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DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗

NASA NTRS1971

The feasibility of electrohydrodynamic heat pipes

The effect of a nonuniform electrostatic field on insulating dielectric liquids in heat pipes was studied. Topics discussed include the theory of operation, design criteria, and evaluation of optimal design features. It is concluded that the electrodynamic heat pipes offer advantages that must be weighed against the disadvantages in order to arrive at a proper assessment of their value in solving heat transfer problems.

Jones, T. B.↗

NASA NTRS1975

Status and trends in active control technology

The emergence of highly reliable fly-by-wire flight control systems makes it possible to consider a strong reliance on automatic control systems in the design optimization of future aircraft. This design philosophy has been referred to as the control configured vehicle approach or the application of active control technology. Several studies and flight tests sponsored by the Air Force and NASA have demonstrated the potential benefits of control configured vehicles and active control technology. The present status and trends of active control technology are reviewed and the impact it will have on aircraft designs, design techniques, and the designer is predicted.

Rediess, H. A.↗

NASA NTRS2013

Silent and Efficient Supersonic Bi-Directional Flying Wing

The supersonic bi-directional (SBiDir) flying wing (FW) concept has a great potential to achieve low sonic boom with high supersonic aerodynamic performance due to removal of performance conflict between high speed and low speed by rotating goo in flight. This NIAC Phase 1 research has achieved three objectives: 2) prove the concept based on simulation that it can achieve very low boom with smooth Sine wave ground over-pressure signature and excellent aerodynamic efficiency; 3) conduct trade study to correlate the geometric parameters with sonic boom and aerodynamic performance for further automated design optimization in Phase II. The design methodology developed in Phase I includes three parts: 1) an advanced geometry model, which can vary airfoil meanline angle distribution to control the expansion and shock waves on the airplane surface to mitigate sonic boom and improve aerodynamic efficiency. 2) a validated CFD procedure to resolve near field flow with accurate shock strength. The sonic boom propagation from near field to far field ground is simulated by NASA NF Boom code. The surface friction drag prediction is based on fiat plate correlation adopted by Seebass and supported by the experimental study of Winter and Smith, which is on the conservative side and is more reliable than CFD RANS simulation. 3) a mission analysis tool based on Corke's model that provides design requirements and constraints of supersonic airplanes for range, payload, volume, size, weight, etc. The design mission target is a supersonic transport with cruise Mach number 1.6, 100 passengers, and 4000nm range. The trade study has several very important findings: 1) The far field ground sonic boom signature is directly related to the smoothness of the flow on the airplane surface. The meanline angle distribution is a very effective control methodology to mitigate surface shock and expansion wave strength, and mitigating compression wave coalescing by achieving smooth loading distribution chord-wise. Compared with a linear meanline angle distribution, a design using nonlinear and non-monotonic meanline angle distribution is able to reduce the sonic boom ground loudness by over 20dBP1. The design achieves sonic boom ground loudness less than 70dBP1 and aerodynamic dynamic efficiency 1/D of 8.4. 2) Decreasing sweep angle within the Mach cone will increase 1/D as well as sonic boom. A design with variable sweep from 84° at the very leading edge to 68° at the tip achieves an extraordinarily high 1/D of 10.4 at Mach number 1.6 due to the low wave drag. If no sonic boom constraint is attached, SBiDir-FW concept still has a lot of room to increase the 1/D. 3) The round leading edge and trailing edge under high sweep angle are beneficial to improve aerodynamic performance, sonic boom, and to increase volume of the airplane. 4) Subsonic performance is benefited greatly from the high slenderness of supersonic configuration after rotating goo. A design with excellent supersonic aspect ratio of 0.44, 1/D of 8.g, gives an extraordinary subsonic aspect ration of 10 and 1/D of 1g.7. Two configurations are designed in details to install internal seats, landing gears, and engine installation to demonstrate the feasibility of SBiDir-FW configuration to accommodate all the required volume for realistic airplane. Here we emphasize that the qualitative findings in Phase I are very encouraging, more important than the quantitative results. Qualitative findings give the understanding of physics and provide the path to achieve the ultimate high performance design. The promising quantitative results achieved in Phase I need to be confirmed by wind tunnel testing in Phase II and ultimately proved by flight test. The other important step forward will be made to study the rotation transition from both CFD unsteady simulation and wind tunnel testing.

