Variable-Fidelity Multidisciplinary Design Optimization Based on Analytical Target Cascading Framework

†‡ § ** In traditional structural optimization, the geometric properties of the product are optimized for a specific set of material properties. This paper seeks to extend the domain of product optimization to also include the material modeling parameters by treating the product and material as a hierarchical multilevel system that can be decomposed and solved using the analytical target cascading (ATC) method. Two material models are considered here. The low-fidelity model is based on a simple elastic-plastic constitutive law with linear hardening whereas the high-fidelity model is based on an internal-state-variable representation that is influenced by the microstructural features of the material. The ATC method is applied to product-material design optimization of a multi-cell, thin-walled tube for impact energy absorption and durability. The integrated product-material optimization problem is solved using both single and multilevel approaches with results compared.

A Randomized Control Trial Focused on Breast Core Needle Biopsy Skill Acquisition Using High Fidelity vs Low Fidelity Simulation Models in Rwanda: A Baseline Analysis

Decomposition-based strategies, such as analytical target cascading (ATC), are often employed in design optimization of complex systems. Achieving convergence and computational efficiency in the coordination strategy that solves the partitioned problem is a key challenge. A new convergent strategy is proposed for ATC, which coordinates the interactions among subproblems using sequential lineralizations. Linearity of subproblems is maintained using L∞ norms to measure deviations between targets and responses. A subproblem suspension strategy is used to temporarily suspend inclusion of subproblems that do not need significant redesign, based on trust region and target value step size. The proposed strategy is intended for use in optimization problems where sequential linearizations are typically effective, such as problems with extensive monotonicities, large number of constraints relative to variables, and propagation of probabilities with normal distributions. Experiments with test problems show that, relative to standard ATC coordination, the number of subproblem evaluations is reduced considerably while maintaining accuracy.

Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models

Summary The determination of optimal well settings is very demanding computationally because the simulation model must be run many times during the course of the optimization. For this reason, reduced-order modeling procedures, which are a family of techniques that enable highly efficient simulations, may be very useful for optimization problems. In this paper, we describe a recently developed reduced-order modeling (ROM) technique that has been used in other application areas, the trajectory piecewise linearization (TPWL) procedure, and incorporate it in production-optimization computations. The TPWL methodology represents solutions encountered during the optimization runs in terms of Taylor-series expansions around previously simulated states. This requires a small number of preprocessing (training) simulations using the full (high-fidelity) model, during which pressure and saturation states and Jacobian matrices are saved. These states and matrices are then projected into a low-dimensional space using proper orthogonal decomposition (POD). Simulations in this reduced space can be performed very efficiently; in this work, we observe runtime speedups of a factor of 450. Overall speedups are, however, less because of the preprocessing overhead. We assess the TPWL representation for simulations of waterflood in a heterogeneous 3D model containing more than 20,000 gridblocks and six wells. The high degree of accuracy of the TPWL model is first demonstrated for several testing simulations in which producer- and injector-well settings differ from those used in the training runs. The TPWL representations are then used in optimizations involving the determination of optimal bottomhole pressures (BHPs) for a reservoir model with four production wells and two injection wells. A gradient-based algorithm is applied for the optimizations. In the first case, the BHPs of the producers and injectors are optimized at six different times (36 control variables) and in the second case at 15 different times (90 control variables). Results for optimized net present value (NPV) using TPWL are shown to be in consistently close agreement with those computed using high-fidelity simulations. Most significantly, when the optimal well settings obtained using the TPWL procedure are applied in high-fidelity models, the resulting NPVs are within approximately 0.5% of the values determined using the high-fidelity simulations. Our overall conclusion is that the TPWL representation may be quite useful in production-optimization problems.

