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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Presentation explaining the new advancements for optimal design of sequential experimental campaign. Scalable computation was achieved through decomposition strategies on models relevant to initiatives across the IDAES software and supporting research portfolio (e.g., PrOMMiS, CCSI2).
Global CO 2 emissions are increasing at about a 1.5% rate per year. Fossil fuel-based plants are one of the main contributors to this rise. In the power generation industry, fossil fuel plants are dominant, and many plants are under development. In this study, a natural gas combined cycle (NGCC) power plant with postcombustion capture using a leading water-lean solvent is considered. For optimal design and operating schedule, large-scale dynamic optimization is undertaken for net present value (NPV) optimization. The first principle dynamic model of NGCC is developed, including a model of the highly efficient H-class gas turbines. For computational tractability of the dynamic optimization problem, a reduced-order model is developed by using the Hankel singular value decomposition. A waterlean solvent, N-(2-ethoxyethyl)-3-morpholinopropan-1-amine, is used for carbon capture. A model of the capture system is developed in Aspen Plus, which is used to develop a reduced-order model by using ALAMO, a machine learning software. In addition, a reduced model of the CO 2 compression system with a dehydration unit is also considered. The integrated system is used for NPV optimization by using the Python-based PYOMO platform. The PCC process is analyzed for three configurations-conventional packed bed, rotating packed bed (RPB), and a combination of RPB and direct contact cooler. The NPV optimization is performed for 14 regional markets by considering year-long clustered and continuous locational marginal price data with a 1 h interval. Optimization results show that the PCC can achieve 90% CO 2 capture with a positive NPV for six regions. Sensitivity studies conducted by using the PCC configurations indicate that the process is economically feasible for 9 regions out of 14 regional electricity markets with NPV values in the range of 33−540 $MM.
Here, we explore the potential uses of hash-based algorithms in the remap operation, mapping one computational mesh onto another. We implement and test optimizations designed to reduce memory operations and compare these optimizations in order to evaluate the performance impact across different meshes. In particular, we created a new remap method that creates a structured hierarchy of hashes. Tests were run on 2D meshes on up to 14 levels of mesh refinement and hundreds of millions of cells to measure the algorithms' relative speeds under differing circumstances. We show that our methods perform two orders of magnitude better than the standard kD-tree algorithm and that on different meshes different hash methods can be optimal. We explore these algorithms on serial CPUs, GPUs using OpenCL, and multi-core CPUs using OpenMP.
The Contingency Contractor Optimization Tool – Prototype (CCOT-P) database is used to store input and output data for the linear program model described in [1]. The database allows queries to retrieve this data and updating and inserting new input data.
The overall goal of this program is to develop a low NOx combustion system which enables a gas turbine combined cycle efficiency of > 65%. The proposed combustion system is a combination of three advanced technologies under development by the Recipient: advanced transition design, the Distributed Combustion System (DCS), and advanced head end. Of note is that the DCS and advanced transition were initially developed with DOE funding under previous Cooperative Agreements. The continued development of these designs has resulted in a proof of concept design which was successfully tested in the Recipient’s state-of-the-art high-pressure rig. The initial rig testing indicated a substantial benefit with respect to high firing temperature operation as well as low NOx operation for the advanced transition and the DCS. Even lower NOx and higher firing temperatures are possible with the addition of the advanced head end to the design. Potential hurdles to the combustion design have been identified, such as high pressure drop and acoustics. The goal of this program is to develop variations of this design which address these hurdles and create an optimized design for implementation into the Recipient’s new high efficiency gas turbine engine.
