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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Coupling subsurface and above-surface models for optimizing the design of borefields and district heating and cooling systems in the presence of varying water-table depth. In: Proceedings, 46th Workshop on Geothermal Reservoir Engineering

Dynamic energy simulation is important for the design and sizing of district heating and cooling systems with geothermal heat exchange. Current modeling approaches in building and district energy simulation tools typically consider heat conduction through the ground between boreholes, without flow of groundwater. On the other hand, detailed simulation tools for subsurface heat and mass transfer exist, but these fall short in simulating above-surface energy systems. To support the design and operation of such systems, we have developed a coupled model including a software package for building and district energy simulation, and software for detailed heat and mass transfer in the ground. For the first, we use the open-source Modelica Buildings Library, which includes dynamic simulation models for building and district energy and control systems. For the heat and mass transfer in the soil, we use the TOUGH simulator. TOUGH can model heat and multi-phase, multi-component mass transport for a variety of fluid systems, as well as chemical reactions, in fractured porous media. In previous work, we described the coupling of these software packages, including how time-dependent boundary conditions for the borehole walls are synchronized for use in Modelica and TOUGH. We verified that the coupled Modelica/TOUGH code produced consistent results with the original Modelica code for an idealized problem in which heat transfer was purely by conduction in a uniform geologic medium. Here, we examine less idealized problems for which TOUGH’s advanced capabilities for modeling fluid flow are required. The first problem has a shallow vadose zone, and the second problem has a thicker vadose zone with a water-table depth that varies in time, which requires a fine vertical grid discretization for the TOUGH model.

Doughtry, Christine↗
DOE OSTI2018

Robust Operation of Soft Open Points in Active Distribution Networks with High Penetration of Photovoltaic Integration

Distributed generators (DGs) including photovoltaic panels (PVs) have been integrated dramatically in active distribution networks (ADNs). Due to the strong volatility and uncertainty, the high penetration of PV generation immensely exacerbates the conditions of voltage violation in ADNs. However, the emerging flexible interconnection technology based on soft open points (SOPs) provides increased controllability and flexibility to the system operation. For fully exploiting the regulation ability of SOPs to address the problems caused by PV, this paper proposes a robust optimization method to achieve the robust optimal operation of SOPs in ADNs. A two-stage adjustable robust optimization model is built to tackle the uncertainties of PV outputs, in which robust operation strategies of SOPs are generated to eliminate the voltage violations and reduce the power losses of ADNs. A column-and-constraint generation (C&CG) algorithm is developed to solve the proposed robust optimization model, which are formulated as second-order cone program (SOCP) to facilitate the accuracy and computation efficiency. Here, case studies on the modified IEEE 33-node system and comparisons with the deterministic optimization approach are conducted to verify the effectiveness and robustness of the proposed method.

14 SOLAR ENERGY↗
DOE OSTI2023

Modeling and Optimal Design of a Desalination System Integrated Between a Glass-Covered Solar Collection Water Chamber and a Heat Dissipating Chimney

Abstract Solar thermal energy is trapped in a glass-covered water chamber/basin to provide the heat for evaporation of brackish water in a novel desalination system. To harvest clean water, a chimney is integrated with the water basin, which draws air into the chamber to be humidified by the vapor and then being ventilated via buoyance force. Uniquely, thermal conductive metal sheet is recommended to make the chimney, which allows the vapor in the humid air condenses easily when flowing up. The condensate at the inner wall of the chimney flows down to be collected as clean water. Mathematical modeling and numerical computation have been carried out to delineate the coupling of the buoyance-force-driven flow with the heat and mass transfer of air and water in the solar collection chamber and the condensation of vapor in the heat-dissipating chimney. The objective of the simulation and optimization of the system is to find the best match of the dimensions of the water chamber with a chimney to maximize the production of clean water and energy efficiency. The model has been used to simulation several cases (of water from 40 °C to 50 °C) with available experimental data from the authors’ previous work, and the agreement was satisfactory. The optimization studies found that there is a maximum air flowrate corresponding to a critical chimney height due to the requirement that the chimney is designated to dissipate heat as much as possible to condense water vapor. With the chimney height greater than the critical height, the airflow rate will have a slight decrease. Nevertheless, higher than a critical height is still needed for a chimney to condense more moisture. Optimized chamber diameters at different chimney heights are provided for reference of optimal system designs.

