A method for parameterized design and rapid optimization of axial flow pumps

Machine Learning User Preferences for Structural Design

A large variety of design optimization methods have been proposed in recent years. Comparison of the relative performance of each method is a difficult task, and attempts to do so are often based on a limited number of numerical experiments. Recently, a ‘basins of attraction’ construction has been proposed as a graphical tool for investigating global performance of iterative design optimization methods, and as a basis for comparison of different methods. The phase‐space representation presented in this paper is a companion to the basins of attraction construction. Basins of attraction reveal the relationship between starting design and final outcome of the solution process; the phase‐space construction reveals that nature of the paths connecting the starting design and final outcome. The two constructions complement one another in summarizing the performance of design optimization processes. Both constructions are demonstrated in this paper, applied to the optimal design of an elastic grillage structure using Newton's method and the stress ratio method.

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet the new market demands while keeping the development time and the cost to a minimum. Many researchers have studied the derivative design process, but these research considered the baseline and the derivatives together, while using the whole set of design variables. Therefore, an efficient process that can reduce the cost and the time for the aircraft derivative design is needed. In this dissertation, Aircraft Derivative Design Optimization process (ADDOPT) was developed which obtains the global changes from the local changes in the aircraft design to develop the aircraft derivatives efficiently. The sensitivity analysis was implemented to ignore design variables that have low impact on the objective function. This avoids wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, the classification of uncertainty from its characteristics and sources of uncertainty involved in the aircraft design process were suggested to consider with design optimization. Uncertainty from the fidelity of analysis tools was applied in design optimization to increase the probability of optimization results. To handle uncertainty in low fidelity analysis tools on aircraft conceptual design optimization, Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were performed. In this research, Extended Fourier Amplitude Sensitivity Test (eFAST) method was implemented in ADDOPT for Global Sensitivity Analysis (GSA) method and Collaborative Optimization (CO) based framework with RBDO and PBDO were also used. These methods were evaluated using numerical examples. ADDOPT was carried through on the civil jet aircraft derivative design. The objective of the optimization problem was to increase cruise range while satisfying the requirement such as the number of passengers. The proposed process reduced computation effort by reducing the number of design variables and achieved the target probability of failure when considering uncertainty from low fidelity analysis tools.

Design of haptic devices requires trade-off between many conflicting requirements, such as high stiffness, large workspace, small inertia, low actuator force/torque, and a small size of the device. With the traditional design and optimization process, it is difficult to effectively fulfill the system requirements by separately treating the different discipline domains. To solve this problem and to avoid sub-optimization, this work proposes a design methodology, based on Multidisciplinary Design Optimization (MDO) methods and tools, for design optimization of six degree-of-freedom (DOF) haptic devices for medical applications, e.g. simulators for surgeon and dentist training or for remote surgery. The proposed model-based and simulation-driven methodology aims to enable different disciplines and subsystems to be included in the haptic device optimization process by using a robust model architecture that integrates discipline-specific models in an optimization framework and thus enables automation of design activities in the concept and detail design phase. Because of the multi-criteria character of the performance requirements, multi-objective optimization is included as part of the proposed methodology. Because of the high-level requirements on haptic devices for medical applications in combination with a complex structure, models such as CAD (Computer Aided Design), CAE (Computer Aided Engineering), and kinematic models are considered to be integrated in the optimization process and presenting a systems view to the design engineers. An integration tool for MDO is used as framework to manage, integrate, and execute the optimization process. A case study of a 6-DOF haptic device based on a TAU structure is used to illustrate the proposed methodology. With this specific case, a Multi-objective Genetic Algorithm (MOGA) with an initial population based on a pseudo random SOBOL sequence and Monte Carlo samplings is used for the optimization.

Preliminary designs with deterministic approaches used to solve the uncertainty related information from a ship design results preliminary design of conventional. In order to overcome the imbalance of optimization in the design process, the preliminary design is equipped with a method Collaborative Optimization (CO), which is part of the Multidisciplinary Design Optimization (MDO). Process design optimization in preliminary design with a deterministic approach based on the value subsystem optimization required by the vessel (d). Ship design requirements of this study are deadweigth requirements, volume cargo requirements and speed/resistance requirements. The preliminary design hull form general cargo for shipping Jakarta to Makassar by using deterministic approach is expected to provide an alternative principal dimension and design variations of the optimal hull form based on information dock and owner reqiurement. In addition, this study is also expected to be an alternative method used in the preliminary process design of modern ships because in the preliminary designs have efforts to optimize an element ship design.

