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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.

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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Verification of Upcoming MCNP Features For Estimating Nuclear Data Sensitivities in Fixed Source Simulations [Abstract]

Predictive simulation codes, like the Monte Carlo N-Particle (MCNP) transport code, are used throughout the nuclear community. These simulations are based on nuclear data. Maximizing the accuracy and precision of nuclear data maximizes the accuracy and precision of the overall simulation. This is imperative to applications that rely on simulations. For example, improving nuclear data for special nuclear material improves simulation accuracy in stockpile stewardship applications, which results in larger safety margins and decreased operational costs. The improvement and validation of nuclear data is completed through integral benchmark experiments. Past benchmarks have primarily been limited to focus on the effective multiplication factor ($\kappa$ eff ); broadening the purview of benchmarks beyond $\kappa$ eff -dependent nuclear data addresses nuclear data deficiencies. Different response types depend on different areas of nuclear data. This dependence is quantified as nuclear data sensitivity: the change in response due to perturbation of a contributing parameter. The larger the nuclear data sensitivity of a response, the more the experiment is influenced by the uncertainties of the nuclear data. The optimization of nuclear data sensitivities in future benchmarks would result in more detailed validation of lesser studied areas of nuclear data. Currently, direct sensitivity capabilities are not easily found for all experiment types and parameters. An MCNP tool to directly estimate the cross section sensitivities of tallied values is under development. Additionally, updates have been made to the perturbation feature of MCNP, which can be used in a less direct approach to estimating sensitivities. This work verifies these features to estimate nuclear data sensitivities in fixed source simulations of a 4.5-kg sphere of alpha- phase weapons-grade plutonium surrounded by differing amounts of copper and polyethylene. Integrated estimates made using MCNP’s tools were found to statistically agree with integrated estimates made from manual perturbation of nuclear data proving the validity of the MCNP tools.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Energy-efficient multimodal mobility networks in transportation digital twins: Strategies and optimization

The study proposes a comprehensive Transportation Mobility (TransitMo) framework covering conceptual design, model formulation, optimization, simulation, and impact analysis of the transportation mobility system. TransitMo is composed of a transportation digital twin developed in Simulation of Urban MObility (SUMO) and an Intelligent Traffic Management and Control Center (ITMCC) that identifies the best ways to improve the movement of people within urban areas using various modes of transportation. This study encompasses advanced modeling techniques, algorithms, and strategic testing to optimize energy efficiency and mobility in a multimodal shared mobility network. TransitMo’s practical applications are exemplified through a city-scaled simulation network in Chattanooga, TN, employing demographic data to analyze historical traffic patterns and forecast future demands. Central to this methodology are three models: the User Preference Model (UP), the Energy Consumption Model (EC), and the System Optimization Model (SO). These models work in concert to iteratively devise the optimal travel incentives and minimize the total system cost in a real-time manner. In conclusion, test results verified that the proposed adaptive incentive program and optimized bus scheduling can improve network performance by increasing public transit ridership.

42 ENGINEERING↗
DOE OSTI2021

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO 3 , with results showing that, with just 20% of the area sampled, most high-response clusters were captured. Furthermore, this approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.

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

An integrated approach to optimizing concentration shock wave electrodialysis using 2D multicell simulation and response surface models

Shock wave electrodialysis (SWED) is a highly promising technique for energy-efficient ion separation in the context of a circular economy. This paper presents a approach way of modeling and improving SWED using a two-dimensional multicell model combined with the COMSOL program and response surface methodology. The model integrates the Nernst-Planck equation, Darcy's law, and first-order electroosmosis to examine the local concentration, flux of ionic species, distribution of current, and velocity of flow in SWED cells under various operating conditions. We first illustrate the clear depiction of concentration, velocity, and electric potential distribution through contours which aids in identifying optimal operating conditions and designing scalable SWED systems. The results emphasize the significance of surface charge density and voltage in influencing the features of shock waves for obtaining effective ion separation while optimizing energy consumption and improving current efficiency by controlling the retention time of feed flow. Here, this study defines two crucial characteristics of shock waves, namely the length of the flat depletion zone of a fully developed shock wave (shock wave height) and the distance of shock wave propagation (shock wave length). These properties significantly impact separation performance, as determined by the simulation results. Additionally, the response surface methodology is incorporated with the COMSOL models to develop predictive models and graph responses, enabling a more comprehensive understanding of the interactions between parameters and performance indicators, such as removal ratio, energy consumption, and water recovery. Finally, this work suggests design tactics for expanding SWED processes and outlines potential areas for further research. This research provides valuable insights into the prospective applications, design optimization, and scalability of SWED in the field of electrokinetic separation technologies for green chemistry and a circular economy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Recovery Act: Low Cost Integrated Substrate for OLED Lighting Development

