Coastal flooding and erosion are growing issues for coastal communities as their severity continues to worsen with climate change. As a result, there is increasing interest in the use of nature-based engineering as a sustainable and cost-effective strategy for protecting many coastlines globally. Among these approaches, reef engineering aims to integrate both the physical and biological aspects of reef communities to attenuate incident wave energy while still maintaining ecological values. However, few examples currently exist on reef engineering for coastal defense due to the multidisciplinary challenge of constraining physical and biological interactions with artificial reefs. Here, we present the first design iteration of a novel artificial hybrid reef system that intends to provide both coastal defense benefits as well as refugia for corals to enable their future growth. To balance these performance objectives, the pyramidal low-crested reef designs developed here combine two hexagonal sub-units: SEAHIVE (R) and lattice with tunable porosity. The hydrodynamic performance of these sub-units was tested using a numerical wave tank (NWT), based on the computational fluid dynamics (CFD) modeling suite OpenFOAM, to determine the best configuration of the sub-units for a given set of wave conditions, both as single reefs and as a three-row reef system. The goal was to produce a small subset of reef designs to be tested in a wave flume facility to support model calibration and future design iteration. The reef designs explored herein offer wave energy reduction values greater than 70%, consistent with natural coral reefs as well as other conventional submerged breakwater designs. Further, the highly porous sub-units provide further tunability of hydrodynamic performance when compared with traditional low-crested breakwaters.

Implementing Prognostic and Predictive Maintenance (PPMx) for the U.S. Army’s ground vehicle fleet requires the design and integration of on-platform predictive analytics. To support the design process, U.S. Army DEVCOM Ground Vehicle Systems Center (GVSC) and Applied Research Laboratory (ARL) Penn State researchers are developing a systematic approach that uses reliability modeling in a guiding role. The key steps of the process are building the initial reliability model from available data (e.g., system diagrams and physical layouts), augmenting with information on observed states and failure modes via subject matter experts, and then conducting trades on additional sensors and algorithms to determine a suitable predictive analytics capability. In this paper we provide an example of this process as applied to an Army ground vehicle, first focusing on a simplified sub-problem to demonstrate the technique, then providing statistics on the large scale process. Citation: M. Majcher, L. Bennett, J. Banks, M. Lukens, E. Nulton, M. Yukish, J. Merenich, “Reliability Modeling to Inform the Development of On-Platform Predictive Analytics”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 10-12, 2021.

Hybrid powered locomotives can provide significant savings on the energy consumed to move freight on railways. The SCORE toolset is an open source, web-based application to assess the impact of new powering technologies on railroad performance, specifically technologies capable of both putting power into the rail (motive force) and taking power from the rail (regeneration). SCORE’s primary goal is generate trade studies to analyze different powering and train make-up options to minimize energy usage and greenhouse gas generation. At the core of generating these trade spaces is calculating the optimal powering policy for the train given the makeup of the train, the route, and time constraints. This paper presents details on the algorithms used in SCORE to compute this powering policies that is fast and accurate, discusses its implementation in an Energy-Longitudinal Train Dynamics (E-LTD) model, compares it to naïve approaches, and demonstrates its use across a variety of train/route pairs.

Adding battery locomotives to traditional diesel locomotives to form a hybrid consist has the potential to reduce fuel consumption and emissions in freight rail operations. This paper provides route-based estimates of diesel (hpd/ton) and battery (hpb/ton) power requirements and diesel (gal/ton) and battery (hpbhr/ton) energy requirements for hybrid consists. Given a route-specific power profile, the power split between the battery pack and the diesel engine is optimized to minimize fuel consumption by running the diesel engine at maximum efficiency during the entire route. Simulations for 200-mile round trips between Chicago and Harrisburg show that the diesel power is maximum in low-gradient regions at 1.05 (hpd/ton) and battery power is maximum in hilly regions at 2 (hpb/ton) for discharge and 6.5 (hpb/ton) for charge. Maximum fuel efficiency gains of approximately 60% are seen in the mountains, where 2.09 (hpbhr/ton) of battery energy is needed. Minimum fuel economy gains are observed in flat regions, with 20% fuel consumption reduction. With battery current and voltage limits, the battery pack size increases, and fuel savings decrease.