Timing closure is a complex process that involves many iterative optimization steps applied in various phases of the physical design flow. Cell sizing and transistor threshold selection, as well as datapath and clock buffering, are some of the tools available for design optimization. At the moment, design optimization methods are integrated into EDA tools and applied incrementally in various parts of the flow, while the optimal order of their application is yet to be determined. In this work, we rely on reinforcement learning – through the use of the Multi-Armed Bandit model for decision making under uncertainty – to automatically suggest online which optimization heuristic should be applied to the design. The goal is to improve the performance metrics based on the rewards learned from the previous applications of each heuristic. Experimental results show that automating the process of design optimization with machine learning not only results in designs that are close to the best-published results derived from deterministic approaches, but it also allows for the execution of the optimization flow without any human in the loop, and without any need for offline training of the heuristic-orchestration algorithm.

With environmental awareness growing in many countries, governments are taking measures to reduce mortality of migrating fish in pumping stations. Manufacturers seek to develop pumps that are less damaging to fish and still provide good hydraulic performance, but little is known about the implications design modifications may have on internal flow characteristics and overall hydraulic performance. In this paper, an integrated design method is proposed that combines a validated blade strike model for fish damage and a computational fluid dynamics method to assess the pump performance. A redesign of an existing, conventional, axial flow pump is presented as an example in this paper. It shows how the design of the impeller blades was modified stepwise in order to reduce fish mortality while its hydraulic performance was monitored. Computational fluid dynamics analysis of the flow near the hub of the highly skewed blades indicated that unconventional design modifications were required to ensure optimum flow behavior. In the final fish-friendly design, the risk of fish mortality has reduced considerably while the hydraulic performance of the pump is still acceptable for practical application.

The blade angle has a great effect on hydraulic performance and internal flow field for axial-flow pumps. This research investigated the effect of the blade angle on hydraulic performance and tip leakage vortex (TLV) of a slanted axial-flow pump. The hydraulic performance and the TLV are compared with different setting angles. The dimensionless turbulence kinetic energy (TKE) is used to investigate the TLV. A novel variable fv is utilized to analyze the relation among the TLV, strain tensor and vorticity tensor. The proper orthogonal decomposition (POD) method is used to analyze TLV structure. The results show that with the increase of the blade angle, the pump head is getting larger, the flow rate of the best efficiency moves to be larger, and both the primary TLV (P-TLV) and the secondary TLV (S-TLV) are getting stronger. The P-TLV often exists in the outer edge of TKE distribution and S-TLVs often exist in the largest value area of TKE. This phenomenon is more evident with blade angle increasing. Through POD method, it shows that the first six modes contain more than 90% of TKE. The reason why the TKE value near the region of S-TLV is high is that the tip leakage flow is a kind of jet-like flow with high kinetic energy. The main structure of the P-TLV is shown in modes 4−6, resulting in a reflux zone but not with the highest TKE.

With the rapid advancement of nuclear power industry, 85Kr, a fission product of 235U, is considered a potential nuclear pollutant. Consequently, developing 85Kr detectors to monitor 85Kr gas in the atmospheric environment is crucial. This paper establishes a design process and optimization method for 85Kr detectors based on two critical indicators: Measurement Efficiency (ME) and Minimum Detectable Activity (MDA). Utilizing Monte Carlo simulation, the study investigates the combined effects of crystal geometry, chamber geometry, and lead shield thickness in classical 85Kr detector structures on the measurement of β-rays produced by the decay of 85Kr. Ultimately, the article presents five geometric design schemes for high ME and low MDA 85Kr detectors of different sizes, achieving a maximum ME of 54.2% for 85Kr in sample vial, and the MDA for the samples is less than 30 Bq/m3 within one hour. Finally, based on one of the design schemes, this paper developed a set of 85Kr detectors, and further validated the scientific rigor and reliability of the entire design process through experiments on ME and MDA. The design process and methods proposed in this paper fill the gaps in the geometric parameter constraint process for 85Kr detectors.

Molded interconnect device (MID) technology is a key enabling technology with growing markets in automotive, communications, consumer electronics through integration with lighting and sensor technologies. The MID technology is yet to be explored for high temperature applications in automotive or consumer lighting. One of the hindering factors for such implementation in the serial production and time to market is the improper electronic and thermal packaging of the light emitting diodes (LEDs) on the MID substrates. This paper addresses the optimization of mold design, surface metallization and soldering process for the effective thermal management of the high-power LED systems. By using a simulation model, the thermal distribution and the resultant decrease in temperatures for varying forward electrical currents in high-power LEDs by design optimization is demonstrated. In addition, the optimization of solder process with respect to solder profile in context of vacuum vapor-phase soldering for void-free solder connections is discussed.

