CAE-based proper design method for the efficient production of hydraulic cylinder

A study on the simulation-based installation shape design method of steel lazy wave riser (SLWR) in ultra deepwater depth

Testability design of power distribution system considering noise immunity characteristics

The flip-chip technology is a high chip density solution to meet the demand for very large scale integration design. For wireless sensor node or some similar RF applications, due to the growing requirements for the wearable and implantable implementations, flip-chip appears to be a leading technology to realize the integration and miniaturization. In this paper, flip-chip is considered as part of the whole system to affect the RF performance. A simulation based design is presented to transfer the surface mount PCB board to the flip-chip die package for the RF applications. Models are built by Q3D Extractor to extract the equivalent circuit based on the parasitic parameters of the interconnections, for both bare die and wire-bonding technologies. All the parameters and the PCB layout and stack-up are then modeled in the essential parts' design of the flip-chip RF circuit. By implementing simulation and optimization, a flip-chip package is re-designed by the parameters given by simulation sweep. Experimental results fit the simulation well for the comparison between pre-optimization and post-optimization of the bare die package's return loss performance. This design method could generally be used to transfer any surface mount PCB to flip-chip package for the RF systems or to predict the RF specifications of a RF system using the flip-chip technology.

The uncertainties of design variables, noise parameters, and metamodel are important factors in simulation-based robust design optimization. Most conventional metamodel construction methods only consider one or two uncertainties. In this paper, a new surrogate modeling method simultaneously measuring all the uncertainties is proposed for simulation-based robust design optimization of complex product. The effect of metamodel uncertainty on product performance uncertainty is quantified through uncertainty propagation analysis among design variables uncertainty, noise parameters uncertainty, metamodel uncertainty, and performance uncertainty. Then, the sampling points are selected and the metamodel is constructed based on the predictive interval of product performance and mean square error of the Kriging metamodel. The constructed metamodel is applied to robust design optimization considering multiple uncertainties. Results of two mathematical examples show that the proposed metamodel considering multiple uncertainties increases the result accuracy of robust design optimization. Finally, the proposed algorithm is applied to robust design optimization of a heat exchanger, and the total heat transfer rate is enhanced under uncertainties of fin structural parameters, operation conditions parameters and simulation metamodel.

Bayesian experimental design for a clinical trial involves specifying a utility function that models the purpose of the trial, in this case the selection of patients for a diagnostic test. The best sample of patients is selected by maximizing expected utility. This optimization task poses difficulties due to a high-dimensional discrete design space and, also, to an expected utility formula of high complexity. A simulation-based optimal design method is feasible in this case. In addition, two deterministic algorithms that perform a systematic search over the design space are developed to address the computational issues.

A wind turbine transformer internal resonance, excited by high frequency transients i.e.; under its energization through a short length of cable, is considered to obtain a proper transformer design method. A multi-objective optimum design method (using TOC and inrush current energy factor as objective functions) is employed and the optimal Pareto solutions for this design are derived. Selected design from the Pareto optimal solutions are then modeled using the lumped RLC ladder network, valid over a wide frequency range. The obtained frequency responses are then compared with each other to investigate the effect of multi-objective design on different aspects of the transformer winding frequency response.

Asphalt treated base (ATB) materials have shown good road performance. There are some differences in the mix design methods used for ATB and Asphalt Concrete (AC) and this paper investigates four different design methods for ATB. Each design method is analyzed by using volume and mechanical indexes, and pavement performance. Based on this analysis, the proper design method of ATB is proposed. 1. ATB Design Method This paper describes the comparison of experimental data with specification criteria recommend in the SUPERPAVE method, the Bailey method, and a mechanical method. The analysis includes determining the effect of different design methods on asphalt pavement performance.

Hierarchical design scenarios arise when the performance of large-scale, complex systems can be affected through the optimal design of several smaller functional units or subsystems. Monte Carlo simulation provides a useful technique to evaluate probabilistic uncertainty in customer-specified requirements, design variables, and environmental conditions while concurrently seeking to resolve conflicts among competing subsystems. This paper presents a framework for multidisciplinary simulation-based design optimization, and the framework is applied to the design of a Formula 1 racecar. The results indicate that the proposed hierarchical approach successfully identifies designs that are robust to the observed uncertainty.

Numerical optimization of an inverted-bow trimaran is carried out through three simulation-based design (SBD) frameworks. Different positions of the trimaran’s side hull are investigated based on a computational fluid dynamics solver using the non-dominated sorting genetic algorithm-III (NSGA-III), simultaneous hybrid exploration that is robust, progressive and adaptive (SHERPA) and response surface (RS) multi-objective optimization for resistance at cruise and sprint speeds. The aims are to create and develop a convenient tool for optimization and investigate the appropriate position of the side hull. An automated, low-cost optimization platform is achieved that can be implemented in other maritime projects. A 10.5% drag reduction for cruise speed and 6.6% reduction for sprint are obtained, corresponding to lower longitudinal and large transversal distances of the side hull. SHERPA and NSGA-III produce the same results, but SHERPA is 2.5 times faster than NSGA-III. RS obtains less desirable results, but in the lowest central processing unit time.