Adding battery locomotives to traditional diesel locomotives to form a hybrid consist has the potential to reduce fuel consumption and emissions in freight rail operations. This paper provides round trip, route-based estimates of diesel (hpd/ton) and battery (hpb/ton) power requirements and diesel (gal/ton) and battery (hpbhr/ton) energy requirements for hybrid consist. Given the route-specific power profile, the power split between the battery pack and the diesel engine is optimized to minimize fuel consumption by running the diesel engine at maximum efficiency during the entire trip. The results from 100-mile out/back routes between Chicago and Harrisburg indicate that the required diesel power for hybrid consist is maximum in low-gradient regions at 1.17 (hpd/ton) and the required battery power is maximum in hilly regions at 2.04 (hpb/ton) for discharge and 9.43 (hpb/ton) for charge. Maximum fuel efficiency gains of approximately 60% are seen in hilly routes, where 2.09 (hpbhr/ton) of battery energy is needed. Minimum fuel economy gains are observed in flat regions, with a 20% fuel consumption reduction. This ideal case analysis of hybrid consist can serve as an easy-to-use tool for the US freight rail sector to estimate fuel savings and sort routes based on the potential results for electrification, without requiring substantial computation.

The use of lattice structure in the Design for Additive Manufacturing (DfAM) engineering practice offers the ability to tailor the properties (and therefore the response) of an engineered component independent of the material and overall geometry. The selection of a lattice topology is critical in maximizing the value of the lattice structure and its unique properties for the intended application. To support this, we have compiled a catalog of lattice structures from the literature that includes all Triply Periodic Minimal Surfaces (TPMS) for which a low-order Fourier series fit is known (so that they can be modeled and manufactured). We also include equations that do not directly correspond to known TPMS but do produce a triply periodic structure without sharp corners that would give rise to stress concentrations. This catalog includes images, elastic mechanical property data, and CAD models useful for the visualization, selection, and implementation of these lattice structures for any engineered structure.

This work explores the design space for a wing built with desktop 3D printers. The choice of manufacturing technology leads to an unconventional design pattern compared to a standard spar-rib built-up wing, and results in a wing design such that the the internal structure is sized primarily to support the load-bearing skin during the manufacturing process and during flight. Experimental bend testing was performed to validate the design pattern. Design space exploration is performed to find the limitations of fabricating wings from polymers. The design pattern coupled with a custom-developed design tool allows a designer to go from wing requirements to a complete wing design almost instantly, and wing printed in a day.

A design study was conducted to explore the space of combining light-weight, low-cost, slow-flying, and self-deploying features into small unmanned motor gliders. This study specifically focused on developing the structures, mechanisms, and layout/sizing necessary to rapidly fabricate a functional, unfolding, conventional fixed-wing configuration. Several aircraft were rapidly designed, built, and tested. Key tradeoff decisions explored were weight, cost, ease of fabrication, lift over drag ratio, and flight speed. Recommendations for best practices and general lessons learned for this vehicle type are discussed.

Lifting Line theory is used to rapidly solve for the spanwise distributions of chord and twist. Recent lifting line work demonstrates wing chord and twist calculated from lift distributions at two different angles of attack of the wing. The present work expands the research to include solving for wing chord and twist from (1) two local 2D lift coefficient distributions and (2) one lift distribution and one lift coefficient distribution.

