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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Switching Device-Cognizant Sequential Distribution System Restoration

This paper presents an optimization framework for sequential reconfiguration using an assortment of switching devices and repair process in distribution system restoration. Compared to existing studies, this paper considers types, capabilities and operational limits of different switching devices, making it applicable in practice. We develop a novel multi-phase method to find the optimal sequential operation of various switching devices and repair faulted areas. We consider circuit breakers, reclosers, sectionalizers, load breaker switches, and fuses. The switching operation problem is decomposed into two mixed-integer linear programming (MILP) subproblems. The first subproblem determines the optimal network topology and estimates the number of steps to reach that topology, while the second subproblem generates a sequence of switching operations to coordinate the switches. For repairing the faults, we design an MILP model that dispatches repair crews to clear faults and replace melted fuses. After clearing a fault, we update the topology of the network by generating a new sequence of switching operations, and the process continues until all faults are cleared. To improve the computational efficiency, a network reduction algorithm is developed to group line sections, such that only switchable sections are present in the reduced network. The proposed method is validated on the IEEE 123-bus and 8500-bus systems.

distribution system↗
DOE OSTI2024

Sequential infiltration of two-photon polymerized 3D photonic crystals for mid-IR spectroscopic applications

Photonic crystals (PhCs) are spatially organized structures with lattice parameters equivalent to the operational wavelength of light. PhCs have been subject to extensive research efforts in the last two decades and are known for controlling light propagation with applications in sensing and time-delayed communication due to the slow-light phenomenon. Despite their exceptional properties, PhCs are difficult to fabricate using planar micromachining techniques due to their periodic structures. Techniques like two-photon stereolithography have been discussed for PhC fabrication in the literature, but the inherent disadvantage of poor refractive index (RI) contrast results in limited application. In this work, we present sequential infiltration synthesis performed on two-photon stereolithographically printed 3D PhCs for infiltration with zinc oxide to increase the RI of 3D PhCs. Further, finite element analysis was performed over a range of RI contrast values to study the change in photonic bandgap (PBG) with RI contrast. The transmission spectra were recorded on 3D PhCs before and after infiltration to demonstrate the change experimentally. An increase in the PBG width and absorbance is seen postinfiltration due to enhanced RI. This work presents the first, to our knowledge, sequentially infiltrated enhanced 3D PhC fabricated with two-photon stereolithography.

3D printing↗
DOE OSTI2021

Sequential Training ToolKit - STTK

Sequential Training ToolKit, or STTK, is a software toolkit plugin for the Unity development platform that enables software developers to easily develop structured Sequential Training applications for gaming and extended reality (XR) environments. STTK is architected to easily import the toolkit as a plugin for the Unity platform and then interact with the graphical user interface (GUI) for drag-and-drop functionality. STTK also includes easy add-on plugins for Prefabs and auto-builds a State Machine. 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. SAND2021-3059 O

Klein, BrandonThorin↗
DOE OSTI2025

Data and scripts associated with “Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics within Water-Tagging Enabled Hydrologic Models” (v3)

