Search NASA⌕ Search

SEARCH · Search NASA

Results for “Selection optimization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Piperidinyl Ureas Chemically Control Defective in Cullin Neddylation 1 (DCN1)-Mediated Cullin Neddylation

We previously discovered and validated a class of piperidinyl ureas that regulate defective in cullin neddylation 1 (DCN1)-dependent neddylation of cullins. Here, we report preliminary structure–activity relationship studies aimed at advancing our high-throughput screen hit into a tractable tool compound for dissecting the effects of acute DCN1–UBE2M inhibition on the NEDD8/cullin pathway. Structure-enabled optimization led to a 100-fold increase in biochemical potency and modestly increased solubility and permeability as compared to our initial hit. The optimized compounds inhibit the DCN1–UBE2M protein–protein interaction in our TR-FRET binding assay and inhibit cullin neddylation in our pulse-chase NEDD8 transfer assay. The optimized compounds bind to DCN1 and selectively reduce steady-state levels of neddylated CUL1 and CUL3 in a squamous cell carcinoma cell line. Furthermore, we anticipate that these studies will identify early lead compounds for clinical development for the treatment of lung squamous cell carcinomas and other cancers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Photodiode-based machine learning for optimization of laser powder bed fusion parameters in complex geometries

We report the quality of parts produced through laser powder bed fusion additive manufacturing can be irregular, with complex geometries sometimes exhibiting dimensional inaccuracies and defects. For optimal part quality, laser process parameters should be selected carefully prior to printing and adjusted during the print if necessary. This is challenging since approaches to control and optimize the build parameters need to take into account the part geometry, the material, and the complex physics of laser powder bed fusion. This work describes a data-driven approach using experimental diagnostics for the optimization of laser process parameters prior to printing. A training dataset is generated by collecting high speed photodiode signal data while printing simple parts containing key geometry features with various process parameter strategies. Supervised learning approaches are employed to train both a forward model and an inverse model. The forward model takes as inputs track-wise geometry features and laser parameters and outputs the photodiode signal along the scan path. The inverse model takes as inputs the geometry features and photodiode signal and predicts the laser parameters. Given the part geometry and a desired photodiode signal, the inverse model can thus determine the required laser parameters. Two test parts which contain defect-prone features are used to assess the validity of the inverse model. The use of the model leads to improved part quality (higher dimensional accuracy, reduced dross, reduced distortion) for both test geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Effects of Pore Size on Na/Ca Ion Selectivity Using Flow Through Electrode Capacitive Deionization

In flow through electrode capacitive deionization (fte-CDI), low salinity brackish water flows through two porous carbon electrodes. When an electrical potential difference is applied over the electrodes, ions migrate towards micropores, cleaning the water. Ion selectivity can be controlled by taking advantage of charge and ionic radius differences as well as the applied potential and pore size of the electrodes. The research outlined in this paper consists primarily of running fte-CDI cells with electrodes of varying pore size distributions to experimentally determine the parameters for optimal total salt removal and sodium/calcium ion selectivity. Five fte-CDI cells of different activation were fabricated using microporous hierarchical carbon aerogel materials (HCAMs) as electrodes. For each cell, cyclic voltammetry using 20 mM NaCl solution and 10 mM CaCl2 solution was performed at a scan rate of 0.5, 0.75, and 1 mV/s with a potential window from -0.8 V to +0.8 V. The same pure salts were desalinated in hourly charge/discharge cycles at ±1 V. A mixture of 5 mM NaCl and 2.5 mM CaCl2 was desalinated at 0.6, 0.8, 1.0, and 1.2 V. The collected charge and discharge streams were analyzed with ion chromatography (IC). The IC data for the two least activated cells showed minimal calcium adsorption compared to sodium. All cells adsorbed sodium, regardless of activation, while only highly activated cells removed calcium. This data indicates a tradeoff between more total salt removed at high voltages and selectivity of sodium over calcium at low voltages. Future research with nitrogen porosimetry could provide a quantitative value for the optimal pore size distribution.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Operando XAS and DFT Uncover Structure-Performance Relationships in Re/TiO 2 for Selective CO 2 Hydrogenation to Methanol