The objective of this study is to characterize the trade space for the structural design of small uncrewed aerial vehicle wings fabricated using Material Extrusion Additive Manufacturing, specifically the trade-off between maintaining the wing external shape while minimizing its internal structure. Beam bending analysis shows that the structural requirements associated with flight loads are easily met with a single perimeter extrusion monocoque construction, however this approach leads to large, unsupported, thin-walled structures that can deform during the build process, creating a potential need for additional structure to maintain wing shape. To characterize the relationship between structure/weight and wing deformation, wing sections were fabricated with varying internal structures for two airfoil shapes. Weight and 3-D laser measurements were taken of the printed parts to capture the final as-built geometry. The as-built geometries were then compared to the as-designed geometries to quantify the deformation, and a coupled viscous-inviscid flow solver was used to determine the aerodynamic effects. The results indicate that while significant aerodynamic performance penalties exist for the monocoque construction, a small amount of well-placed internal structure provides sufficient improvement at minimal weight penalty. Results also showed that less internal structure is required to minimize deformation for an airfoil with larger initial curvature.

This work develops a technique for printing an unmanned aerial vehicle wing with its span parallel to the build plate using material extrusion. Stair-stepping from planar additive manufacturing often leads a designer to choose a vertical build orientation; however, this limitation can now be overcome by leveraging recent advances in nonplanar printing techniques. This paper documents available nonplanar methods and demonstrates the use of nonplanar slicing combined with gcode post-processing to print a wing lying down. The advantages of this technique are the potential for faster printing times, improved resistance to bending along the wing’s span, and open access to internal components. The disadvantages of this technique are larger time requirements in the design and slicing phases, the need for a large support structure or complex build platform, and additional post-processing of the printed wing. By presenting this technique, this paper expands the available design space for engineers producing small-scale aircraft with material extrusion.

In this paper, we consider a combination of traditional and modern methods to perform data-driven system identification (SysID) of a prototype lighter-than-air vehicle. We explore the methods of linear least squares (LS), subsampling based threshold sparse Bayesian regression (SubTSBR), and a novel implementation using both methods to form a constrained optimization problem. Notably, linear LS system identification is used to solve for parameters that are defined by a proposed dynamic model and SubTSBR is used to discover remaining unmodeled dynamics given the error in the LS model. This allows for a high fidelity model of the prototype LTAV from flight test data that outperforms the LS method and reduces negative effects of sparse SysID.

his work proposes a new method to design curvilinear spars for additively manufactured wings. A wing is treated as a 1D beam subjected to a spanwise loading distribution. At any given spanwise location, the number and chordwise locations of spars are “tuned” to provide the desired bending stiffness using a scalar spacing factor. The underlying physics behind these structures is given, and an algorithm for generating this structure is provided. The proposed method takes less than a minute to run on a typical laptop, and generates a structure that is easily 3D printed. Example wings are provided, one of which was 3D printed and successfully flight tested on a small UAV.

Although necessary for complex problem solving, such as engineering design, team agility is often difficult to achieve in practice. The evolution of Artificial Intelligence (AI) affords unique opportunities for supporting team problem solving. While integrating assistive AI agents into human teams has at times improved team performance, it is still unclear if, how, and why AI affects team agility. A large-scale human experiment answers these questions, revealing that, with appropriately interfaced AIs, AI-assisted human teams enjoy improved coordination and communications, leading to better performance and adaptations to team disruptions, while devoting more effort to information handling and exploring the solution space more broadly. In sum, working with AI enables human team members to think more and act less. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/).

Through a low-order fidelity simulation study, it was determined that adding a wing does indeed have the potential to increase the range of a multirotor unoccupied aerial system (UAS). A modular, parameterized system model was developed and validated over a range of platform speeds, then used to explore the potential range extension that could be realized by adding a wing to a multirotor vehicle. Over the small range of wing sizes explored in this first study, the range extension was as high as 2:1 compared to no-wing. The study elucidated that the effect of adding a reasonable size wing for the platform, however, can induce undesirably high pitch angles. The next step is to examine wings that can perform well under multiple flight conditions (no payload and with payloads, cruise speeds lower than the optimal speed for the wing) to extend the platform range while maintaining a reasonable pitch during forward flight.