This data package is associated with the publication “Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics within Water-Tagging Enabled Hydrologic Models” submitted to Journal of Advances in Modeling Earth Systems (Butler et al. 2025). This study developed the Sequential Precipitation Input Tagging (SPIT) framework to tag input precipitation and estimate water transit times and hydrologic tracers. SPIT tags all precipitation events at regular intervals over an extended period (monthly tags over seven years) in a hydrologic model from 2016-2022. SPIT is applied at six National Ecological Observatory Network (NEON) sites across the continental United States to calculate transit time distributions (TTD) and derive from these mean transit times (MTT), fractions of young water (Fyw), and hydrologic tracer concentrations in stream water (δ18O) within a water-tagging enabled version of the Weather Research and Forecast (WT-WRF-Hydro) model with national water model (NWM) configurations. We go on to validate WT-WRF-Hydro estimates against Butler et al. (2023), who analyzed the same NEON sites using stable water isotope data to estimate water transit times. This new tracking method provides a detailed picture of water movement and helps improve predictions about water availability in the future. This data package was originally published in January 2025. It was updated May 2025 (v2; new and modified files) and October 2025 (v3; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. This data package contains the data and scripts used to develop the SPIT framework WT-WRF-Hydro (Water Tagging Weather Research and Forecasting Hydrologic) model and is associated with the following GitHub repository: https://github.com/zbutler33/SPIT-Framework. This data package contains five parent folders: (1) “Manipulated_outputs”, (2) “Metadata”, (3) “Observed”, (4) “Outputs”, and (5) “Scripts”. Each of these parent folders contains additional subfolders and files. Please see the FLMD (“v*_Butler_2024_WT_WRF_Hydro_flmd.csv”) for a list of all the files contained in this data package and descriptions for each. See the data dictionary (“v*_Butler_2024_WT_WRF_Hydro_dd.csv”) for definitions and units of all of the tabular (files ending in “.csv” and ".tsv") column headers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Application of Sequential Design of Experiments (SDoE) to Large Pilot-Scale Solvent-Based CO2 Capture Process at Technology Centre Mongstad (TCM)

The United States Department of Energy’s Carbon Capture Simulation for Industry Impact (CCSI2) program has developed a framework for sequential design of experiments (SDoE) that aims to maximize knowledge gained from budget- and schedule-limited pilot scale testing. SDoE was applied to the planning and execution of campaigns for testing CO2 capture systems at pilot-scale in order to optimally allocate resources available for the testing. In this methodology, a stochastic process model is developed by quantifying the parametric uncertainty in submodels of interest; for a solvent-based CO2 capture system, these may include physical properties and equipment performance submodels (e.g., mass transfer, interfacial area). This uncertainty is propagated through the full process model, over variable operating conditions, for estimating the resulting uncertainty in key model outputs (e.g., percentage of CO2 capture, solvent regeneration energy requirement). In developing a data collection plan, the predicted output uncertainty is incorporated into an algorithm that seeks simultaneously to select process operating conditions for which the predicted uncertainty is relatively high and to ensure that the entire space of operation is well represented. This test plan is then used to guide operation of the pilot plant at varying steady-state conditions, with resulting process data incorporated into the existing model using Bayesian inference to refine parameter distributions. The updated stochastic model, with reduced parametric uncertainty from data collected, is then used to guide additional data collection, thus the sequential nature of the experimental design. The SDoE process was implemented at the pilot test unit (12 MWe in scale) at Norway’s Technology Centre Mongstad (TCM) in a summer 2018 test campaign with aqueous monoethanolamine (MEA). During the test campaign, the varied operating conditions included the flowrates of circulated solvent, flue gas, and reboiler steam and the CO2 concentration in the flue gas. The process data were used to update probability distributions of mass transfer and interfacial area parameters of a stochastic process model developed by the CCSI2 team. Two iterations of the SDoE process were executed, resulting in the uncertainty in model predicted CO2 capture percentage decreasing by an average of 58.0 ± 4.7% over the full input space of interest. This work demonstrates the potential of the SDoE process for model refinement through reduction in process model parametric uncertainty, and ultimately risk in scale-up, in CO2 capture technology performance.

carbon capture↗
DOE OSTI2023

Surface properties and RF performance of Vapor Diffused Nb3Sn on Nb after sequential anneals below 1000 °C

Nb3Sn is a next-generation superconducting material that can be used for future superconducting radiofrequency (SRF) accelerator cavities, promising better performance, cost reduction, and higher operating temperature than Nb. The Sn vapor diffusion method is currently the most preferred and successful technique to coat niobium cavi-ties with Nb3Sn. Among post-coating treatments to opti-mize the coating quality, higher temperature annealing without Sn is known to degrade Nb3Sn because of Sn loss. We have investigated Nb3Sn/Nb samples briefly annealed at 800-1000 °C for 10 and 20 minutes to poten-tially improve the surface to enhance the performance of Nb3Sn-coated cavities. Following the sample studies, a coated single-cell cavity was sequentially annealed at 900 °C and tested its performance each time, improving the cavity's quality factor relatively. This paper summarizes the sample studies and discusses the RF test results from sequentially annealed SRF Nb3Sn/Nb cavity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Sequential Bayesian Methods for Analyzing Computer Models

