Interactive CAD Layout of Reflection-Based Mirror Metrology Systems
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Brimigion, Felicia Marie↗
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A comparison of eight optimization methods applied to a wind farm layout optimization problem
Abstract. Selecting a wind farm layout optimization method is difficult. Comparisons between optimization methods in different papers can be uncertain due to the difficulty of exactly reproducing the objective function. Comparisons by just a few authors in one paper can be uncertain if the authors do not have experience using each algorithm. In this work we provide an algorithm comparison for a wind farm layout optimization case study between eight optimization methods applied, or directed, by researchers who developed those algorithms or who had other experience using them. We provided the objective function to each researcher to avoid ambiguity about relative performance due to a difference in objective function. While these comparisons are not perfect, we try to treat each algorithm more fairly by having researchers with experience using each algorithm apply each algorithm and by having a common objective function provided for analysis. The case study is from the International Energy Association (IEA) Wind Task 37, based on the Borssele III and IV wind farms with 81 turbines. Of particular interest in this case study is the presence of disconnected boundary regions and concave boundary features. The optimization methods studied represent a wide range of approaches, including gradient-free, gradient-based, and hybrid methods; discrete and continuous problem formulations; single-run and multi-start approaches; and mathematical and heuristic algorithms. We provide descriptions and references (where applicable) for each optimization method, as well as lists of pros and cons, to help readers determine an appropriate method for their use case. All the optimization methods perform similarly, with optimized wake loss values between 15.48 % and 15.70 % as compared to 17.28 % for the unoptimized provided layout. Each of the layouts found were different, but all layouts exhibited similar characteristics. Strong similarities across all the layouts include tightly packing wind turbines along the outer borders, loosely spacing turbines in the internal regions, and allocating similar numbers of turbines to each discrete boundary region. The best layout by annual energy production (AEP) was found using a new sequential allocation method, discrete exploration-based optimization (DEBO). Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem, given that they are applied correctly.
17 WIND ENERGY↗
A Tool for the Automated Design and Evaluation of Habitat Interior Layouts
The objective of space habitat design is to minimize mass and system size while providing adequate space for all necessary equipment and a functional layout that supports crew health and productivity. Unfortunately, development and evaluation of interior layouts is often ignored during conceptual design because of the subjectivity and long times required using current evaluation methods (e.g., human-in-the-loop mockup tests and in-depth CAD evaluations). Early, more objective assessment could prevent expensive design changes that may increase vehicle mass and compromise functionality. This paper describes a new interior design evaluation method to enable early, structured consideration of habitat interior layouts. This interior layout evaluation method features a comprehensive list of quantifiable habitat layout evaluation criteria, automatic methods to measure these criteria from a geometry model, and application of systems engineering tools and numerical methods to construct a multi-objective value function measuring the overall habitat layout performance. In addition to a detailed description of this method, a C++/OpenGL software tool which has been developed to implement this method is also discussed. This tool leverages geometry modeling coupled with collision detection techniques to identify favorable layouts subject to multiple constraints and objectives (e.g., minimize mass, maximize contiguous habitable volume, maximize task performance, and minimize crew safety risks). Finally, a few habitat layout evaluation examples are described to demonstrate the effectiveness of this method and tool to influence habitat design.
Simon, Matthew A.↗
Sparsity for Gradient-Based Optimization of Wind Farm Layouts
Optimizing wind farm layouts is an important step in designing an efficient wind farm. Optimizing wind farm layouts is also a difficult task due to computation times increasing with the number of turbines present in the farm. The most computationally expensive part of gradient-based optimization is calculating the gradient. In order to reduce the expense of gradient calculation, we performed a study on the use of sparsity in wind farm layout optimization. This paper presents the findings of the sparsity study and provides a method to use sparsity in wind farm layout optimization. We tested this sparsity method by optimizing multiple farms with sparse methods and compared the results to optimizations of the same farms using traditional methods. By using the sparse method to optimize multiple farms, we found that the objective results were comparable between sparse and traditional methods and that sparse methods were 4 times faster than traditional methods on average. We expect more speedups with improved methodology and larger wind farms. By using sparse methods, it is possible to solve the wind farm layout optimization problem more efficiently, thus allowing for a more thorough study of the wind farm layout design space without excessive computational costs. Further work is required to refine the method and prepare for testing on real-world wind farm layout applications.
gradient↗
Accelerating network layouts using graph neural networks
Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.