In this project, our MIT, Purdue, and GE team aims to design, model, fabricate, and test a novel high temperature, compact, and durable ceramic heat exchanger to be operated under high temperature and pressure conditions for aerospace applications. Our approach is grounded in introducing multiscale porosity, i.e., centimeter-scale channels embedded with micrometer-scale channels, into the ceramic heat exchanger to significantly improve its heat transfer performance and mechanical strength while maintaining minimal pressure losses. We first developed high-fidelity thermal-fluid-mechanical model capable of precisely capturing the heat transfer rate, temperature profile, pressure drop, and mechanical stress throughout the entire heat exchanger design. Guided by our model, we identified the optimal design parameters for the SiC heat exchanger body and manifolds. Then, we established a completed fabrication procedure to create multiscale features in the ceramic heat exchanger, including co-extrusion, lamination, burnout, and sintering. We fabricated multiple heat exchanger bodies consisting of 6 × 6 and 3 × 3 centimeter-scale channels where each individual centimeter-scale channel comprises 625 crack-free microchannels with 90 μm × 90 μm opening. Owing to the multiscale features, our fabricated heat exchanger bodies exhibited desirable mechanical strength with 156 MPa flexural strength under 1300 Celsius degree. Despite the demonstrated highly tailorable microscopic features and superior mechanical strength, we identified delamination due to the complex interaction among ceramic, polymer, and gas species can be a critical challenge to create fully defect-free heat exchanger, which requires further fundamental investigations in future works. To test the heat exchanger performance, we constructed a high-temperature and high-pressure experimental apparatus that can be safely operated under 400 Celsius degree and 4 bar. With insights gained from mechanistic modeling, material development, and detailed characterization, a cost model was finally developed to understand the market potential of the developed technology, where a cost of $43,000 Celsius degree/kW was envisioned. This project developed a transformative approach to high-performance heat exchanger design. The thermal-fluid-mechanical design approach developed in this project can serve as a generic tool to guide the design of various heat-exchangers operated under high-temperature and high-pressure conditions. The material fabrication approach established in this project can be a useful guide for ceramic processing at extreme length scales.
NREL and industrial partners GE and Nel seek to advance affordable green hydrogen production by optimizing wind turbine design specifically for hydrogen production and validating designs using NREL's ARIES facilities. This project will couple wind turbine, wind plant, solar plant, and electrolyzer models to predict hydrogen production from variable, renewable power sources. It will then use gradient-based optimization with exact-analytic gradients to optimize wind turbine rotor diameter, hub height, and power rating for optimal hydrogen production. Finally, it will validate the optimal turbine designs using the ARIES research platform, where it will compare a baseline turbine to the optimal design for a variety of scenarios and resources.
Dataset contains MHK Hydrofoils Design and Optimization and CFD Analysis Report for the Aquantis 2.5 MW Ocean Current Generation Device, as well as MHK Hydrofoils Wind Tunnel Test Plan and Checkout Test Report.
In this work, we present an integrated approach to synthesize process intensification systems with guaranteed flexibility and safety performances. The synthesis of intensified equipment/flowsheets is addressed through the Generalized Modular Representation Framework (GMF), which utilizes an aggregation of multifunctional mass/heat exchange modules to represent chemical processes. Thus, the optimal design options are investigated as mass- and heat-transfer opportunities using superstructure-based optimization techniques without a prepostulation of plausible configurations. To ensure that the designs can be operated under a specified range of uncertain parameters, a multiperiod GMF representation is developed based on the critical operating conditions identified by flexibility test. Risk assessment, accounting for equipment failure frequency and consequence severity, is incorporated as a constraint into this synthesis model to derive inherently safer designs. The resulting safely operable intensified systems, which are represented via phenomenological modules, are then identified as corresponding equipment-based flowsheets and validated with steady-state simulation. We demonstrate the proposed approach through a case study for the production of methyl tert-butyl ether. Here, the results indicate that safety and operability considerations can result in significant changes in the structural and operating parameters of the optimal intensified design configuration.