Efficient analysis of computer models is essential for the validation and uncertainty quantification of those models. Surrogate models, and Gaussian processes in particular, are a common and powerful approach to analyzing computer models that treat computer models as a black-box function. Gaussian processes form a Bayesian model over a space of functions that gives a measure of uncertainty about the computer model output at unobserved locations and a framework for sequential sampling. This tutorial will show how to fit Gaussian processes on a series of test functions and apply sequential design techniques to estimate extrema, level sets, and reliabilities of those test functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Applying colloidal silica suspensions injection and sequential gelation to block vertical water flow in well annulus: laboratory testing on rheology, gelation, and injection

We evaluated the application of silica suspension injection and sequential gelation to block vertical water flow in the annuli of long-screened wells. First, we studied the viscosity, rheological behavior, and gelation performance of colloidal silica suspensions in batch tests. Then, we tested the injection of silica suspensions and the water flow blocking efficiency of the later formed silica gel in column and bench-scale sandbox experiments. Micron-sized fumed powder silica suspensions and nanosized silica suspensions recovered from geothermal fluids were tested in this work. Fumed silica suspensions showed shear thinning, while nanosized silica suspensions exhibited Newtonian flow behavior. During the gelation process, the nanosized silica suspension changed from a Newtonian fluid to a shear thinning fluid while increasing its overall viscosity. At comparable concentrations, the nanosized silica suspensions have much lower viscosity than that of the fumed silica suspensions. Increases in the Na + concentration and silica particle concentration in these suspensions shortened the gelation time. Silica suspension gelation in sand columns completely blocked the water flow and sustained the injection pressure up to 50 psig (344.7 kPa). A silica suspension was successfully injected into the target zone in the annulus of a bench-scale sandbox mimicking long-screened wells in the field. The silica gel formed in the annulus effectively blocked chemical transport through the gelled zone. Our research reveals that a process using silica suspension injection and sequential gelation technology is promising for blocking the vertical water flow and chemical transport through the filter pack in targeted zones within the annulus of long-screened well systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Relativistic Hybrid Stars with Sequential First-order Phase Transitions in Light of Multimessenger Constraints

In this work, we consider the properties of compact stars in which quark matter has low- and high-density phases that are separated by a first-order phase transition. Thus, unlike the commonly considered case of a single phase transition from hadronic to quark matter, our models of hybrid stars contain sequential phase transitions from hadronic matter to low- and then to high-density quark matter phases. We extend our previous study of the parameter space of hybrid stars with a single phase transition to those with sequential phase transitions, taking into account the constraints on the mass and radius of neutron stars from the NICER experiment, the experimental inferences of the neutron skin thickness of the lead nucleus by the PREX-II experiment, and constraints on the tidal deformability from the gravitational-wave event GW170817. We determine the range of the masses for which both twin and triplet configurations, i.e., identical-mass stars with two and three different values of radii, arise.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Anomaly Detection in Sequential Health Care Data using Higher-Order Network Representation

The ever-increasing use of information technology (IT) in health care presents new challenges to patient care. Information errors arising from the use of Health IT and their implications on care delivery and patient outcomes have been widely reported. Information errors can lead to changes in clinical decisions, care processes, and care outcomes, among others. An altered care process, for example, could interrupt the sequence of care, which could lead to changes in care decisions and/or changes in care outcomes. We define interruptions in the care process as anomalies in the care sequence. In this paper, we present a new approach based on the higher-order network (HON) representation to detect anomalies in sequential health care data using electronic health records. The results show that there are higher-order dependencies in health care data; and that the use of HON representation is more effective than the first-order network representation for detecting anomalies in sequential health care data.