PPG pursued the development of an integrated substrate, including the anode, external, and internal extraction layers. The objective of PPG's program was to achieve cost reductions by displacing the existing expensive borosilicate or double-side polished float glass substrates and developing alternative electrodes and scalable light extraction layer technologies through focused and short-term applied research. One of the key highlights of the project was proving the feasibility of using PPG's high transmission Solarphire® float glass as a substrate to consistently achieve organic lightemitting diode (OLED) devices with good performance and high yields. Under this program, four low-cost alternatives to the Indium Tin Oxide (ITO) anode were investigated using pilot-scale magnetron sputtered vacuum deposition (MSVD) and chemical vapor deposition (CVD) technologies. The anodes were evaluated by fabricating small and large phosphorescent organic lightemitting diode (PHOLED) devices at Universal Display Corporation (UDC). The device performance and life-times comparable to commercially available ITO anodes were demonstrated. A cost-benefit analysis was performed to down-select two anodes for further low-cost process development. Additionally, PPG developed and evaluated a number of scalable and compatible internal and external extraction layer concepts such as scattering layers on the outside of the glass substrate or between the transparent anode and the glass interface. In one external extraction layer (EEL) approach, sol-gel sprayed pyrolytic coatings were deposited using lab scale equipment by hand or automated spraying of sol-gel solutions on hot glass, followed by optimizing of scattering with minimal absorption. In another EEL approach, PPG tested large-area glass texturing by scratching a glass surface with an abrasive roller and acid etching. Efficacy enhancements of 1.27x were demonstrated using white PHOLED devices for 2.0mm substrates which are at par with the standard diffuser sheets used by OLED manufacturers. For an internal extraction layer (IEL), PPG tested two concepts combining nanoparticles either in a solgel coating inserted between the anode and OLED or anode and glass interface, or incorporated into the internal surface of the glass. Efficacy enhancements of 1.31x were demonstrated using white PHOLED devices for the IEL by itself and factors of 1.73x were attained for an IEL in combination of thick acrylic block as an EEL. Recent offline measurements indicate that, with further optimization, factors over 2.0x could be achieved through an IEL alone.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Identifying key factors of the seawater intrusion model of Dagu river basin, Jiaozhou Bay

Highlights: • The key factors of seawater intrusion model is identified by mutual entropy analysis. • Precipitation rate and groundwater pumping are the key factors for seawater intrusion. • The hydraulic conductivity of zone 1 has significant influence on seawater intrusion. Seawater intrusion is a complex groundwater - seawater interaction process, and it is influenced by many factors from ground surface to underground, from groundwater to seawater. Generally, for seawater intrusion model, some model parameters and boundary conditions are always specified by model users’ personal experiences or literature's reference value. The defective model would damage the groundwater management for controlling and preventing seawater intrusion when making decisions are based on this model. In order to improve the reliability of seawater intrusion model, the influences of model inputs on output should be identified prior at optimizing model inputs. Dagu river basin, Jiaozhou Bay is one of the most serious areas of seawater intrusion in China, and it is chosen as the study area in this study. The seawater intrusion model of Dagu river basin is built based on a general program SEAWAT4. The key influence factors of model output are analyzed by two sensitivity analysis methods, i.e., stepwise regression and mutual entropy. The results demonstrated that the most important influence factors which have largest sensitivities to groundwater Cl{sup -} concentration are the precipitation rate and groundwater pumping in agriculture area. In addition, the hydraulic conductivity of zone 1 has a non-negligible influence on seawater intrusion process. Stepwise regression analysis is capable of identifying most important influence factor, and it can’t handle complicated nonlinear input-output relationship. Mutual entropy analysis is reliable for identifying the influence factors for complex seawater intrusion model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Forest residue harvest optimization: spanning the bridge between plant biology and biorefinery performance

