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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Experiences performing conceptual design optimization of transport aircraft

Optimum Preliminary Design of Transports (OPDOT) is a computer program developed at NASA Langley Research Center for evaluating the impact of new technologies upon transport aircraft. For example, it provides the capability to look at configurations which have been resized to take advantage of active controls and provide and indication of economic sensitivity to its use. Although this tool returns a conceptual design configuration as its output, it does not have the accuracy, in absolute terms, to yield satisfactory point designs for immediate use by aircraft manufacturers. However, the relative accuracy of comparing OPDOT-generated configurations while varying technological assumptions has been demonstrated to be highly reliable. Hence, OPDOT is a useful tool for ascertaining the synergistic benefits of active controls, composite structures, improved engine efficiencies and other advanced technological developments. The approach used by OPDOT is a direct numerical optimization of an economic performance index. A set of independent design variables is iterated, given a set of design constants and data. The design variables include wing geometry, tail geometry, fuselage size, and engine size. This iteration continues until the optimum performance index is found which satisfies all the constraint functions. The analyst interacts with OPDOT by varying the input parameters to either the constraint functions or the design constants. Note that the optimization of aircraft geometry parameters is equivalent to finding the ideal aircraft size, but with more degrees of freedom than classical design procedures will allow.

Arbuckle, P. D.↗
NASA NTRS1984

Fundamental differences between optimization code test problems in engineering applications

The purpose here is to suggest that there is at least one fundamental difference between the problems used for testing optimization codes and the problems that engineers often need to solve; in particular, the level of precision that can be practically achieved in the numerical evaluation of the objective function, derivatives, and constraints. This difference affects the performance of optimization codes, as illustrated by two examples. Two classes of optimization problem were defined. Class One functions and constraints can be evaluated to a high precision that depends primarily on the word length of the computer. Class Two functions and/or constraints can only be evaluated to a moderate or a low level of precision for economic or modeling reasons, regardless of the computer word length. Optimization codes have not been adequately tested on Class Two problems. There are very few Class Two test problems in the literature, while there are literally hundreds of Class One test problems. The relative performance of two codes may be markedly different for Class One and Class Two problems. Less sophisticated direct search type codes may be less likely to be confused or to waste many function evaluations on Class Two problems. The analysis accuracy and minimization performance are related in a complex way that probably varies from code to code. On a problem where the analysis precision was varied over a range, the simple Hooke and Jeeves code was more efficient at low precision while the Powell code was more efficient at high precision.

Eason, E. D.↗
DOE OSTI2020

Optimal charging scheduling and management for a fast-charging battery electric bus system

Herein we discuss how battery electric buses (BEBs) are rapidly being embraced by public transit agencies because of their environmental and economic benefits. To address the problems of limited driving range and time-consuming charging for BEBs, manufacturers have developed rapid on-route charging technology that utilizes typical layovers at terminals to charge buses in operation using high power. With on-route fast-charging, BEBs are as capable as their diesel counterparts in terms of range and operating time. However, on-route fast-charging makes it more challenging to schedule and manage charging events for a BEB system. First, on-route fast-charging may lead to high electricity power demand charges. Second, it may increase electricity energy charges because of charging that occurs during on-peak hours. Without careful charging scheduling and management, on-route fast-charging may significantly increase fuel costs and reduce the economic attractiveness of BEBs. The present study proposes a network modeling framework to optimize the charging scheduling and management for a fast-charging BEB system, effectively minimizing total charging costs. The charging schedule determines when to charge a BEB, while the charging management strategically controls the actual charging power. Charging costs include both electricity demand charges and energy charges. The charging scheduling and management problem is first formulated as a nonlinear nonconvex program with time-continuous variables. A discretizing method and a linear reformulation technique are then adopted to reformulate the model as a linear program, which can be easily solved using off-the-shelf solvers, even for large-scale problems. Finally, the model is demonstrated with extensive numerical studies based on two real-world bus networks. The results demonstrate that the proposed model can effectively determine the optimal charging scheduling and management for a fast-charging BEB system, which carries the potential for use in large-scale real-world bus networks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. We report a systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions.

