Cavitating Flow Suppression for a Two-Phase Liquefied Natural Gas Expander Through Collaborative Fine-Turning Design Optimization of Impeller and Exducer Geometric Shape

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Peng Song ◽  
Jinju Sun ◽  
Changjiang Huo
Keyword(s):  
Natural Gas ◽  
Objective Function ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Liquefied Natural Gas ◽  
Cavitating Flow ◽  
Two Phase ◽  
Phase Liquid ◽  
Adaptive Kriging

Abstract Cryogenic liquid turbine expanders have been increasingly used in liquefied natural gas (LNG) production plants to save energy. However, high-pressure LNG commonly needs to be throttled to or near a two-phase state, which makes the LNG turbine expander more vulnerable to cavitation. Although some work has been reported on cryogenic turbomachine cavitation, no work has been reported on designing a cavitation-resistant two-phase LNG liquid turbine expander. Motivated by the urgent requirement for two-phase liquid turbine expanders, an effective design optimization method is developed that is well-suited for designing the cavitation-resistant two-phase liquid turbine expanders. A novel optimization objective function is constituted by characterizing the cavitating flow, in which the overall efficiency and local cavitation flow behavior are incorporated. The adaptive-Kriging surrogate model and cooperative coevolutionary algorithm (CCEA) are incorporated to solve the highly nonlinear design optimization problem globally and efficiently. The former maintains high-level prediction accuracy of the objective function but uses much reduced computational fluid dynamics (CFD) simulations while the later solves the complex optimization problem at a high convergence rate through decomposing them into some readily solved parallel subproblems. By means of the developed optimization method, the impeller and exducer blade geometries and their axial gap and circumferential indexing are fine-tuned. Consequently, cavitating flow in both the impeller and exducer of the two-phase LNG expander is effectively mitigated.

A Deterministic Global Design Optimization Method for Nonconvex Generalized Polynomial Problems

1990 ◽  
Author(s):  
Chihsiung Lo ◽  
Panos Y. Papalambros
Keyword(s):  
Objective Function ◽  
Design Optimization ◽  
Global Solutions ◽  
Optimization Method ◽  
Original Model ◽  
Two Phase ◽  
Generalized Polynomials ◽  
Generalized Polynomial ◽  
New Variables ◽  
Global Design Optimization

Abstract A new design optimization method is described for finding global solutions of models with a nonconvex objective function and nonlinear constraints. All functions are assumed to be generalized polynomials. By introducing new variables, the original model is transformed into one with a linear objective function, one convex and one reversed convex constraint. A two-phase algorithm that includes global feasible searches and local optimal searches is used for globally optimizing the transformed model. Several examples illustrate the method.

A Deterministic Global Design Optimization Method for Nonconvex Generalized Polynomial Problems

1996 ◽  
Vol 118 (1) ◽  
pp. 75-81 ◽  
Author(s):  
Chihsiung Lo ◽  
P. Y. Papalambros
Keyword(s):  
Objective Function ◽  
Design Optimization ◽  
Global Solutions ◽  
Optimization Method ◽  
Original Model ◽  
Two Phase ◽  
Generalized Polynomials ◽  
Generalized Polynomial ◽  
New Variables ◽  
Global Design Optimization

A new design optimization method is described for finding global solutions of models with a nonconvex objective function and nonlinear constraints. All functions are assumed to be generalized polynomials. By introducing new variables, the original model is transformed into one with a linear objective function, one convex and one reversed convex constraint. A two-phase algorithm that includes global feasible search and local optimal search is used for globally optimizing the transformed model. Several examples illustrate the method.

scholarly journals Optimum Arrangement Design of Mastic Ropes for Membrane-Type LNG Tanks Considering the Flatness of Thermal Insulation Panel and Production Cost

2020 ◽  
Vol 8 (5) ◽  
pp. 353
Author(s):  
Do-Hyun Chun ◽  
Myung-Il Roh ◽  
Seung-Ho Ham
Keyword(s):  
Objective Function ◽  
Thermal Insulation ◽  
Production Cost ◽  
Cryogenic Temperature ◽  
Optimization Problem ◽  
Optimization Method ◽  
Liquefied Natural Gas ◽  
Optimum Arrangement ◽  
Membrane Type ◽  
Production Errors