In order to reduce the time and resources devoted to design-space exploration during simulation-based design and optimization, the use of surrogate models, or metamodels, has been proposed in the literature. Key to the success of metamodeling efforts are the experimental design techniques used to generate the combinations of input variables at which the computer experiments are conducted. Several adaptive sampling techniques have been proposed to tailor the experimental designs to the specific application at hand, using the already-acquired data to guide further exploration of the input space, instead of using a fixed sampling scheme defined a priori. Though mixed results have been reported, it has been argued that adaptive sampling techniques can be more efficient, yielding better surrogate models with less sampling points. In this paper, we address the problem of adaptive sampling for single and multi-response metamodels, with a focus on Multi-stage Multi-response Bayesian Surrogate Models (MMBSM). We compare distance-optimal latin hypercube sampling, an entropy-based criterion and the maximum cross-validation variance criterion, originally proposed for one-dimensional output spaces and implemented in this paper for multi-dimensional output spaces. Our results indicate that, both for single and multi-response surrogate models, the entropy-based adaptive sampling approach leads to models that are more robust to the initial experimental design and at least as accurate (or better) when compared with other sampling techniques using the same number of sampling points.

Simulation-based design and optimization of detachable louver for modification of a heat pump assisted dryer for efficient air flow and performance enhancement

Simulation-based design, optimization, and validation of autonomous vehicles have proven to be crucial for their improvement over the years. Nevertheless, the ultimate measure of effectiveness is their successful transition from simulation to reality (sim2real). However, existing sim2real transfer methods struggle to address the autonomy-oriented requirements of balancing: (i) conditioned domain adaptation, (ii) robust performance with limited examples, (iii) modularity in handling multiple domain representations, and (iv) real-time performance. To alleviate these pain points, we present a unified framework for learning cross-domain adaptive representations through conditional latent diffusion for sim2real transferable automated driving. Our framework offers options to leverage: (i) alternate foundation models, (ii) a few-shot fine-tuning pipeline, and (iii) textual as well as image prompts for mapping across given source and target domains. It is also capable of generating diverse high-quality samples when diffusing across parameter spaces such as times of day, weather conditions, seasons, and operational design domains. We systematically analyze the presented framework and report our findings in terms of performance benchmarks and ablation studies. Additionally, we demonstrate its serviceability for autonomous driving using behavioral cloning case studies. Our experiments indicate that the proposed framework is capable of bridging the perceptual sim2real gap by over 40%.

All-terrain vehicle is famously used for various purposes. The design of the chassis of this vehicle is critical in determining the overall strength. In this paper, the design chassis frame for the use of all-terrain vehicle (ATV) is presented. In designing the chassis frame, a proper design method was employed. Finite Element Analysis (FEA) was utilized to determine the maximum stress and displacement of the frame when a particular load is applied onto it. Structure modifications need to be done if the chassis frame could not sustain the applied load. After the design process is completed, the fabrication of the frame is conducted by students of the engineering faculty. The fabricated frame will be used as the main part for a project of which a complete ATV will be developed. The main purpose of the project is to instill the interest among the student in engineering through the application of classroom.

This paper presents eco-friendly planting method to overcome the problems of existing concrete retaining wall and gabion retaining wall, respectively, based on the examination on existing concrete and gabion retaining wall. Prior to doing this, proper design method was provided through pull out test. Planting method using gabion metal net and L shape green net retaining wall were compared and analyzed. According to this study, it is confirmed that reduction of construction period and economical profit in construction can be achieved by both manufacturing at the factory and self procurement at the job site as well as the use of metal net, which is applied as a substitution of existing strengthening material. For the effect of planting method, the use of L shape green net retaining wall shows superiority to environment-friendly gabion retaining wall in its ability to rootage and germination of the grass. The L shape green net retaining wall had excellent performance in helping rootage of grass and prevention of soil leakage, and even if raining period, remarkable damage of planting mat does not occur when planting mat is applied.

A software package has been developed in which a gas simulation model and Nodal analysis model are used in conjunction with fracture simulation. The software package is also tied to a microcomputer database system to provide pertinent data for well analyses. The gas simulation model generates inflow performance for various production conditions while Nodal analysis studies the inflow and outflow conditions. Since specific Appalachian production conditions are used for both models, the software package will prove to be a useful tool for improving production in the Appalachian area. Artifical lift "rabbit" production, bailing/swabbing operation, restimulation of the new zone and deeper drilling are the conventional methods to improve the production in the Appalachian area. Nodal analysis technique has been used with specific wells to evaluate the potential results of using each recovery enhancement method. Nodal analysis is an optimization technique to analyze production problems and predict solutions. This technique can predict the "critical" pressure for artificial lift and can predict the inflow performance of a well. The new bailing operation can then be scheduled accordingly. In addition, this technique can also estimate the skin damage within an existing well and be used to justify restimulating the new zone or drilling deeper. In the Appalachian area, tight formations and relatively depleted reservoirs are major problems. The current marginal economic situation does not allow producers to improve production by pressure enhancement and multiple stimulation. A solution can be obtained by analyzing the problem and predicting the outcome of using various production enhancement methods. Then, the operator can select the proper method to use.