Engineering↗
DOE OSTI2016

Fuzzy Energy and Reserve Co-optimization With High Penetration of Renewable Energy

In this study, we propose a fuzzy-based energy and reserve co-optimization model with consideration of high penetration of renewable energy. Under the assumption of a fixed uncertainty set of renewables, a two-stage robust model is proposed for clearing energy and reserves in the first stage and checking the feasibility and robustness of re-dispatches in the second stage. Fuzzy sets and their membership functions are introduced into the optimization model to represent the satisfaction degree of the variable uncertainty sets. The lower bound of the uncertainty set is expressed as fuzzy membership functions. The solutions are obtained by transforming the fuzzy mathematical programming formulation into traditional mixed integer linear programming problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

SimCCS: An open-source tool for optimizing CO 2 capture, transport, and storage infrastructure

Commercial-scale carbon capture and storage (CCS) technology will involve deploying infrastructure on a massive and costly scale. This effort will require careful and comprehensive planning to ensure that capture locations, storage sites, and the dedicated CO 2 distribution pipelines are selected in a robust and cost-effective manner. Presented in 2009, SimCCS is an optimization model for integrated system design that enables researchers, stakeholders, and policy makers to design CCS infrastructure networks. SimCCS 2.0 is a complete, ground-up redesign that is now a portable software package, useable and shareable by the CCS research, industrial, policy, and public communities. SimCCS 2.0 integrates multiple new capabilities including a refined optimization model, novel candidate network generation techniques, and optional integration with high-performance computing platforms. Accessing user-provided CO 2 source, sink, and transportation data, SimCCS 2.0 creates candidate transportation routes and formalizes an optimization problem that determines the most cost-effective CCS system design. This optimization problem is further solved either through a high-performance computing interface, or through third-party software on a local desktop computing platform. Finally, SimCCS 2.0 employs an open-access geographic information system framework to enable analysis and visualization capabilities. SimCCS 2.0 is written in Java and is publicly available via GitHub to encourage collaboration, modification, and community development.

42 ENGINEERING↗
DOE OSTI2022

Optimization-based modeling and analysis of brine reflux osmotically assisted reverse osmosis for application toward zero liquid discharge systems

Significant amounts of high-salinity wastewater generated by water-intensive industrial activities such as shale oil and gas production have raised serious environmental concerns in recent years. Existing and emerging desalination technologies offer promise to manage these high salinity wastewater streams while simultaneously producing fresh water that could be diverted for beneficial uses. Osmotically assisted reverse osmosis (OARO) is one such emerging desalination technology capable of handling hypersaline brines and achieving high recoveries. However, rigorous modeling and analysis is needed to evaluate the process performance, energy consumption, and treatment cost of various OARO configurations. Here, this work presents detailed modeling and analysis of brine-reflux OARO (BR-OARO) system and compares it with other commonly discussed configurations, including cascading osmotically mediated reverse osmosis (COMRO), consecutive loop OARO, and split feed counterflow RO, through a cost optimization-based framework. We analyze and compare the treatment costs, membrane area, specific energy consumption, and design parameters of the aforementioned configurations with the ultimate goal of achieving zero liquid discharge (ZLD). The results indicate that the BR-OARO system with treatment cost of 5.1 US $/m 3 of produced water with 10% salinity outperforms other configurations in terms of number of stages needed, treatment cost, membrane area, and energy consumption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Integration of graphical approaches into optimization-based design of multistage liquid extraction

We propose two optimization models for designing two liquid extraction systems: simple multistage liquid extractors and extractors with extract reflux. Both models are motivated by the concepts of the modified McCabe-Thiele graphical method for multistage extractor design. The operating and equilibrium curves in the McCabe-Thiele method are represented by material balances and piece-wise linearized thermodynamics properties. The use of piece-wise approximations improves computational tractability of both optimization models. In addition, we consider some extensions such as dilute systems, insoluble solvents, and non-ideal stages. In conclusion, the applicability of the proposed models is demonstrated with four illustrative examples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Fast strength model characterization using Bayesian statistics