The conversion of CO 2 into value-added chemicals, such as methanol, offers a promising pathway toward a renewable energy future. However, a precise kinetic control and a highly selective catalyst are necessary to overcome the thermodynamic preference for CO 2 hydrogenation to methane. Rhenium-based catalysts, particularly Re/TiO 2 , demonstrate high activity and selectivity for methanol under high-pressure conditions. For example, at 100 bar and 200 °C, a methanol selectivity of 97−99% was obtained. Catalysts with 1 wt % Re and 5 wt % Re/ TiO 2 were used to study the effect of cluster sizes. At 250 °C, the 1 wt % catalyst achieves 97% selectivity at 23% conversion, whereas 5 wt % Re/TiO 2 achieves 74% selectivity at 40% conversion, corresponding to a drop in space-time yield from 65 to 16 g CH 3 OH ·g Re −1 ·h −1 , respectively. X-ray absorption spectroscopy provided insights into the structure of the active sites, while density functional theory calculations revealed the effects of cluster size on the energy barriers for H 2 activation, CH 3 OH dissociation, and CH 3 OH desorption, all of which directly influence conversion and selectivity. These results underscore the importance of balancing cluster size for optimal catalyst performance and provide insights into the design of efficient and selective catalysts for renewable methanol production.

XAS↗
DOE OSTI2025

Methods for Carbon Mass Closure in Polyolefin Hydrocracking

Heterogeneous catalytic hydrocracking of polyolefins is a promising approach for the processing of postconsumer plastics, but product quantification methods remain inconsistent across the literature. In systems that generate a large fraction of vapor-phase products, typical product capture methods can result in large carbon balance deficits, exceeding 50%, compromising reported yields and selectivities. Here, we identify the major sources of product loss and develop enhanced capture methods to improve the quantification accuracy. Seven supplemental techniques were evaluated, targeting either increased vapor recovery (by increasing the volatility or system volume) or enhanced retention in the liquid phase (by decreasing volatility). Among these, a flow collection approach using a continuous helium sweep and downstream gas sampling bag capture yielded the highest recovery, achieving a 96 ± 9.2% carbon balance closure. We show that the efficacy of these methods is strongly dependent on product distribution. In general, solvent addition was most effective when condensable species dominate the product distribution, while flow collection was preferred when both condensable species and light gases are present in high concentrations. These results highlight the need for method-specific workup strategies and demonstrate that no single protocol is universally optimal. We provide general guidelines for selecting and implementing robust product capture techniques, enabling accurate yield and selectivity determinations in polyolefin hydrocracking systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Methanol Partial Oxidation on Cu(111) and PtCu(111) Single-Atom Alloy Surfaces: Effect of Surface Oxygen Coverage on Selectivity

The selective oxidation of methanol to formaldehyde on Cu surfaces is an important and well-studied reaction. However, a systematic analysis of product selectivity as a function of oxygen coverage on Cu(111) and Cu-based single-atom alloys (SAAs) has not been previously reported. In this work, we present a comprehensive investigation of deuterated methanol (CD 3 OH) partial oxidation on Cu(111) and 1% PtCu(111) SAA surfaces as a function of preadsorbed oxygen coverage. Temperature-programmed desorption (TPD) and X-ray photoelectron spectroscopy (XPS) reveal that isolated Pt atoms inhibit the initial surface oxidation of Cu(111) under low oxygen exposures. Despite this difference in oxidation kinetics, the product selectivity remains largely unaffected: on both Cu(111) and PtCu(111), formaldehyde (CD 2 O) is the predominant partial oxidation product over a broad range of oxygen coverages. The selectivity toward formaldehyde peaks at intermediate oxygen coverages (∼0.3 monolayers, ML), indicating the existence of an optimal oxygen loading for partial oxidation. Notably, the similar product selectivities on Cu(111) and PtCu(111) over a range of surface oxygen coverage indicate that Pt single atoms do not significantly alter the reaction pathway or shift the optimal oxygen coverage for formaldehyde formation. Control experiments confirm that Cu(111) is unreactive toward methanol in the absence of oxygen, while PtCu(111) surfaces produce a small amount of formaldehyde even when oxygen is not preadsorbed, indicating that isolated Pt atoms facilitate O−H activation at below 150 K, leading to H 2 desorption, followed by C−D activation at higher temperatures (∼350 K). Density functional theory (DFT)-based calculations show that Pt atoms increase the O 2 dissociation barrier relative to Cu(111), consistent with the observed inhibition of oxidation at low exposures. Overall, this work provides the first detailed selectivity map for methanol oxidation on oxidized Cu(111) and PtCu(111) SAA surfaces. By linking classical mechanistic insights such as methoxy- and formate-mediated pathways with single-atom alloy catalyst design, this work demonstrates that while Pt substitution modulates the oxidation kinetics and oxygen binding, the overall selectivity toward formaldehyde is governed primarily by oxygen coverage. These findings underscore the potential of isolated dopants to tune surface oxidation behavior without compromising the intrinsic partial oxidation selectivity of copper-based catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Combined selection of the dynamic model and modeling error in nonlinear aeroelastic systems using Bayesian Inference