A high-fidelity engineering simulation model has been developed in FLIGHTLAB for a Sikorsky production helicopter to support future design modifications. The simulation model consists of major subsystems for main rotor, tail rotor, fuselage, empennage, landing gear, flight control system, and propulsion system. As the manufacturer, Sikorsky was able to provide a complete and validated set of model data and a large database of flight test records to ensure the model quality and fidelity. Although the model correlation with test data is satisfactory in most flight conditions including hover, low-speed flight, level flight, and vertical climb, some model-data discrepancies were seen in the forward climb/descent and autorotation test cases. An additional study was conducted at Sikorsky to investigate these discrepancies. Based on the study, a set of model enhancements were developed to improve the model correlation with test data in forward climb/descent and autorotation. These enhancements allow for adjustment of certain semi-empirical corrections to address model limitations at these challenging conditions such as fuselage characteristics and interference at high angles of attack and rotor inflow and interference at low collective settings and near 90-degree wake skew. These enhancements were carefully designed such that the effects were localized so that the model-data correlation was not adversely impacted in other flight conditions. The model-data correlation in forward climb/descent and autorotation were significantly improved by implementing these model enhancements with little to no impact on the other flight conditions resulting in a high-fidelity engineering simulation model validated in the entire flight envelope.

Analytical target cascading (ATC) is a multidisciplinary design optimization method for multilevel hierarchical systems. To improve computational efficiency, especially for problems under uncertainty or with strong monotonicity, a sequential linear programming (SLP) algorithm was previously employed as an alternate coordination strategy to solve ATC and probabilistic ATC problems. The SLP implementation utilizes L∞ norms to maintain the linearity of SLP subsequences. This note offers a proof that there exists a set of weights such that the ATC algorithm converges when L∞ norms are used. Examples are also provided to illustrate the effectiveness of using L∞ norms as a penalty function to maintain the formulation linear and differentiable. The examples show that the proposed method provides more robust results for linearized ATC problems due to the robustness of the linear programming solver.

BackgroundA key reason for premature cessation of breastfeeding is inadequate support from healthcare providers. Most physicians and nurses do not feel confident in their ability to support families with breastfeeding initiation or maintenance. Increasing health professional confidence in clinical lactation skills is key to improving maternal and child health outcomes. High-fidelity (realistic) simulators encourage learner engagement, resulting in increased clinical skills competency, confidence, and transfer to patient care. Lactation educators teach with low-fidelity cloth and single breast models. There are no high-fidelity breast simulators for health professional education in clinical lactation.Development and evaluation of a high-fidelity lactation simulation modelIn this commentary we describe the development of a high-fidelity Lactation Simulation Model (LSM) and how physician residents, nurse-midwifery students, and clinical lactation experts provided feedback on LSM prototypes.LimitationsThe user-testing described in this commentary does not represent comprehensive validation of the LSM due to small sample sizes and the significant conflict of interest.ConclusionFor breastfeeding rates to improve, mothers need support from their nurses, midwives, pediatricians, obstetricians and gynecologists, and all healthcare staff who interact with pregnant and lactating women. Clinical education with high-fidelity breastfeeding simulators could be the ideal learning modality for trainees and hospital staff to build confidence in clinical lactation skills. The ability of a high-fidelity breastfeeding simulator to increase a learner’s lactation knowledge and psychomotor skills acquisition, retention, and transfer to patient care still needs to be tested.

In the development of complex engineering systems, many engineers from different disciplines collaborate to identify feasible designs that satisfy all system requirements. Analytical target cascading (ATC) is a method for the design optimization of hierarchical, multilevel systems and has been successfully employed in the design of complex engineering systems. In this paper, we propose a novel data‐driven set‐based ATC (SBATC) method for hierarchical design optimization problems using machine learning techniques. The proposed SBATC offers two advantages in engineering processes. First, it decomposes hierarchical design optimization problems into sets of suboptimization problems. The feasible regions of the suboptimization problems are explored and cascaded to lower‐level optimization problems. Using the set‐based approach, couplings between two adjacent levels in the optimization process are not required. Second, an efficient strategy is employed to determine feasible regions based on Bayesian active learning (BAL). In BAL, the Gaussian process (GP) of all cost functions is trained. An acquisition function that combines the probability of feasibility and entropy search is evaluated using posterior distributions of the trained GP. The acquisition function is maximized to generate new sampling points around the feasible regions by balancing the exploitation and exploration of the design space. To verify the effectiveness of the proposed method, numerical examples of hierarchical optimization problems are evaluated.

