforcement learning based technique is applied to enhance the searching method under high-dimensional scenarios.

Multistage pressure-retarded osmosis configurations: A unifying framework and thermodynamic analysis

Pressure-retarded osmosis has enjoyed increasing research interest over the last decade. Recent studies focusing on single-stage PRO designs have raised doubts regarding the long-term economic viability of the technology. While most of the analyses are based on single-stage operation, comprehensive analysis of multistage PRO which shows promise for better energetic performance is absent. Previous studies on multistage PRO differ in their design philosophies and performance metrics, leading to an incomplete assessment regarding the potential benefits of multistaging. In this paper, we develop a unifying framework to classify several existing multistage configurations. In addition, we analyze the multistage PRO system from a thermodynamic perspective. Among the two major multistage design strategies, namely interstage pressure control and independent feed inputs to each stage, we found the latter to be more effective towards increasing net power density. In comparison to a single-stage device, a 10-stage system achieves around 9% higher net power density while using the same membrane area.

Design optimization of building geometry and fenestration for daylighting and energy performance

With the increasing demand for sustainable design and green buildings, building performance is having a greater influence on design decisions. Design decisions on building envelop, especially on building geometry, window and skylight size and placement are essential in the early design stage. This research proposes a building performance optimization process that can help designers simultaneously evaluate the daylighting and energy performance of numerous design options and generate optimized design. The proposed method utilizes parametric design, building simulation modeling, and genetic algorithms. A case study of a small office building is conducted to test and verify the effectiveness of the optimization process. The geometry of the case study building is optimized in three different climates, Miami, Atlanta, and Chicago. After the optimization, the daylighting performance metric UDI is increased by 38.7%, 31.6%, and 28.8%, and the energy performance metric EUI is decreased by 20.2%, 18.5%, and 17.9% compared to average performance values. Sensitivity analysis is performed to analyze the relationship between design variables and performance metrics. The skylight width and length are the most important variables for all locations, while the influence of the other variables varies greatly.

Optimization of Multi-Component Topology and Material Orientation Design of Composite Structures Using AI

Composite materials offer a wide range of advantages in terms of strength, weight, and versatility, making them a popular choice for structural applications. The performance of composite structures depends not only on the material constituents but also on the topology and arrangement of these components. However, designing an optimal configuration that maximizes the desired performance metrics can be a challenging task due to the large design space and complex interactions between the components. It presents a novel approach for optimizing the multi-component topology and material orientation design of composite structures using artificial intelligence (AI) techniques. The objective is to develop an automated and efficient methodology that can identify the most optimal configuration for a given set of performance requirements. The proposed methodology combines AI algorithms, such as genetic algorithms and machine learning, with advanced computational modeling techniques. The genetic algorithms are employed to explore the design space and search for the best combination of topology and material orientations. Concurrently, machine learning techniques are utilized to model the complex relationships between the design variables and performance metrics, enabling the identification of design patterns and accelerating the optimization process.
 To achieve this, a comprehensive framework is established, encompassing the generation of an initial population of candidate designs, evaluation of their performance using numerical simulations or experimental testing, application of genetic algorithms to iteratively evolve the population by selecting the fittest designs and introducing variations, and utilization of machine learning models to predict the performance of new designs and guide the optimization process. The effectiveness of the proposed methodology is demonstrated through a case study involving the design of a composite aerospace structure. The results reveal that the AI-based optimization approach significantly outperforms traditional trial-and-error methods, leading to improved performance metrics such as strength, stiffness, and weight.
 The findings have significant implications for the design and manufacturing of composite structures in various industries, including aerospace, automotive, and civil engineering. The automated optimization process enabled by AI techniques can efficiently explore the design space, identify innovative solutions, and ultimately enhance the overall performance of composite structures.

