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At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Towards optimal sensor placement for inverse problems in spaces of measures

The objective of this work is to quantify the reconstruction error in sparse inverse problems with measures and stochastic noise, motivated by optimal sensor placement. To be useful in this context, the error quantities must be explicit in the sensor configuration and robust with respect to the source, yet relatively easy to compute in practice, compared to a direct evaluation of the error by a large number of samples. In particular, we consider the identification of a measure consisting of an unknown linear combination of point sources from a finite number of measurements contaminated by Gaussian noise. The statistical framework for recovery relies on two main ingredients: first, a convex but non-smooth variational Tikhonov point estimator over the space of Radon measures and, second, a suitable mean-squared error based on its Hellinger–Kantorovich distance to the ground truth. To quantify the error, we employ a non-degenerate source condition as well as careful linearization arguments to derive a computable upper bound. This leads to asymptotically sharp error estimates in expectation that are explicit in the sensor configuration. Thus they can be used to estimate the expected reconstruction error for a given sensor configuration and guide the placement of sensors in sparse inverse problems.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Integration of Design Data Into Automated Fiber Placement Process Planning Metrics

With the ever-expanding aviation industry, a need is arising for more rapid production of composite aircraft to meet increasing demand. State-of-the-art aircraft such as the Boeing 787showcase 50% composite material usage by weight, highlighting this emerging industry-wide adoption of the material system. Currently, many of the large structures associated with these aircraft are manufactured additively via Automated Fiber Placement (AFP). The AFP process shows great potential for efficient manufacturing, however unavoidable defects still occur because of tool surface geometry, placement errors, or poor process planning, resulting in decreased quality and throughput. Due to such effects, it is critical to incorporate design for manufacturing (DFM) principles to achieve the optimal manufacturing plan and resulting structure. This work will develop a methodology for incorporating design information into process planning metrics in an automated fashion to achieve an optimal set of process inputs.The analysis incorporates HyperX, Computer Aided Process Planning (CAPP) and Vericut Composite Programming (VCP). Safety margins from HyperX are imported into CAPP where AFP defects are mapped to the values. The resulting margins are then incorporated into the CAPP manufacturability algorithms, creating a design informed process planning analysis

Automated Fiber Placement↗

NASA NTRS2026

Smart Process Planning for Automated Fiber Placement

Many industries, including aerospace, automotive, wind energy, maritime, and sporting goods, rely on strong, lightweight materials called composites. These materials are made by layering fibers, which can come in the form of narrow strips or wider sheets, and setting them in a polymer matrix. One of the most advanced ways to make these parts is through automated fiber placement, where a machine lays down the fibers in precise patterns. This method can create very efficient and strong designs, but it is complex, expensive, and often depends heavily on the experience of skilled engineers. Today, the design, manufacturing, and inspection stages of composite production are usually handled separately. This separation means that important information, such as how a part will be built or what defects might occur, is not always shared between stages. As a result, parts may not be as lightweight, strong, or defect-free as possible, and the process can take longer and cost more. This research develops a smart process planning system that connects design, manufacturing, and inspection into one continuous process. Built as software that works with existing tools, the system can automatically plan how the fibers are placed, predicting and reducing defects while improving both manufacturability and strength. The system optimizes not only individual layers but also how defects are distributed across all layers, preventing them from stacking up in ways that weaken the final part. It also uses inspection results from completed parts to improve future designs, creating a feedback loop where each stage informs the others. The system was tested by designing a composite panel using this new approach and comparing it to a panel made with state-of-the-art manual planning methods. The results showed that the system could intentionally control where defects appeared and increase the efficiency of the planning process. By unifying design, manufacturing, and inspection, this research shows a way to make advanced composite manufacturing more efficient, consistent, and cost-effective. This approach lowers the barrier to using automated fiber placement and opens the door for its wider adoption not only in aerospace but also in industries such as automotive, wind energy, maritime, and sporting goods, where strong and lightweight structures are essential.

Computer-Aided Process Planning↗

DOE OSTI2024

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Planning green infrastructure placement based on projected precipitation data

Continued urbanization has led to tremendous changes on the landscape. These changes have exacerbated the effects of extreme climatic events such as flooding because of constrained water infiltration and increased surface flow. Typical runoff control measures involve sophisticated gray infrastructure that guide excess surface flow into storage and disposal sites. In a dynamic climate system, these measures are not sustainable since they cannot be easily modified to accommodate large volumes of runoff. Green Infrastructure (GI) is an adaptable technique that can be used to minimize runoff, in addition to offering an array of additional benefits (urban heat regulation, aesthetics, improved air quality etc.). Strategic placement of GI is key to achieving maximum utility. While physical site characteristics play a major role in determining suitable GI placement sites, knowledge of future precipitation patterns is crucial to ensure successful flood mitigation. Here, suitable GI sites within the city of Knoxville, Tennessee, were determined based on potential impact of an extreme flood event as indicated by site characteristics. Then, the relative potential likelihood of a flood event was determined based on projected precipitation data and knowledge of existing flood zones. By combining potential impact with likelihood information, low, medium, and high priority GI implementation sites were established. Results indicate that high priority sites are in the central parts of the city with priority decreasing outward. The GI prioritization scheme presented here, offers valuable guidance to city planners and policy makers who wish to exploit the GI approach for flood mitigation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Utilizing Single-Crystalline Transformations for Precise Atom Placement in Multicomponent Cluster-Based Coordination Networks