Under dynamic conditions, the dynamic force between the suspended equipment and the car body is substantially increased. This increase not only affects the ride comfort but also substantially raises the likelihood of fatigue damage to the suspended equipment. A multiobjective analytical target cascading (ATC) optimization is proposed to improve the ride comfort of high-speed trains and reduce the vibration of the suspended equipment. A mathematical simulation model of the vehicle equipment system is established, and the acceleration frequency-response function expression of the car body and the suspension system is obtained. The comfort index of the car body and the acceleration root mean square (RMS) of the suspended equipment are calculated by combining the German vertical irregular track spectrum and the comfort filter function. Meanwhile, the ATC method is used to optimize the car body comfort index and the acceleration RMS of the underframe suspended equipment. Then, the availability of the optimization method is evaluated via numerical simulation. Compared with the original suspension scheme of the underframe equipment, when the running speed of the vehicle is 300 km/h, the RMS value of the vibrational acceleration of the underframe equipment after optimization decreases by 19.9 %, and the ride comfort indexes at the car body center and above the front and rear bogies are improved by 6.4 %, 0.1 %, and 1.1 %, respectively. Simulation results demonstrate that ATC optimization can improve the railway vehicle ride comfort and reduce the vibration of the suspended equipment. This paper provides a new approach to the suspension parameter design of equipment.

This paper presents a physics-based data-driven method to learn predictive reduced-order models (ROMs) from high-fidelity simulations and illustrates it in the challenging context of a single-injector combustion process. The method combines the perspectives of model reduction and machine learning. Model reduction brings in the physics of the problem, constraining the ROM predictions to lie on a subspace defined by the governing equations. This is achieved by defining the ROM in proper orthogonal decomposition (POD) coordinates, which embed the rich physics information contained in solution snapshots of a high-fidelity computational fluid dynamics model. The machine learning perspective brings the flexibility to use transformed physical variables to define the POD basis. This is in contrast to traditional model reduction approaches that are constrained to use the physical variables of the high-fidelity code. Combining the two perspectives, the approach identifies a set of transformed physical variables that expose quadratic structure in the combustion governing equations and learns a quadratic ROM from transformed snapshot data. This learning does not require access to the high-fidelity model implementation. Numerical experiments show that the ROM accurately predicts temperature, pressure, velocity, species concentrations, and the limit-cycle amplitude, with speedups of more than five orders of magnitude over high-fidelity models. Our ROM simulation is shown to be predictive 200% past the training interval. ROM-predicted pressure traces accurately match the phase of the pressure signal and yield good approximations of the limit-cycle amplitude.

Surgical Simulation of Posterior Sagittal Anorectoplasty for Rectovestibular Fistula: Low-Cost High-Fidelity Animal-Tissue Model

D-RM Builder: A software tool for generating fast and accurate nonlinear dynamic reduced models from high-fidelity models

Active learning with multifidelity modeling for efficient rare event simulation

Computational fluid dynamics (CFD) simulations are a fundamental tool in aerodynamic design. Unfortunately, accurate, high-fidelity CFD models may be computationally too expensive to conduct the design using numerical optimization procedures. Recently, variable-fidelity optimization algorithms have attracted attention for their ability to reduce high CPU-cost related to the design process solely based on accurate CFD models. Low-fidelity simulation models are the most critical components of such algorithms. They normally employ the same CFD solver as the high-fidelity model but with reduced discretization density and reduced number of flow solver iterations. Typically, the selection of the appropriate model parameters has only been guided by the designer experience. In this chapter, an automated low-fidelity model selection technique is described. By defining the model setup task as a constrained nonlinear optimization problem, suitable grid and flow solver parameters are obtained. The approach is compared to two conventional methods of generating a family of variable-fidelity models. Comparison of the standard and the proposed approach is carried out in the context of aerodynamic design of transonic airfoils using a multi-level optimization algorithm. The results obtained for several test cases indicate that the automated model generation may lead to significant computational savings of the CFD-based airfoil design process. Illustration of the entire optimization cycle involving automated low-fidelity model preparation and B-spline-parameterized airfoil design using space mapping algorithm is also provided.