Lifetime Optimizing Clustering Structure Using Archimedes’ Spiral-Based Deployment in WSNs

In wireless sensor networks (WSNs), preserving energy requires utmost attention, as they are highly resource constrained. Clustering is commonly considered as one of the efficient energy conservation techniques. In a clustered WSN, due to the nature of operation, cluster heads (CHs) near the sink bear the major share of data forwarding compared with the CHs far away from the sink, resulting in an energy hole problem. First, we have analyzed the optimization of network lifetime by balancing the energy consumption among different CHs. To meet the requirement of optimization of network lifetime, we have devised a routing-aware optimal clustering strategy. Further, keeping the network model in mind, we identify Archimedes' spiral, based on which a deployment function is proposed for distributing a member node and a CH. The performance of the optimal clustering strategy is evaluated in terms of energy balance and network lifetime. Simulation results show that our scheme provides satisfactory network performance in terms of end-to-end delay and throughput. Finally, all the results are compared with two competing schemes that confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics.

Micro-sized tunable liquid crystal optical filters.

Liquid crystal arrayed microcavities (LCAM) is a new technology for ultra-narrow optical filtering (FWHM ∼0.1 nm) that uses picoliter volume Fabry-Perot-type optical cavities filled with liquid crystal for tuning. LCAMs are sub-nm spectral resolution filters, which utilize well-established laser writing, thin film deposition, and wafer manufacturing techniques. These filters are compact, robust, and inexpensive. Compact, high-resolution optical filters have applications, including biomedical imaging, chemical detection, and environmental monitoring. Here, we describe the LCAM design and initial performance metrics.

Design and Implementation of Performance Metrics for Evaluation of Assessments Data

<p>Evocative evaluation of assessment data is essential to quantify the achievements at course and program levels. The objective of this paper is to design performance metrics and respective formulas to quantitatively evaluate the achievement of set objectives and expected outcomes at the course levels for program accreditation. Even though assessment processes for accreditation are well documented but existence of an evaluation process is assumed. This work provides performance metrics such as attainment, student achievement, and x-th percentile for the evaluation of assessment data at course and program level. Then, a sample course data and uniformly distributed synthetic data are used to analyze the results from designed metrics. The primary findings of this work are twofold: (i) analysis with sample course assessment data reveals that qualitative mapping between marks obtained in assessments to the defined outcomes is essential for meaningful results, (ii) analysis with synthetic data shows that higher values of one metric does not imply higher values of the other metrics and they depend upon the obtained marks distribution. In particular, for uniformly distributed marks, achievement < attainment for meanOfUniformDistr. < averageMarks < passingT hreshold(α). Authors hope that the articulated description of evaluation formulas will help convergence to high quality standard in evaluation process.</p>

Lifetime enhancement of wireless sensor networks by avoiding energy-holes with Gaussian distribution

Wireless sensor networks (WSNs) are receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In WSNs, preserving energy requires utmost attention, as they are highly resource constrained. One fundamental way of conserving energy is judicious deployment of nodes within the network for balancing energy flow throughout the network. Node deployment using Gaussian distribution is a standard practice and is widely acceptable when random deployment is used. Initially, an analysis is done to establish that Gaussian distribution based node deployment is not energy balanced. Standard deviation of Gaussian distribution is identified as the parameter responsible for energy balancing. A deployment strategy is proposed for energy balancing using customized Gaussian distribution by discretizing the standard deviation. Performance of the scheme is evaluated in terms of energy balance and network lifetime. Exhaustive simulation is performed to measure the extent of achieving our design goal of enhancing network lifetime while attaining energy balancing. The simulation results show that our scheme also provides satisfactory network performance in terms of end-to-end delay and throughput. Finally, all the results are compared with three competing schemes and the results confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics.

Lifespan prolonging location-wise predetermined deployment strategy for visual sensor networks

Visual sensor networks are widely used for surveillance, environmental monitoring and tracking. However, due to the funneling effect, visual sensor nodes (SNs) close to the sink tend to deplete their energy sooner than SNs farther away from the sink resulting in energy hole problem. This energy hole problem significantly increases the unbalanced energy usage among SNs and leads to premature decrease in network lifetime. Initially, we analyzed network lifetime and found number of relay nodes and their location has significant influence on limiting the energy hole problem and enhancement of network lifetime. Based on the principle of energy balancing, derived from this analysis, we develop a heterogeneous SNs deployment strategy leading to an enhancement of network lifetime. Exhaustive simulation is performed, primarily to measure the extent of achieving our design goal of limiting memory and computation overheads, enhancing network lifetime while attaining energy balancing. We also measure the effect of placement errors on the performance and show that even in presence of placement error the performance is comparable with the other competing scheme. Further, the simulation results show that our scheme does not compromise with other network performance metrics such as end-to-end delay and throughput while achieving the design goal. Finally, all the results are compared with a competing scheme and the results confirm our scheme׳s supremacy in terms of both design performance metrics as well as network performance metrics.