A variety of flow stress models exist with new models constantly being developed. These models aim to approximate the strength of materials in a variety of regimes from quasistatic loading through shock scenarios. All models contain an array of parameters which need to be tuned to the material under study. Some models perform well under limited conditions, requiring adjustment of the parameters when venturing outside of those predefined ranges. Other models perform well over a wide range of conditions with a set of parameters, but may be outperformed by other models optimized on a tighter range of conditions. Recent research by Los Alamos demonstrated the ability to optimize the Johnson Cook (JC) model using a set of 3 plate-impact experiments on Aluminum. They utilized Bayesian statistics and emulation to determine optimal parameters for the model with a quantification of parameter uncertainty. We present an extension of this capability to incorporate velocimetry from plate-impact tests, stress-strain data from split Hopkinson pressure bar and quasistatic compression tests, plus profiles from Taylor cylinders in a unified fashion. Statistically robust comparisons of the performance and uncertainty of different realizations of the JC flow stress model were carried out based on calibration to several possible combinations of these three different experiment types.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Optimizing multiple reliable forward contracts for reservoir allocation using multitime scale streamflow forecasts

Streamflow forecasts at multiple time scales provide a new opportunity for reservoir management to address competing objectives. Market instruments such as forward contracts with specified reliability are considered as a tool that may help address the perceived risk associated with the use of such forecasts in lieu of traditional operation and allocation strategies. A water allocation process that enables multiple contracts for water supply and hydropower production with different durations, while maintaining a prescribed level of flood risk reduction, is presented. The allocation process is supported by an optimization model that considers multitime scale ensemble forecasts of monthly streamflow and flood volume over the upcoming season and year, the desired reliability and pricing of proposed contracts for hydropower and water supply. It solves for the size of contracts at each reliability level that can be allocated for each future period, while meeting target end of period reservoir storage with a prescribed reliability. The contracts may be insurable, given that their reliability is verified through retrospective modeling. The process can allow reservoir operators to overcome their concerns as to the appropriate skill of probabilistic forecasts, while providing water users with short–term and long–term guarantees as to how much water or energy they may be allocated. An application of the optimization model to the Bhakra Dam, India, provides an illustration of the process. In conclusion, the issues of forecast skill and contract performance are examined. A field engagement of the idea is useful to develop a real–world perspective and needs a suitable institutional environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Cascade energy optimization for waste heat recovery in distributed energy systems

The efficiency of distributed energy systems can be significantly increased through waste heat recovery from industry or power generation. The technologies used for this process are typically dependent on the quality and temperature grades of waste heat. To maximize the efficiency of cascade heat utilization, it is important to optimize the choice of waste heat recovery technologies and their operation. Here, a detailed mixed integer linear programming optimization model is proposed for waste heat recovery in a district-scale microgrid. The model can distinguish waste heat quality for planning and operation optimization of distributed energy systems. Heat utilization technologies are formulated in this developed model and categorized in different temperature grades. The developed model is validated using four typical cases under different settings of system operation and business models. It is found that the optimization model, by distinguishing waste heat temperature, can increase energy cost savings by around 5%, compared to models that do not consider waste heat temperature grades. Additionally, the results indicate that the developed model can provide more realistic configuration and technologies dispatch.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Optical modeling toward optimizing monitoring of intestinal perfusion in trauma patients

Trauma is the number one cause of death for people between the ages 1 and 44 years in the United States. In addition, according to the Centers of Disease Control and Prevention, injury results in over 31 million emergency department visits annually. Minimizing the resuscitation period in major abdominal injuries increases survival rates by correcting impaired tissue oxygen delivery. Optimization of resuscitation requires a monitoring method to determine sufficient tissue oxygenation. Oxygenation can be assessed by determining the adequacy of tissue perfusion. In this work, we present the design of a wireless perfusion and oxygenation sensor based on photoplethysmography. Through optical modeling, the benefit of using the visible wavelengths 470, 525 and 590nm (around the 525nm hemoglobin isobestic point) for intestinal perfusion monitoring is compared to the typical near infrared (NIR) wavelengths (805nm isobestic point) used in such sensors. Specifically, NIR wavelengths penetrate through the thin intestinal wall (~4mm) leading to high background signals. However, these visible wavelengths have two times shorter penetration depth that the NIR wavelengths. Monte-Carlo simulations show that the transmittance of the three selected wavelengths is lower by 5 orders of magnitude depending on the perfusion state. Due to the high absorbance of hemoglobin in the visible range, the perfusion signal carried by diffusely reflected light is also enhanced by an order of magnitude while oxygenation signal levels are maintained. In addition, short source-detector separations proved to be beneficial for limiting the probing depth to the thickness of the intestinal wall.