The assembly of cluster or superatom building-blocks into extended solids has revolutionized materials design, enabling the synthesis of modular semiconductors with well-defined structures and tunable electronic, magnetic or optical properties. This strategy has recently advanced the synthesis of complex metal oxides with multifunctional or emergent behaviors, but precise atom placement of multiple elements with similar chemistries or preferred coordination environments remains a significant challenge. Here, in this study, we present a strategy for synthesizing polyoxometalate (POM)-based coordination networks with up to three different cations in precisely defined positions. Our approach leverages a single-crystal-to-single-crystal (SCSC) transformation in which the spatial placement of cations is governed by their availability at distinct stages of crystallization and transformation. Specifically, [ZP 5 W 30 O 110 ] (15-n)- (Z = Na + , K + , Ca 2+ , Ag + , Bi 3+ , Y 3+ , any Ln 3+ , Th 4+ ) is coordinatively assembled with various bridging metal cations (Y 3+ , any Ln 3+ , Th 4+ ). By using the encapsulated cation (Z) to "label" the POM, we track the phase-transformation and confirm the retention of single crystallinity. The integrated use of POM labeling and SCSC transformation enables rational control over cation distribution and establishes a versatile strategy for constructing multicomponent materials with high compositional and spatial precision.

Chen, Linfeng [Univ. of California, San Diego, CA ↗

DOE OSTI2022

Block copolymer self-assembly to pattern gold nanodots for site-specific placement of DNA origami and attachment of nanomaterials

Directed placement of DNA origami could play a key role in future integrated nanoelectronic devices. Here we demonstrated the site-selective attachment of DNA origami on gold dots formed using a pattern transfer method through block copolymer self-assembly. First, a random copolymer brush layer is grafted on the Si surface and then poly (styrene-b-methylmethacrylate) block copolymer is spin-coated to give a hexagonal nanoarray after annealing. UV irradiation followed by acetic acid etching is used to remove the PMMA, creating cylindrical holes and then oxygen plasma etching removes the random copolymer layer inside those holes. Next, metal evaporation, followed by lift-off creates a gold dot array. We evaluated different ligand functionalization of Au dots, as well as DNA hybridization to attach DNA origami to the nanodots. DNA-coated Au nanorods are assembled on the DNA origami as a step towards creating nanowires and to facilitate electron microscopy characterization of the attachment of DNA origami on these Au nanodots. The DNA hybridization approach showed better DNA attachment to Au nanodots than localization by electrostatic interaction. Here, this work contributes to the understanding of DNA-templated assembly, nanomaterials, and block copolymer nanolithography. Furthermore, the work shows potential for creating DNA-templated nanodevices and their placement in ordered arrays in future nanoelectronics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Sensor Placement Optimization Software Applied to Site-Scale Methane-Emissions Monitoring

Advances in sensor technology have increased our ability to monitor a wide range of environments. However, even as the cost of sensors decline, only a limited number of sensors can be installed at any given site. The physical placement of sensors, along with the sensor technology and operating conditions, can have a large impact on our ability to adequately monitor environmental change. This paper introduces a new open-source Python package, called Chama, that determines optimal sensor placement and technology to improve a sensor network’s detection capabilities. Additionally, the methods are demonstrated using site-specific methane emission scenarios that capture uncertainty in wind conditions and emission characteristics. Mixed-integer linear programming formulations are used to determine sensor locations and detection thresholds that maximize detection of the emission scenarios. The optimized sensor networks consistently increase the ability to detect leaks, as compared to sensors placed near each potential emission source or along the perimeter of the site.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems

Optimal design of experiments for Bayesian inverse problems has recently gained wide popularity and attracted much attention, especially in the computational science and Bayesian inversion communities. An optimal design maximizes a predefined utility function that is formulated in terms of the elements of an inverse problem, an example being optimal sensor placement for parameter identification. The state-of-the-art algorithmic approaches following this simple formulation generally overlook misspecification of the elements of the inverse problem, such as the prior or the measurement uncertainties. This work presents an efficient algorithmic approach for designing optimal experimental design schemes for Bayesian linear inverse problems such that the optimal design is robust to misspecification of elements of the inverse problem. Specifically, we consider a worst-case scenario approach for the uncertain or misspecified parameters, formulate robust objectives, and propose an algorithmic approach for optimizing such objectives. Furthermore, both relaxation and stochastic solution approaches are discussed with detailed analysis and insight into the interpretation of the problem and the proposed algorithmic approach. Extensive numerical experiments to validate and analyze the proposed approach are carried out for sensor placement in a parameter identification problem.