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

Economic evaluation of infrastructures for thermochemical upcycling of post-consumer plastic waste

Thermochemical technologies, such as pyrolysis, offer a potentially scalable pathway for upcycling diverse types of plastic waste (PW) into value-added chemicals. However, deploying these technologies in waste management infrastructures is not straightforward because such systems involve a wide range of interdependent stakeholders, processing facilities, and products. In this work, we present a holistic optimization framework that integrates value-chain analysis, techno-economic analysis, and life-cycle analysis for investigating the economic viability and environmental benefits of upcycling infrastructures that collect, sort, clean, and process post-consumer PW for producing virgin polymer resins. The framework is applied to a case study in the upper Midwest region of the US. Our analysis reveals that the infrastructures are economically viable and could activate a regional circular economy that generates over 1 billion USD in annual profit. Moreover, our analysis reveals that this economy can reduce the carbon footprint of PW incineration by half. Our framework also determines the inherent values of post-consumer PW and of derived products such as plastic bales and pyrolysis oil; we find that, in these infrastructures, PW becomes a highly valuable feedstock with a market value of 500 USD per tonne. Here, we discuss how this market value can generate incentives that foster more effective waste pre-sorting practices by consumers that can help bypass material recycling facilities and increase total system profit.

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

Risk-Informed MultiPphysics BestEestimate Plus Uncertainties (BEPU) Application Development of RELAP5-3D Perturbation Model

The United States nuclear industry is facing a strong challenge to ensure maximum safety while enhancing economic benefit. Safety is a key parameter to all aspects related to light water reactor (LWR) nuclear power plants (NPPs), especially cost savings. Since the goal is to extend the lifetimes of these NPPs, the traditional deterministic safety concept may not guarantee a current economic asset. The Light Water Reactor Sustainability (LWRS) Program has been promoting a wide range of research and development (R&D) in this field to maximize the safety, economics, and performance of these NPPs through improved scientific understanding. One of the best practices to achieve this goal is to identify and optimize safety margins, which can lead to cost reduction. To do this, under the LWRS framework, the Risk-Informed Systems Analysis (RISA) Pathway will focus on the optimization of safety margin and minimization of uncertainties to ensure both safety and economics at the highest level. The RISA Pathway will provide enhanced capabilities for analyzing and characterizing LWR systems performance by developing and demonstrating methods, tools, and data to enable risk-informed margins management (RIMM). The goals of the RISA Pathway are twofold: (1) deploy the risk-informed tools and methods that enable better representation of safety margins and factors that contribute to cost and safety; and (2) conduct advanced risk assessment applications with industry to support margin management strategies that enable more cost-effective plant operation. The tools and methods provided by the RISA Pathway will support effective margin management for both active and passive safety systems, structures, and components (SSC) of an NPP. The tools and methods used in the RISA Pathway should have high confidence and highest technical maturity for and implementation to industry at its current setting. They should also have a capability to support risk-informed decision making for both probabilistic and deterministic elements of safety. The RISA Pathway will, therefore, perform a comprehensive assessment of verification and validation (V&V) status of RISA Toolkit to enhance credibility RISA Toolkit which be used by industry. This report summarizes RELAP5-3D development activities for best estimated and uncertainty (BEPU) capability to support ongoing RISA Pathway pilot projects for risk informed uncertainty quantification applications. Work scope includes (1) development of initial perturbation model for selected closure laws and (2) testing of suitable probability distribution functions (PDF) for the selected correlations. Developed BEPU modules are tested with selected separate effect cases and compared with conventional thermal-hydraulics model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRSDate not supplied

Multidisciplinary Design Optimization of a Transonic Truss-Braced Wing using Physics-Based Models