Unlike phononic crystals or systems designed by topology optimization, waveguides designed by shape optimization do not have voids or internal defects, making the fabrication process more suitable for additive manufacturing. By designing a Y-junction waveguide through shape optimization, an ultrasonic wave can be controlled so that it propagates to a predetermined location just by adjusting its frequency. These demultiplexed ultrasonic waves can be used to transport signals or stimulate nearby materials. As an example, the ultrasonic wave is converted to heat at different locations, which can be applied to mechanisms that can take advantage of heating. First, shape optimization is performed on a cylindrical structure to selectively propagate ultrasonic waves of a particular frequency while attenuating others, which is analyzed through a finite element model. The numerical study results are compared with experimental measurements from samples fabricated through additive manufacturing methods. After verifying the concept, the Y-junction waveguide is fabricated to demultiplex the wave and selectively heat different locations. Here the results show that the method of combining shape optimization with additive manufacturing is exceptionally simple and capable of demultiplexing ultrasonic waves, which can replace complex electrical components with single-material waveguides.
Designing an PWR loading pattern is a combinatorial problem challenging to solve by brute force or traditional methods due to the sheer amount of possible combination, and constraints. Nature-inspired algorithms, such as the genetic algorithm, have demonstrated the potential to tackle this problem. The goal of this work was to improve and demonstrate the capabilities for constrained, multi-objective optimization (MOO) of loading patterns using NSGA-II in RAVEN’s optimization framework.
Wind turbine blade design is a highly multidisciplinary process that involves aerodynamics, structures, controls, manufacturing, costs, and other considerations. More efficient blade designs can be found by controlling the airfoil cross-sectional shapes simultaneously with the bulk blade twist and chord distributions. Prior work has focused on incorporating panel-based aerodynamic solvers with a blade design framework to allow for airfoil shape control within the design loop in a tractable manner. Including higher fidelity aerodynamic solvers, such as computational fluid dynamics, makes the design problem computationally intractable. In this work, we couple an invertible neural network trained on high-fidelity airfoil aerodynamic data to a turbine design framework to enable the design of airfoil cross sections within a larger blade design problem. We detail the methodology of this coupled framework and showcase its efficacy by aerostructurally redesigning the IEA 15-MW reference wind turbine blade. The coupled approach reduces the cost of energy by 0.9% compared to a more conventional design approach. This work enables the inclusion of high-fidelity aerodynamic data earlier in the design process, reducing cycle time and increasing certainty in the performance of the optimal design.
optimas is a Python library that can coordinate a large number of numerical simulations on high-performance computing resources, with the aim of optimizing a given simulation result. More specifically, this library is currently used for design optimization of laser-plasma particle accelerators. In this context, the performance of particle accelerator designs are often evaluated with large-scale simulation codes, and many separate simulations need to be run - with different design parameters - in order to find the most performant design. optimas facilitates this process by providing a convenient interface to advanced optimizers (e.g. Bayesian optimization), and by coordinating the execution of the different simulations on HPC resources. (This is done by leveraging the library libensemble.) Compared to other open-source optimization libraries (e.g. Ax), optimas is more tailored towards execution on DOE HPC resources (e.g. Perlmutter, Summit, etc.) and is specialized for the type of simulation codes and workflows that are used in the community of laser-plasma acceleration.
The aim of this work is to present the numerical design optimization, construction, and experimental proof of concept of a graphite probe calorimeter (GPC) conceived for dose measurement in the clinical environment (U.S. provisional patent 61/652,540). A finite element method (FEM) based numerical heat transfer study was conducted using a commercial software package to explore the feasibility of the GPC and to optimize the shape, dimensions, and materials used in its design. A functioning prototype was constructed inhouse and used to perform dose to water measurements under a 6 MV photon beam at 400 and 1000 MU/min, in a thermally insulated water phantom. Heat loss correction factors were determined using FEM analysis while the radiation field perturbation and the graphite to water absorbed dose conversion factors were calculated using Monte Carlo simulations. The difference in the average measured dose to water for the 400 and 1000 MU/min runs using the TG-51 protocol and the GPC was 0.2% and 1.2%, respectively. Heat loss correction factors ranged from 1.001 to 1.002, while the product of the perturbation and dose conversion factors was calculated to be 1.130. The combined relative uncertainty was estimated to be 1.4%, with the largest contributors being the specific heat capacity of the graphite (type B, 0.8%) and the reproducibility, defined as the standard deviation of the mean measured dose (type A, 0.6%). By establishing the feasibility of using the GPC as a practical clinical absolute photon dosimeter, this work lays the foundation for further device enhancements, including the development of an isothermal mode of operation and an overall miniaturization, making it potentially suitable for use in small and composite radiation fields. It is anticipated that, through the incorporation of isothermal stabilization provided by temperature controllers, a subpercent overall uncertainty will be achieved.