Niu, Haoran↗
DOE OSTI2023

Surface Properties and RF Performance of Vapor Diffused Nb3Sn on NB After Sequential Anneals Below 1000°C

Nb3Sn is a next-generation superconducting material that can be used for future superconducting radiofrequency (SRF) accelerator cavities, promising better performance, cost reduction, and higher operating temperature than Nb. The Sn vapor diffusion method is currently the most preferred and successful technique to coat niobium cavi-ties with Nb3Sn. Among post-coating treatments to opti-mize the coating quality, higher temperature annealing without Sn is known to degrade Nb3Sn because of Sn loss. We have investigated Nb3Sn/Nb samples briefly annealed at 800-1000°C for 10 and 20 minutes to poten-tially improve the surface to enhance the performance of Nb3Sn-coated cavities. Following the sample studies, a coated single-cell cavity was sequentially annealed at 900°C and tested its performance each time, improving the cavity's quality factor relatively. This paper summarizes the sample studies and discusses the RF test results from sequentially annealed SRF Nb3Sn/Nb cavity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Versatile Sequential Casting Processing for Highly Efficient and Stable Binary Organic Photovoltaics

Forming an ideal bulk heterojunction (BHJ) morphology is a critical issue governing the photon to electron process in organic solar cells (OSCs). Complementary to the widely-used blend casting (BC) method for BHJ construction, sequential casting (SC) can also enable similar or even better morphology and device performance for OSCs. Here, BC and SC methods on three representative donor:acceptor (D:A) blends are utilized, that is, PM6:PC 71 BM, PM6:IT-4F and PM6:L8-BO. Higher power conversion efficiencies (PCEs) in all cases by taking advantage of beneficial morphology from SC processing are achieved, and a champion PCE of 18.86% (certified as 18.44%) based on the PM6:L8-BO blend is reached, representing the record value among binary OSCs. The observations on phase separation and vertical distribution inspire the proposal of the swelling–intercalation phase-separation model to interpret the morphology evolution during SC processing. Further, the vertical phase segregation is found to deliver an improvement of device performance via affecting the charge transport and collection processes, as evidenced by the D:A-ratio-dependent photovoltaic properties. Besides, OSCs based on SC processing show advantages on device photostability and upscale fabrication. Finally, this work demonstrates the versatility and efficacy of the SC method for BHJ-based OSCs.

14 SOLAR ENERGY↗
DOE OSTI2025

Sequential Infiltration Synthesis of Cadmium Sulfide Discrete Atom Clusters

Exposure of soft material templates to alternating volatile chemical precursors can produce inorganic deposition within the permeable template (e.g. a polymer thin film) in a process akin to atomic layer deposition (ALD). While such sequential infiltration synthesis (SIS) processes have now been demonstrated for many metal oxides, we report an SIS process for a transition metal sulfide – CdS. Gas phase dimethyl cadmium and hydrogen sulfide precursors infiltrated into poly(4-vinylpyridine) thin films result in the 3D-nucleation of clusters consistent with a cubane-type Cd 4 S 4 core that are variably terminated with methyl, thiol and hydroxy capping ligands. First principles models and simulation of few-atom Cd-based clusters are consistent with electronic and vibrational spectroscopy and grazing-incidence total X-ray scattering measurements of 3D-cluster-arrays synthesized at 80 °C. The direct synthesis of few-atom transition metal sulfide clusters within polymer thin films will provide a versatile new route to precision architectures for light-absorbing materials including solar energy harvesting and conversion applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians

In this study, we consider solving nonlinear optimization problems with a stochastic objective and deterministic equality constraints. We assume for the objective that its evaluation, gradient, and Hessian are inaccessible, while one can compute their stochastic estimates by, for example, subsampling. We propose a stochastic algorithm based on sequential quadratic programming (SQP) that uses a differentiable exact augmented Lagrangian as the merit function. To motivate our algorithm design, we first revisit and simplify an old SQP method Lucidi developed for solving deterministic problems, which serves as the skeleton of our stochastic algorithm. Based on the simplified deterministic algorithm, we then propose a non-adaptive SQP for dealing with stochastic objective, where the gradient and Hessian are replaced by stochastic estimates but the stepsizes are deterministic and prespecified. Finally, we incorporate a recent stochastic line search procedure Paquette and Scheinberg into the non-adaptive stochastic SQP to adaptively select the random stepsizes, which leads to an adaptive stochastic SQP. The global "almost sure" convergence for both non-adaptive and adaptive SQP methods is established. Numerical experiments on nonlinear problems in CUTEst test set demonstrate the superiority of the adaptive algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation

Abstract We consider the problem of learning the level set for which a noisy black-box function exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian process (GP) metamodels. Our focus is on strongly stochastic simulators, in particular with heavy-tailed simulation noise and low signal-to-noise ratio. To guard against noise misspecification, we assess the performance of three variants: (i) GPs with Student- t observations; (ii) Student- t processes (TPs); and (iii) classification GPs modeling the sign of the response. In conjunction with these metamodels, we analyze several acquisition functions for guiding the sequential experimental designs, extending existing stepwise uncertainty reduction criteria to the stochastic contour-finding context. This also motivates our development of (approximate) updating formulas to efficiently compute such acquisition functions. Our schemes are benchmarked by using a variety of synthetic experiments in 1–6 dimensions. We also consider an application of level set estimation for determining the optimal exercise policy of Bermudan options in finance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The effects of sequential hydrothermal-mechanical refining pretreatment on cellulose structure changes and sugar recoveries

The recalcitrance of lignocellulosic biomass necessitates an efficient pretreatment protocol for operating a successful cellulosic biorefinery. It is critical to improve cellulose accessibility for hydrolysis and fermentation by altering the plant cell wall’s physical structure and chemical composition. Sequential hydrothermal-mechanical refining pretreatment (HMR) allows efficient recovery of cellulosic sugars without utilizing any hazardous chemicals. HMR has been successfully applied to Liberty switchgrass, a bioenergy cultivar released by the USDA, and now it is being applied to oilcane, a recently developed transgenic sugarcane variety engineered to accumulate lipids in its vegetative tissues. Sugar yields of oilcane bagasse (OCB) and switchgrass (SG) treated with HMR are 96.4% and 75.4%, respectively. This study sought to correlate cellulosic sugar yields with structural changes within the cell wall caused by HMR on two distinct bioenergy crops. Simon’s staining technique for the specific surface area analysis showed that HMR increased the specific surface area of pretreated biomass residues by 80–112%. In addition, ATR-FTIR was performed to determine the effects of HMR on physical structures based on the total crystallinity index (TCI) and hydrogen bonding intensity (HBI). Irrespective of biomass type, HMR decreased the initial crystalline cellulose contents of untreated biomass residues by 3.5% and reduced TCI and HBI by 7–13%. Here, the study found that sugar yields were negatively correlated to reducing values of hydrogen bonding intensity, crystalline cellulose content, and total crystallinity index.

09 BIOMASS FUELS↗
DOE OSTI2023

Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning

We present a mathematical framework and computational methods for optimally designing a finite sequence of experiments. This sequential optimal experimental design (sOED) problem is formulated as a finite-horizon partially observable Markov decision process (POMDP) under a Bayesian setting and with information-theoretic utilities. The formulation is general and may accommodate continuous random variables, non-Gaussian posteriors, and nonlinear forward models. The sOED design policy incorporates elements of feedback and lookahead simultaneously, and we show it to generalize the commonly-used batch and greedy design strategies. We solve for the sOED policy using the policy gradient (PG) method from reinforcement learning, and provide a derivation for the PG expression in the sOED context. Adopting an actor-critic approach, the policy and value functions are parameterized using deep neural networks and improved via PG estimates produced from simulated episodes of designs and observations. The new PG-sOED algorithm is first validated on a linear-Gaussian benchmark, and then compared against other design baselines on a sensor movement problem for contaminant source inversion in a convection-diffusion field. As a result, we provide explanation for the policy behaviors using knowledge of the underlying physical process.

97 MATHEMATICS AND COMPUTING↗