Optimizing the Size and Placement of Piezoelectric Actuators for Modal Testing.
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Optimization procedures are described for the rotor blade design process by integrating appropriate disciplines and accounting for important interactions among the disciplines. Progress is reported in the areas of aerodynamic performance optimization, dynamic optimization, optimum placement of tuning masses for vibration reduction, and structural optimization. Selected results from these activities are highlighted in this appendix.
Abstract While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom–up (i.e. activity level) or top–down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom–up and top–down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom–up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom–up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.
This study quantifies the robustness and sensitivity of an outbound cislunar trajectory for a lunar lander in the form of mission-maps, or topological maps that allows either a computer program or mission designer to intuitively optimize the placement of critical outbound correction burns from the derived sensitivity data. The non-linear multi-body dynamics are applied to generate an outbound cislunar reference profile used by a linear covariance analysis (LinCov) tool to compute the expected Δv and trajectory dispersions due to the initial state uncertainty, sensor errors, maneuver execution errors, and disturbance accelerations along the outbound cislunar profile. The rapid performance analysis capabilities of LinCov are complimented with parallel processing techniques to evaluate hundreds and thousands of different translational burn locations, placements, and targeting constraints to identify the combination that minimizes the total Δv usage (nominal plus 3σ Δv) and trajectory dispersions at lunar orbit insertion. This study utilizes a generalized reference targeting algorithm to quickly assess the integrated closed-loop GN&C system performance due to different targeting configurations and constraints. The resulting mission maps provide an intuitive insight to ascertain each trajectory correction maneuver’s (TCM) sensitivity to different burn times along an outbound cislunar trajectory and quickly identify desirable engineering tradeoffs when performing analysis on the number and placement of these burns that nominally zero. Multiple mission maps are generated for a variety of different performance parameters that allow engineers to visually identify optimal solutions for trajectory correction maneuver placements, the number of correction burns, and the targeting constraints for each burn.
There has been considerable research on choosing actuator and sensor locations in large flexible spacecraft in order to optimize the controllability and observability of the system, or to maximize some objective function of control system performance. Future large flexible spacecraft may require on-orbit identification of the structure to tune the control system, because such tests cannot be performed in a one-g environment before launch. This indicates that the choice of actuator and sensor locations must serve a dual purpose, for control and for identification. This paper develops concepts for a degree of identifiability and studies placement of actuators and sensors on a free-free beam to optimize such objective functions. The results in this simple situation suggest that in free-free spacecraft structures in orbit, placement for control and placement for identification may often be consistent objectives rather than conflicting objectives.
This paper describes the Rack Equipment Placement and Optimization System. The primary objective of this system is to assist engineers with the placement of equipment into the racks of the modules of Space Station Freedom. It accomplishes this by showing a user where equipment placement is possible and by generating potential layouts. The system uses an explicit representation of integration constraints to search for potential solutions for individual rack equipment items. A simulated annealing process is being evaluated for total solution generation as well. Versions of this system are in use now and are assisting with the development of the Space Station Freedom at the Marshall Space Flight Center in Huntsville, Alabama.
The effectiveness of viscous elements in introducing damping in a structure is a function of several variables, including their number, their location in the structure, and their physical properties. In this paper several optimization problems are posed to optimize these variables. The paper investigates various metrics to define the optimization problem, and compares the damping profiles that are obtained. Both discrete and continuous optimization problems are formulated and solved, corresponding, respectively, to the problems of placement of damping elements and to the tuning of their parameters. The paper particularly emphasizes techniques to make feasible the large scale problems resulting from the optimization formulations. Numerical results involving a lightly damped tested structure are presented.
The performance of a variety of parity placement schemes are defined and investigated to demonstrate that, at relatively large request sizes of hundreds of kilobytes, the choice of parity placement significantly affects performance (20 to 30 percent for the disk array configurations that are common today). It is shown that the left-symmetric, extended-left-symmetric and flat-left-symmetric are the best RAID level 5 parity placements. The placement with the highest read performance, flat-left-symmetric, has the lowest write performance, while the placement with the lowest read performance, left-symmetric, has the highest write performance. Suggestions for optimizing parity placements are included.