Frameworks for organising design performance metrics

ABSTRACTComputational tools for aiding design-by-analogy have focused on function-flow and keyword-based retrieval of analogues. Because of the critical role of performance and benchmarking in design, the need for performance metrics-driven analogy retrieval is currently unmet. Towards meeting this need, a study has been done to investigate and propose a combined framework for organising the myriad of technical performance metrics used in engineering design (e.g. efficiency, cost, weight, etc.). Such organisational frameworks are needed for the implementation of a computational tool which can retrieve relevant analogies using performance metrics. The study, which takes a deductive approach, defines a hierarchical taxonomy of performance metrics akin to the Functional Basis vocabulary of function and flow terms. To support this approach, an empirical study was conducted to verify the identification of criticality with use in analogy designs.

Discrete event simulation: a vital tool for a concurrent life cycle design

Purpose – The purpose of this study is to examine whether discrete event simulation (DES) can be equally utilised in the design phase of the architecture, engineering and construction industry (AEC) projects to achieve a more efficient facility layout. Facility design is a complex process involving diverse disciplines, tasks, tools and events. Integrating key participants involved in the design generally leads to a more satisfied end-user. The AEC thoroughly examined different approaches to enhance this integration through improved communication, visualisation and coordination among the different project participants. DES has been used extensively as a tool for analysis and evaluation, especially during the construction process. Design/methodology/approach – A facility planning framework is illustrated that combines both qualitative and quantitative analysis to achieve a performance-driven design. An investigative qualitative research approach is used to determine the design criteria and performance metrics based on the end-user and authority requirements. This approach is achieved by conducting critical reviews, surveys, focus groups and interviews. The research findings and collected data are used to perform a quantitative analysis to determine the effectiveness of the proposed design if constructed using DES. The potential of the method is shown through a case study to design a mall parking facility. Findings – The case study illustrated the capability of DES to improve construction design by comparing the artificially designed facility following the proposed framework to an existing facility. Increasing customer satisfaction by enhancing safety, minimising waiting time and maximising parking spot availability were the performance metrics used to evaluate the designs. DES was used as a tool to measure these criteria. Utilising DES in facility design increased resource utilisation and resulted in a safer layout that satisfied the end-user, client and authority requirements. Originality/value – Previous studies focused on integrating other modules such as energy, HVAC, lighting, acoustics and life cycle analysis to achieve a performance-driven design. The overwhelming majority of the literature focused on the use of DES for improving construction operations. Research literature about integrating DES as a tool for concurrent life cycle design was scarce. This research demonstrated that DES is an effective method and a vital key for determining the facility’s operational efficiency after construction.

Building neighborhood emerging properties and their impacts on multi-scale modeling of building energy and airflows

This paper provides a critical review on the building neighborhood properties influencing the energy and airflows in urban neighborhoods. Specifically, the review focus is on the multi-scale modeling required to quantify this influence of the building neighborhood properties on the energy consumption in buildings. The energy consumption patterns of buildings located in dense city centers are highly dependent on the surrounding urban neighborhood, compared to the low density, suburban/rural regions, where the building energy consumption patterns are similar to an isolated building energy consumption patterns. Due to the complex nature of the outdoor airflow around the buildings in urban neighborhoods, a practical modeling approach utilizes multi-scale modeling to account for different spatial and temporal scales for the relevant transport processes. Specifically, this modeling approach aims to identify the most important neighborhood properties influencing building energy consumption. The urban morphology parameters, such as the urban plan area density, frontal area density, and mean height of the buildings represent successful examples of emerging properties suitable for development of generalized solutions and physical models at the neighborhood scale. This paper also reviews different modeling approaches that account for the impacts of the urban neighborhood properties on the thermo-fluid property of the air, surfaces, and sky in the built environment as the required inputs for accurate assessment of building energy consumption. Furthermore, these emerging properties of urban neighborhoods directly affect (1) the mitigation strategies for a better adaptation, (2) design performance metrics of neighborhoods for the green building rating systems, and (3) socio-environmental factors.

