A Conformal Decomposition Finite Element Method with Guaranteed Quality.
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Interface-conforming elements generated by the conformal decomposition finite element method can have arbitrarily poor quality due to the arbitrary intersection of the base triangular or tetrahedral mesh with material interfaces. This can have severe consequences for both the solvability of linear systems and for the interpolation error of fields represented on these meshes. The present work demonstrates that snapping the base mesh nodes to the interface whenever the interface cuts close to a node results in conforming meshes of good quality. Theoretical limits on the snapping tolerance are derived, and even conservative tolerance choices result in limiting the stiffness matrix condition number to within a small multiple of that of the base mesh. Interpolation errors are also well controlled in the norms of interest. In 3D, use of node-to-interface snapping also permits a simpler and more robust vertex ID-based element decomposition algorithm to be used with no serious detriment to mesh quality.
Purpose: To efficiently find quality-guaranteed treatment plans with the minimum number of beams for stereotactic body radiation therapy using RayStation. Methods: For a pre-specified pool of candidate beams we use RayStation (a treatment planning software for clinical use) to identify the deliverable plan which uses all the beams with the minimum dose to organs at risk (OARs) and dose to the tumor and other structures in specified ranges. Then use the dose matrix information for the generated apertures from RayStation to solve a linear program to find the ideal plan with the same objective and constraints allowing use of all beams. Finally we solve a mixed integer programming formulation of the beam angle optimization problem (BAO) with the objective of minimizing the number of beams while remaining in a predetermined epsilon-optimality of the ideal plan with respect to the dose to OARs. Since the treatment plan optimization is a multicriteria optimization problem, the planner can exploit the multicriteria optimization capability of RayStation to navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus OARs sparing, and then use the proposed technique to reduce the number of beams while guaranteeing quality. For the numerical experiments two liver cases and one lung case with 33 non-coplanar beams are considered. Results: The ideal plan uses an impractically large number of beams. The proposed technique reduces the number of beams to the range of practical application (5 to 9 beams) while remaining in the epsilon-optimal range of 1% to 5% optimality gap. Conclusion: The proposed method can be integrated into a general algorithm for fast navigation of the ideal dose distribution Pareto surface and finding the treatment plan with the minimum number of beams, which corresponds to the delivery time, in epsilon-optimality range of the desired ideal plan. The project was supported by the Federal Share of program income earned by Massachusetts General Hospital on C06 CA059267, Proton Therapy Research and Treatment Center and partially by RaySearch Laboratories.
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ROL-PEBBL is a C++, MPI-based parallel code for mixed-integer PDE-constrained optimization (MIPDECO). In these problems we wish to optimize (control, design, etc.) physical systems, which must obey the laws of physics, when some of the decision variables must take integer values. ROL-PEBBL combines a code to efficiently search over integer choices (PEBBL = Parallel Enumeration Branch-and-Bound Library) and a code for efficient nonlinear optimization, including PDE-constrained optimization (ROL = Rapid Optimization Library). In this report, we summarize the design of ROL-PEBBL and initial applications/results. For an artificial source-inversion problem, finding sources of pollution on a grid from sparse samples, ROL-PEBBLs solution for the nest grid gave the best optimization guarantee for any general solver that gives both a solution and a quality guarantee.
Systems, apparatuses, and methods for enforcing processor quality of service guarantees when servicing system service requests (SSRs) are disclosed. A system includes a first processor executing an operating system and a second processor executing an application which generates SSRs for the first processor to service. The first processor monitors the number of cycles spent servicing SSRs over a previous time interval, and if this number of cycles is above a threshold, the first processor starts delaying the servicing of subsequent SSRs. In one implementation, if the previous delay was non-zero, the first processor increases the delay used in the servicing of subsequent SSRs. If the number of cycles is less than or equal to the threshold, then the first processor services SSRs without delay. As the delay is increased, the second processor begins to stall and its SSR generation rate falls, reducing the load on the first processor.
