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The Pajarito Corridor Office Space Management tool is a reference used to assess and inform the management of the current portfolio of workstations and office space planning along the Pajarito Corridor. This work is in collaboration with the Associate Laboratory Directorate for Weapons Production (ALDWP)/Strategic Planning Office.
The monitoring and diagnostics of induced fractures are important for the real-time performance evaluation of hydraulic fracturing operations. Previous electromagnetic (EM) based studies show that single backbone tri-axial induction logging tools are promising candidates for real-time monitoring and diagnosis of fractures in open-hole wells. To support the development of field deployable tools, the concept must be tested in experiments, in a controllable environment, before it is tested under field-like conditions. To this end, we have developed numerical tools which can simulate any wellbore environment while logging hydraulic fractures with the induction tool. We have designed and built a prototype induction tool and performed two sets of tests to compare with numerical simulation results. The computational and experimental setup consists of tri-axial transmitter- and receiver-coils in co-axial, co-planar and cross-polarized configurations. Both lab and shallow earth measurements are shown to be in good agreement with simulations for all examined cases. The average relative and maximum discrepancies of the measured signals from the simulated ones were lower than 3% and 10%, respectively. With the prototype tool, strong signals sensitive to the fracture’s surface area and dip-angle were measured in co-axial coil configuration, while weaker signals sensitive to the fracture’s aspect ratio were observed in the co-planar configuration. Cross-polarized signals are also shown to be strong and sensitive to the fracture’s dip. Lastly, we resolved the detectable components of the signal tensor to obtain parameters of simplified fracture geometries. The inversion algorithm, a derivative free directional search model, uses an objective function defined as a combination of co-axial and cross-polarized signals from different tool spacings to provide a well behaved global minimum. The robustness of inversion algorithm is tested on synthetic data for single cluster fractures in homogeneous and heterogeneous background electrical conductivity. All the effective model parameters for different cases, fracture conductivity (width), propped area (length) and dip-angle, are shown to be recovered with sufficient accuracy. We also evaluated the effect of multiple neighboring fractures and suggested a multi-cluster inversion algorithm that can accurately recover the proppant distribution in each fracture. Based on these results we provide a downhole tool design, with detailed specifications, that can effectively recover far-field proppant distribution in hydraulic fractures.
A method of determining the position of the surface modules within the working space of a machine tool is outlined. The method includes the formulation of rectangular coordinate systems for the machine tool and the surface modules and establishing their relative position.
We use real-time density functional theory on a real-space grid to calculate electronic excitations of bacteriochlorophyll chromophores of the light-harvesting complex 2 (LH2). Comparison with Gaussian basis set calculations allows us to assess the numerical trust range for computing electron dynamics in coupled chromophores with both types of techniques. Tuned range-separated hybrid calculations for one bacteriochlorophyll as well as two coupled ones are used as a reference against which we compare results from the adiabatic time-dependent local density approximation (TDLDA). The tuned range-separated hybrid calculations lead to a qualitatively correct description of the electronic excitations and couplings. They allow us to identify spurious charge-transfer excitations that are obtained with the TDLDA. When we take into account the environment that the LH2 protein complex forms for the bacteriochlorophylls, we find that it substantially shifts the energy of the spurious charge-transfer excitations, restoring a qualitatively correct electronic coupling of the dominant excitations also for TDLDA.