Zha, Gecheng↗

NASA NTRSDate not supplied

Power System Redundancy Design Trends for All-Electric eVTOL Quadrotors

Electrical vertical takeoff and landing (eVTOL) vehicles promise to enable a variety of new local and intraregional air transport missions. However, there are significant challenges that must be solved to enable their use. One challenge is the relatively low reliability of power components (e.g. motors, power electronic devices, and batteries). No clear development path to create reliable eVTOL power systems has been articulated in the literature, though it is generally assumed redundancy is part of the solution. This work proposes an analysis process to examine the redundancy design space and select mass optimal designs for eVTOL power systems. The process is applied to a six passenger quadrotor vehicle, and results are used to highlight redundancy design trends for this class of vehicle. This process and associated data can help vehicle designers more effectively develop eVTOLs that meet reliability requirements.

Electric vertical takeoff and landing↗

NASA NTRSDate not supplied

Power System Redundancy Design Trends for All-Electric eVTOL Quadrotors

Electric vertical takeoff and landing↗

DOE OSTI2017

Adaptive OFDM Waveform Design for Spatio-Temporal-Sparsity Exploited STAP Radar

In this chapter, we describe a sparsity-based space-time adaptive processing (STAP) algorithm to detect a slowly moving target using an orthogonal frequency division multiplexing (OFDM) radar. The motivation of employing an OFDM signal is that it improves the target-detectability from the interfering signals by increasing the frequency diversity of the system. However, due to the addition of one extra dimension in terms of frequency, the adaptive degrees-of-freedom in an OFDM-STAP also increases. Therefore, to avoid the construction a fully adaptive OFDM-STAP, we develop a sparsity-based STAP algorithm. We observe that the interference spectrum is inherently sparse in the spatio-temporal domain, as the clutter responses occupy only a diagonal ridge on the spatio-temporal plane and the jammer signals interfere only from a few spatial directions. Hence, we exploit that sparsity to develop an efficient STAP technique that utilizes considerably lesser number of secondary data compared to the other existing STAP techniques, and produces nearly optimum STAP performance. In addition to designing the STAP filter, we optimally design the transmit OFDM signals by maximizing the output signal-to-interference-plus-noise ratio (SINR) in order to improve the STAP performance. The computation of output SINR depends on the estimated value of the interference covariance matrix, which we obtain by applying the sparse recovery algorithm. Therefore, we analytically assess the effects of the synthesized OFDM coefficients on the sparse recovery of the interference covariance matrix by computing the coherence measure of the sparse measurement matrix. Our numerical examples demonstrate the achieved STAP-performance due to sparsity-based technique and adaptive waveform design.

Sen, Satyabrata↗

DOE OSTI2020

Optimal Operation of a Hybrid Hydraulic Electric Architecture (HHEA) for Off-Road Vehicles Over Discrete Operating Decisions

Many off-highway machines including construction and agriculture equipment use hydraulics for power transmission and throttling as a means for control. Trends towards better efficiency and electrification have led to the creation of a novel Hybrid Hydraulic-Electric Architecture (HHEA) which could significantly reduce energy consumption and maintain control performance, even in machines that are too large to be directly electrified. This is achieved by using a set of common pressure rails to transmit the majority of power via power dense hydraulics and modulating the power with small electric motor-drives to achieve precise control. This paper proposes a computationally efficient method for computing the optimal sequence of pressure rail selections for the HHEA over finite drive cycles. This is useful for fairly comparing the novel architecture’s energy performance to existing architectures and for use in iterative optimal design of the architecture. The optimal control technique makes use of a static model of the architecture and losses. Constraints are enforced such that the drive cycle is repeatable. The constrained optimal operation is solved using a Lagrange multiplier technique that transforms the optimization into a small set of sub-problems by considering combinations of active constraints. Each of these sub-problems can be solved efficiently because loss calculations for all time steps can be computed in parallel. A case study of an off-road construction machine demon-strates that the HHEA reduces energy consumption by 2/3 compared to the baseline load sensing architecture.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2016