Forestry residues have immense potential as alternative feedstocks to petroleum, yet their inherent complexity remains a major challenge to widespread use. Pairing the temporal rhythms of plant biology with biorefinery performance is critical to industrial-scale biorefinery development. Here, we provide the first report of a techno-economic analysis (TEA) and life cycle assessment (LCA) for a model integrated reductive catalytic fractionation (RCF)–molten salt hydrolysis process for forestry residues varying in tree part, species, and phenophase. All forestry residues resulted in net-negative greenhouse gas (GHG) emissions vs. comparable petroleum feedstocks, with GHG emissions potentially reduced >4.0× through composition-based feedstock selection (e.g., harvesting American beech bark in spring vs. summer). Moreover, American beech twigs/branchlets and bark in leafed and emergence phenophases, respectively, had 7.9× lower predicted phenolic minimum selling prices (MSPs) vs. other feedstocks and MSPs within the current global phenolic market range. Hemicellulose content and RCF yield emerged as key parameters impacting GHG emissions and biorefinery revenue, identifying hardwood twigs/branchlets in the leafed phenophase as optimal biofeedstocks. Biorefinery expenses were dominated by purchased equipment, raw materials, and utility costs, highlighting essential areas for future study. Notably, RCF reactor pressures drove 85–90% of equipment costs, but sensitivity analysis revealed that decreasing the pressure 20% could reduce the phenol MSP 4-fold. Structural carbohydrate dynamics were also investigated using a two-step acid hydrolysis method to resolve tissue- and species-level patterns in biomass composition throughout the year to enable harvest optimization based on TEA/LCA findings. Ultimately, elucidating the impact of biofeedstock dynamics on biorefinery performance enables harvest optimization, informed engineering design, and progress towards an expanded bioeconomy.

Shapiro, Alison J. [University of Delaware, Newark↗
DOE OSTI2014

The latest developments and outlook for hydrogen liquefaction technology

Liquefied hydrogen is presently mainly used for space applications and the semiconductor industry. While clean energy applications, for e.g. the automotive sector, currently contribute to this demand with a small share only, their demand may see a significant boost in the next years with the need for large scale liquefaction plants exceeding the current plant sizes by far. Hydrogen liquefaction for small scale plants with a maximum capacity of 3 tons per day (tpd) is accomplished with a Brayton refrigeration cycle using helium as refrigerant. This technology is characterized by low investment costs but lower process efficiency and hence higher operating costs. For larger plants, a hydrogen Claude cycle is used, characterized by higher investment but lower operating costs. However, liquefaction plants meeting the potentially high demand in the clean energy sector will need further optimization with regard to energy efficiency and hence operating costs. The present paper gives an overview of the currently applied technologies, including their thermodynamic and technical background. Areas of improvement are identified to derive process concepts for future large scale hydrogen liquefaction plants meeting the needs of clean energy applications with optimized energy efficiency and hence minimized operating costs. Compared to studies in this field, this paper focuses on application of new technology and innovative concepts which are either readily available or will require short qualification procedures. They will hence allow implementation in plants in the close future.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Optimization of Energy Absorbing Materials for Radioactive Packaging