Thermal insulation panels are installed on the inner walls of liquefied natural gas (LNG) tanks of an LNG carrier to maintain the cryogenic temperature. Mastic ropes are used to attach thermal insulation panels to the inner walls and to fill the gap between the walls and panels. Because the inner walls of the LNG tanks can be corrugated owing to production errors, a large amount of mastic ropes are required to maintain the flatness of the thermal insulation panels. Therefore, in this study, an optimization method is proposed to minimize the total amount of mastic ropes for satisfying the flatness criterion of thermal insulation panels. For this purpose, an optimization problem is mathematically formulated. An objective function is used to minimize the total amount of mastic ropes considering constraints to flatten the thermal insulation panels. This function is applied to the design of membrane-type LNG tanks to verify the effectiveness and feasibility of the proposed method. Consequently, we confirm that the proposed method can provide a more effective arrangement design of mastic ropes compared with manual design.

scholarly journals Numerical Model for Cavitational Flow in Hydraulic Poppet Valves

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Sandor I. Bernad ◽  
Romeo Susan-Resiga
Keyword(s):  
Liquid Flow ◽  
Bubble Dynamics ◽  
Flow Analysis ◽  
Cavitating Flow ◽  
Absolute Pressure ◽  
Two Phase ◽  
Current Effort ◽  
Flow Models ◽  
Density Distributions ◽  
Phase Liquid

The paper presents a numerical simulation and analysis of the flow inside a poppet valve. First, the single-phase (liquid) flow is investigated, and an original model is introduced for quantitatively describing the vortex flow. Since an atmospheric outlet pressure produces large negative absolute pressure regions, a two-phase (cavitating) flow analysis is also performed. Both pressure and density distributions inside the cavity are presented, and a comparison with the liquid flow results is performed. It is found that if one defines the cavity radius such that up to this radius the pressure is no larger than the vaporization pressure, then both liquid and cavitating flow models predict the cavity extent. The current effort is based on the application of the recently developed full cavitation model that utilizes the modified Rayleigh-Plesset equations for bubble dynamics.

Sequential Radial Basis Function-Based Optimization Method Using Virtual Sample Generation

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Yifan Tang ◽  
Teng Long ◽  
Renhe Shi ◽  
Yufei Wu ◽  
G. Gary Wang
Keyword(s):  
Radial Basis Function ◽  
Design Optimization ◽  
Basis Function ◽  
Optimization Problem ◽  
Optimization Method ◽  
Support Vector ◽  
Virtual Sample ◽  
Real Samples ◽  
Virtual Samples ◽  
Radial Basis

Abstract To further reduce the computational expense of metamodel-based design optimization (MBDO), a novel sequential radial basis function (RBF)-based optimization method using virtual sample generation (SRBF-VSG) is proposed. Different from the conventional MBDO methods with pure expensive samples, SRBF-VSG employs the virtual sample generation mechanism to improve the optimization efficiency. In the proposed method, a least squares support vector machine (LS-SVM) classifier is trained based on expensive real samples considering the objective and constraint violation. The classifier is used to determine virtual points without evaluating any expensive simulations. The virtual samples are then generated by combining these virtual points and their Kriging responses. Expensive real samples and cheap virtual samples are used to refine the objective RBF metamodel for efficient space exploration. Several numerical benchmarks are tested to demonstrate the optimization capability of SRBF-VSG. The comparison results indicate that SRBF-VSG generally outperforms the competitive MBDO methods in terms of global convergence, efficiency, and robustness, which illustrates the effectiveness of virtual sample generation. Finally, SRBF-VSG is applied to an airfoil aerodynamic optimization problem and a small Earth observation satellite multidisciplinary design optimization problem to demonstrate its practicality for solving real-world optimization problems.