Augmenting Parametric Optimal Ascent Trajectory Modeling with Graph Theory

It has been well documented that decisions made in the early stages of Conceptual and Pre-Conceptual design commit up to 80% of total Life-Cycle Cost (LCC) while engineers know the least about the product they are designing [1]. Once within Preliminary and Detailed design however, making changes to the design becomes far more difficult to enact in both cost and schedule. Primarily this has been due to a lack of detailed data usually uncovered later during the Preliminary and Detailed design phases. In our current budget-constrained environment, making decisions within Conceptual and Pre-Conceptual design which minimize LCC while meeting requirements is paramount to a program's success. Within the arena of launch vehicle design, optimizing the ascent trajectory is critical for minimizing the costs present within such concerns as propellant, aerodynamic, aeroheating, and acceleration loads while meeting requirements such as payload delivered to a desired orbit. In order to optimize the vehicle design its constraints and requirements must be known, however as the design cycle proceeds it is all but inevitable that the conditions will change. Upon that change, the previously optimized trajectory may no longer be optimal, or meet design requirements. The current paradigm for adjusting to these updates is generating point solutions for every change in the design's requirements [2]. This can be a tedious, time-consuming task as changes in virtually any piece of a launch vehicle's design can have a disproportionately large effect on the ascent trajectory, as the solution space of the trajectory optimization problem is both non-linear and multimodal [3]. In addition, an industry standard tool, Program to Optimize Simulated Trajectories (POST), requires an expert analyst to produce simulated trajectories that are feasible and optimal [4]. In a previous publication the authors presented a method for combatting these challenges [5]. In order to bring more detailed information into Conceptual and Pre-Conceptual design, knowledge of the effects originating from changes to the vehicle must be calculated. In order to do this, a model capable of quantitatively describing any vehicle within the entire design space under consideration must be constructed. This model must be based upon analysis of acceptable fidelity, which in this work comes from POST. Design space interrogation can be achieved with surrogate modeling, a parametric, polynomial equation representing a tool. A surrogate model must be informed by data from the tool with enough points to represent the solution space for the chosen number of variables with an acceptable level of error. Therefore, Design Of Experiments (DOE) is used to select points within the design space to maximize information gained on the design space while minimizing number of data points required. To represent a design space with a non-trivial number of variable parameters the number of points required still represent an amount of work which would take an inordinate amount of time via the current paradigm of manual analysis, and so an automated method was developed. The best practices of expert trajectory analysts working within NASA Marshall's Advanced Concepts Office (ACO) were implemented within a tool called multiPOST. These practices include how to use the output data from a previous run of POST to inform the next, determining whether a trajectory solution is feasible from a real-world perspective, and how to handle program execution errors. The tool was then augmented with multiprocessing capability to enable analysis on multiple trajectories simultaneously, allowing throughput to scale with available computational resources. In this update to the previous work the authors discuss issues with the method and solutions.

Patrick D Dees↗

NASA NTRS1989

Tailoring of composite links for optimal damped elasto-dynamic performance

A method is developed for the optimal design of composite links based on dynamic performance criteria directly related to structural modal damping and dynamic stiffness. An integrated mechanics theory correlates structural composite damping to the parameters of basic composite material systems, laminate parameters, link shape, and modal deformations. The inclusion of modal properties allows the selective minimization of vibrations associated with specific modes. Ply angles and fiber volumes are tailored to obtain optimal combinations of damping and stiffness. Applications to simple composite links indicate wide margins for trade-offs and illustrate the importance of various design variables to the optimal design.

Saravanos, D. A.↗

NASA NTRS1989

Tailoring of composite links for optimal damped elasto-dynamic performance

Saravanos, D. A.↗

NASA NTRS1990

Robust integrated autopilot/autothrottle design using constrained parameter optimization

A multivariable control design method based on constrained parameter optimization was applied to the design of a multiloop aircraft flight control system. Specifically, the design method is applied to the following: (1) direct synthesis of a multivariable 'inner-loop' feedback control system based on total energy control principles; (2) synthesis of speed/altitude-hold designs as 'outer-loop' feedback/feedforward control systems around the above inner loop; and (3) direct synthesis of a combined 'inner-loop' and 'outer-loop' multivariable control system. The design procedure offers a direct and structured approach for the determination of a set of controller gains that meet design specifications in closed-loop stability, command tracking performance, disturbance rejection, and limits on control activities. The presented approach may be applied to a broader class of multiloop flight control systems. Direct tradeoffs between many real design goals are rendered systematic by this method following careful problem formulation of the design objectives and constraints. Performance characteristics of the optimization design were improved over the current autopilot design on the B737-100 Transport Research Vehicle (TSRV) at the landing approach and cruise flight conditions; particularly in the areas of closed-loop damping, command responses, and control activity in the presence of turbulence.