A novel approach for the immediate sealing of traumatic wounds is under development. A portable microwave generator and handheld antenna are used to seal wounds, binding the edges of the wound together using a biodegradable protein sealant or solder. This method could be used for repairing wounds in emergency settings, by restoring the wound surface to its original strength within minutes. This technique could also be utilized for surgical purposes involving solid visceral organs (i.e., liver, spleen, and kidney) that currently do not respond well to ordinary surgical procedures. A miniaturized microwave generator and a handheld antenna are used to deliver microwave energy to the protein solder, which is applied to the wound. The antenna can be of several alternative designs optimized for placement either in contact with or proximity to the protein solder covering the wound. In either case, optimization of the design includes the matching of impedances to maximize the energy delivered to the protein solder and wound at a chosen frequency. For certain applications, an antenna could be designed that would emit power only when it is in direct contact with the wound. The optimum frequency or frequencies for a specific application would depend on the required depth of penetration of the microwave energy. In fact, a computational simulation for each specific application could be performed, which would then match the characteristics of the antenna with the protein solder and tissue to best effect wound closure. An additional area of interest with potential benefit that remains to be validated is whether microwave energy can effectively kill bacteria in and around the wound. Thus, this may be an efficient method for simultaneously sterilizing and closing wounds. Using microwave energy to seal wounds has a number of advantages over lasers, which are currently in experimental use in some hospitals. Laser tissue welding is unsuitable for emergency use because its large, bulky equipment cannot be easily moved between operating rooms, let alone relocated to field sites where emergencies often occur. In addition, this approach is highly dependent on the uniformity and thickness of the protein solder as well as the surgeon s skills. In contrast, the use of microwave energy is highly tolerant of the thickness of the protein solder, level of fluids in and around the wound, and other parameters that can adversely affect the outcome of laser welding. However, controlling the depth of penetration of the microwave energy into the wound is critical for achieving effective wound sealing without damaging the adjacent tissue. In addition, microspheres that encapsulate metallic cores could also be incorporated into the protein solder to further control the depth of penetration of the microwave energy.
A novel approach for the immediate sealing of traumatic wounds is under development. A portable microwave generator and handheld antenna are used to seal wounds, binding the edges of the wound together using a biodegradable protein sealant or solder. This method could be used for repairing wounds in emergency settings by restoring the wound surface to its original strength within minutes. This technique could also be utilized for surgical purposes involving solid visceral organs (i.e., liver, spleen, and kidney) that currently do not respond well to ordinary surgical procedures. A miniaturized microwave generator and a handheld antenna are used to deliver microwave energy to the protein solder, which is applied to the wound. The antenna can be of several alternative designs optimized for placement either in contact with or in proximity to the protein solder covering the wound. In either case, optimization of the design includes the matching of impedances to maximize the energy delivered to the protein solder and wound at a chosen frequency. For certain applications, an antenna could be designed that would emit power only when it is in direct contact with the wound. The optimum frequency or frequencies for a specific application would depend on the required depth of penetration of the microwave energy. In fact, a computational simulation for each specific application could be performed, which would then match the characteristics of the antenna with the protein solder and tissue to best effect wound closure. An additional area of interest with potential benefit that remains to be validated is whether microwave energy can effectively kill bacteria in and around the wound. Thus, this may be an efficient method for simultaneously sterilizing and closing wounds.
The objective of traditional PMU placement approaches is to use the minimal number of PMUs to cover system complete observability. Very few consider the probability and the consequence severity of observability loss. For example, following line trip, grid topology changes, which may results in observability loss and overloaded lines. As long as the overloaded lines are still observable, then we can take action in time to correct the system; and the consequence of the observability loss is not severe, although some parts of the system is not observable. Otherwise, if the overloaded lines become unobservable, then we lose the situation awareness of the system and the consequence of the observability loss is severe. To address the above points in PMU placement, this paper proposes a risk-oriented PMU placement approach. The proposed approach uses two indices, namely the probability of observability loss and its corresponding consequence of severity to design the optimal PMU placement strategy. We show that our proposed approach is able to capture the critical events while the traditional approaches fail to do so. Extensive simulation results carried out on the IEEE 39-bus and 118-bus systems demonstrate its effectiveness.