Here, we report a Bayesian framework for concurrent selection of physics-based models and (modeling) error models. We investigate the use of colored noise to capture the mismatch between the predictions of calibrated models and observational data that cannot be explained by measurement error alone within the context of Bayesian estimation for stochastic ordinary differential equations. Proposed models are characterized by the average data-fit, a measure of how well a model fits the measurements, and the model complexity measured using the Kullback–Leibler divergence. The use of a more complex error models increases the average data-fit but also increases the complexity of the combined model, possibly over-fitting the data. Bayesian model selection is used to find the optimal physical model as well as the optimal error model. The optimal model is defined using the evidence, where the average data-fit is balanced by the complexity of the model. The effect of colored noise process is illustrated using a nonlinear aeroelastic oscillator representing a rigid NACA0012 airfoil undergoing limit cycle oscillations due to complex fluid–structure interactions. Several quasi-steady and unsteady aerodynamic models are proposed with colored noise or white noise for the model error. The use of colored noise improves the predictive capabilities of simpler models.

42 ENGINEERING↗
DOE OSTI2023

Assessment of the Degradation Mechanisms of Cu Electrodes during the CO 2 Reduction Reaction

Catalyst degradation and product selectivity changes are two of the key challenges in the electrochemical reduction of CO 2 on copper electrodes. Yet, these aspects are often overlooked. Here, we combine in situ X-ray spectroscopy, in situ electron microscopy, and ex situ characterization techniques to follow the long-term evolution of the catalyst morphology, electronic structure, surface composition, activity, and product selectivity of Cu nanosized crystals during the CO 2 reduction reaction. We found no changes in the electronic structure of the electrode under cathodic potentiostatic control over time, nor was there any build-up of contaminants. In contrast, the electrode morphology is modified by prolonged CO 2 electroreduction, which transforms the initially faceted Cu particles into a rough/rounded structure. In conjunction with these morphological changes, the current increases and the selectivity changes from value-added hydrocarbons to less valuable side reaction products, i.e. , hydrogen and CO. Hence, our results suggest that the stabilization of a faceted Cu morphology is pivotal for ensuring optimal long-term performance in the selective reduction of CO 2 into hydrocarbons and oxygenated products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Dynamic Binary Complexes (DBC) as Super-Adjustable Viscosity Modifiers for Hydraulic Fracturing Fluids