intestinal perfusion↗
DOE OSTI2019

A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization: Preprint

This paper considers time-varying nonconvex optimization problems, utilized to model optimal operational trajectories of systems governed by possibly nonlinear physical or logical models. Algorithms for tracking a Karush-Kuhn-Tucker point are synthesized, based on a regularized primal-dual gradient method. In particular, the paper proposes a feedback-based primal-dual gradient algorithm, where analytical models for system state or constraints are replaced with actual measurements. When cost and constraints functions are twice continuously differentiable, conditions for the proposed algorithms to have bounded tracking error are derived, and a discussion of their practical implications is provided. Illustrative numerical simulations are presented for an application in power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Maintaining environmental quality while expanding biomass production: Sub-regional U.S. policy simulations

This paper evaluates environmental policy effects on ligno-cellulosic biomass production and environ- mental outcomes using an integrated bioeconomic optimization model. The environmental policy integrated climate (EPIC) model is used to simulate crop yields and environmental indicators in current and future potential bioenergy cropping systems based on weather, topographic and soil data. The crop yield and environmental outcome parameters from EPIC are combined with biomass transport costs and economic parameters in a representative farmer profit-maximizing mathematical optimization model. The model is used to predict the impact of alternative policies on biomass production and environmental outcomes. We find that without environmental policy, rising biomass prices initially trigger production of annual crop residues, resulting in increased greenhouse gas emissions, soil erosion, and nutrient losses to surface and ground water. At higher biomass prices, perennial bioenergy crops replace annual crop residues as biomass sources, resulting in lower environmental impacts. Simulations of three environmental policies namely a carbon price, a no-till area subsidy, and a fertilizer tax reveal that only the carbon price policy systematically mitigates environmental impacts. The fertilizer tax is ineffectual and too costly to farmers. The no-till subsidy is effective only at low biomass prices and is too costly to government.

Egbendewe-Mondzozo, Aklesso↗
DOE OSTI2022

Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices

The integration of thermal energy storage into a concentrating solar power system allows for mitigating some of the risk associated with uncertain solar irradiance and uncertain energy prices. We solve a 48 h dispatch optimization model with continually updated conditional point forecasts of both direct normal irradiance (DNI) and electricity prices with a rolling-horizon scheme at hourly resolution over the course of a year. Joint, conditional forecasts for DNI and prices are formed using an autoregressive moving-average time series model with exogenous weather predictors. We guide dispatch using a mixed-integer programming model, but in order to evaluate performance we use the System Advisor Model (SAM) of the National Renewable Energy Laboratory. SAM is a techno-economic simulation model that accounts for plant thermodynamics with higher fidelity. Our conditional DNI forecasts improve annual revenue by 4%–12% over using historical forecasts based on data from previous years. Conditional price forecasts improve annual revenue by 6%–19% in the real-time market over analogous historical forecasts. Updating these forecasts every six hours, rather than every 24 h, further improves annual revenue by 5%–6%. Here, we also investigate a method that values terminal inventory in our dispatch optimization model, again when used in a rolling-horizon scheme.

14 SOLAR ENERGY↗
DOE OSTI2020

Stochastic Analysis for Long Term Capital Structures, Systems, and Components Refurbishment and Replacement