A numerical study employing a simplified model of the High Speed Civil Transport mixer/ejector nozzle has been conducted to investigate the effect of tabs (vortex generators) on the mixing process. More complete mixing of the primary and secondary flows within the confined ejector lowers peak exit velocity resulting in reduced jet noise. Tabs were modeled as vortex pairs and inserted into the computational model. The location, size, and number of tabs were varied and its effect on the mixing process is presented here both quantitatively and qualitatively. A baseline case (no tabs) along with six other cases involving two different vortex strengths at three different orientations have been computed and analyzed. The case with the highest vorticity (six vortices representing large tabs) gives the best mixing. It is shown that the influence of the vorticity acts primarily in the forward or middle portions of the duct, significantly alters the flow structure, and promotes some mixing in the lateral direction. Unmixed pockets were found at the top and bottom of the lobe, and more clever placement of tabs improved mixing in the vertical direction. The technique of replacing tabs with vortices shows promise as an efficient tool for quickly optimizing tab placement in lobed mixers.
The rapid increase in resident space objects, including satellites and orbital debris, poses a significant threat to the safety and sustainability of space missions. This paper explores orbital debris remediation using a network of collaborative space-based lasers, leveraging laser ablation for momentum transfer on debris. A novel delta-v vector analysis framework quantifies the e↵ects of multiple simultaneous laser-to-debris (L2D) engagements by using vector composition of the imparted delta-v vectors. The paper introduces the Concurrent LocationScheduling Problem (CLSP), which optimizes the placement of laser platforms and the scheduling of L2D engagements to maximize debris remediation capacity. Due to the computational complexity of the CLSP, it is decomposed into two sequential subproblems: (1) optimal laser platform locations are determined using the Maximal Covering Location Problem, and (2) a novel integer linear programming-based approach schedules L2D engagements within the network configuration to maximize remediation capacity. Computational experiments are conducted to evaluate the proposed framework’s e↵ectiveness under various mission scenarios, demonstrating key network functions such as collaborative nudging, deorbiting, and just-in-time collision avoidance. A sensitivity analysis further examines how varying the number and distribution of laser platforms a↵ects debris remediation capacity, providing insights into optimizing the performance of space-based laser networks.
With the sharp increasing volume of user data, Deep Learning Recommendation Model (DLRM) becomes an indispensable infrastructure in large technology companies. However, large-scale DLRM on the multi-GPU platform is still inefficient due to unbalanced workload partitioning and intensive inter-GPU communication. To this end, we propose OPER, an OPtimality guided Embedding table placement for large-scale Recommendation model training and inference. OPER explores the potential of mitigating remote memory access latency in DLRM through fine-grained embedding table placement. Specifically, OPER proposes a theoretical modeling that builds up the relationship between EMT placement and the embedding communication latency in both training and inference. OPER proves the NP hardness of finding the optimal embedding table placement and proposes a heuristic algorithm that yields near optimal placement. OPER implements a SHMEM-based embedding table training system and a unified embedding index mapping to support fine-grained embedding table sharding and placement. Comprehensive experiments reveal that OPER achieves on average 3.4× and 5.1× speedup on training and inference respectively over state-of-the-art DLRM frameworks.
Abstract Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.