In the preceding project year two, we refined three DBC formulations from a selection of over 50 different chemistries. The optimization study primarily encompassed testing for (i) reversibility extent, (ii) performance in the presence of chemical additives, (iii) adhesion and friction behavior during displacement in wellbores and pipelines, (iv) corrosion protection performance, and (v) injection performance with model fracture systems at the laboratory scale. Highly promising results obtained from all these tests signify the significant potential of DBCs in enhancing hydrocarbon recovery from unconventional reservoirs. The primary activities in the third project year included publishing experimental findings across multiple articles and conducting outreach initiatives. Throughout the year, we undertook tasks such as replicating experimental results, further optimizing various formulations and their associated experimental sets, and conducting additional tests to address missing components based on reviewer feedback and suggestions. We also explored the surfactant and friction-reduction aspects of selected formulations through drag reduction tests. In addition, we constructed an improved fracturing performance setup and performed flow injection tests. The specific DBC formulations focused on during this project period were A8/B1, A12/B5, and A10/B12. We also obtained results for additional DBC formulations and a select few commercial fracturing fluids for the purpose of comparison. Within the project's scope, we aim to enhance the experimental findings with the development of various models. The first two years focused on two key aspects: (i) the creation of a high-fidelity hydraulic fracturing model for non-Newtonian fluids to gain insights into the implementation of DBC fluids in fracking environments, and (ii) the development of a multiphase flow simulator for estimating total production, fluid saturation in the reservoir, and the creation of a fracture propagation model and kinetic Monte Carlo (kMC) models for diverse applications. In the third year, we delved into the fundamental nanostructural properties of DBCs, exploring aspects such as material chemistry, pH tunability, and control of DBC formation and stability. Subsequently, in the extension year, we conducted a systematic investigation of various building blocks containing primary, secondary, and tertiary amine functional groups to understand their impact on rheological and viscoelastic properties. Furthermore, we explored a Dissipative Particle Dynamics (DPD) model to simulate self-assembly processes with precision, creating a high-fidelity representation of relevant nanostructures. The tasks performed this year with the significant results obtained have been discussed in Section 2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Individualized Selection of Beam Angles and Treatment Isocenter in Tangential Breast Intensity Modulated Radiation Therapy

Purpose and Objective: Propose a novel method for individualized selection of beam angles and treatment isocenter in tangential breast intensity modulated radiation therapy (IMRT). Methods and Materials: For each patient, beam and isocenter selection starts with the fully automatic generation of a large database of IMRT plans (up to 847 in this study); each of these plans belongs to a unique combination of isocenter position, lateral beam angle, and medial beam angle. The imposed hard planning constraint on patient maximum dose may result in plans with unacceptable target dose delivery. Such plans are excluded from further analyses. Owing to differences in beam setup, database plans differ in mean doses to organs at risk (OARs). These mean doses are used to construct 2-dimensional graphs, showing relationships between: (1) contralateral breast dose and ipsilateral lung dose; and (2) contralateral breast dose and heart dose (analyzed only for left-sided). The graphs can be used for selection of the isocenter and beam angles with the optimal, patient-specific tradeoffs between the mean OAR doses. For 30 previously treated patients (15 left-sided and 15 right-sided tumors), graphs were generated considering only the clinically applied isocenter with 121 tangential beam angle pairs. For 20 of the 30 patients, 6 alternative isocenters were also investigated. Results: Computation time for automatic generation of 121 IMRT plans took on average 30 minutes. The generated graphs demonstrated large variations in tradeoffs between conflicting OAR objectives, depending on beam angles and patient anatomy. For patients with isocenter optimization, 847 IMRT plans were considered. Adding isocenter position optimization next to beam angle optimization had a small impact on the final plan quality. Conclusion: A method is proposed for individualized selection of beam angles in tangential breast IMRT. This may be especially important for patients with cardiac risk factors or an enhanced risk for the development of contralateral breast cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Critical differences between electron beam melted and selective laser melted Ti-6Al-4 V

Effective optimization of the production of Ti-6Al-4 V using AM requires a fundamental understanding of the relative importance of different microstructural features to the deformation and failure mechanisms, particularly features that vary between production methods. In this study, the tensile response and deformation mechanisms of electron beam melted (EBM) AM Ti-6Al-4 V material loaded in different orientations and produced using various powder sizes were compared to those of selective laser melted (SLM) AM Ti-6Al-4 V material. The density and morphology of pores, phase fractions, prior-β grains, and defect microstructures were evaluated using scanning electron microscopy, X-ray computed tomography, electron backscatter diffraction, and transmission electron microscopy before and after deformation. The results were used to evaluate the relative importance of each feature on strengthening, deformation, and failure initiation mechanisms. Results focused primarily on coarse-powder EBM materials indicated that phase distribution and defect density were most influential for determining material yield strength as well as maximum possible strain to failure. Porosity was lower overall in EBM Ti-6Al-4 V than in SLM, allowing for occasional increases in part strain to failure, but remained a limiting factor determining overall part ductility.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fundamental Studies of Bifunctional Catalysts for Tandem Reactions

