Virtual Design Space Research Articles

Designing a Super-Fast Prius: Systematic Design of Multispeed eCVT via Virtual Design Space

Despite its excellent fuel economy (FE), the Toyota Prius has a poor acceleration performance (AP). Its performance is improved by using bigger propulsion components and a multi-speed transmission for SUVs and high-performance models. Nevertheless, this performance improvement is limited as the input-split architecture cannot use its small motor/generator for motoring. To overcome this shortcoming, this paper suggests adding fixed gear (FG) modes to the Prius system. FG modes can boost the AP thanks to the power amplification resulting from using both motor/generators as motors. With a focus on the architectural design of FG modes, all potential placements of clutches and gears are enumerated. The analysis of candidate physical FG modes revealed a redundancy as one transmission ratio (ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>E</i></sub> /ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>O</i></sub> ) can be created by different clutch and gear pairs. To omit this redundancy, this paper conducts the architecture search within the concise virtual design space. The virtual design space consists of all different combinations of candidate input-split and virtual FG architectures. The performance is evaluated within the virtual design space and virtual designs with good FE and AP and feasible gradeability are selected. An optimal virtual design that is drastically faster (67.82 % faster) than the single-mode Prius and with a better FE (5.56 % higher) is selected and converted into fifty physical multi-speed architectures.

Accelerated design of stable halide perovskite heterostructure film in hostile condition via surface modifier

The bulk halide perovskite heterostructure has been adopted for perovskite-based optoelectronic devices owing to their interesting interfacial phenomena; however, most of them are unstable in hostile condition and the extremely large virtual design space causes problems for traditional trial-and-error methods to optimize them. In this study, we combine photoelectrochemical experiments, machine learning and first-principles calculations (Exp + ML + DFT) to accelerate the design of halide perovskite heterostructures in aqueous solution, in an effort to improve stability of halide perovskite materials in hostile condition (prototypical CH3NH3PbI3 quickly deactivates in water within seconds). The optimization of halide perovskite heterojunction with surface modifier is speeded up via machine learning using extra tree algorithm, and a large virtual design space consisting of halide perovskite heterostructure composites is predicted. Five new water-stable perovskite heterostructure candidates predicted via machine learning are experimentally validated, and the first-principles calculations reveal the detailed atomic and electronic structures of an exemplar stable heterostructure candidate. This study demonstrates the effectiveness of machine learning to accelerate the design of stable halide perovskite heterostructure materials in hostile condition.

Accelerated Multisolvent Prediction for Aqueous Stable Halide Perovskite Materials.

Solvent treatment is critical to improving the stability of halide perovskite materials that suffer from notorious issues that inhibit their industrial deployment; however, the complicated perovskite virtual design space with different types of solvent modifiers is inaccessible to traditional trial-and-error methods. In this study, machine learning is employed to predict stable multiple solvent-modified perovskite films under hostile conditions, and a complicated quinary solvent system "DMSO + DMF + toluene + NMP + GBL" is effectively identified to significantly improve the optoelectronic stability of CH3NH3PbI3 in water. The "combinatorial solvent design" approach is realized by an extra tree machine learning model, which leads to a prediction dataset containing aqueous stability labels of 6720 new quinary solvent/perovskite systems. Importantly, the accuracy of the machine learning model is verified via photoelectrochemical experiments, achieving an experimental accuracy of 80%. A machine learning-predicted quinary solvent system offers significantly enhanced aqueous stability and 1000 times larger aqueous photocurrents, compared with the control CH3NH3PbI3 film under the same hostile conditions. This study demonstrates the efficacy of machine learning for solvent design toward stable halide perovskite materials under hostile conditions.

Designing Two-Dimensional Halide Perovskites Based on High-Throughput Calculations and Machine Learning.

The interactions between ions and the low-dimensional halide perovskites are critical to realizing the next-generation energy storage devices such as photorechargeable ion batteries and ion capacitors. In this study, we performed high-throughput calculations and machine-learning analysis for ion adsorption on two-dimensional A2BX4 halide perovskites. The first-principles calculations obtained an initial data set containing adsorption energies of 640 compositionally engineered ion/perovskite systems with diverse ions including Li+, Zn2+, K+, Na+, Al3+, Ca2+, Mg2+, and F-. The machine learning algorithms including k-nearest neighbors (KNN), Kriging, Random Forest, Rpart, SVM, and Xgboost algorithms were compared, and the Xgboost algorithm achieved the best accuracy (r = 0.97, R2 = 0.93) and was selected to predict the virtual design space consisting of 11 976 ion/perovskite systems. The features were then analyzed and ranked according to their Pearson correlations to the output values. In particular, to better understand the features, diverse feature selection methods were employed to comprehensively evaluate the features. The machine-learning-predicted virtual design space was subsequently screened to select stable lead-free ion/perovskite systems with suitable band gaps and halogen mixing features. The present study provides a theoretical foundation to design halide perovskite materials for ion-based energy storage applications such as secondary ion batteries, ion capacitors, and solar-rechargeable batteries.

Efficient Design Space Exploration of Multi-Mode, Two-Planetary-Gear, Power-Split Hybrid Electric Powertrains via Virtual Levers

The recent industry trend of incorporating multiple planetary gears (PG) and clutches in power-split hybrid electric vehicles has enhanced their potential fuel economy and acceleration performance. However, this increase in performance potential comes at the cost of increased design and control complexity. In this paper, we propose a highly efficient design methodology that finds the optimal multi-mode, two-PG powertrain by extending our recently developed virtual lever, a modeling tool that eliminates the redundancy in the physical design space and hence minimizes the computational load associated with optimizing over <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple</i> different types of PGs. First, every operating mode of every powertrain architecture is modeled using the virtual lever, whose parameters comprise the virtual design space. Second, the fuel economy and acceleration time are evaluated for every design in this continuous, virtual design space. Then, the powertrain architectures are compared by their respective Pareto frontiers in the fuel economy — acceleration time plane. Furthermore, a design space conversion method is used to group the evaluated designs by their respective feasible physical realizations to compare the 16 possible physical realizations of the generic Volt 2nd, which is found to be the best powertrain architecture. The proposed method uncovered several different physical realizations of the generic Volt 2nd that outperforms General Motors’ existing Chevy Volt 2nd powertrain.