97 MATHEMATICS AND COMPUTING↗
FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization
Annual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free-stream wind conditions. Physics-based estimates of wind farm power production typically rely on low-fidelity engineering wake models that approximate the steady-state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed-form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient-based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction.
17 WIND ENERGY↗
Floating Wind Farm Layout Optimization Considering Moorings and Seabed Variations
This paper presents a method for optimizing the layout of floating wind farms that accounts for realistic seabed variations and the consequent adjustments to the mooring systems required for different turbine positions. The mooring lines of floating wind farms create large spatial constraints that are depth-dependent, since mooring designs must adapt to variations in seabed conditions over the array area. We develop a layout optimization methodology that addresses this, adjusting mooring system designs based on the local seabed characteristics as the layout changes and using steady-state models for the wake effects and mooring lines. The approach includes design algorithms that adjust the anchor positions and line length to achieve the desired mooring line profile for different water depths, and a layout optimization framework that implements spatial constraints between the turbines, mooring lines, and lease area boundaries. Demonstrating the method on several cases shows the effect of the seabed and spatial-constraint factors, as well as their interactions, on the optimal array layout. This demonstration paves the way for scaling up the method, using more powerful optimization algorithms to handle larger farm sizes and situations with more intensely varied seabed conditions.
17 WIND ENERGY↗
Regen: An object layout regenerator on large-scale production HPC systems
This article proposes an object layout regenerator called Regen which regenerates and removes the object layout dynamically to improve the read performance of applications. Regen first detects frequent access patterns from the I/O requests of the applications. Second, Regen reorganizes the objects and regenerates or preallocates new object layouts according to the identified access patterns. Finally, Regen removes or reuses the obsolete or regenerated object layouts as necessary. As a result, Regen accelerates access to objects by providing a flexible object layout. We implement Regen as a framework on top of Proactive Data Container (PDC) and evaluate it on Cori supercomputer, a production-scale HPC system, by using realistic HPC I/O benchmarks. The experimental results show that Regen improves the I/O performance by up to 16.92 × compared with an existing system.
Distributed file system↗
Collimated phase measuring deflectometry II: Re-design of the optical layout for high-curvature surfaces
Collimated phase measuring deflectometry (CPMD) is an optical metrology technique developed to improve upon traditional phase measuring deflectometry (PMD). CPMD utilizes telecentric imaging and collimated structured light illumination to eliminate the height-slope ambiguity present in traditional PMD measurements. After the publication of the first CPMD paper, efforts began to optimize the optical layout of the CPMD system. The first proposed change, and the one detailed in this work, was to move the Fourier transform (FT) lens closer to the surface under test (SUT). Moving the FT lens closer to the SUT meant that for a given FT lens diameter, a larger range of surface slopes on the SUT could be measured. This change to the optical layout was not trivial and introduced at least two concerns that had to be addressed: telecentricity in the imaging path and possible ghost reflections from the re-located FT lens. Here, in this work, we examine how these concerns were addressed and present results showing that the revised optical layout is capable of measurement results at least as good as the original CPMD optical layout. We also demonstrate the increased slope measuring range of the revised optical layout.
47 OTHER INSTRUMENTATION↗
WPGNN and PLayGen (Wind Plant Graph Neural Network and Plant Layout Generator) [SWR-21-90]
WPGNN is the graph neural network machine learning based surrogate model and software that provides a streamlined approximation of wind plant wake models. It can rapidly estimate the energy production of the plant and turbines for any arbitrarily sized wind plant and layout under any inflow condition. Associated tools include graphing and visualization capabilities as well as a wind plant generator capable of creating randomized realizations of canonical wind plant layouts. The WPGNN architecture and application is extendable to multiple wake models, turbine technologies and features, and supports downstream optimizations of wind plant layouts and control strategies. In addition to the WPGNN, we include the code for the plant layout generator (PLayGen) playgen.py. This generator can produce random realizations of realistic wind plant layouts from one of the four canonical styles: cluster, single string, multiple string, or parallel string. The PLayGen_demo.ipynb notebook provides a demonstration of how to use the generator tool.