Highlights: Black-Right-Pointing-Pointer Definition of parameters characterising agricultural plastic waste (APW) quality. Black-Right-Pointing-Pointer Analysis of samples to determine APW quality for recycling or energy recovery. Black-Right-Pointing-Pointer Majority of APW samples from various countries have very good quality for recycling. Black-Right-Pointing-Pointer Upper limit of 50% w/w soil contamination in APW acceptable for energy recovery. Black-Right-Pointing-Pointer Chlorine and heavy metals content in APW below the lowest limit for energy recovery. - Abstract: A holistic environmentally sound waste management scheme that transforms agricultural plastic waste (APW) streams into labelled guaranteed quality commodities freely traded in open market has been developed by the European research project LabelAgriWaste. The APW quality is defined by the APW material requirements, translated to technical specifications, for recycling or energy recovery. The present work investigates the characteristics of the APW quality and the key factors affecting it from the introduction of the virgin product to the market to the APW stream reaching the disposer. Samples of APW from different countries were traced from their application to the field through their storage phase and transportation to the final destination. The test results showed that the majority of APW retained their mechanical properties after their use preserving a 'very good quality' for recycling in terms of degradation. The degree of soil contamination concerning the APW recycling and energy recovery potential fluctuates depending on the agricultural plastic category and application. The chlorine and heavy metal content of the tested APW materials was much lower than the maximum acceptable limits for their potential use in cement industries.
We present several formulations to solve the MPBP for the recovery of REE/CMs from produced water and propose a decomposition approach thatleverages strategies in general disjunctive programming to enhance its performance. We compare these proposed formulations/strategies via two illustrative case studies on recovering lithium from a network of produced water sources.
This paper investigates the ability of a collection of deferrable heating, ventilation and air conditioning (HVAC) loads to directly emulate energy storage devices (ESD), that is, to absorb any low frequency fluctuations in solar generation. We find the optimal number of aggregated HVAC loads to absorb solar fluctuation in the same frequency band based on frequency analysis. To guarantee quality of service in afleet of residential buildings, an optimization problem is formed to compute optimal schedule of the HVAC loads considering temperature comfort band and available solar generation. By minimizing the tracking error between solar generation and total consumption, the proposed method also quantifies the minimum capacity of ESD (battery or fly-wheel) . The simulation results show that the proposed techniques are able to achieve good solar tracking performance as well as obtain a minimal ESD capacity. Therefore, we show that most of the renewable generation can be consumed locally through an intelligent coordination of HVAC loads, their impacts on the grid would be minimal and the required ESD capacity can be minimized.
One fundamental task in network analysis is detecting “hotspots” or “anomalies” in the network; that is, detecting subgraphs where there is significantly more activity than one would expect given historical data or some baseline process. Scan statistics is one popular approach used for anomalous subgraph detection. This methodology involves maximizing a score function over all connected subgraphs, which is a challenging computational problem. A number of heuristics have been proposed for these problems, but they do not provide any quality guarantees. Here, we propose a framework for designing algorithms for optimizing a large class of scan statistics for networks, subject to connectivity constraints. Our algorithms run in time that scales linearly on the size of the graph and depends on a parameter we call the “effective solution size,” while providing rigorous approximation guarantees. In contrast, most prior methods have super-linear running times in terms of graph size. Extensive empirical evidence demonstrates the effectiveness and efficiency of our proposed algorithms in comparison with state-of-the-art methods. Our approach improves on the performance relative to all prior methods, giving up to over 25% increase in the score. Further, our algorithms scale to networks with up to a million nodes, which is 1–2 orders of magnitude larger than all prior applications.