Systematic Design of Input- and Output-Split Hybrid Electric Vehicles With a Speed Reduction/Multiplication Gear Using Simplified-Lever Model

Power-split hybrid electric vehicles (HEV) employ a speed reduction gear (SRG) mainly to enhance acceleration performance. However, the full potentials of an additional gear on fuel economy and acceleration performance have not yet been thoroughly investigated due to a vast design space: 432 configurations with three design variables (i.e. two planetary gear ratios and a final drive gear ratio). In this paper, a systematic speed reduction gear design methodology is proposed to analyze the impact of a speed reduction (multiplication) gear on the performance of input- and output-split HEVs and select an optimal configuration. First, the physical and virtual design spaces of SRG (SMG) are defined and the relationship between two design spaces are identified. Second, performance metrics are evaluated within the virtual design space using the proposed simplified lever. The proposed approach completely eliminates the redundancy present in the physical design spaces, and thus, the impact of SRG (SMG) on the performance of input- and output-split configurations can be analyzed with the minimum computational burden. Lastly, the selected gear ratio is converted back to the physical planetary gear connections. The results confirm that SRG (SMG) can potentially either improve or deteriorate the acceleration performance and the fuel economy of split HEVs. Therefore, the full potential of speed reduction or multiplication gear should be thoroughly analyzed when designing split hybrid electric vehicles.

Algorithmic investigation of the actual and virtual design space of historic hexagonal-based Islamic patterns

This research challenges the long-standing paradigm that considers compositional analysis to be the key to researching historical Islamic geometric patterns. Adopting a mathematical description shows that the historical focus on existing forms has left the relevant structural similarities between historical Islamic geometric patterns understudied. The research focused on the hexagonal-based Islamic geometric patterns and found that historical designs correlate to each other beyond just the formal dimension and that deep, morphological connections exist in the structures of historical singularities. Using historical evidence, this article identifies these connections and presents a categorization system that groups designs together based on their “morphogenetic” characteristics.

Optimizing a reconfigurable material via evolutionary computation.

Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design space that simulates potential material behaviors and after completion needs to be validated by experiment. However, in principle an evolutionary optimizer can also operate on an actual physical structure or laboratory experiment directly, provided the relevant material parameters can be accessed by the optimizer and information about the material's performance can be updated by direct measurements. Here we provide a proof of concept of such direct, physical optimization by showing how a reconfigurable, highly nonlinear material can be tuned to respond to impact. We report on an entirely computer controlled laboratory experiment in which a 6×6 grid of electromagnets creates a magnetic field pattern that tunes the local rigidity of a concentrated suspension of ferrofluid and iron filings. A genetic algorithm is implemented and tasked to find field patterns that minimize the force transmitted through the suspension. Searching within a space of roughly 10^{10} possible configurations, after testing only 1500 independent trials the algorithm identifies an optimized configuration of layered rigid and compliant regions.

DESIGNING DESIGN TEAM EXPERIENCES: CREATING STEAMER

A challenge in teaching engineering design lies in guiding the collaboration of students in a design team environment. We have identified a need for a virtual design space application that enhances the user experience so as to encourage teamwork and project management skills. The Student Team Electronic Resource (STeamER) project arose out of lessons learned over the past nine years teaching project-based design courses to engineering students. The overall objective of STeamER is to support students and instructors involved in team-based projects. We describe the rationale as well as identified requirements for the STteamER application.

Development of Integrated System for Virtual Design and Performance Testing of Automobile Seat

Most of traditional design processes of mechanical parts are regarded as sequential and discrete, since different kinds of softwas should be introduced. In this paper, we develop an integrated fram ework for virtual design and performance testing of an automobile seat. The system is composed of four modules, i.e. CAD, static analysis, dynamic analysis, and draft drawing module. In the CAD module, PRO/ENGINEER 3D seat model is created using parameters to be modified with the result of static and dynamic analysis. In the static analysis, headrest tere used in each design stage make it difficult to feedback their results to upstream process. These discrete processes may result in time loss and cost rise. In recent years, life cycle of product is reduced. To have competence with others, new concept design processt is simulated using ANSYS. In the dynamic analysis module, FMVSS201 test is simulated using DADS. Overall data flow is controlled by Motif. The advantage of the system is that even a novice can perform and review the whole design process, without a good hand at professional design/analysis S/W in each stage. The system also provides a virtual design space, where engineers in different development stage can access common data of design models. The concept could be applied to other fields and it could reduce time and money required in design process.

A Context-Specific Interface Model for Architectural Design in the Virtual Environment

There is an ongoing debate on the success of architectural software in meeting the designers' wishes and in being familiar to the way designers design. One dominant belief is that as architectural software introduces a work environment closer to that of the paper-based techniques, the efficiency of the use of such software in the profession will increase. We argue that it is not the question of making the digital environment familiar to the paper-based, but designing interfaces through which the users will be able to customize the digital environment according to their wishes. This study introduces a context-specific transformation model to convert a state in the ‘user+need space’ to a digital aid in the virtual design space. This model incorporates a customization scale menu (CSM) to act with the menu options of the architectural software. In this model, the menu options are customized through the selections made on the CSM by the user. These selections will determine the required level of interaction between the software and the user, thus customizing the digital environment according to the user's needs.