Bayesian inverse problems↗

DOE OSTI2023

Energy Storage Sizing, Placement and Valuation for Distribution Networks

SAND2023-05682O The Energy Storage Sizing, Placement, and Valuation for Distribution software allows a user to simulate sizing, placement, and operation of one or more storage assets on a distribution grid. Among its features are measuring differences to determine the best combinations, and options for configuring and comparing results. This new simulation capability leverages the Open Distribution System Simulator and the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Tamrakar, Ujjwol↗

DOE OSTI2025

Sensor Placement Optimization Study for the Built Environment: Operational Use Cases

Systems of fixed-position radiation sensors can provide information that assists emergency responders following nuclear and radiological incidents. State, local, tribal, and territorial (SLTT) government agencies that implement systems of fixed-position sensors are faced with numerous decisions regarding sensor selection, quantity, and placement. To develop guidance on implementation of radiation detection systems, we simulated the release of radioactive material in an urban environment using a combination of three models: the Weather Research Forecasting (WRF), Quick Urban and Industrial Complex (QUIC), and Monte Carlo N-Particle (MCNP) models. We then evaluated the performance of several hypothetical sensor systems. The small number of simulations we conducted are not sufficient to generate definitive design guidance for radiation sensor systems, but we did identify trends that would be of interest to emergency planners. For a scenario that releases 1000 curies of Cs-137, radiation detectors were needed at 500-meter intervals to have a high likelihood of event detection and to estimate source location and plume detection. We also noted that optimal detector altitude varied with distance to the source. We recommend additional research in this area be conducted to support developing sensor placement guidelines that expand on a range of locations, isotopes, activity levels, and different weather conditions. Original simulation strategies included a range of environments, additional radioisotopes (Am241 and AmBe), and a larger selection of sensor types. These types of expansions would support SLTT guidance on sensor system recommendations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1975

Some effects of the atmosphere and microphone placement on aircraft flyover noise measurements

The effects of varying atmospheric conditions on certification-type noise measurements were studied. Tests were made under various atmospheric conditions at two test sites, Fresno, California, and Yuma, Arizona, using the same test aircraft, noise, and weather measuring equipment, and operating personnel. Measurements were made to determine the effects of the atmosphere and of microphone placement on aircraft flyover noise. The measurements were obtained for characterization of not only the acoustic signature of the test aircraft, but also specific atmospheric characteristics. Data are presented in the form of charts and tables which indicate that for a wide range of weather conditions, at both site locations, noise data were repeatable for similar aircraft operating conditions. The placement of microphones at ground level and at 1.2 m over both spaded sand and concrete illustrate the effects of ground reflections and surface impedance on the noise measurements.

Hosier, R. N.↗

NASA NTRS1979

Control of large flexible space structures using pole placement design techniques

A design approach via the pole placement techniques for the class of large flexible space structures is developed. The numerical problems in pole placement algorithm, arising from large dimension systems and the extremely low frequency eigenvalues which occur in large space structure models are examined. It shows these numerical difficulties may be overcome by properly selecting the sensor/actuator locations and introducing a frequency scaling scheme. The concepts of this paper are illustrated by some numerical studies on the linear feedback control design of a representative large spacecraft consisting of a small rigid core with ten radial booms (five booms 1000 ft long and five shorter booms 700 ft long) lying in a plane.

Wu, Y. W.↗

NASA NTRS1980

Design of the annular suspension and pointing system /ASPS/ through decoupling and pole placement

A decoupling and pole-placement technique has been developed for the Annular Suspension and Pointing System (ASPS) of the Space Shuttle which uses bandwidths as performance criteria. The dynamics of the continuous-data ASPS allows the three degrees of freedom to be totally decoupled by state feedback through constant gains, so that the bandwidth of each degree of freedom can be independently specified without interaction. Although it is found that the digital ASPS cannot be completely decoupled, the bandwidth requirements are satisfied by pole placement and a trial-and-error method based on approximate decoupling.

Kuo, B. C.↗

NASA NTRS1979

On the algebraic geometry of the output feedback pole placement map

Pole placement by output feedback is based on a concept that a transfer function can be considered as a curve in a Grassmanian manifold, so that pole placement consists of finding a special hypersurface in the manifold which intersects the curve at a prescribed set of points. An equation is derived for the number of different gains which yield the same set of poles in the case where the number of output feedback gains equals the number of poles of the system.

Brockett, R. W.↗

NASA NTRS1983

Optimum placement of controls for static deformations of space structures

The placement of thermal or force actuators to control slowly varying disturbances in the shape of large space structures is investigated analytically. Expressions are derived based on disturbances defined as the worst linear combination of a given set of functions and minimized numerically using the NEWSUMT optimization package. Results are presented in tables and graphs for a free-free beam with equidistant controls and polynomial-form (order 2-6) disturbances. Control placement is found to be of greater importance for force actuators, but is also of significance for thermal actuators.

Haftka, R. T.↗

NASA NTRS1985

Initial placement of STO instruments

The current plans for the placement of the Solar Terrestrial Observatory (STO) space station will make use of each of the currently planned space station elements - the manned space station, the polar platform, and the coorbiting platform. A designation of the instrument placement on each element along with a summary of the mass, volume, power and data requirements which these instrument will impose is provided in tabular form.

Source record↗

NASA NTRS1986

Initial placement of STO instruments

Source record↗