Hierarchical Arrangement of Characteristics in Product Design Optimization Problems for Deeper Insight Into Optimized Results

2004 ◽  
Author(s):  
Masataka Yoshimura ◽  
Masahiko Taniguchi ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Optimum Design ◽  
Design Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Method ◽  
Hierarchical Level ◽  
Hierarchical Arrangement ◽  
Insight Into

This paper proposes a design optimization method for machine products that is based on the decomposition of performance characteristics, or alternatively, extraction of simpler characteristics, to accommodate the specific features or difficulties of a particular design problem. The optimization problem is expressed using hierarchical constructions of the decomposed and extracted characteristics and the optimizations are sequentially repeated, starting with groups of characteristics having conflicting characteristics at the lowest hierarchical level and proceeding to higher levels. The proposed method not only effectively enables achieving optimum design solutions, but also facilitates deeper insight into the design optimization results, and aids obtaining ideas for breakthroughs in the optimum solutions. An applied example is given to demonstrate the effectiveness of the proposed method.

Development of a Multistage Reliability-Based Design Optimization Method

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Eric J. Paulson ◽  
Ryan P. Starkey
Keyword(s):  
Objective Function ◽  
Design Optimization ◽  
Technology Development ◽  
Computational Cost ◽  
Optimization Method ◽  
Comparison Method ◽  
Reliability Based Design Optimization ◽  
Numerical Example ◽  
Reliability Based Design ◽  
System Acquisition

Complex system acquisition and its associated technology development have a troubled recent history. The modern acquisition timeline consists of conceptual, preliminary, and detailed design followed by system test and production. The evolving nature of the estimates of system performance, cost, and schedule during this extended process may be a significant contribution to recent issues. The recently proposed multistage reliability-based design optimization (MSRBDO) method promises improvements over reliability-based design optimization (RBDO) in achieved objective function value. In addition, its problem formulation more closely resembles the evolutionary nature of epistemic design uncertainties inherent in system design during early system acquisition. Our goal is to establish the modeling basis necessary for applying this new method to the engineering of early conceptual/preliminary design. We present corrections in the derivation and solutions to the single numerical example problem published by the original authors, Nam and Mavris, and examine the error introduced under the reduced-order reliability sampling used in the original publication. MSRBDO improvements over the RBDO solution of 10–36% for the objective function after first-stage optimization are shown for the original second-stage example problem. A larger 26–40% improvement over the RBDO solution is shown when an alternative comparison method is used than in the original. The specific implications of extending the method to arbitrary m-stage problems are presented, together with a solution for a three-stage numerical example. Several approaches are demonstrated to mitigate the computational cost increase of MSRBDO over RBDO, resulting in a net decrease in calculation time of 94% from an initial MSRBDO baseline algorithm.

The Response Surface Single Loop Reliability-Based Design Optimization Method With Reliability Requirement on System Failure

2016 ◽  
Author(s):  
Rami Mansour ◽  
Mårten Olsson
Keyword(s):  
Design Optimization ◽  
Response Surface ◽  
Optimization Problem ◽  
Optimization Method ◽  
Reliability Based Design Optimization ◽  
Single Loop ◽  
Reliability Based Design ◽  
Design Variables ◽  
Reliability Method ◽  
Deterministic Solution

