An official website of the United States government
Abstract Addressing challenges in urban water infrastructure systems, including aging infrastructure, supply uncertainty, extreme events, and security threats, depends highly on water distribution networks modeling emphasizing the importance of realistic assumptions, modeling complexities, and scalable solutions. In this study, we propose a derivative‐free, linear approximation for solving the network water flow problem. The proposed approach takes advantage of the special form of the nonlinear head loss equations, and, after the transformation of variables and constraints, the water flow problem reduces to a linear optimization problem that can be efficiently solved by modern linear solvers. Ultimately, the proposed approach amounts to solving a series of linear optimization problems. We demonstrate the proposed approach through several case studies and show that the approach can model arbitrary network topologies and various types of valves and pumps, thus providing modeling flexibility. Under mild conditions, we show that the proposed linear approximation converges. We provide sensitivity analysis and discuss in detail the current limitations of our approach and suggest solutions to overcome these. All the codes, tested networks, and results are freely available on Github for research reproducibility.
more »
« less
An Empirical Study of Cycle Toggling Based Laplacian Solvers
We study the performance of linear solvers for graph Laplacians based on the combinatorial cycle adjustment methodology proposed by [Kelner-Orecchia-Sidford-Zhu STOC-13]. The approach finds a dual flow solution to this linear system through a sequence of flow adjustments along cycles. We study both data structure oriented and recursive methods for handling these adjustments. The primary difficulty faced by this approach, updating and querying long cycles, motivated us to study an important special case: instances where all cycles are formed by fundamental cycles on a length n path. Our methods demonstrate significant speedups over previous implementations, and are competitive with standard numerical routines.
more »
« less
- Award ID(s):
- 1637564
- PAR ID:
- 10040899
- Date Published:
- Journal Name:
- Proceedings of the Seventh SIAM Workshop on Combinatorial Scientific Computing
- Page Range / eLocation ID:
- 33-41
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Standard estimators of the global average treatment effect can be biased in the presence of interference. This paper proposes regression adjustment estimators for removing bias due to interference in Bernoulli randomized experiments. We use a fitted model to predict the counterfactual outcomes of global control and global treatment. Our work differs from standard regression adjustments in that the adjustment variables are constructed from functions of the treatment assignment vector, and that we allow the researcher to use a collection of any functions correlated with the response, turning the problem of detecting interference into a feature engineering problem. We characterize the distribution of the proposed estimator in a linear model setting and connect the results to the standard theory of regression adjustments under SUTVA. We then propose an estimator that allows for flexible machine learning estimators to be used for fitting a nonlinear interference functional form. We propose conducting statistical inference via bootstrap and resampling methods, which allow us to sidestep the complicated dependences implied by interference and instead rely on empirical covariance structures. Such variance estimation relies on an exogeneity assumption akin to the standard unconfoundedness assumption invoked in observational studies. In simulation experiments, our methods are better at debiasing estimates than existing inverse propensity weighted estimators based on neighborhood exposure modeling. We use our method to reanalyze an experiment concerning weather insurance adoption conducted on a collection of villages in rural China.more » « less
-
Abstract It is now well established that changes in the zonal wind stress over the Antarctic Circumpolar Current (ACC) do not lead to changes in its baroclinicity nor baroclinic transport, a phenomenon referred to as “eddy saturation.” Previous studies provide contrasting dynamical mechanisms for this phenomenon: on one extreme, changes in the winds lead to changes in the efficiency with which transient eddies transfer momentum to the sea floor; on the other extreme, structural adjustments of the ACC’s standing meanders increase the efficiency of momentum transfer. In this study the authors investigate the relative importance of these mechanisms using an idealized, isopycnal channel model of the ACC. Via separate diagnoses of the model’s time-mean flow and eddy diffusivity, the authors decompose the model’s response to changes in wind stress into contributions from transient eddies and the mean flow. A key result is that holding the transient eddy diffusivity constant while varying the mean flow very closely compensates for changes in the wind stress, whereas holding the mean flow constant and varying the eddy diffusivity does not. This implies that eddy saturation primarily occurs due to adjustments in the ACC’s standing waves/meanders, rather than due to adjustments of transient eddy behavior. The authors derive a quasigeostrophic theory for ACC transport saturation by standing waves, in which the transient eddy diffusivity is held fixed, and thus provides dynamical insights into standing wave adjustment to wind changes. These findings imply that representing eddy saturation in global models requires adequate resolution of the ACC’s standing meanders, with wind-responsive parameterizations of the transient eddies being of secondary importance.more » « less
-
Leveraging soil mapping and machine learning to improve spatial adjustments in plant breeding trialsAbstract Spatial adjustments are used to improve the estimate of plot seed yield across crops and geographies. Moving means (MM) and P‐Spline are examples of spatial adjustment methods used in plant breeding trials to deal with field heterogeneity. Within the trial, spatial variability primarily comes from soil feature gradients, such as nutrients, but a study of the importance of various soil factors including nutrients is lacking. We analyzed plant breeding progeny row (PR) and preliminary yield trial (PYT) data of a public soybean breeding program across 3 years consisting of 43,545 plots. We compared several spatial adjustment methods: unadjusted (as a control), MM adjustment, P‐spline adjustment, and a machine learning‐based method called XGBoost. XGBoost modeled soil features at: (a) the local field scale for each generation and per year, and (b) all inclusive field scale spanning all generations and years. We report the usefulness of spatial adjustments at both PR and PYT stages of field testing and additionally provide ways to utilize interpretability insights of soil features in spatial adjustments. Our work shows that using soil features for spatial adjustments increased the relative efficiency by 81%, reduced the similarity of selection by 30%, and reduced the Moran's I from 0.13 to 0.01 on average across all experiments. These results empower breeders to further refine selection criteria to make more accurate selections and select for macro‐ and micro‐nutrients stress tolerance.more » « less
-
A large diversity of fluid pumps is found throughout nature. The study of these pumps has provided insights into fundamental fluid dynamic processes and inspiration for the development of micro-fluid devices. Recent work by Thiria and Zhang [Appl. Phys. Lett. 106, 054106 (2015)] demonstrated how a reciprocal, valveless pump with a geometric asymmetry could drive net fluid flow due to an impedance mismatch when the fluid moves in different directions. Their pump's geometry is reminiscent of the asymmetries seen in the chains of contractile chambers that form the insect heart and mammalian lymphangions. Inspired by these similarities, we further explored the role of such geometric asymmetry in driving bulk flow in a preferred direction. We used an open-source implementation of the immersed boundary method to solve the fluid-structure interaction problem of a viscous fluid moving through a sawtooth channel whose walls move up and down with a reciprocal motion. Using a machine learning approach based on generalized polynomial chaos expansions, we fully described the model's behavior over the target 3-dimensional design space, composed of input Reynolds numbers (Rein), pumping frequencies, and duty cycles. Scaling studies showed that the pump is more effective at higher intermediate Rein. Moreover, greater volumetric flow rates were observed for near extremal duty cycles, with higher duty cycles (longer contraction and shorter expansion phases) resulting in the highest bulk flow rates.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1137/1.9781611974690.ch4