Ly, Uy-Loi↗

DOE OSTI2026

Multipoint Aerostructural Optimization of Wind Turbine Rotors Using a Coupled Blade‐Resolved Aerostructural Solver

Physics‐based design optimization workflows thread the needle between computational cost limitations and simulation complexity, often compromising between modeling detail and the range of operating design conditions. Multipoint aerostructural optimization of wind turbine rotors has so far been confined to low‐fidelity analyses or to high‐fidelity studies with simplified structural models, leaving the most complex design trade‐offs unexplored. We close this gap by performing the first tightly coupled gradient‐based multipoint aerostructural rotor optimization using 3D aerodynamic and structural solvers with discrete coupled adjoints. The optimizer simultaneously varies blade planform, airfoil shapes, and structural thickness through more than 270 design variables, minimizing a weighted combination of rotor mass and power across multiple wind speeds. Applied to a modified DTU 10‐MW benchmark under conservative structural and aerodynamic constraints, our multipoint optimization reduces rotor mass by up to 36% and increases power by 12%–15% across the main operating conditions; biasing the objective toward power yields power gains up to 18% and a 17% mass reduction. For a nominal wind distribution, 3‐point rotor designs accounting for low RPM and high thrust conditions capture dominant trade‐offs and outperform single‐point designs. Adding two off‐design points changes individual‐condition power by less than 3% but leaves the weighted average within 0.5%, and the mass‐power bias has a stronger effect on the final design than the operating‐point weighting itself. Our framework extends naturally to richer load cases and site‐specific wind distributions, providing a basis for high‐fidelity multipoint design earlier in industrial workflows.

17 WIND ENERGY↗

NASA NTRSDate not supplied

A Framework for Optimization-Based ISRU Tool Design Using Discrete Element Modeling

Novel robotic excavation technologies are needed to perform in-situ resource utilization (ISRU) tasks at levels required to sustain a long-term presence on the lunar surface. Developing and testing multiple iterations of functional hardware is time and cost prohibitive, thus slowing down the pace of progress and delaying humanity’s settlement of the Moon. High-fidelity, physics-based simulation can reduce the time and effort required to develop and deploy robotic systems [1]. We have adopted this approach to create high-fidelity models of robotic test hardware to enable rapid virtual design and optimization of excavation technologies [2]. Such models can leverage modern computational tools like Discrete Element Method (DEM) simulations that can be coupled with automated design approaches like topology optimization to reduce the amount of prototyping and physical testing needed to realize useful tools.

ISRU↗

NASA NTRS1977

Wing design by numerical optimization

A study was conducted to assess the feasibility of performing computerized wing design by numerical optimization. The design program combined a full potential, inviscid aerodynamics code with a conjugate gradient optimization algorithm. Three design problems were selected to demonstrate the design technique. The first involved modifying the upper surface of the inboard 50% of a swept wing to reduce the shock drag subject to a constraint on wing volume. The second involved modifying the entire upper surface of the same swept wing (except the tip section) to increase the lift-drag ratio subject to constraints on wing volume and lift coefficient. The final problem involved modifying the inboard 50% of a low-speed wing to achieve good stall progression. Results from the three cases indicate that the technique is sufficiently accurate to permit substantial improvement in the design objectives.

Hicks, R. M.↗

NASA NTRS1997

CONDUIT: A New Multidisciplinary Integration Environment for Flight Control Development

A state-of-the-art computational facility for aircraft flight control design, evaluation, and integration called CONDUIT (Control Designer's Unified Interface) has been developed. This paper describes the CONDUIT tool and case study applications to complex rotary- and fixed-wing fly-by-wire flight control problems. Control system analysis and design optimization methods are presented, including definition of design specifications and system models within CONDUIT, and the multi-objective function optimization (CONSOL-OPTCAD) used to tune the selected design parameters. Design examples are based on flight test programs for which extensive data are available for validation. CONDUIT is used to analyze baseline control laws against pertinent military handling qualities and control system specifications. In both case studies, CONDUIT successfully exploits trade-offs between forward loop and feedback dynamics to significantly improve the expected handling, qualities and minimize the required actuator authority. The CONDUIT system provides a new environment for integrated control system analysis and design, and has potential for significantly reducing the time and cost of control system flight test optimization.