Distance correlation-based nearly orthogonal space-filling experimental designs

In many real-world applications of optimisation, the required model (function) evaluations are determined by expensive and time-consuming physical experiments or numerical procedures. Within this context, the objective of experimental design is to obtain meaningful information based on a strongly limited number of experiments or function evaluations. In order to generate informative designs, space-filling and orthogonality are widely considered to be essential. In this study, we review several existing design performance metrics that address these criteria, and the distance correlation-based metric is proposed for achieving improved experimental design. Three closely related randomised sampling schemes are proposed to generate nearly orthogonal designs with good space-filling properties in real time. The effectiveness of our approach is demonstrated by numerical examples and an illustrative borehole model case study.

Design of an Archimedes’ spiral based node deployment scheme targeting enhancement of network lifetime in wireless sensor networks

Wireless sensor networks (WSNs) have been receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In WSNs, unbalanced energy consumption is an intrinsic problem and this can considerably decrease network lifetime. One primary way of mitigating uneven energy consumption is judicious deployment of sensor nodes so that the energy flow remains balanced throughout the network. In this work, at first we analyze layered network architecture and find out the conditions that need to be satisfied for optimal deployment setting in order to achieve complete area coverage, energy balance and prolonged network lifetime. We also analyze two standard distribution functions to find out their suitability as node deployment function and found neither of the distribution functions ensures balanced energy consumption, thereby failing to prolong network lifetime. Keeping the conditions of optimal node deployment setting in mind, and considering the reasons of unsuitability of the two standard distributions mentioned above, we identify Archimedes’ spiral based on which a node deployment function is proposed. Furthermore, we show the proposed deployment function provides optimal deployment setting and derive the constraint for balanced energy consumption, thereby subsequent enhancement of network lifetime. Exhaustive simulation is performed primarily to measure the extent of achieving our design goal of enhancing network lifetime while attaining energy balancing and maintaining coverage. We also measure the effect of placement errors on the performance of the scheme and show that even in presence of placement error the performance is comparable with the other competing schemes. Further, the simulation results show that our scheme does not compromise with other network performance metrics such as end-to-end delay and throughput while achieving the design goal. Finally, all the results are compared with three competing schemes and the results confirm our scheme’s supremacy in terms of both design performance metrics as well as network performance metrics.

Parametric analysis of CO2 geologic sequestration in closed volumes

geologic sequestration has been recognized as a potential greenhouse gas mitigation strategy. Regional geologic storage in deep saline formations will likely involve the injection of to 100 million metric tons (11 to 110 million tons) of per year using a network of to 50 wells over an area covering sq. miles (). Some of the wells will be injecting into closed volumes because of symmetry, thus providing the bounding case in terms of pressurization and brine efflux. This study describes a parametric analysis of the problem using characteristics typical of the Arches Province in the United States Midwest where Paleozoic rocks form broad arch and platform structures. Two-dimensional radial-cylindrical models developed with the numerical simulator STOMP (Subsurface Transport Over Multiple Phases) are utilized to investigate the impact of well spacing, injection depth, and reservoir characteristics of the injection zone (Mount Simon) and cap rock (Eau Claire) on system performance. Multiple linear regression analysis is then used to develop correlation equations between these design variables and performance metrics, such as cumulative -mass injected and -plume extent. The correlations are tested on new synthetic test sites, and are found to predict the performance metrics quite accurately. These results serve as a proxy simulator to quickly evaluate various design options, instead of having to run time-consuming numerical simulations, and can therefore be applied for developing optimal injection strategies for regional storage in the Arches Province.