A design study was conducted to bridge the gap between conventional hinged aileron and compliant mechanism controlled morphing wing aileron designs for small unmanned aircraft systems using additive manufacturing methods. Taking advantage of the rapid prototyping capabilities of fused filament fabrication machines, several design options were designed and fabricated using multiple materials. Final design selection was determined by part usability and by metrics of reduced part count, weight, material use, assembly time, and other factors are enabled by additive manufacturing. The final compliant design was printed, tested and compared to a printed, conventional-style hinged aileron on a flying test-bed to identify aerodynamic benefits and to prove functionality. Data suggests some aerodynamic benefit to be gained with the proposed compliant morphing wing aileron. Additional improvements include a reduction in total part count and time to assemble with improvements in final component weight and material usage possible using this design process. The use of additive manufacturing enabled rapid prototyping of concepts, greatly accelerating the design process and resulting in a novel design.

Human subject experiments are often used in research efforts to understand human behavior in design. However, such research is often time-consuming, expensive, and limited in scope due to the need to experimentally control specific variables. This work develops an initial digital simulation of team-based multidisciplinary design, where the actions of individual team members are simulated using deep learning models trained on historical human design trends. The main benefit of this work is to simulate design session events and interactions without human participants, developing a complimentary method to rapidly perform digital team-based experiments. This research merges the benefits of purely data-driven modeling with minimal assumptions about process, along with the strengths of agent-based modeling in which it is possible to tailor agent behavior. Initial results show that the simulated design team sessions are able to replicate trends and distributions compared to human-based team sessions, but run approximately 21 times faster than equivalent human subject studies. The multi-disciplinary design problem currently simulated is loosely coupled, in the sense that agent behaviors can be modeled in isolation of other agents and yet replicate the behavior of the ensemble. Future work will extend the agents to sense and respond behaviors that can be used to model tightly coupled problems, and truly evaluate team formulations.

We present a form-aware reinforcement learning (RL) method to extend control knowledge from one design form to another without losing the ability to control the original design. A major challenge in developing control knowledge is the creation of generalized control policies across designs of varying form. Our presented RL policy is form-aware because in addition to receiving dynamic state information about the environment, it also receives states that encode information about the form of the design that is being controlled. In this paper, we investigate the impact of this mixed state space on transfer learning. We present a transfer learning method for extending a control policy to a different design form, while continuing to expose the agent to the original design during the training of the new design. To demonstrate this concept, we present a case study of a multi-rotor aircraft simulation, wherein the designated task is to achieve a stable hover. We show that by introducing form states, an RL agent is able to learn a control policy to achieve the hovering task with both a four rotor and three rotor design at once, whereas without the form states it can only hover with the four rotor design. We also benchmark our method against a test case that removes the transfer learning component, as well as a test case that removes the continued exposure to the original design to show the value of each of these components. We find that form states, transfer learning, and parallel learning all contribute to a more robust control policy for the new design, and that parallel learning is especially important for maintaining control knowledge of the original design.

At the heart of many numerical implementations of lifting line theory (LLT) is the matrix relating vortex strength to downwash. This type of matrix is a symmetric Toeplitz matrix which has well-studied properties, including recent research that has developed methods to identify the asymptotics of individual eigenvalues and eigenvectors of infinite Toeplitz matrices. This paper explores applying the method to the lifting line matrix to identify its eigenstructure, and demonstrates using them to develop an accurate estimate of the coefficients of lift and induced drag of a rectangular wing.

Human-computer hybrid teams can meet challenges in designing complex engineered systems. However, the understanding of interaction in the hybrid teams is lacking. We review the literature and identify four key attributes to construct design research platforms that support multi-phase design, hybrid teams, multiple design scenarios, and data logging. Then, we introduce a platform for unmanned aerial vehicle (UAV) design embodying these attributes. With the platform, experiments can be conducted to study how designers and intelligent computational agents interact, support, and impact each other.