Further optimal design of an axial-flow water pump calls for a thorough recognition of the characteristics of the complex turbulent flow field in the pump, which is however extremely difficult to be measured using the up-to-date experimental techniques. In this study, a numerical simulation procedure based on computational fluid dynamics (CFD) was elaborated in order to obtain the fully three-dimensional unsteady turbulent flow field in an axial-flow water pump. The shear stress transport (SST) k-ω model was employed in the CFD calculation to study the unsteady internal flow of the axial-flow pump. Upon the numerical simulation results, the characteristics of the velocity field and pressure field inside the impeller region were discussed in detail. The established model procedure in this study may provide guidance to the numerical simulations of turbomachines during the design phase or the investigation of flow and pressure field characteristics and performance. The presented information can be of reference value in further optimal design of the axial-flow pump.

Simulation-based design optimization utilizes computational models that rely on assumptions and approximations. There is a need therefore, to ensure that the obtained designs will exhibit the desired behavior as anticipated given the model predictions. The common approach to accomplish that is to validate the utilized computational models prior to the design optimization process. However, this is practically an impossible task especially for design problems with high-dimensional design and parameter spaces. We have recently proposed a different approach for maximizing confidence in the designs generated during a sequential simulation-based optimization process based on calibrating the computational models when necessary and within local subdomains of the design space. In that work, the size of the local domains was held fixed and not linked to uncertainty, and the confidence in designs was quantified using Bayesian hypothesis testing. In this article, we present an improved methodology. Specifically, we use a statistical methodology to account for uncertainty and to determine the size of the local domains at each stage of the sequential design optimization process using parametric bootstrapping that involves maximum likelihood estimators of model parameters. The sequential process continues until the local domain does not change from stage to stage during the design optimization process, ensuring convergence to an optimal design. The proposed methodology is illustrated with the design of a thermal insulator using one-dimensional, linear heat conduction in a solid slab with heat flux boundary conditions.

This paper presents the methods and results of a bi-level optimization process used to select and size the components of an energy centre as well as to determine their optimal operation. The optimization process used a single-objective optimization of operational control nested within a two-objective optimization of design parameters that minimized capital costs and carbon emissions. Different cases were examined in which the ‘credit’ assigned for the export of excess electricity to the grid was varied. Results are presented including the overall trade-off front, trends amongst design variables, operational schedules for a particular solution, and aggregated parameters like total energy supplied by each device. Constraints on roof area were critical at the design-level, and complex interactions between demand and supply were identified at the operational level. Comparison of the two cases highlights the impact of the export of excess power to the grid.

Computational design optimization has been widely considered a promising technique to help designers address complex design challenges regarding building performance. However, a barrier to applying it to real-world projects is the difficulty in incorporating functional requirements and constraints into the design optimization process. In response, this study presents an optimization-assisted design approach for early-stage architectural design. The approach combines the application of EvoMass, an integrated building mass design generation and optimization tool, and the soft constraint strategy. The combination allows designers to integrate various design requirements and constraints into the optimization, which makes it produce results with higher practical values. To demonstrate the efficacy of the approach, two case studies are presented, which show that the application of optimization facilitates designers to better formulate the design problem and rapidly investigate different design directions for exploration and information extraction.

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet new market demands while keeping the development time and cost to a minimum. Many researchers have studied the derivative design process, but these research efforts consider baseline and derivative designs together, while using the whole set of design variables. Therefore, an efficient process that can reduce cost and time for aircraft derivative design is needed. In this research, a more efficient design process is proposed which obtains global changes from local changes in aircraft design in order to develop aircraft derivatives efficiently. Sensitivity analysis was introduced to remove unnecessary design variables that have a low impact on the objective function. This prevented wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, uncertainty from the fidelity of analysis tools was considered in design optimization to increase the probability of optimization results. The Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were proposed to handle the uncertainty in aircraft conceptual design optimization. In this paper, Collaborative Optimization (CO) based framework with RBDO and PBDO was implemented to consider uncertainty. The proposed method was applied for civil jet aircraft derivative design that increases cruise range and the number of passengers. The proposed process provided deterministic design optimization, RBDO, and PBDO results for given requirements.