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Results for “Optimization modeling”

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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

A model for optimizing file access patterns using spatio-temporal parallelism. In: UltraVis '13 Proceedings of the 8th International Workshop on Ultrascale Visualization, Article No. 4

For many years now, I/O read time has been recognized as the primary bottleneck for parallel visualization and analysis of large-scale data. In this paper, we introduce a model that can estimate the read time for a file stored in a parallel filesystem when given the file access pattern. Read times ultimately depend on how the file is stored and the access pattern used to read the file. The file access pattern will be dictated by the type of parallel decomposition used. We employ spatio-temporal parallelism, which combines both spatial and temporal parallelism, to provide greater flexibility to possible file access patterns. Using our model, we were able to configure the spatio-temporal parallelism to design optimized read access patterns that resulted in a speedup factor of approximately 400 over traditional file access patterns.

Nouanesengsy, Boonthanome↗
DOE OSTI2018

Cost Optimization of Osmotically Assisted Reverse Osmosis

We develop a nonlinear optimization model to identify minimum cost designs for osmotically assisted reverse osmosis (OARO), a multistaged membrane-based process for desalinating high-salinity brines. The optimization model enables comprehensive evaluation of a complex process configuration and operational decision space that includes nonlinear process performance and implicit relationships among membrane stages, saline sweep cycles, and makeup, purge, and recycle streams. Here, the objective function minimizes cost, rather than energy or capital expenditures, to accurately account for the trade-offs in capital and operational expenses inherent in multistaged membrane processes. Generally, we find that cost-optimal OARO processes minimize the number of stages, eliminate the use of saline makeup streams, purge from the first sweep cycle, and successively decrease stage membrane area and sweep flow rates. The optimal OARO configuration for treating feed salinities of 50–125 g/L total dissolved solids with water recoveries between 30–70% results in costs less than or equal to $6 per m3 of product water. Sensitivity analysis suggests that future research to minimize OARO costs should focus on minimizing the membrane structural parameter while maximizing the membrane burst pressure and reducing the membrane unit cost.

42 ENGINEERING↗
DOE OSTI2024

Model-predictive optimal control of ferrofluidic microrobots in three-dimensional space

Ferrofluid microrobots have emerged as promising tools for minimally invasive medical procedures. Their unique properties to navigate complex fluids and reach otherwise inaccessible regions of the human body have enabled new applications in targeted drug delivery, tissue engineering, and diagnostics. Here, this paper proposes a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots in three dimensions (3D). The internal optimization routine of the controller determines appropriate changes in the applied electromagnetic field to minimize the deviation between the actual and desired trajectories of the microrobot. A linear system governing locomotion is derived and used as the equality constraints of the optimization problems associated with the feedback index. In addition to ferrofluid droplets, the controller presented in this work may be applied to other magnetically-pulled microrobots. Several experiments are performed to validate the controller and showcase its ability to adapt to changes in system parameters such as the desired tracking trajectory and the size, orientation, deformation, and velocity of the microrobot. The accuracy of the controller is analyzed for each experiment, and the average error is found to be within 0.25 mm for small velocities. An additional experiment is performed to demonstrate significant improvement over a PID controller that is optimally tuned using Bayesian optimization. The results presented in this paper suggest that the proposed control algorithm could enable new microrobotic capabilities in minimally invasive medical procedures, lab-on-a-chip applications, and microfluidics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Development of Reduced Glass Furnace Model to Optimize Process Operation

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and PPG Industries to develop a reduced-order glass furnace model that enables plant operators to make informed, realtime process adjustments. This CRADA project is sponsored under the High-Performance Computing for Manufacturing (HPC4Mfg) Program of the Department of Energy's Advanced Manufacturing Office (AMO) within the Energy Efficiency and Renewable Energy (EERE) Office. This program ran from June 2016 to July 2017 with no extensions.

42 ENGINEERING↗
DOE OSTI2017

Development of Reduced Glass Furnace Model to Optimize Process Operation (Final Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and PPG Industries to develop a reduced-order glass furnace model that enables plant operators to make informed, real time process adjustments. This CRADA project is sponsored under the High-Performance Computing for Manufacturing (HPC4Mfg) Program of the Department of Energy's Advanced Manufacturing Office (AMO) within the Energy Efficiency and Renewable Energy (EERE) Office. This program ran from June 2016 to July 2017 with no extensions.