Finding similarities between protein structures is a crucial task in molecular biology. Most of the existing tools require proteins to be aligned in order-preserving way and only find single alignments even when multiple similar regions exist. We propose a new seed-based approach that discovers multiple pairs of similar regions. Its computational complexity is polynomial and it comes with a quality guarantee—the returned alignments have both root mean squared deviations (coordinate-based as well as internal-distances based) lower than a given threshold, if such exist. We do not require the alignments to be order preserving (i.e., we consider nonsequential alignments), which makes our algorithm suitable for detecting similar domains when comparing multidomain proteins as well as to detect structural repetitions within a single protein. Because the search space for nonsequential alignments is much larger than for sequential ones, the computational burden is addressed by extensive use of parallel computing techniques: a coarse-grain level parallelism making use of available CPU cores for computation and a fine-grain level parallelism exploiting bit-level concurrency as well as vector instructions.
Previous NREL research shows that solar photovoltaic (PV) customers tend to obtain lower installation quote prices on online quote platforms. In new research reported in Solar Buyer's Markets: Unlocking Lower Photovoltaic and Battery Prices on Online Quote Platforms, we corroborate those results using data from the Pick My Solar quote platform. Furthermore, for the first time, we evaluate quote prices for batteries on online quote platforms. Given that few customers currently use quote platforms, we discuss various pathways to increase use of online quote platforms, including developing strategic partnerships, building customer trust, increasing market transparency, and providing quality guarantees.
In engineering system design, minimizing the variations of the quality measurements while guaranteeing their overall quality up to certain levels, namely the robust parameter design (RPD), is crucial. Recent works have dealt with the design of a system whose response-control variables relationship is a deterministic function with a complex shape and function evaluation is expensive. In this work, we propose a Bayesian optimization method for the RPD of stochastic functions. Dual stochastic response models are carefully designed for stochastic functions. The heterogeneous variance of the sample mean is addressed by the predictive mean of the log variance surrogate model in a two-step approach. We establish an acquisition function that favors exploration across the feasible and optimality-improvable regions to effectively and efficiently solve the stochastic constrained optimization problem. Further, the performance of our proposed method is demonstrated by the extensive numerical and case studies. Note to Practitioners-Many manufacturing processes involve undesirable variations, which create variations in the final products. For example, many emerging manufacturing processes, such as nanomanufacturing, involve complex physical and chemical dynamics and transformation, creating variations in the manufacturing output. In such processes, it is crucial to design the manufacturing processes or products so that they have minimum variations in their quality. Meanwhile, it is also important to maintain the overall quality of the designed processes or products. Furthermore, acquiring data from many advanced manufacturing processes is often very costly, especially in the designing stage. In this work, we propose a data-driven method that automatically finds the best setting of manufacturing processes or products with the minimum variations of quality and a given constraint on the average quality satisfied. Our proposed method is used before conducting every experiment; It analyzes the historical data from previous experiments and provides a setting to be used in the next experiment. Our proposed method efficiently utilizes the historical data, and thus finds the best robust setting by conducting only a small number of experiments.
Solar-driven interfacial evaporation shows great prospects for seawater desalination with its rapid fast evaporation rate and high photothermal conversion efficiency. Here, a sustainable, biodegradable, non-toxic, and highly efficient full ocean biomass-based solar-driven evaporator is reported, which is composed of chitosan (CS) hydrogel as the hydratable skeleton and cuttlefish ink (CI) as the photothermal material. Under solar irradiation, the cuttlefish ink powder harvests solar energy and heats the surrounding water. Simultaneously, the water in the three-dimensional network of chitosan hydrogel is rapidly replenished by the interconnected porous structure and the hydrophilic functional groups attached to the polymer chains. With its enlarged evaporation surface, high solar absorptance, adequate water transportation, good salt drainage, and heat localization, the CI/CS-based evaporator achieves a remarkable evaporation rate of 4.1 kg m-2 h-1 under one sun irradiance (1 kW m-2) with high-quality freshwater yields. This full ocean biomass-based evaporator with abundant raw material availability provides new possibilities for an efficient, stable, sustainable, and environmentally friendly solar evaporator with guaranteed water quality.