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DOE OSTI2023

Techno-Economic Analysis for a Potential Geothermal District Heating System in Tuttle, Oklahoma: Preprint

Geothermal deep direct use (DDU) has potential across a wide swath of the United States but is underutilized due to challenging project economics associated with developing a deep geothermal resource for a large-scale and variable heat demand. The National Renewable Energy Laboratory (NREL) and University of Oklahoma evaluated the feasibility of a geothermal district heating (GDH) and cooling system in two schools and 250 houses by utilizing existing oil and gas (O&G) wells in Tuttle, Oklahoma. Heating and cooling demand in the two schools and a typical single-family house were modeled using EnergyPlus building energy simulation software. The modeling results indicated that annual heating demand in two schools and 250 houses is approximately 2.61 GWhth, and cooling demand in the two schools is approximately 2.65 GWhth. In this scope, the techno-economic analysis (TEA) was conducted using the GEOPHIRES tool combined with the TOUGH2 reservoir simulator. The reservoir performance, including geothermal heat production capacity, was modeled by the reservoir simulator TOUGH2. Then, levelized cost of heat (LCOH) was calculated using GEOPHIRES version 3.0, which includes new features such as hourly heat load optimization and peak performance evaluation. Geothermal reservoir temperature was estimated as 90.5 degrees C at a total depth of 3.3 km by the regional average temperature gradient of 22.8 degrees C/km and validated by cation geothermometer calculations. Four production scenarios with two different well configurations and two different heat load profiles have been developed for well flow rates ranging between 3.1 kg/s and 9.3 kg/s. The LCOH of the district heating and cooling system was calculated between $95 and $210/MWh ($28/MMBtu to $62/MMBtu) for four different production scenarios. Typical natural gas prices for residential customers in Oklahoma have ranged from 9 to 19 $/MMBtu over the past decade, which indicates a challenge for deployment of such a GDH system.

deep direct-use↗

DOE OSTI2023

Techno-Economic Analysis for a Potential Geothermal District Heating System in Tuttle, Oklahoma

deep direct-use↗

NASA NTRSDate not supplied

Simulated Ice Shapes on the High Lift Common Research Model Using LEWICE3D

Computational icing tools consist of predicting two distinct problems, the dry air aerodynamics around the iced or uniced vehicle and the accretion of ice due to inclement weather. A desire exists to advance these tools such that they can be reliably used earlier in the design process to limit the need for more costly flight and wind tunnel testing. To achieve these goals the computational tools need to be benchmarked and validated against high quality experimental data sets to raise the software’s Technology Readiness Level. An ongoing collaborative effort between NASA and Boeing Commercial Airplanes is focused on obtaining this required experimental data and subsequently benchmarking and/or validating the tools that predict the aerodynamics around the iced and uniced vehicle. Currently the scope of this effort is focused on the usage of simulated ice shapes to be representative of the iced vehicle. This presentation discusses the generation of those simulated ice shapes.