As commercial Nuclear Power Plants (NPPs) pursue extended plant operation in the form of Second License Renewal (SLR), opportunities exist for these plants to provide capital investments to ensure long-term safe and economic performance. At the current time, several utilities have announced an intention to pursue extended operation for one or more of their NPPs via SLR . The goal of this research is to develop a risk-informed approach to evaluate and prioritize plant capital investments made in preparation for, and during the period of, extended plant operations to support decisions for NPP operations. Since the capital investments are influenced by various factors, such as markets, safety and regulatory, the decision-making process of NPP operations should take into account relevant factors for balancing risks, costs and profits. The traditional method of capital budgeting is based on the priority list of candidate projects using economic measures such as benefit-investment ratio, net present value (NPV) and internal rate of return. In the literatures, the problem of capital budgeting or the variant can be represented by an appropriate knapsack problem. The knapsack approach to capital budgeting takes as input as investment, along with the cost and profit of each project. The objective of capital budgeting is to find the combination of the binary decisions for every investment such that the overall profit is as large as possible. The output is a collection of projects to be carried out, and we refer this selected collection of projects as a project portfolio. One limitation of traditional optimization models for capital budgeting is that they do not account for risk/uncertainty in profit and cost streams associated with individual projects, they do not account for risk in resource availability in future years [1,2,3]. Projects can incur cost over-runs, especially when projects are large, performed infrequently, and when there is risk regarding technical viability, external contractors, and/or suppliers of requisite parts and materials. Occasionally, projects are performed ahead of schedule and with cost savings. Planned budgets for capital improvements can be cut and key personnel may be lost. Or, there may be surprise windfalls in budgets for maintenance activities due to decreased costs for “unplanned” maintenance. In these cases, how should we resolve capital budgeting when we have risk forecasts for costs, profits and budgets? One approach we proposed in this summary is to re-solve the optimization models based on assumed statistical distributions of given parameters. If these distributions were not available, a two-stage stochastic optimization approach can be used to provide priority lists to decision-makers to support better risk-informed decisions [4, 5]. In this summary, we will only focus on the first approach.

42 ENGINEERING↗
DOE OSTI2015

Stochastic Optimal Scheduling of Residential Appliances with Renewable Energy Sources

This paper proposes a stochastic, multi-objective optimization model within a Model Predictive Control (MPC) framework, to determine the optimal operational schedules of residential appliances operating in the presence of renewable energy source (RES). The objective function minimizes the weighted sum of discomfort, energy cost, total and peak electricity consumption, and carbon footprint. A heuristic method is developed for combining different objective components. The proposed stochastic model utilizes Monte Carlo simulation (MCS) for representing uncertainties in electricity price, outdoor temperature, RES generation, water usage, and non-controllable loads. The proposed model is solved using a mixed integer linear programming (MILP) solver and numerical results show the validity of the model. Case studies show the benefit of using the proposed optimization model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

On combining the conductor-like screening model and optimally tuned range-separated hybrid density functionals

Range-separated hybrid functionals whose range-separation parameter γ has been nonempirically tuned to a particular molecule have been shown to yield frontier orbital energies and other properties in very good agreement with experiments. Yet, many cases, such as organic optoelectronic devices, require the description of molecules embedded in an environment. This can be done by combining the γ-tuning procedure with polarizable continuum models in general and the very versatile conductor-like screening model in particular. There are at least two different ways of performing this combination. The partially vertical γ-tuning employs equilibrium solvation throughout. The strictly vertical γ-tuning, on the other hand, employs nonequilibrium solvation to obtain ionization energies. Here, we compare ground-state and excited-state properties of several different molecules relevant to organic optoelectronics that were obtained using both of the two different tuning procedures. While there are significant differences in the ground-state properties, we see virtually no difference in the excited-state properties. Given these conclusions, we find that both tuning procedures have to be used in conjunction for the correct description of both ground-state and excited-state properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Modeling and optimization of steady flow of natural gas and hydrogen mixtures in pipeline networks

Here, we extend the canonical problems of simulation and optimization of steady-state gas flows in pipeline networks with compressors to the transport of mixtures of highly heterogeneous gases injected throughout a network. Our study is motivated by proposed projects to blend hydrogen generated using clean energy into existing natural gas pipeline systems as part of efforts to reduce the reliance of energy systems on fossil fuels. Flow in a pipe is related to endpoint pressures by a basic Weymouth equation model, with an ideal gas equation of state, where the wave speed depends on the hydrogen concentration. At vertices, in addition to mass balance, we also consider mixing of incoming flows of varying hydrogen concentrations. The problems of interest are the heterogeneous gas flow simulation (HGFS), which determines system pressures and flows given fixed boundary conditions and compressor settings, as well as the heterogeneous gas flow optimization (HGFO), which extremizes an objective by determining optimal boundary conditions and compressor settings. We examine conditions for uniqueness of solutions to the HGFS, as well as compare and contrast mixed-integer and continuous nonlinear programming formulations for the HGFO. We develop computational methods to solve both problems, and examine their performance using four test networks of increasing complexity.

08 HYDROGEN↗