Most applications of honeycomb materials are for lightweight structural members. This area of honeycomb material behavior has been extensively researched. In packaging design, honeycomb materials are used for impact energy absorption. Data concerning the deformation of stainless steel honeycomb structures is not widely available. It is being proposed to use honeycomb stainless steel as an impact energy absorber in a new radioactive material shipping package. Honeycomb materials can be useful for energy absorption applications because the geometry causes the structure to have a constant compressive force during buckling. This Finite Element Analysis is used to predict the compressive buckling strength of various honeycomb structures in order to design an energy absorber for a specific package weight and geometry. The model simulates a drop test where a 50 pound plate lands flat on a honeycomb stainless steel structure from a height of 30 feet. Some actual drop tests have already been performed on real specimens. Most of the resulting deformations of these tests include the bottom of the honeycomb structure buckling while the rest of the structure was either slightly deformed or straight. The deformation of the simulated honeycomb specimen matched the deformation from the actual drop tests. This is one verification that the model is working properly. It is evident that after about 2.5% strain the strength is a relatively linear value, and eventually flattens out to about 875 psi. The crush strength measured from the actual drop tests for the same density honeycomb was just over 800 psi. This is further verification that the model is working correctly. The results gathered from the FEA model successfully mirror the results from a real drop test. The deformation looks realistic, and the compressive strength found is consistent with the empirically measured value. Future work for this model includes modeling different density honeycomb structures which have not been tested before. This will allow for optimization of honeycomb stainless steel use for impact absorption in radioactive material shipping packages.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

Memory effect in composites of liquid crystal and silica aerosil

Aerosil silica nanoparticles dispersed in a liquid crystal (LC) possess the interesting property of keeping memory of an electric- or magnetic-field-induced orientation. Two types of memory have been identified: thermally erasable memory arising from the pinning of defect lines versus a 'permanent' memory where the orientation persists even after thermal cycling the samples up to the isotropic phase. To address the source of the latter type of memory, solid-state nuclear magnetic resonance spectroscopy and conventional x-ray diffraction (XRD) were first combined to characterize the LC orientational order as a function of multiple in-field temperature cycles. Microbeam XRD was then performed on aligned gels of different concentrations to gain knowledge of the structural properties at the origin of the memory effect. No detectable anisotropy of the gel or significant breaking of silica strands with heating ruled out the formation of an anisotropic silica network as the source of the permanent memory as previously proposed. Instead, support for a role of the surface memory effect, well known for planar substrates, in stabilizing the permanent memory was deduced from 'training' of the composites, that is, optimizing the orientational order through the thermal in-field cycling. The ability to train the composites is inversely proportional to the strength of the random-field disorder. The portion of thermally erasable memory also decreases as the silica density increases. We propose that the permanent memory originates from the surface memory effect operating at points of intersection in the silica network. These areas, where the LC is strongly confined with conflicted surface interactions, are trained to achieve an optimized orientation and subsequently act as sites from which the LC orientational order regrows after zero-field thermal cycling up to the isotropic phase.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Learning concave-convex profiles of data transport over dedicated connections

Dedicated data transport infrastructures are increasingly being deployed to support distributed big-data and high-performance computing scenarios. These infrastructures employ data transfer nodes that use sophisticated software stacks to support network transport among sites, which often house distributed file and storage systems. Throughput measurements collected over such infrastructures for a range of round trip times (RTTs) reflect the underlying complex end-to-end connections, and have revealed dichotomous throughput profiles as functions of RTT. In particular, concave regions of throughput profiles at lower RTTs indicate near-optimal performance, and convex regions at higher RTTs indicate bottlenecks due to factors such as buffer or credit limits. We present a machine learning method that explicitly infers these concave and convex regions and transitions between them using sigmoid functions. We also provide distribution-free confidence estimates for the generalization error of these concave-convex profile estimates. Throughput profiles for data transfers over 10 Gbps connections with 0–366 ms RTT provide important performance insights, including the near optimality of transfers performed with the XDD tool between XFS filesystems, and the performance limits of wide-area Lustre extensions using LNet routers. A direct application of generic machine learning packages does not adequately highlight these critical performance regions or provide as precise confidence estimates.