During the past few years of our funding from DOE Basic Energy Sciences, we have focused on the catalytic conversion of biomass-derived 5-hydroxymethyl furfural (HMF) as a platform molecule for sustainable synthesis of value-added chemicals. The combination of the diene and aldehyde functionalities in HMF enables catalytic production of acetalized HMF derivatives with diol or epoxy reactants to allow reversible synthesis of norcantharimide derivatives upon Diels-Alder reaction with maleimides. Whereas the electron-withdrawing nature of aldehyde group inhibits the reactivity of diene group in HMF to undergo Diels-Alder reactions with dienophiles, by converting the aldehyde group in HMF to an electron-donating hydroxyl group, HMF can append maleimide-based chemicals through Diels-Alder coupling. Acetalization of HMF not only produces an electron-rich diene for Diels-Alder reaction, but it also alters the reversibility of acetal formation and thereby allows for the controlled release of chemicals by hydrolysis. Therefore, we synthesized various norcantharimide derivatives from biomass-derived HMF by acetalization over an acid catalyst, Amberlyst-15, followed by Diels-Alder reaction with maleimide. The norcantharimides release the starting materials by retro Diels-Alder reaction that is triggered by acetal hydrolysis under acidic (≤pH 3) conditions. In subsequent work, we explored the synthesis and properties of functional polyurethanes and polyesters with tunable properties from biomass-derived (HMF)-Acetone-HMF (HAH) monomers. HAH can be selectively hydrogenated over Cu and Ru catalysts to produce partially-hydrogenated HAH (PHAH) and fully-hydrogenated HAH (FHAH). HAH contains functional groups, including hydroxyl, furan, enone, and ketone functionalities, that can be exploited to further tune the polymer properties. The π-electron conjugation between the enone and furan groups in HAH monomer stabilizes the furan group to prevent the uncontrolled degradation of furans during reactions, such as etherification. Therefore, HAH is a renewable monomer that can be used to synthesize polymers, having high molecular weight, symmetric functionalities, an inexpensive production price, and being derived from renewable resources without competition with food resources. We demonstrated the synthesis of functional polyurethanes and a renewable polyester from HAH-derived monomers, and 4,4’-methylenebis(phenyl isocyanate) (MDI). We showed that it is essential to be able to selective hydrogenate C=C double bonds in HAH to be able to achieve effective Diels Alder reactions of the furan ring. Therefore, we studied the hydrogenation at temperatures from 313 – 393 K of HAH over Pd, Ru, and Cu based catalysts. HAH was selectively hydrogenated to produce partially-hydrogenated monomers (PHAH) over Cu and Ru catalysts and to fully-hydrogenated HAH monomers (FHAH) over the Ru catalyst. Pd based catalysts yielded a mixture of partially and fully hydrogenated monomers. Reaction kinetics models were employed to quantify the kinetic behavior for hydrogenation over Ru, Cu, and Pd catalysts. A 5-step pathway exhibited over Pd and Ru catalysts consists of both series and parallel reaction steps, where HAH is both converted to fully hydrogenated products sequentially via series reactions of partially hydrogenated intermediates, as well as converted directly in parallel reactions to form the fully hydrogenated products. In contrast, a 3-step pathway over the Cu catalyst consists only of the consecutive reaction steps, where the final product was formed via series reactions of intermediate products. Additionally, reaction over the Cu catalyst did not hydrogenate the furan rings of the HAH molecule and yielded a different final product than those hydrogenation over Pd and Ru catalysts. Using the results from our detailed reaction kinetics studies, we developed universal conditions for the maximum production of the various hydrogenated products for both batch and plug flow systems. In subsequent work, we employed reaction schemes to optimize the yields of products from selective hydrogenations of HAH in isopropanol solvent and in the presence of liquid water. Reaction schemes consisting of 7, 9, and 11 steps were examined to describe the rates of formation of the observed products and reaction intermediates for hydrogenation of HAH over Ru and Pd catalysts, and a 3-step scheme was studied over Cu catalysts. Rate constants and activation energies were calculated using these reaction schemes, and we then apply these reaction schemes to explore the effects of water addition on the hydrogenation pathways. The effects of water addition to isopropanol (IPA) solvents on the hydrogenation of HAH were markedly different over Pd, Ru, and Cu catalysts. Over the Pd catalyst, the addition of water to IPA increased hydrogenation rates and promoted hydrogenation of furan rings. The addition of water to IPA yielded significant carbon losses over the Ru catalyst, and slowed hydrogenation steps over Cu, while significantly inhibiting hydrogenation of the ketone group. This behavior opened routes toward increased production rates of PHAH=O (a partially hydrogenated form of HAH containing a C=O bond), a product in which the diene groups of the furan rings were not hydrogenated. Addition of water also allowed increased feed concentrations of HAH that were previously not possible in pure IPA solvents.