The historical use and disposal of chlorinated solvents throughout Department of Defense and Department of Energy sites has resulted in subsurface contamination by dense non-aqueous phase liquids (DNAPL). The nature and environmental persistence of DNAPLs makes delineation and remediation of these compounds challenging and costly. This paper examines the utilization of data from a membrane interface probe (MIP) and hydraulic profile tool (HPT) to reduce a typical 18 to 24 month field investigation to 3 months; thereby, significantly reducing costs and resulting in a three-dimensional (3D) imagery of a deep DNAPL plume in order to support an aggressive remedial design for Space Launch Complex 16 (SLC16) at Cape Canaveral Air Force Station, Florida. During the early stages of the U.S. space program, chlorinated solvents were routinely used at space complexes. Spent solvents were discharged to surface impoundments where they migrated into the subsurface. Over time, these chlorinated solvents reached concentrations high enough to form a DNAPL plume. Recent investigations near SLC16 identified a previously unknown DNAPL plume co-mingled with dissolved-phase chlorinated compounds. The DNAPL source area (concentrations of trichloroethene [TCE] as high as 1,000,000 micrograms per liter [μg/L]) was discovered approximately 1,200 feet (ft) west of the existing monitoring network area. The U.S. Air Force, along with the U.S. Army Corps of Engineers, developed data quality objectives (DQO) to delineate the extent of the DNAPL and the dissolved-phase contaminant plume. A multi-phase investigation approach was developed by HydroGeoLogic, Inc. (HGL) emphasizing the use of MIP and HPT sampling technologies, conventional direct push technology (DPT), and monitoring well sampling methods. This approach allowed the collection of real-time data that supported the project DQOs and significantly reduced overall project costs and the field implementation schedule. (authors)
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As interest in biobased chemicals grows, and their application space expands, computational tools to navigate molecule space as a complement to experimental approaches are imperative. This work expands upon previous work that identified candidate bioprivileged molecules from the C 6 H x O y (C6) subspace. It refines the framework that was developed previously to better refine the molecules according to their biological origin and applies it to three new subspaces of chemical structure: C 4 H x O y (C4), C 5 H x O y (C5), and C 7 H x O y (C7). For C5 and C7, roughly the top 100 bioprivileged candidates were identified, and the enhanced framework was applied to recast slightly the previous list of the top 100 C6 molecules. In addition, all top candidates were analyzed for their key functional moieties using a random forest model, and this algorithm was applied to compare the functional group space occupied by bioprivileged molecules of various databases of molecules with a focus on evaluating how closely the molecules were aligned with those known to biology. Furthermore, with the present work's focus on automation and data science principles, the framework can be easily expanded to include other chemical formulae to screen for bioprivileged candidates. As a result, this in turn facilitates the retrosynthesis process inherent in the framework to identify those bioprivileged intermediates in other subspaces that lead to target molecules.
Here, the present work addresses the issue of accurate stochastic approximations in high-dimensional parametric space using tools from uncertainty quantification (UQ). The basis adaptation method and its accelerated algorithm in polynomial chaos expansions (PCE) were recently proposed to construct low-dimensional approximations adapted to specific quantities of interest (QoI). The present paper addresses one difficulty with these adaptations, namely their reliance on quadrature point sampling, which limits the reusability of potentially expensive samples. Projection pursuit (PP) is a statistical tool to find the “interesting” projections in high-dimensional data and thus bypass the curse-of-dimensionality. In the present work, we combine the fundamental ideas of basis adaptation and projection pursuit regression (PPR) to propose a novel method to simultaneously learn the optimal low-dimensional spaces and PCE representation from given data. While this projection pursuit adaptation (PPA) can be entirely data-driven, the constructed approximation exhibits mean-square convergence to the solution of an underlying governing equation and thus captures the supports and probability distributions associated with the physics constraints. The proposed approach is demonstrated on a borehole problem and a structural dynamics problem, demonstrating the versatility of the method and its ability to discover low-dimensional manifolds with high accuracy with limited data. In addition, the method can learn surrogate models for different quantities of interest while reusing the same data set.
Application energy optimization in HPC data centers face two critical gaps. Systematic methodologies that connect data center policies to application decisions and accessible monitoring tools that enable data-driven optimization. We address both gaps through two complementary pillars. First, we present a methodology based on extended weighted Energy Delay Product (EDP) to translate data center operational priorities and integrate energy considerations into the energy optimization workflow which starts from continuous monitoring through targeted optimization. Second, we present a user-space monitoring tool, Omnistat, that enables this methodology by providing developers with direct access to actionable energy telemetry. Through deployment on the Frontier supercomputer and case studies exploring performance-energy trade-offs, we show how these pillars help energy as an integral optimization target for developers as active participants in data center efficiency.
Abstract In‐silico screening of novel biofuel molecules based on chemical and fuel properties is a critical first step in the biofuel evaluation process due to the significant volumes of samples required for experimental testing, the destructive nature of engine tests, and the costs associated with bench‐scale synthesis of novel fuels. Predictive models are limited by training sets of few existing measurements, often containing similar classes of molecules that represent just a subset of the potential molecular fuel space. Software tools can be used to generate every possible molecular descriptor for use as input features, but most of these features are largely irrelevant and training models on datasets with higher dimensionality than size tends to yield poor predictive performance. Feature selection has been shown to improve machine learning models, but correlation‐based feature selection fails to provide scientific insight into the underlying mechanisms that determine structure–property relationships. The implementation of causal discovery in feature selection could potentially inform the biofuel design process while also improving model prediction accuracy and robustness to new data. In this study, we investigate the benefits causal‐based feature selection might have on both model performance and identification of key molecular substructures. We found that causal‐based feature selection performed on par with alternative filtration methods, and that a structural causal model provides valuable scientific insights into the relationships between molecular substructures and fuel properties.