Carry-based reduction parallel counter design

Parallel counters are one of the important components to construct high performance DSP units including column compression multipliers (i.e. tree multipliers). As the multiplier size increases, designing optimised wider counters is critical for its performance, which is the motive of this study. This article proposes a novel approach to design (2 n −1, n) parallel counter using a reduction stage and a single (2 n −1 −1, n − 1) parallel counter. An algorithm to construct the reduction stage for any counter size is presented. The designs of parallel counter examples using the proposed algorithm are discussed. Finally, the proposed design performance metrics (in terms of delay, power and energy-delay-product) are compared with the conventional parallel counter designs. The proposed designs achieve 13% average speed up, 10% power reduction and 32% improvement in energy-delay-product compared with conventional counters.

Enhancement of wireless sensor network lifetime by deploying heterogeneous nodes

Energy is one of the scarcest resources in wireless sensor network (WSN). One fundamental way of conserving energy is judicious deployment of sensor nodes within the network area so that energy flow remains balanced throughout the network. This avoids the problem of occurrence of ‘energy holes’ and ensures prolonged network lifetime. We have first investigated the problem for enhancing network lifetime using homogeneous sensor nodes. From our observation it is revealed that energy imbalance in WSN occurs due to relaying of data from different parts of the network towards sink. So for improved energy balance instead of using only sensor nodes it is desirable to deploy relay nodes in addition to sensor nodes to manage such imbalance. We have also developed a location-wise pre-determined heterogeneous node deployment strategy based on the principle of energy balancing derived from this analysis, leading to an enhancement of network lifetime. Exhaustive simulation is performed primarily to measure the extent of achieving our design goal of enhancing network lifetime while attaining energy balancing and maintaining coverage. The simulation results also show that our scheme does not compromise with other network performance metrics such as end-to-end delay, packet loss, throughput while achieving the design goal. Finally all the results are compared with two competing schemes and the results confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics.

Empirical models of jackup rig lightship displacement

A jackup rig is a mobile offshore drilling unit used to drill wells to find and produce hydrocarbons. The weight of a rig is an important variable in cost estimation and determining the amount of steel required in construction. Rig weight is generally considered proprietary because it indicates design benchmarks and performance metrics that are central to the competitive nature of the construction industry. Methods to predict ship weight cannot be used for jackups because of the structural differences between jackup rigs and ships. The purpose of this note is to present an empirically-derived jackup lightship displacement function. We compile a dataset of jackup lightship displacements and develop a linear regression model that explains over 90% of the variation in rig weight.

FEATURES OF WSN AND VARIOUS ROUTING TECHNIQUES FOR WSN: A SURVEY

A Wireless Sensor Network is the collection of large number of sensor nodes, which are technically or economically feasible and measure the ambient condition in the environment surrounding them. The difference between usual wireless networks and WSNs is that sensors are sensitive to energy consumption. Most of the attention is given to routing protocols, for energy awareness, since they might differ depending on the application and network architecture. Routing techniques for WSN are classified into three categories based on network structure: Flat, hierarchical and location-based routing. Furthermore, these protocols can be classified into multipath based, query based, negotiation-based, QoS-based, and coherent–based, depending on the protocol operation. In this paper the survey of routing techniques in WSNs is shown. It is also outlined the design challenges and performance metrics for routing protocols in WSNs. Finally We also highlight the advantages and performance issues of different routing techniques by it’s comparative analysis. Future-directions for routing in sensor network is also described.

Identifying Feasible Design Concepts for Products with Competing Performance Requirements by Metamodeling of Loss-Scaled Principal Components

ABSTRACT Engineering design often involves the determination of design variable settings to optimize competing performance requirements. In the early design stages we propose narrowing down the domain of design solutions using metamodels of principal components of the multiple performance levels that have been scaled by a multivariate quadratic loss function. The multivariate quadratic loss function is often used as the objective function in reaching optimal solutions because it utilizes the correlation structure of the design's performance metrics and penalizes off-target performance in a symmetrical manner. We also compare the computational performance of these loss-scaled principal components when used to solve for the design with minimal expected multivariate quadratic loss under three modeling approaches: response surface methodology, multivariate adaptive regression splines, and spatial point modeling. We demonstrate the technique on the design of the mechanical frame of an electric vehicle with six desired performance levels determined simultaneously by the dimensions of eight mechanical design elements. The method is the focus in this work.