09 BIOMASS FUELS↗
DOE OSTI2018

Schedule Management Optimization (SMO) Domain Model (V.1.2)

Schedule Management Optimization (SMO) is a tool for automatically generating a schedule of project tasks. Project scheduling is traditionally achieved with the use of commercial project management software or case-specific optimization formulations. Commercial software packages are useful tools for managing and visualizing copious amounts of project task data. However, their ability to automatically generate optimized schedules is limited. Furthermore, there are many real-world constraints and decision variables that commercial packages ignore. Case-specific optimization formulations effectively identify schedules that optimize one or more objectives for a specific problem, but they are unable to handle a diverse selection of scheduling problems. SMO enables practitioners to generate optimal project schedules automatically while considering a broad range of real-world problem characteristics. SMO has been designed to handle some of the most difficult scheduling problems – those with resource constraints, multiple objectives, multiple inventories, and diverse ways of performing tasks. This report contains descriptions of the SMO modeling concepts and explains how they map to real-world scheduling considerations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Co-optimization of CO 2 -EOR and Storage Processes under Geological Uncertainty

This paper presents an integrated numerical framework to co-optimize EOR and CO 2 storage performance in the Farnsworth field unit (FWU), Ochiltree County, Texas. The framework includes a field-scale compositional reservoir flow model, an uncertainty quantification model and a neural network optimization process. The reservoir flow model has been constructed based on the field geophysical, geological, and engineering data. A laboratory fluid analysis was tuned to an equation of state and subsequently used to predict the thermodynamic minimum miscible pressure (MMP). A history match of primary and secondary recovery processes was conducted to estimate the reservoir and multiphase flow parameters as the baseline case for analyzing the effect of recycling produced gas, infill drilling and water alternating gas (WAG) cycles on oil recovery and CO 2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO 2 storage. The uncertainty quantification model comprising the Latin Hypercube sampling, Monte Carlo simulation, and sensitivity analysis, was used to study the effects of uncertain variables on the defined objective functions. Uncertain variables such as bottom hole injection pressure, WAG cycle, injection and production group rates, and gas-oil ratio among others were selected. The most significant variables were selected as control variables to be used for the optimization process. A neural network optimization algorithm was utilized to optimize the objective function both with and without geological uncertainty. The vertical permeability anisotropy (Kv/Kh) was selected as one of the uncertain parameters in the optimization process. The simulation results were compared to a scenario baseline case that predicted CO 2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO 2 storage in the FWU. The optimization process predicted more than 94% of CO 2 storage and most importantly about 28% of incremental oil recovery. The sensitivity analysis reduced the number of control variables to decrease computational time. A risk aversion factor was used to represent results at various confidence levels to assist management in the decision-making process. The defined objective functions were proved to be a robust approach to co-optimize oil recovery and CO 2 storage. The Farnsworth CO 2 project will serve as a benchmark for future CO 2 –EOR or CCUS projects in the Anadarko basin or geologically similar basins throughout the world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Evaluation of Hydrogen Production Feasibility for a Light Water Reactor in the Midwest