This effort describes experiments for the validation of the response of an additively manufactured stiffness-optimized short-fiber reinforced composite. A Direct Ink Write (DIW) method is used to additively manufacture a clevis joint plate which was designed such that its compliance would be minimized (i.e., stiffness maximized) for far-field axial loading. A unique aspect of the optimization scheme is that it accounts for manufacturing constraints, such as tool turn radius and tool path spacing. Along with the optimized clevis joint plate, additively manufactured 0°–90° and ± 45° composite plates of the same number of layers, as well as a control 0°–90° sample of resin only, were also studied. In addition to far-field load-displacement, near-field strain was monitored using digital image correlation (DIC). The results showed that the optimized plate did indeed exhibit the largest stiffness, based on far-field measurements. DIC-measured strains showed that locally the axial strain component, which is the largest, was also minimized for the optimized specimen, although the other strain components followed different trends. Furthermore, in the experiments it was seen that the optimized design exhibited a nonlinear/hysteretic behavior which is believed to be a consequence of its internal gap/cell structure. This internal structure, which results by optimized placement of material, although explicitly not accounted for in the optimization, may affect global response. Lastly, although not a part of the optimization study itself, the failure load for the optimized joint plate was also seen to be the largest of all the cases studied.
Lithium-ion battery energy storage systems (BESS) packs have emerged as a leading solution for grid-scale energy storage, enhancing resiliency and balancing load fluctuations. Yet, experimental characterization of large-format LIB packs-particularly to assess performance and degradation over hundreds of cycles - demands substantial hardware investment and multi-year testing campaigns. In this work, we couple a hierarchical, physics-based modeling framework agnostic to electrode chemistries with high-performance computing to accelerate systems level evaluation by upto two orders of magnitude. Building on the open-source liionpack platform, we implement cell, module, and pack-scale electrochemical models enriched with mechanistic aging mechanisms and deploy them on an HPC cluster to simulate 150−200kWh systems over 500 - 1,000 cycles with in days. We subject these virtual B ESS to both constant-current cycling and realistic grid service profiles spanning frequency regulation, ramp-rate support, and energy arbitrage-and quantify the resulting degradation patterns. Our results reveal that localized cell aging can induce substantial nonuniformity at module and pack levels, with service-specific cycling protocols driving distinct aging modes. This rapid, multiscale modeling approach provides a powerful design-space exploration tool for optimizing electrical architecture, control strategies, and operational schedules to prolong pack lifetime and lower total cost of ownership.
Existing retrosynthesis tools generally traverse production routes from a source to a sink metabolite using known enzymes or de novo steps. Generally, important considerations such as blending known transformations with putative steps, complexity of pathway topology, mass conservation, cofactor balance, thermodynamic feasibility, microbial chassis selection, and cost are largely dealt with in a posteriori fashion. The computational procedure we present here designs bioconversion routes while simultaneously considering any combination of the aforementioned design criteria. First, we track and codify as rules all reaction centers using a prime factorization-based encoding technique (rePrime). Reaction rules and known biotransformations are then simultaneously used by the pathway design algorithm (novoStoic) to trace both metabolites and molecular moieties through balanced bio-conversion strategies. We show the use of novoStoic in bypassing steps in existing pathways through putative transformations, assembling complex pathways blending both known and putative steps toward pharmaceuticals, and postulating ways to biodegrade xenobiotics.
Abstract Machine learning is receiving more attention in classical engineering fields, and in particular, recurrent neural networks (RNNs) coupled with ensemble regression tools have demonstrated the capability of modeling nonlinear dynamic processes. In Part I of this two‐article series, the Lyapunov‐based model predictive control (LMPC) method using a single RNN model and an ensemble of RNN models, respectively, was rigorously developed for a general class of nonlinear systems. In the present article, computational implementation issues of this new control method ranging from training of the RNN models, ensemble regression of the RNN models, and parallel computation for accelerating the real‐time model calculations are addressed. Furthermore, a chemical reactor example is used to demonstrate the implementation and effectiveness of these machine‐learning tools in LMPC as well as compare them with standard state‐space model identification tools.