In its 2022 commercial market outlook, Boeing forecasted an 80% increase in the global fleet through 2041 compared to 2019 pre-pandemic levels. This sharp rise in demand will drive pressure onto airframe manufacturers to ramp up production and find more efficient ways to design and manufacture airplanes. Complicating this challenge is the industry’s recent transformation from traditional metal-based airframes towards hybrid composite-metal aircraft. While composites have been used in aviation for decades, aircraft manufacturers are still struggling to design and manufacture quality parts at a high rate. Automated Fiber Placement (AFP) is one of the main manufacturing techniques used to produce large-scale composite parts. After a design has been created, a manufacturing strategy has to be developed based on the working material, part geometry, and machine capabilities. This process planning stage is essential to the AFP workflow and currently requires a high level of manual input from an experienced process planner. In an effort to automate and optimize this stage, the Computer Aided Process Planning (CAPP) module was developed. CAPP assists process planners in identifying optimal starting point location and layup strategy for each ply of a laminate. This Ply-Level Optimization (PLO) phase operates on the quantification of ply quality through predictable geometry-based defects such as gaps, overlaps, angle deviation, and steering. As you move from PLO to Laminate-Level Optimization (LLO) the design space grows exponentially, emphasizing the need for automated optimization. The work presented in this thesis expands CAPP’s functionality by comparing the planned fiber paths through the thickness of the laminate to mitigate stacked area defects and achieve an optimal laminate-level manufacturing strategy. Within CAPP, predicted gap and overlap defects are imported from Vericut Composites Programming (VCP) and then discretized to streamline the through-thickness comparison. Two objective functions are used to score different combinations of ply layup strategies based on defect stacking both globally and locally. Four combinatorial optimization algorithms were coupled with these objective functions to investigate the laminate-level manufacturing strategy design space and converge on the optimal plan. These algorithms were evaluated based on accuracy and efficiency through virtual testing on a complex tool surface. A separate LLO approach was developed to achieve near-optimal laminates in significantly less time. The end result is a software package which greatly reduces the required input from process planners, shortening the design-build cycle time and improving part quality.
Control of a large jet transport aircraft without the use of conventional control surfaces was studied. Engine commands were used to attempt to recreate the forces and moments typically provided by the elevator, ailerons, and rudder. Necessary conditions for aircraft controllability were developed pertaining to aircraft configuration such as the number of engines and engine placement. An optimal linear quadratic regulator controller was developed for the Boeing 707-720, in particular, for regulation of its natural dynamic modes. The design used a method of assigning relative weights to the natural modes, i.e., phugoid and dutch roll, for a more intuitive selection of the cost function. A prototype pilot command interface was then integrated into the loop based on pseudorate command of both pitch and roll. Closed loop dynamics were evaluated first with a batch linear simulation and then with a real time high fidelity piloted simulation. The NASA research pilots assisted in evaluation of closed loop handling qualities for typical cruise and landing tasks. Recommendations for improvement on this preliminary study of optimal propulsion only flight control are provided.
Abstract There is a growing interest in developing data‐driven reduced‐order models for atmospheric and oceanic flows that are trained on data obtained either from high‐resolution simulations or satellite observations. The data‐driven models are non‐intrusive in nature and offer significant computational savings compared to large‐scale numerical models. These low‐dimensional models can be utilized to reduce the computational burden of generating forecasts and estimating model uncertainty without losing the key information needed for data assimilation (DA) to produce accurate state estimates. This paper aims at exploring an equation‐free surrogate modeling approach at the intersection of machine learning and DA in Earth system modeling. With this objective, we introduce an end‐to‐end non‐intrusive reduced‐order modeling (NIROM) framework equipped with contributions in modal decomposition, time series prediction, optimal sensor placement, and sequential DA. Specifically, we use proper orthogonal decomposition (POD) to identify the dominant structures of the flow, and a long short‐term memory network to model the dynamics of the POD modes. The NIROM is integrated within the deterministic ensemble Kalman filter (DEnKF) to incorporate sparse and noisy observations at optimal sensor locations obtained through QR pivoting. The feasibility and the benefit of the proposed framework are demonstrated for the NOAA Optimum Interpolation Sea Surface Temperature (SST) V2 data set. Our results indicate that the NIROM is stable for long‐term forecasting and can model dynamics of SST with a reasonable level of accuracy. Furthermore, the prediction accuracy of the NIROM gets improved by almost one order of magnitude by the DEnKF algorithm.