In reliability-based design optimization (RBDO), an optimal design which minimizes an objective function while satisfying a number of probabilistic constraints is found. As opposed to deterministic optimization, statistical uncertainties in design variables and design parameters have to be taken into account in the design process in order to achieve a reliable design. In the most widely used RBDO approaches, the First-Order Reliability Method (FORM) is used in the probability assessment. This involves locating the Most Probable Point (MPP) of failure, or the inverse MPP, either exactly or approximately. If exact methods are used, an optimization problem has to be solved, typically resulting in computationally expensive double loop or decoupled loop RBDO methods. On the other hand, locating the MPP approximately typically results in highly efficient single loop RBDO methods since the optimization problem is not necessary in the probability assessment. However, since all these methods are based on FORM, which in turn is based on a linearization of the deterministic constraints at the MPP, they may suffer inaccuracies associated with neglecting the nonlinearity of deterministic constraints. In a previous paper presented by the authors, the Response Surface Single Loop (RSSL) Reliability-based design optimization method was proposed. The RSSL-method takes into account the non-linearity of the deterministic constraints in the computation of the probability of failure and was therefore shown to have higher accuracy than existing RBDO methods. The RSSL-method was also shown to have high efficiency since it bypasses the concept of an MPP. In RSSL, the deterministic solution is first found by neglecting uncertainties in design variables and parameters. Thereafter quadratic response surface models are fitted to the deterministic constraints around the deterministic solution using a single set of design of experiments. The RBDO problem is thereafter solved in a single loop using a closed-form second order reliability method (SORM) which takes into account all elements of the Hessian of the quadratic constraints. In this paper, the RSSL method is used to solve the more challenging system RBDO problems where all constraints are replaced by one constraint on the system probability of failure. The probabilities of failure for the constraints are assumed independent of each other. In general, system reliability problems may be more challenging to solve since replacing all constraints by one constraint may strongly increase the non-linearity in the optimization problem. The extensively studied reliability-based design for vehicle crash-worthiness, where the provided deterministic constraints are general quadratic models describing the system in the whole region of interest, is used to demonstrate the capabilities of the RSSL method for problems with system reliability constraints.

scholarly journals Optimizing Virtual Private Network Design Using a New Heuristic Optimization Method

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Hongbing Lian ◽  
András Faragó
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Optimization Method ◽  
Virtual Private Network ◽  
Successful Model ◽  
Search Speed ◽  
Private Network

In virtual private network (VPN) design, the goal is to implement a logical overlay network on top of a given physical network. We model the traffic loss caused by blocking not only on isolated links, but also at the network level. A successful model that captures the considered network level phenomenon is the well-known reduced load approximation. We consider here the optimization problem of maximizing the carried traffic in the VPN. This is a hard optimization problem. To deal with it, we introduce a heuristic local search technique called landscape smoothing search (LSS). This study first describes the LSS heuristic. Then we introduce an improved version called fast landscape smoothing search (FLSS) method to overcome the slow search speed when the objective function calculation is very time consuming. We apply FLSS to VPN design optimization and compare with well-known optimization methods such as simulated annealing (SA) and genetic algorithm (GA). The FLSS achieves better results for this VPN design optimization problem than simulated annealing and genetic algorithm.

scholarly journals Determination of the LNG Bunkering Optimization Method for Ports Based on Geometric Aggregation Score Calculation

2021 ◽  
Vol 9 (10) ◽  
pp. 1116
Author(s):  
Yong-Ung Yu ◽  
Young-Joong Ahn ◽  
Jong-Kwan Kim
Keyword(s):  
Natural Gas ◽  
Evaluation Method ◽  
Optimization Method ◽  
Liquefied Natural Gas ◽  
Economic Feasibility ◽  
International Standards ◽  
International Maritime Organization ◽  
Supply Risk

Owing to stricter environmental regulations of the International Maritime Organization (IMO) 2020, the demand of liquefied natural gas (LNG) bunkering is expected to grow by approximately 15% during 2020–2025 along with increased investments in eco-friendly ships by global shipping companies. Thus, determining optimal methods for LNG bunkering using existing ports that lack LNG bunkering infrastructure is necessary. Here, a method is proposed to determine the optimal LNG bunkering method for existing ports. Analyzing previous studies, we selected four evaluation factors: assessment of LNG supply for ships, suitability of fuel supply, risk of spillage, and domestic and international standards, which were used to calculate a geometric aggregation score via normalization, weight, and aggregation for selecting an appropriate LNG bunkering method. The analytical results indicated that the ship to ship (STS) method, evaluated based on the size and type of ships, is optimal for the Busan port. This is expected to contribute to the competitiveness of ports and their safety and economic feasibility by serving as a basis for determining the optimal LNG bunkering implemented in existing ports. It is necessary to expand the follow-up research to improve the evaluation method by aggregating more improved data through real cases.

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