Harrison-Atlas, Dylan↗
Improving the Spacelab mass memory unit tape layout with a simulation model
A tape drive called the Mass Memory Unit (MMU) stores software used by Spacelab computers. MMU tape motion must be minimized during typical flight operations to avoid a loss of scientific data. A projection of the tape motion is needed for evaluation of candidate tape layouts. A computer simulation of the scheduled and unscheduled MMU tape accesses is developed for this purpose. This simulation permits evaluations of candidate tape layouts by tracking and summarizing tape movements. The factors that affect tape travel are investigated and a heuristic is developed to find a good tape layout. An improved tape layout for Spacelab I is selected after the evaluation of fourteen candidates. The simulation model will provide the ability to determine MMU layouts that substantially decrease the tape travel on future Spacelab flights.
Noneman, S. R.↗
Optimal Control Surface Layout for an Aeroservoelastic Wingbox
This paper demonstrates a technique for locating the optimal control surface layout of an aeroservoelastic Common Research Model wingbox, in the context of maneuver load alleviation and active utter suppression. The combinatorial actuator layout design is solved using ideas borrowed from topology optimization, where the effectiveness of a given control surface is tied to a layout design variable, which varies from zero (the actuator is removed) to one (the actuator is retained). These layout design variables are optimized concurrently with a large number of structural wingbox sizing variables and control surface actuation variables, in order to minimize the sum of structural weight and actuator weight. Results are presented that demonstrate interdependencies between structural sizing patterns and optimal control surface layouts, for both static and dynamic aeroelastic physics.
Stanford, Bret K.↗
Internal Layout of a Lunar Surface Habitat
NASA is currently developing concepts for sustained crewed missions to the lunar surface. Sustained missions will occur on annual basis for durations of 30 days or more. To house and support the crew, NASA is exploring the use of a lunar Surface Habitat. NASA relies on conceptual spacecraft design to assist in planning and mission analysis. A crucial component of this habitation design is the internal layout: the interior location of systems, workstations, crew quarters, etc. The purpose of this paper is to describe the process and resulting layout of a specific Surface Habitat design. This paper describes the SH’s hybrid inflatable structure then details the habitat’s interior layout. The proposed SH’s internal layout is depicted inside a hybrid inflatable structure using a low-fidelity CAD model. Multiple NASA references were consulted to determine the SH’s required functionality, minimal Net Habitable Volume, and minimal functional dimensions. These functions and volumes contribute to the placement and location of systems and spaces inside the habitat. In addition to functional volume, the layout includes systems and logistics placement. The resulting design will aid NASA in mission planning and further systems analysis.
Callie Burke↗
Robust wind farm layout optimization
Wake interactions in wind farms cause losses in annual energy production (AEP) on the order of 10%. Wind farm designers optimize the layout of the farm to mitigate wake losses, especially in the dominant site-specific wind directions. As wind turbines and wind farms grow in scale, optimization becomes more complex. Offshore wind farms regularly comprise more than 100 wind turbines and are characterized by complex boundaries due to shipping lanes, neighboring wind farms, and other constraints. Layout optimization methods are broadly split between gradient-based and gradient-free approaches. Gradient-based approaches can converge quickly and perform well for smaller, academic problems but are often sensitive to initial conditions and tuning parameters and require expert knowledge to use. On the other hand, gradient-free approaches can be more robust to problem complexities. We present a robust layout optimization approach based on a random search algorithm. The algorithm is intended for those who are not optimization experts and has few tuning parameters that need specification to achieve satisfactory results. Unlike off-the-shelf methods, which use generally available, non-domain-specific optimization routines that accept as inputs an optimization function and constraint definitions, this approach takes advantage of the relative computational costs of the different evaluations by evaluating cheaper computations first (boundary and minimum distance constraints) and running expensive AEP evaluations only if all other checks pass. Moreover, an outer genetic algorithm allows multiple solutions to evolve in parallel, enabling rapid solution development on high-performance computers. We discuss the relative ease of selecting necessary tuning parameters and demonstrate the efficacy of the genetic random search on a complex layout problem consisting of placing 70 turbines in a nonconvex and unconnected boundary region.