Icing↗

DOE OSTI2022

Port of New York and New Jersey Drayage Electrification Analysis

The National Renewable Energy Laboratory (NREL) evaluated the potential for drayage electrification in the Port of New York and New Jersey (PoNYNJ), with a focus on operators: Harbor Freight Transport (HF), Safeway Trucking (SWT), and International Motor Freight Inc (IMF). This report summarizes the data collection and electrification evaluation of all three drayage operators, includes detailed operational data, and identifies the performance requirements for battery electric tractors (BETs) and corresponding infrastructure operated within the context of PoNYNJ drayage operation. This report also details a methodology to evaluate opportunities, strategies, and challenges associated with future expansions of BETs in meeting PANYNJ emissions goals. The Port Authority has established a goal of achieving Net Zero carbon emissions by 2050 across all facilities, including from tenant and stakeholder sources such as drayage trucks. NREL used real-world performance data collected on the three PoNYNJ drayage operations, along with modeling and analysis tools to compare BET to diesel trucks. From March to July 2021, NREL collected 1Hz vehicle and engine data from 46 drayage trucks at the three operators totaling nearly 121,000 miles of operation, providing enough information to assess vehicle operations for electrification potential. A Future Automotive Systems Technology Simulator (FASTSim) electric truck powertrain model was validated using PoNYNJ data and scenarios were run to evaluate drayage truck electrification requirements over the real-world cycles. The first scenario examined BET viability with minimal changes to existing operations. This assumes the trucks charge when stopped for two hours or longer, have a functional battery size of 375 kWh, and can charge at 270 kilowatts (kW) average which are the specification of the commercially available Freightliner eCascadia. The second scenario looked at what operational, charging infrastructure, and BET technology changes would be needed to fully electrify. Finally, detailed analysis was run on charging rate structure to understand operational costs to the fleets. The studied drayage trucks averaged 5.1 MPG, spent roughly 9% of their energy at idle, and drove an average of 140 miles per day with a maximum daily distance of 573 miles. The FASTSim model results indicate a comparable BET would use 417 kWh of energy per day on average accounting for cargo weight, which is close to the full usable capacity of the eCascadia currently available on the market. Based on the daily average operating data, partial fleet electrification is possible with current technology. However, some specific days of operation would require over 1,600 kWh of energy due to longer distances traveled by the trucks and more intense operation. Trucks used for long distance and intense operation cannot be readily electrified with current technology without operational changes. Full adoption of BETs could reduce CO 2 emissions from these fleets by roughly 75% today, eliminating 76 metric tons of CO 2 (MTCO 2 ) per vehicle each year, which equates to 24,100 MTCO 2 per year for all three operators. Commercially available direct current fast chargers (DCFC) have charge rates up to 350 kW. Based on the average daily modeled energy use for each operator, current industrial rate structures, and the assumption of 350 kW peak charging, full drayage electrification would increase electricity consumption. In addition, peak demand usage would increase with unmanaged charging along with cost of electricity having a direct impact on cost per mile for electric vehicles. The resulting cost per mile for BETs along with comparable cost per mile for conventional diesel trucks are also examined at $\$$4.00 per gallon of diesel. It will be important for PANYNJ and the drayage operators within the PoNYNJ to consider these load impacts to their existing electrical infrastructure and devise operational strategies that avoid coincident charging of vehicles to mitigate demand charges. Despite these electricity cost increases, savings from reductions in diesel consumption will help offset the costs of this increased electricity consumption. However, prices of both electricity and diesel are subject to change based on various factors meaning the realized savings will vary over time. This shows BETs could be cost-competitive on an energy cost per mile basis for all scenarios while diesel is above $\$$3.00/gal. Further, if diesel prices dropped to the 15-year low of $2.33/gal, it would still be cost competitive to operate the EVs with electricity costs of 16.3 ¢/kWh or less.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Machine learning predictions of high-Curie-temperature materials

Technologies that function at room temperature often require magnets with a high Curie temperature, $T$ C , and can be improved with better materials. Discovering magnetic materials with a substantial $T$ C is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known datasets of experimental Curie temperatures, we develop machine-learning models to make rapid $T$ C predictions solely based on the chemical composition of a material. We train a random-forest model and a k -NN one and predict on an initial dataset of over 2500 materials and then validate the model on a new dataset containing over 3000 entries. The accuracy is compared for multiple compounds' representations (“descriptors”) and regression approaches. A random-forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. Further, a random-forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-$T$ C materials and under-predict high-$T$ C materials. For exhaustive searches to find new high-$T$ C materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Feasibility of Process Intensification of Water-Gas Shift Reaction Using a Microreactor with Integrated Cooling

Hydrogen is an increasingly attractive low-carbon energy carrier for a variety of stationary and mobile applications. The water-gas shift (WGS) reaction is a key processing step used for large-scale hydrogen production via the steam methane reforming process. However, the thermodynamics and kinetics of the reaction are such that standard two-stage adiabatic reactors used in these systems are large, increasing catalyst volume and cost. To intensify the process and realize the economical distributed production of hydrogen, adiabatic WGS microreactors with integrated cooling directly regulate the reaction temperature via integrated coolant channels to promote higher conversion within a smaller reactor volume. This study investigates the conversion efficiency of a single WGS microchannel operating under such cooling conditions. A COMSOL Multiphysics model is developed and validated with isothermal experimental data from the literature. The model is then used to evaluate improvements in conversion efficiency when the reaction is cooled via a specified wall temperature profile. Lastly, the model is modified to include cooling channels with a secondary fluid that can practically achieve a similar conversion profile as the specified wall temperature profile previously applied. Initial results show that reactor conversion can be significantly increased by the inclusion of appropriate cooling and that there is a potential for the recovery of energy from the reaction stream that can be used for other applications within the overall process.

energy recovery↗

NASA NTRS1997

Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

A cost effective model which uses the artificial intelligence techniques in the selection and approval of parts is presented. The knowledge which is acquired from the specialists for different part types are represented in a knowledge base in the form of rules and objects. The parts information is stored separately in a data base and is isolated from the knowledge base. Validation, verification and performance issues are highlighted.