Tischler, Mark B.↗

DOE OSTI2013

Optimization Using Metamodeling in the Context of Integrated Computational Materials Engineering (ICME)

Predictive Design Technologies, LLC (PDT) proposed to employ Integrated Computational Materials Engineering (ICME) tools to help the manufacturing industry in the United States regain the competitive advantage in the global economy. ICME uses computational materials science tools within a holistic system in order to accelerate materials development, improve design optimization, and unify design and manufacturing. With the advent of accurate modeling and simulation along with significant increases in high performance computing (HPC) power, virtual design and manufacturing using ICME tools provide the means to reduce product development time and cost by alleviating costly trial-and-error physical design iterations while improving overall quality and manufacturing efficiency. To reduce the computational cost necessary for the large-scale HPC simulations and to make the methodology accessible for small and medium-sized manufacturers (SMMs), metamodels are employed. Metamodels are approximate models (functional relationships between input and output variables) that can reduce the simulation times by one to two orders of magnitude. In Phase I, PDT, partnered with Mississippi State University (MSU), demonstrated the feasibility of the proposed methodology by employing MSU?s internal state variable (ISV) plasticity-damage model with the help of metamodels to optimize the microstructure-process-property-cost for tube manufacturing processes used by Plymouth Tube Company (PTC), which involves complicated temperature and mechanical loading histories. PDT quantified the microstructure-property relationships for PTC?s SAE J525 electric resistance-welded cold drawn low carbon hydraulic 1010 steel tube manufacturing processes at seven different material states and calibrated the ISV plasticity material parameters to fit experimental tensile stress-strain curves. PDT successfully performed large scale finite element (FE) simulations in an HPC environment using the ISV plasticity model in Abaqus FE analyses of the tube forming, sizing, drawing, welding, and normalizing processes. The simulation results coupled with the manufacturing cost data were used to develop prototype metamodeling (quick response) codes which could be used to predict and optimize the microstructure-process-property-cost relationships. The developed ICME metamodeling toolkits are flexible enough to be applied to other manufacturing processes (e.g. forging, forming, casting, extrusion, rolling, stamping, and welding/joining) and metamodeling codes can run on laptop computers. Based on the work completed in Phase I, in Phase II, PDT proposes to continue to refine the ISV model by correlating and incorporating the uncertainties in the microstructure, mechanical testing, and modeling. Following the model refinement, FE analyses will be simulated and will provide even more realistic predictions as they include an appropriate window of uncertainty. Using the HPC output (FE analyses) as input, the quick-response metamodel codes will more accurately predict and optimize the microstructure-process-property-cost relationships. Furthermore, PDT propose to employ the ICME metamodeling toolkits to help develop a new tube product using entirely new high strength steel. The modeling of the high strength steel manufacturing process will replace the costly and time consuming trial-and-error methods that were used in the tubing industry previously. This simulation-based process prototyping will greatly benefit our industrial partners by opening up new market spaces due to new products with greater capabilities.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Reduced Order Models Generation for HTGRs Pebble Shuffling Procedure Optimization Studies

This report provides an initial study for producing reduced-order models (ROMs) of pebble-bed high temperature gas reactor (HTGR) models for the purposes of design optimization. As an initial study, this work is meant to be exploratory---identifying useful workflows and methods for ROM generation---and not meant to be a catch-all analysis of HTGR ROM generation and usage for optimization. This report summarizes three tasks performed in Fiscal Year 2022: 1) the creation of HTGR model, 2) the sensitivity analysis of model design parameters, and 3) an introduction to ROM generation techniques. The representative HTGR model created in this work is a multiphysics equilibrium-core using the BlueCRAB (comprehensive reactor analysis bundle) reactor analysis application, coupling four physical phenomena: neutronics, streamline depletion, porous flow thermal hydraulics, and pebble heat conduction. Part of the model creation was identifying some design parameters and quantities of interest that are relevant in an optimization analysis and adjustable in the model. The sensitivity analysis utilized a polynomial chaos expansion methodology to compute global sensitivity metrics. This analysis showed that thermal hydraulics parameters and quantities of interest had a relatively small impact on simulation results. Finally, the ROM generation work involved exploring three different ROM methodologies: polynomial regression, a Gaussian process, and artificial neural networks. Using a cross-validation technique to characterize ROM performance, the Gaussian process and single-layer artificial neural networks showed the most promising results. Overall, this study was insightful and the lessons learned will be invaluable for the eventual development of an HTGR design optimization workflow.

97 MATHEMATICS AND COMPUTING↗