17 WIND ENERGY↗
Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients
Wind farm layout optimization (WFLO) studies often aim to maximize the annual energy production (AEP) of a wind farm by choosing an arrangement of turbines that minimizes wake interactions. One way to reduce the cost of WFLO studies is by using more computationally efficient AEP models. The cost of standard AEP modeling approaches, based on the numerical integration of low-fidelity engineering wake models, scales poorly with the number of simulated discrete wind conditions. A second way to reduce cost when using a gradient-based algorithm is to supply exact gradient information instead of finite-difference estimates. However, analytical functions for the derivatives of AEP with respect to turbine positions are not always available in the conventional modeling approach. FLOWERS is a computationally inexpensive, analytical model for wind farm AEP that is specifically developed for WFLO applications. In this paper, we analyze the performance of the FLOWERS AEP model with analytic gradients in a layout optimization study compared with a reference optimization framework across three wind farm case studies. We find that the FLOWERS-based approach reduces computation time by a factor of 50–4000 and improves optimal AEP by about 0.3% with less than half of the variability in AEP across instances with randomized initial conditions. We also find the optimal layouts to be insensitive to model parameter tuning, making FLOWERS-based layout optimization a streamlined, user-friendly approach.
17 WIND ENERGY↗
Integrated floating wind farm layout design and mooring system optimization to increase annual energy production
As we cluster wind turbines in wind farms to gain energy from sites with high wind speeds, wake losses occur within the wind farm. Wake loss is a term used to describe the lower energy production of a downwind turbine that is totally or partially in the wake of an upwind turbine. To decrease wake losses inside the wind farm, the wind farm’s layout is optimized. However, a variety of factors constrain the wind farm layout optimization, such as the size of the lease area relative to the number of turbines to be placed, or the shape of the lease area. Therefore, many wind farms end up with a regular grid layout, such as the Horns Rev 1 wind farm in the North Sea. The ability of a floating offshore wind turbine (FOWT) to change its position based on the wind direction and its mooring system design presents an opportunity to further decrease wake losses in floating wind farms. In this work, we integrate the design of the FOWT mooring systems with the floating wind farm layout design with the goal of increasing the farm’s annual energy production. We use the Horns Rev 1 wind farm as a case study to demonstrate our method. The results show that allowing the FOWT to relocate can decrease wake losses up to 18%. Moreover, the newly developed mooring systems are less stiff and therefore allow larger motion of the FOWT; hence, the material cost of the mooring system decreases by an estimated 17%.
17 WIND ENERGY↗
Sensitivity analysis of design parameters in RowWise borehole layout for ground heat exchangers
Ground source heat pump systems offer a promising pathway toward energy-efficient building heating and cooling. The performance and cost-effectiveness of these systems heavily depend on the design of the ground heat exchanger (GHE), particularly the spatial placement of boreholes used in large commercial buildings. Among various borefield layout strategies, the RowWise approach generates and optimizes borehole configurations within irregular polygonal land boundaries, providing land-use efficiency and installation flexibility. While multiple geometric design parameters constrain the RowWise layout, their influence on the system thermal performance and total drilling requirements remains unclear. Thus, this study presents a sensitivity analysis of key design parameters influencing the RowWise layout of vertical borehole GHEs, including the perimeter spacing ratio, borehole spacing, borehole field rotation angle, and borehole length. GHEDesigner and Ray Tune are employed to generate and assess different RowWise configurations. A series of parametric simulations was conducted to quantify the impact of each parameter on geometric distribution, economic cost, and computational speed. The results provide critical insights into the sensitivity and relative importance of different design parameters of RowWise method, offering practical guidance for designers aiming to optimize GHE layouts by balancing thermal efficiency, land constraints, and economic feasibility.
Xu, Dikai [Purdue University]↗
Examining the Impact of Data Layout on Tape on Data Recall Performance for ATLAS
Increases in data volumes are forcing high-energy and nuclear physics experiments to store more frequently accessed data on tape. Extracting the maximum performance from tape drives is critical to make this viable from a data availability and system cost standpoint. The nature of data ingest and retrieval in an experimental physics environment make achieving high access performance difficult given the inherent limitations of magnetic tape. Tailoring the layout of data on tape is one key to improving read performance. This paper highlights the work in progress to characterize ATLAS data ingested in the tape system, understand how data layout, i.e. file co-location on tape and file distribution over tapes, affect read performance and how optimal data layout might be achieved in a production environment.
99 GENERAL AND MISCELLANEOUS↗