Economic and environmental forces have placed pressure on the aviation industry to produce future aircraft designs with substantial improvements over those flying today. In order to meet these ambitious goals, new concepts such as the Transonic Truss-Braced Wing (TTBW) are being considered which diverge from the traditional tube-and-wing design. While this concept offers potential performance benefits over its traditional counterpart, it also features more coupling between various physics disciplines that must be considered during the design of the concept. This unique challenge provides a ripe opportunity for the application of multidisciplinary design optimization tool sets. The final work will couple together the mission analysis capability of Aviary with OpenAeroStruct for aerodynamic analysis and TACS for structural analysis. Using these tools we will preform a design optimization on the TTBW concept minimizing design fuel burn. The optimization will include design variables for mission trajectory, wing geometry, and structural sizing variables.

mbsae↗
DOE OSTI2024

Conceptual Design of Integrated Energy Systems with Market Interaction Surrogate Models

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with market and can result in overestimated economic values. In this work, we pro-pose a machine learning surrogate-assisted optimization framework to quantify the IES/market interactions and thus go beyond price taker. We use time series clustering to generate representative IES operation profiles for the IES optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

Chen, Xinhe↗
DOE OSTI2026

Relaxations of the steady optimal gas flow problem for a non-Ideal gas

Natural gas ranks second in U.S. primary energy consumption. Because most production sites are remote, gas must be transported through pipeline networks equipped with compressors, valves, and other components. For both economic efficiency and system reliability, it is desirable to operate these networks optimally. The governing physics across pipeline components entails nonlinear, non-convex equality and inequality constraints, and the most general steady-flow operations problem is a Mixed-Integer Nonlinear Program (MINLP).This work focuses on one such steady-flow problem-the Optimal Gas Flow (OGF) for a natural gas pipeline network-which minimizes production cost subject to the steady-flow physics. For day-to-day operations, the ability to quickly compute a globally optimal solution and a strong lower bound for varying demand profiles is crucial. A promising strategy is to build tight relaxations of the OGF’s nonlinear constraints. However, many nonlinearities arising from non-ideal equations of state either lack relaxations or have relaxations that do not scale to realistic network sizes. We address this gap by combining recent advances in polyhedral relaxations for univariate functions to construct tight, computationally efficient relaxations of the OGF with a non-ideal equation of state. These relaxations solve within seconds on a standard laptop. In conclusion, we demonstrate their quality through extensive numerical experiments on very large-scale test networks from the literature and find that the proposed approach proves optimality in 92% of tested instances.

03 NATURAL GAS↗
DOE OSTI2023

Techno-economic Analysis and Life Cycle Assessment of Gluconic Acid and Xylonic Acid Production from Waste Materials

Organic acid-based bioleaching has attracted significant research interest for the recovery of rare earth elements(REEs) and other critical metals. Utilizing biologically produced leaching agents, known as biolixiviants, derived from waste materials holds great promise for enhancing the economic viability and environmental sustainability of bioleaching processes. Here this study focuses on the modeling and optimization of biolixiviant production using corn stover (CS), date palm clippings (DP), and nonrecyclable paper (NP). Techno-economic analysis revealed that gluconic acid production from NP is more cost-effective than that from CS and DP, with respective costs of $0.04/kg, $0.06−0.08/kg, and $0.06−0.09/kg of the biolixiviant, yielding gluconic acid concentrations of 135.39, 172.90, and 176.87 mM, respectively. Life cycle assessment demonstrated that biolixiviant production from NP exerts the lowest environmental impact compared with the other evaluated substrates. When applied to the bioleaching of a neodymium−iron−boron magnet swarf, the biolixiviant derived from NP exhibits the highest leaching efficiencies, confirming its cost and environmental competitiveness in comparison to CS and DP

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Renewable hydrogen and ammonia for combined heat and power systems in remote locations: Optimal design and scheduling