42 ENGINEERING↗
DOE OSTI2020

Online Modelling and Optimization of Nonlinear Integrable Systems

Nonlinear integrable optics was recently proposed as a design approach to increase the limits on beam brightness and intensity imposed by fast collective instabilities. To study these systems experimentally, a new research electron and proton storage ring, the Integrable Optics Test Accelerator, was constructed and recently commissioned at Fermilab. Beam-based diagnostics and online modelling of nonlinear systems presents unique challenges - in this paper, we report on our efforts to develop optimization methods suited for such lattices. We explore the effectiveness of neural networks as fast online surrogate estimators, and integrate them into a beam-based tuning algorithm. We also develop a method of knob dimensionality reduction and subsequent robust multivariate optimization for maximizing key performance metrics under complicated lattice optics constraints.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Reactive Dye Adsorption from Aqueous Solution on HPEI-Modified Fe{sub 3}O{sub 4} Nanoparticle as a Superadsorbent: Characterization, Modeling, and Optimization

In this work, the optimum condition for the adsorption of Reactive Blue 19 dye onto Fe{sub 3}O{sub 4} functionalized with hyperbranched polyethylenimine (Fe@HPEI) was determined using response surface methodology. The properties of synthesized magnetic Fe@HPEI nanoparticles were ascertained using FTIR, SEM, TEM, VSM and zeta potential measurement. The experimental adsorption data indicating the positive effect of adsorbent dose and contact time, and the negative effect of pH, initial dye concentration and ionic strength on dye adsorption. At optimal condition; pH 3.4, contact time 25 min, adsorbent dose 0.4 g/L and initial dye concentration 113 mg/L, Fe@HPEI nanoparticles removed dye up to 99.27%. The maximum experimental dye adsorption was near to the predicted value of 100%, which confirmed the reliability of the selected statistical model. The dye adsorption data were fitted well to Langmuir isotherm and pseudo-second-order kinetic, and the maximum adsorption capacity was 500 mg/g. Totally, Fe@HPEI as an effective superadsorbent can be repeatedly utilized for the adsorption of dye from the aqueous environment.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2013

Application of stochastic optimization to nuclear power plant asset management decisions

We describe the development and application of stochastic optimization models and algorithms to address an issue of critical importance in the strategic allocation of resources; namely, the selection of a portfolio of capital investment projects under the constraints of a limited and uncertain budget. This issue is significant and one that faces decision-makers across all industries. The objective of this strategic decision process is generally self evident - to maximize the value obtained from the portfolio of selected projects (with value usually measured in terms of the portfolio's net present value). However, heretofore, many organizations have developed processes to make these investment decisions using simplistic rule-based rank-ordering schemes. This approach has the significant limitation of not accounting for the (often large) uncertainties in the costs or economic benefits associated with the candidate projects or in the uncertainties in the actual funds available to be expended over the projected period of time. As a result, the simple heuristic approaches that typically are employed in industrial practice generate outcomes that are non-optimal and do not achieve the level of benefits intended. In this paper we describe the results of research performed to utilize stochastic optimization models and algorithms to address this limitation by explicitly incorporating the evaluation of uncertainties in the analysis and decision making process. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

EFFORT (EFFectiveness Of Rate sTructure for enabling demand response)

This tool helps utilities evaluate potential time of use price-plans and find effective hours for price plan on peak periods and the potential price-elastic demand response. An optimization model can be used to find tiered prices for the time of use tariffs and analyze the impact on load profiles, energy consumption, utility, and customer savings. The tool is equipped with an optimization model to determine the energy consumption of electric appliances that can leverage survey data containing information on the number of appliances owned by customers. Lastly, a statistical model can be used to predict system-level load based on exogenous parameters such as weather, time of day, month, type of day, and year. The tool is scripted in Python and uses the Pyomo python optimization language.