Rao, Nageswara↗
DOE OSTI2021

Secure Communications Concept and API Concept for Integrating XENDEE Positronix with TESLA PowerPack System at Site 300 (Final Deliverable)

The CleanStart DERMS project focuses on the management of Distributed Energy Resources (DER) for enhanced distribution grid resilience. The demonstration site has changed from Riverside Public Utility to the LLNS Site 300 DERS demonstration site. This project has so far focused only on device level controllers and local area controllers. These controllers potentially lack the ability to perform supervisory control and grid interactive control functions, essential for grid-level optimal DER management. This project seeks to close that gap in development of secure communication concept and appropriate Application Programming Interfaces (API) to enable integration with DERs, device level and local area controllers, such as Distributed Energy Resources Management System (DERMS).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Fairness-Aware Distributed Energy Coordination for Voltage Regulation in Power Distribution Systems

The accelerating deployment of solar photovoltaics into low-voltage distribution networks can cause reverse power flow and overvoltage problems. However, if coordinated properly, the real and reactive power flexibility of these resources enables distribution operators to manage their networks more efficiently. Existing literature is rich in droop-based control (Volt-Watt and Volt-VAr) and optimization-based distributed energy coordination for four-quadrant control of photovoltaics to prevent overvoltage issues. While optimal coordination can effectively mitigate overvoltage, it tends to treat resources at sensitive parts of the grid unfairly. Here, to address this concern, we propose a distributed optimal power flow formulation that incorporates fairness in curtailing photovoltaic generation and utilizes the reactive power capability of smart inverters. The proposed distributed formulation allows for scalable resource aggregation that can be leveraged to achieve fairness within a certain segment of the grid and/or fairness across the entire network. Fair curtailment of photovoltaic systems is demonstrated with aggregation at each of two layers in a distribution network: 1) area-level fairness and 2) feeder-level fairness. To explore the trade-off between fairness and optimal utilization, the fairness-aware control actions are compared against the performance of a centralized controller that aims to maximize the aggregate PV generation without incorporating fairness. Simulation results show that introducing area-level fairness increased curtailment by 0.0101 percentage points and feeder-level fairness increased curtailment by 0.0458 percentage points compared to a fairness-agnostic control.

Poudel, Shiva↗
DOE OSTI2022

Latent Space Simulation for Carbon Capture Design Optimization

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. However, previous works such as Deep Fluids (Kim et al., 2019) show that large simulation speed-ups and low error are achievable by replacing CFD simulators with neural-network (NN) surrogates that mimic the CFD simulation process. This raises the possibility for a fast, accurate replacement for a CFD simulator, and thus computationally feasible IA-based CCS-design optimization. As such, here, we explore whether an existing NN-surrogate approach (and variants we develop) can successfully be applied to our complex carbon-capture CFD simulations, with the ultimate goal of obtaining a fast and accurate simulator for our CCS-design application. Our experiments build on the Deep Fluids approach and find that resulting surrogates can produce large speed ups (4000x) while maintaining IA relative errors as low as 4% on unseen CCS configurations (interpolating between configurations seen during training). Thus, despite less faithfulness to the underlying physics of the problem, NN surrogates may be a promising tool for our CCS design optimization problem. Notably, though, the Deep Fluids approach has limitations for our application (such as model non-transferability to CCS-packing changes), which we discuss. We conclude with potential directions for future work that may, like innovations we introduced here (e.g., transformer-based dynamics prediction), improve performance on our complicated dataset of CCS CFD simulations.

AI Surrogate Simulation, Carbon Capture, design op↗
DOE OSTI2017

Optimal sample formulations for DNP SENS: The importance of radical-surface interactions

The efficacy of dynamic nuclear polarization (DNP) surface-enhanced NMR spectroscopy (SENS) is reviewed for alumina, silica, and ordered mesoporous carbon (OMC) materials, with vastly different surface areas, as a function of the biradical concentration. Importantly, our studies show that the use of a “one-size-fits-all” biradical concentration should be avoided when performing DNP SENS experiments and instead an optimal concentration should be selected as appropriate for the type of material studied as well as its surface area. In general, materials with greater surface areas require higher radical concentrations for best possible DNP performance. This result is explained with the use of a thermodynamic model wherein radical-surface interactions are expected to lead to an increase in the local concentration of the polarizing agent at the surface. We also show, using plane-wave density functional theory calculations, that weak radical-surface interactions are the cause of the poor performance of DNP SENS for carbonaceous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Cellulose Fiber Isolation and Characterization from Sweet Blue Lupin Hull and Canola Straw