Increased electricity production from renewable energy resources coupled with low natural gas prices has caused existing light-water reactor (LWRs) to experience ever diminishing returns from the electricity market. Via partnership among Idaho National Laboratory (INL), The National Renewable Energy Laboratory (NREL), Argonne National Laboratory (ANL), Exelon, and Fuel Cell Energy a techno-economic analysis on the viability of retrofitting existing pressurized water reactors (PWRs) to produce hydrogen (H2) via high temperature steam electrolysis (HTSE) has been conducted. Such integration would allow nuclear facilities to expand into additional markets that may be more profitable in the long-term. To accommodate such an integration, a detailed analysis of HTSE process operation, requirements, and flexibility was conducted. Technical analysis includes proposed nuclear system control scheme modifications to allow dynamic operation of the HTSE via both thermal and electrical connection to the nuclear plant. High fidelity Modelica simulations showcase the viability of such control schemes. However, due to limited knowledge of solid oxide fuel cell (SOFC) stack degradation due to thermal gradients; thermal cycling of the HTSE was not included. Therefore, the control schemes proposed are only utilized to re-distribute steam at startup and only the portion of electricity utilized in the electrolyzers is cycled. From the detailed analysis of the nuclear integration and the HTSE process design, a comprehensive cost estimation was conducted in the APEA and H2A models to elucidate capital and operational costs associated with the production, compression, and distribution of hydrogen from a nuclear facility. Alongside this costing analysis, market analyses were conducted by NREL and ANL on the electric and hydrogen markets respectively in the PJM interconnect. Utilizing the electricity data market projections in the PJM interconnect from NREL and hydrogen demand/pricing projections from ANL a five variable sweep over component capacities, discount rates, and hydrogen pricing was completed using the stochastic framework RAVEN (Risk Analysis Virtual ENVironment) through its resource dispatch plugin HERON (Heuristic Energy Resource Optimization Network). Each combination of variables was evaluated stochastically four times over a seventeen year timespan from 2026-2042 (inclusive) to determine the most economically advantageous solution. Following the five variable sweep an optimization was conducted about the best sweep point to determine optimal component sizing and setpoints. Results suggest positive gain is achievable at all projected hydrogen market pricing levels and at all discount rates. However, exact component sizing and net returns vary based on these values and if incorrect sizing is selected; major net losses can occur. The optimal result occurred with set points as follows: high hydrogen prices, the largest possible HTSE unit in the sweep set at 7.47 kg/sec [645.4 tpd], a contractual hydrogen market agreement 7.29 kg/sec [629.8 tpd], and a hydrogen storage size 115,188 kg. The analysis suggested that with a discount rate of 8%, a ΔNPV = 1.2 billion over the seventeen year span can be achieved. The results illuminate that by operating in multiple markets the nuclear facility is capable of avoiding sale of electricity during times of low electricity market pricing, while maintaining the ability to capitalize on the high electricity market pricing. It should be noted that the analysis conducted in this report is a differential cash flow analysis and as such does not present profit levels. Instead it highlights the net benefit between building and competing in the hydrogen market utilizing nuclear facilities and conducting business as usual in the electricity markets. Additionally, results presented in this report exhibit conservatism due to four key assumptions: Given the limited knowledge on SOFC stack degradation due to thermal gradients the high temperature steam electrolysis plant is not allowed to thermally cycle. This limitation decreases electricity generation capacity in the nuclear plant, reducing capacity payments and the maximum output to the electrical grid. This analysis considers building a separate hydrogen pipeline for use in the nuclear facility. Research is currently being conducted to determine if existing natural gas pipelines can accommodate direct hydrogen injection. If hydrogen can be integrated into existing natural gas pipelines then a capital savings of ~$19,000,000 per kg/sec [1 kg/sec = 86.4 tpd] of installed HTSE capacity could be realized. Fast transients in the electric grid are typically served by flywheels and electric batteries. With electrolysis only operation the nuclear plant can flex its electricity just as quickly. However, for this analysis, the ancillary services market has been neglected. Should the nuclear plant have the ability to operate in the ancillary services market during periods when it is producing hydrogen, an additional substantial income stream that would increase overall system profitability could be considered. Subsidies for non-emitting generation technologies are not introduced in the cash flow analysis, but instead are completed assuming zero subsidies. Profitability is based solely on electricity pricing. Electric pricing is assumed to never go negative. Instead, it is assumed overall system curtailment by the Independent System Operator (ISO) will occur in such a manner that pricing remains at or above zero. This assumption inflates the “business as usual” scenario, thus decreasing the ΔNPV calculated. If negative pricing scenarios continue to occur as they do today, one would expect a further increase in ΔNPV. Overall, results advocate that, through market diversification, nuclear power plants have the potential to substantially increase current profit margins, increase market penetration, and ultimately solidify their place as a mainstay in energy production.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