Duwadi, Kapil↗
DOE OSTI2023

Enhancing flexibility for climate change using seasonal energy storage (aquifer thermal energy storage) in distributed energy systems

Long-term energy storage is expected to play a vital role in the deep decarbonization of building energy sectors, while enhancing the flexibility of buildings to withstand future climate variations. However, it is challenging to design distributed multi-energy systems (DMES) while taking into account the uncertainties introduced by climate change, since stochastic optimization of such systems is difficult. The present study introduces a stochastic optimization model to address this bottleneck, taking into account DMES including aquifer thermal energy storage (ATES) as the long-term thermal storage. For the first time, a novel optimization algorithm links ATES with the DMES optimization model with the support of a simplified geotechnical model. Subsequently, a case study was conducted, focusing on a residential district in Chicago where the impact of future climate condition, energy demand, and solar and wind energy potentials were evaluated using Weather Research and Forecasting (WRF) data (up to 2080) and the EnergyPlus model. The study revealed that ATES is an attractive way to improve the renewable energy penetration level and minimize the dependence on fossil fuels with reasonable support from the grid to assist the fluctuations in both demand and generation. Furthermore, ATES notably reduces fuel consumption and dependence while greatly enhancing the flexibility of the energy system to withstand fluctuations in demand and renewable energy generation brought by future climate variations. These qualities will make ATES an important part of distributed energy systems, even though it is not currently the lowest cost alternative due to lack of technology maturity. Furthermore, the design platform introduced in the present study can be used to design DMES enhancing flexibility to accommodate future climate variations.

25 ENERGY STORAGE↗
DOE OSTI2019

Egret v. 0.1 (beta)

Egret is a package that provides tools for building electric grid optimization models. This software includes objects and functions for parsing, storing, and managing electric grid data. Egret also includes the following electric grid models: - ACOPF (alternating current optimal power flow) - DCOPF – a linearized transmission model - ACOPF relaxations - Unit commitment - Economic dispatch

Knueven, Bernard↗
DOE OSTI2019

Method and apparatus for shape-based energy analysis of solids

A method of evaluating response of a physical structure to external stimulus includes defining a mesh of finite elements, each defined by edges, for a model of the physical structure. The method includes identifying a governing differential equation and associated complementary functions, which are each associated with a scalar multiplier. The method includes generating an energy optimization model that minimizes a difference between internal and external energies of the finite elements. The internal energy is based on strain energy in a volume defined by the edges of the finite element and resulting from deformations by the complementary functions. The external energy of each finite element is based on external work done by the external stimulus acting on the finite element as deformed by the complementary functions. The method includes solving the energy optimization model for the scalar multipliers and calculating a resulting parameter of interest of the physical structure.

42 ENGINEERING↗
DOE OSTI2025

Modeling and Optimization of a Rotating Packed Bed Contactor with a Tetraamine-Appended Metal−Organic Framework for CO 2 Capture

A potential contactor technology for sorbent-based CO 2 capture is the rotating packed bed that contains separate sections for continuous adsorption and desorption. A heat exchanger can be embedded to remove heat in the adsorption section and add heat in the desorption section. In this work, we develop a two-dimensional (2D) model of a rotating packed bed for use in CO 2 capture applications. Mass and energy balances for the model are developed based on a Ljungström-type air preheater, which accounts for the counter-current axial flow of gas phases in separate sections of the bed and the rotation of a solid sorbent, which cycles between adsorption and desorption sections. The sorbent used for this analysis is the tetraamine-appended metal−organic framework Mg 2 (dobpdc)(3−4− 3), chosen for its stability and affinity for CO 2 at low partial pressures, such as those from a natural gas power plant source. An optimization problem is solved that considers the trade-off between maximizing the productivity of the bed and minimizing energy consumption. Maximum productivity and minimum energy are found to be 8.53 kg/h/m 3 and 3.84 MJ/kg, respectively, when these objectives are optimized independently. It is observed that the flue gas pressure and bed rotational speed are the desired operating variables to vary for model-based design of experiments to reduce uncertainty in parameter estimation, as these two variables yielded the most information content based on the Fisher information matrix.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control

Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. The simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical white-box implementations. Hundreds of these models, each with different properties, are randomly populated into a modified IEEE 123-bus test system to represent a typical U.S. community. Advanced VPP controls are developed based on the Consumer Technology Association (CTA) 2045 standard to leverage HVAC systems as generalized energy storage (GES) such that BTM solar PV is better utilized locally and occurrences of distribution system power peaks are reduced, while also maintaining occupant thermal comfort. An optimization is performed to determine the best control settings for targeted peak power and total daily energy increase minimization with example peak load reductions of 25+%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Modeling and Optimization of Zeolites for Contaminant Removal from Coal Combustion Impoundment Leachates

Coal Combustion Residual (CCR) landfills in the U.S. can lead to elevated contaminant concentrations in groundwater and runoff, particularly of arsenic, boron, and selenium. Zeolites can be readily synthesized from materials contained within the coal combustion ash impoundments and can be cation-exchanged to enhance adsorption capacity, selectivity, and reactivity. However, the optimization of zeolites becomes a daunting task when accounting for the variety of Si:Al ratios, the species of extra-framework cations present, and the zeolite pore structure. Molecular simulations provide methods to study and guide the design of zeolites for the sorption of contaminants from aqueous solution. In this work, models that predict the sorption of arsenic, boron, and selenium from water using cation-exchanged zeolites. Because of the lack of experimental adsorption data for these species of contaminants, models were parametrized to reproduce the results of first-principles calculations and then used to predict sorption for zeolites for a dataset containing 6000 combinations of zeolites and sorption conditions. Machine learning was used to train a model to predict sorption for materials in this database based on the results of the molecular simulations. Next, a genetic algorithm was used to optimize zeolites for the removal of each contaminant from aqueous solution for individual impoundment sites based on contaminant concentrations reported by the Electric Power Research Institute.

Findley, John↗
DOE OSTI2013

Optimal planning for the sustainable utilization of municipal solid waste

Highlights: • An optimization approach for the sustainable management of municipal solid waste is proposed. • The proposed model optimizes the entire supply chain network of a distributed system. • A case study for the sustainable waste management in the central-west part of Mexico is presented. • Results shows different interesting solutions for the case study presented. - Abstract: The increasing generation of municipal solid waste (MSW) is a major problem particularly for large urban areas with insufficient landfill capacities and inefficient waste management systems. Several options associated to the supply chain for implementing a MSW management system are available, however to determine the optimal solution several technical, economic, environmental and social aspects must be considered. Therefore, this paper proposes a mathematical programming model for the optimal planning of the supply chain associated to the MSW management system to maximize the economic benefit while accounting for technical and environmental issues. The optimization model simultaneously selects the processing technologies and their location, the distribution of wastes from cities as well as the distribution of products to markets. The problem was formulated as a multi-objective mixed-integer linear programing problem to maximize the profit of the supply chain and the amount of recycled wastes, where the results are showed through Pareto curves that tradeoff economic and environmental aspects. The proposed approach is applied to a case study for the west-central part of Mexico to consider the integration of MSW from several cities to yield useful products. The results show that an integrated utilization of MSW can provide economic, environmental and social benefits.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Optimizing dispatch for a concentrated solar power tower

Concentrating solar power (CSP) systems employ a sophisticated thermal receiver, power cycle, and a heliostat field, comprised of thousands of mirrors spread over hundreds of acres of land, and are most cost-effective with relatively large quantities of energy storage which can be scheduled for dispatch. We exercise an existing optimization model that maximizes revenue over a year-long time horizon, solved using a standard 48-h look-ahead policy at hourly fidelity on a CSP system under development in California. The system employs molten salt power tower technology with a 150 MWe (net) turbine, eight hours of thermal storage at full load, and a solar field that provides 1.75 times the rated turbine thermal power. The model considers system configuration and interoperability aspects, such as storage tank size, production capacities, and ramp rates, and determines decisions that expedite financing, permitting, and plant design. Relative to results achieved via industry practice, the existing optimization model reduces the number of power cycle (i.e., turbine) start-up events by 86.4%, thereby improving net revenue by more than 8.5% annually; this corresponds to an approximately $140 M increase in net revenue over the lifetime of the plant, taking a step towards advancing the long-term economic viability of large-scale renewable energy systems. Sensitivity analysis on uncertain parameter values provides insight regarding those values that influence profit most significantly.

14 SOLAR ENERGY↗