In this study, cellulose fibers were removed from crop by-products using a combination of sodium hydroxide treatment followed by acidified sodium chlorite treatment. The objective was to obtain high recovery of cellulose by optimizing treatment conditions with sodium hydroxide (5–20%, 25–75 °C and 2–10 h) followed by acidified sodium chlorite (1.7%, 75 °C for 2–6 h) to remove maximum lignin and hemicellulose, as well as to investigate the effect of lignin content of the starting materials on the treatment efficiency. Samples were characterized for their chemical composition, crystallinity, thermal behavior and morphology to evaluate the effects of treatments on the fibers’ structure. The optimum sodium hydroxide treatment conditions for maximum cellulose recovery was at 15% NaOH concentration, 99 °C and 6 h. Subsequent acidified sodium chlorite treatment at 75 °C was found to be effective in removing both hemicellulose and lignin, resulting in higher recovery of cellulose in lupin hull (~ 95%) and canola straw (~ 93%). The resultant cellulose fibers of both crop by-products had increased crystallinity without changing cellulose I structure (~ 68–73%). Improved thermal stabilities were observed with increased onset of degradation temperatures up to 307–318 °C. Morphological investigations validated the effectiveness of treatments, revealing disrupted cell wall matrix and increased surface area due to the removal of non-cellulosics. The results suggest that the optimized combination of sodium hydroxide and acidified sodium chlorite treatments could be effectively used for the isolation of cellulose fibers from sweet blue lupin hull and canola straw, which find a great number of uses in a wide range of industrial applications.

09 BIOMASS FUELS↗
DOE OSTI2021

Examining the Economic Optimality of Automatic Generation Control

The automatic generation control (AGC) system is temporally situated between economic dispatch and synchronous generator dynamics, and its primary role is to regulate frequency within and tie-line flows between control areas. Given appropriate choice of participation factors (feed-forward controller gains that govern the disaggregation of the area-level power requirement to individual generators), the AGC can be engineered to nudge system dynamics toward a steady-state operating point corresponding to economic optimality. This paper establishes necessary and sufficient conditions under which a widely accepted choice of participation factors guarantees the alignment of steady-state synchronous generator outputs with a global minimum of a prototypical economic dispatch problem. In so doing, it resolves several ambiguities and formalizes technical assumptions governing the role of the standard AGC architecture in the context of economic dispatch and steady-state operation. Numerical case studies tailored to a modified version of the New England 39-bus 10-machine test system validate the theoretical results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Topography-based Flood Planning and Optimization Capability Development Report

Globally, water-related disasters are among the most frequent and costly natural hazards. Flooding inflicts catastrophic damage on critical infrastructure and population, resulting in substantial economic and social costs. NISAC is developing LeveeSim, a suite of nonlinear and network optimization models, to predict optimal barrier placement to protect critical regions and infrastructure during flood events. LeveeSim currently includes a high-performance flood model to simulate overland flow, as well as a network optimization model to predict optimal barrier placement during a flood event. The LeveeSim suite models the effects of flooding in predefined regions. By manipulating a domain’s underlying topography, developers altered flood propagation to reduce detrimental effects in areas of interest. This numerical altering of a domain’s topography is analogous to building levees, placing sandbags, etc. To induce optimal changes in topography, NISAC used a novel application of an optimization algorithm to minimize flooding effects in regions of interest. To develop LeveeSim, NISAC constructed and coupled hydrodynamic and optimization algorithms. NISAC first implemented its existing flood modeling software to use massively parallel graphics processing units (GPUs), which allowed for the simulation of larger domains and longer timescales. NISAC then implemented a network optimization model to predict optimal barrier placement based on output from flood simulations. As proof of concept, NISAC developed five simple test scenarios, and optimized topographic solutions were compared with intuitive solutions. Finally, as an early validation example, barrier placement was optimized to protect an arbitrary region in a simulation of the historic Taum Sauk dam breach.

42 ENGINEERING↗