The U.S. Proliferation Resistance and Optimization (PRO-X) program provides a framework for design optimization of a system, component, or feature of a nuclear facility to minimize potential Special Nuclear Material production and diversion pathways while optimizing system performance for peaceful, asintended missions.
We present that the introduction of carbon fiber reinforced polymer (CFRP) composites to structural components in lightweight automotive structures necessitates an assessment to evaluate that their crashworthiness dynamic response provides similar or higher levels of safety compared to conventional metallic structures. In order to develop, integrate and implement predictive computational models for CFRP composites that link the materials design, molding process and final performance requirements to enable optimal design and manufacturing vehicle systems for this study, the dynamic mechanical response of unidirectional (UD) and 2x2 twill weave CRFP composites was characterized at deformation rates applicable to crashworthiness performance. Non-standardized specimen geometries were tested on a standard uniaxial frame and an intermediate-to-high speed dynamic testing frame, equipped with high speed cameras for 3D digital image correlation (DIC). Specimen cross-sections, according to each fiber orientation tested, were consistent across strain rates to ensure results were comparable. Tensile strength and modulus were experimentally investigated over a wide range of strain rates (0.0001 to 200 s -1 ). DIC was used to estimate strain profiles on composites surfaces, and the modulus was calculated from those strain measurements. Experimental results demonstrated an increase in the tensile strength of UD CFRP composites in the longitudinal (0°) and transverse (90°) direction with increasing strain rates. In contrast, the tensile strength of 2x2 woven composites and the tensile modulus of the UD material were insensitive to increasing strain rates. Finally, comparison of failure modes provided insights on how loading rates influenced failure mechanisms.
Porous electrodes are an integral part of many electrochemical devices since they have high porosity to maximize electrochemical transport and high surface area to maximize activity. Traditional porous electrode materials are typically homogeneous, stochastic collections of small-scale particles and offer few opportunities to engineer higher performance. Fortunately, recent breakthroughs in advanced and additive manufacturing are yielding new methods to structure and pattern porous electrodes across length scales. These architected electrodes are emerging as a promising new technology to continue to drive improvement; however, it is still unclear which structures to employ and few tools are available to guide their design. Here we address this gap by applying topology optimization to the design of porous electrodes. We demonstrate our framework on two applications: a porous electrode driving a steady Faradaic reaction and a transiently operated electrode in a supercapacitor. We present computationally designed electrodes that minimize energy losses in a half-cell. For low-conductivity materials, the optimization algorithm creates electrode designs with a hierarchy of length scales. Further, the designed electrodes are found to outperform undesigned, homogeneous electrodes. Finally, we present three-dimensional porous electrode designs. We thus establish a topology optimization framework for designing porous electrodes.
Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model. Quantifying the impact of the model uncertainty on the given operational objectives is critical for designing optimal experiments that can most effectively reduce the uncertainty that affect the objectives pertinent to the application at hand. In this paper, we propose the concept of mean multi-objective cost of uncertainty (multi-objective MOCU) that can be used for objective-based quantification of uncertainty for complex uncertain systems considering multiple operational objectives. We provide several illustrative examples that demonstrate the concept and strengths of the proposed multi-objective MOCU. Furthermore, we present a real-world example based on the mammalian cell cycle network to demonstrate how the multi-objective MOCU can be used for quantifying the operational impact of model uncertainty when there are multiple, possibly competing, objectives.