Abstract Using hydrogen (H ) and ammonia (NH ) for renewable energy storage has the potential to enable economical power and heat supply with high renewable penetrations, especially in remote locations which are characterized by high energy costs. In this work we assess the economic competitiveness of renewable combined heat and power (CHP) systems in Mahaka HI, Nantucket MA, and Northwest Arctic Borough (NWAB) AK by optimally designing these systems for scenarios in which power and heat can be purchased over a range of historical energy prices as well as when 100% renewable supply is required. We use a combined optimal design and scheduling model which minimizes annualized net present cost by determining optimal technology selection and size simultaneously with optimal schedules for each period of a system operating horizon aggregated from full year hourly resolution data via a consecutive temporal clustering algorithm. We find that renewable generation meets at least 85% of power demands and 75% of heat demands under the lowest energy prices investigated. Higher conventional energy prices lead to increased renewable penetration which is facilitated by renewable NH as a seasonal energy storage medium, as are 100% renewable CHP systems. NH is used for power generation with heat cogeneration in all three locations, as well as directly for heating in NWAB. On an annual cost basis, NH ‐enabled 100% renewable CHP is only 3% more expensive in Mahaka and NWAB than systems which can purchase energy at the lowest prices, while it is 15% more expensive in Nantucket.

Palys, Matthew J.↗
DOE OSTI2022

Techno-Economic Analysis of CSP Incorporating sCO2 Brayton Power Cycles: Trade-Off Between Cost and Performance

Concentrating solar power (CSP) plants, thanks to the implementation of cost-competitive thermal energy storage, represent a dispatchable zero-emission alternative to traditional fossil fuel power plants. Next generation solar towers are expected to adopt high temperature receivers (>700 degrees C) coupled to sCO2-based power blocks, which optimal design is generally pushed towards the maximum cycle efficiency, often neglecting the economic impact with the justification that the main share of the capital cost is represented by the heliostat field. As result, the scientific literature lacks in comprehensive studies on techno-economic evaluation of CSP+sCO2 power plants addressing the important correlation that exists between system cost and performances. This work provides a preliminary techno-economic analysis of a solar power tower comparing four different cycle configurations for the sCO2 power block. Results have been reported on a Pareto front, highlighting the tradeoff between the plant investment cost and the solar-to-electricity plant efficiency. The trends of the optimization variables and cycle results have been reported to give useful insights about proper assumptions for the sCO2 power block design. The recompressed cycle with intercooling resulted as the most promising configuration and it has been further analyzed through a comparison of different solutions on the Pareto front. The cost breakdown of the sCO2 power block has been reported to highlight which components have the greatest impact on the overall plant cost and how they vary along the optimal solutions front. Eventually, the optimization has been repeated introducing a correlation to compute the turbomachinery isentropic efficiencies, to investigate their effect on the techno- economic analysis.

concentrated solar power↗
DOE OSTI2026

A Systematic Review and Integrated Approach to Modeling of Aging Utility Scale PV Systems

The growing deployment of utility-scale photovoltaic (PV) systems has increased the importance of techno-economic modeling operational photovoltaic (PV) systems for predicting energy yield, optimizing asset management, and informing financial decisions. Through a systematic review of literature and current industry practices, we review the different common modeling practices of a system's configuration and age, performance and degradation, operation and maintenance (O&M), while also focusing on specific considerations for repowering, revamping, and decommissioning. Building on the synthesis, we develop a structured framework for techno-economic modeling of operating PV systems that integrate performance and degradation analysis, a decommissioning and repowering cost model that estimates the system's end-of-life costs to reduce uncertainty quantifications and improve consistency across the sector. This research contributes to improved modeling methodologies and potentially to reduced financial performance requirements by providing practitioners with input resources and practical approaches to estimate performance, degradation, and costs associated with continued operation, revamping, repowering, or decommissioning decisions.

14 SOLAR ENERGY↗
DOE OSTI2023

Optimal Sizing and Dispatch of Solar Power with Storage

Designers of utility-scale solar plants with storage, seeking to maximize some aspect of plant performance, face multiple challenges. In many geographic locations, there is significant penetration of photovoltaic generation, which depresses energy prices during the hours of solar availability. An energy storage system affords the opportunity to dispatch during higher-priced time periods, but complicates plant design and dispatch decisions. Solar resource variability compounds these challenges, because determining optimal system sizes requires simultaneously considering how the plant will be operated under the imposed market and weather conditions. We develop an approach to analyze the economic performance of hybrid and single-technology solar power plants, which incorporates optimal dispatch, and considers the expected electricity market and weather conditions. We utilize the System Advisor Model software package to simulate the operation of multiple renewable generation and energy storage technologies, in conjunction with hourly-fidelity generation decisions determined by a revenue-maximizing, mixed-integer linear program. We show that, under our assumed market and weather conditions, the lifetime benefit-to-cost ratio can be improved by 6 to 19 percent, relative to a baseline design without optimizing, and that a concentrating solar power with thermal energy storage design produces significantly more energy per year, but is less profitable under our cost assumptions.

black-box optimization↗
DOE OSTI2021

Dynamic Process Intensification via Data-Driven Dynamic Optimization: Concept and Application to Ternary Distillation

Process intensification is a design philosophy aimed at making chemical processes safer and more efficient. Its implementation often results in significant modifications to the design and structure of the process, with several conventional unit operations occurring in the same physical device. Traditionally, process intensification has focused on steady-state operation. In our previous works, we introduced dynamic process intensification (DPI) as a new intensification paradigm based on operational changes for conventional or intensified units. DPI is predicated on switching operation between two auxiliary steady states selected via a steady-state optimization calculation that ensures that the system generates, on average and over time, the same products as in nominal steady-state operation, but with favorable economics. This paper extends the DPI concept and introduces a novel dynamic optimization-based DPI strategy (Do-DPI) that involves imposing a true cyclic operation rather than switching between two discrete states. We discuss its implementation using surrogate dynamic models learned via system identification. Here, an extensive case study concerning a ternary distillation column separating a canonical hydrocarbon mixture shows that Do-DPI can reduce energy use by more than 4% relative to steady-state operation, with no significant deviations in product quality and production rate.

42 ENGINEERING↗
DOE OSTI2024

Optimization of Conditions for Photoluminescence-Based Sensing of Critical Minerals: Strategies and Outlook

Economically critical minerals and metals are vital to America’s security, with application areas in energy, advanced technologies, and defense systems. The growing implementation of renewable energy sources such as electric vehicles is expected to significantly increase demand for these minerals, while the global supply is monopolistic, with most of the production occurring in a few countries outside of the US. To mitigate potential supply chain vulnerabilities, the domestic production of economically critical metals has become a bipartisan priority of the US government, and unconventional sources such as coal and its utilization byproducts such as fly ash and acid mine drainage are being actively explored as an alternative method for meeting domestic demand. Slow and expensive characterization methods for critical metals present a significant pain point for domestic production, which has led to the exploration of alternative low-cost, portable platforms capable of detecting target metals during resource prospecting and downstream process monitoring. The National Energy Technology Laboratory has developed a portable fiber optic-based luminescence spectrometer that utilizes a metal-organic framework (MOF) material to detect and distinguish parts-per-billion levels of the visible-emitting rare earth elements terbium, dysprosium, samarium, and europium, which are all economically critical metals. Although preliminary results with this system are promising, a crucial barrier to practical deployment is the ability to operate in low pH, high ionic strength environments, as acidic conditions and the presence of other metal ions can significantly reduce luminescence signal. Here, current strategies related to sample treatment, material design, and sensor configuration are discussed in the context of improving sensor performance in application-relevant conditions. Additionally, future opportunities, such as the optimization of the sensing binding environment through computational methods and specific metals to be removed for treatment will be discussed, presenting a forward-looking view for innovation in luminescent sensing of critical metals.

36 MATERIALS SCIENCE↗