Krishnan, G. S.↗

NASA NTRS2001

National Transonic Facility Wall Pressure Calibration Using Modern Design of Experiments (Invited)

The Modern Design of Experiments (MDOE) has been applied to wind tunnel testing at NASA Langley Research Center for several years. At Langley, MDOE has proven to be a useful and robust approach to aerodynamic testing that yields significant reductions in the cost and duration of experiments while still providing for the highest quality research results. This paper extends its application to include empty tunnel wall pressure calibrations. These calibrations are performed in support of wall interference corrections. This paper will present the experimental objectives, and the theoretical design process. To validate the tunnel-empty-calibration experiment design, preliminary response surface models calculated from previously acquired data are also presented. Finally, lessons learned and future wall interference applications of MDOE are discussed.

Underwood, Pamela J.↗

DOE OSTI2021

A Robust Approach to Sequential Information Theoretic Planning

In many sequential planning applications a natural approach to generating high quality plans is to maximize an information reward such as mutual information (MI). Unfortunately, MI lacks a closed form in all but trivial models, and so must be estimated. In applications where the cost of plan execution is expensive, one desires planning estimates which admit theoretical guarantees. Through the use of robust M-estimators we obtain bounds on absolute deviation of estimated MI. Moreover, we propose a sequential algorithm which integrates inference and planning by maximally reusing particles in each stage. We validate the utility of using robust estimators in the sequential approach on a Gaussian Markov Random Field wherein information measures have a closed form. Lastly, we demonstrate the benefits of our integrated approach in the context of sequential experiment design for inferring causal regulatory networks from gene expression levels. Here, our method shows improvements over a recent method which selects intervention experiments based on the same MI objective.

Zheng, Sue↗

DOE OSTI2022

Invertible Neural Networks for Aerodynamic Design of Wind Turbine Blades

The state-of-the-practice methods for aerodynamic design of wind turbine blads use linearized blade element momentum theory (BEM) to optimize the twist and chord profiles from a pre-selected set of 2D airfoil shapes. In this work, we apply invertible neural network (INN) tools to enable the rapid inverse aerodynamic design of wind turbine blades including component airfoils. The INN is trained on data obtained through the use of robust automated mesh generation and the HAMSTR computational fluid dynamics solver with advanced turbulence and transition models validated for turbine applications. Our design technique is a significant improvement over the state-of-the-practice linearized blade element momentum (BEM) techniques in capturing 3D nonlinear aerodynamic effects that are critical for optimal design of the rotors. This is made possible by developing sparse, invertible neural networks (INNs) for inverse design and optimization that realize a 100x cost reduction compared to adjoint-based computational fluid dynamics (CFD) approaches, while enabling increased robustness of the final design. We demonstrate the INN tool for design of a section of the NREL 5-MW blade. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of wind turbine blades.

aerodynamic design↗

NASA NTRS2018

Validation of Heat Transfer Correlations in Line Chill-Down Tests of Cryogenic Fluid in SINDA/FLUINT

Line chill-down is an important process in cryogenic tank propellant management, storage, and usage Complex flow dynamics during these processes: boiling heat transfer (film, transition, and nucleate) Understanding boiling phenomena can lead to efficient line chill-down systems that use less propellant, propellant stored, reducing cost for space missions Line Chill-down heat transfer was modelled using SINDA/FLUINT version 5.8 (SF) Multiple chill-down tests were modelled using: heat transfer correlations readily available in SF using HTN/HTC TIES heat transfer empiricisms developed by the University of Florida (UF) based on a series of liquid nitrogen chill-down tests using SF HTU TIES Chill-down tests modelled: liquid nitrogen tests conducted by the University of Florida horizontal flow, upward flow, and downward flow (Reynolds Numbers ranging 850-231,000)liquid hydrogen tests conducted by NASA Glenn Research Center vertical upward flow (Reynolds Number range of 18,400 - 433,000)The flow rate was measured far downstream of the test section, near the system exit. Where to set the flow rate? SF was highly sensitive, and sometime unstable, setting the test flow rate downstream (the outlet) of the test section model and setting the test pressure upstream (the inlet) of the test section model higher flow rate oscillations at the entrance of the model's test section SF was more stable setting the test flow rate upstream (than the downstream flow rate set case)test pressure was used as an inlet (SF plenum) to set the thermodynamic state (temperature and quality) coming into the system setting the appropriate downstream pressure was the unknown. The pressure drops predicted by SF for the downstream set flow rate boundary condition were much smaller than test section measured pressure drops. The multiphase pressure drop correlations used internally in SF may need to be adjusted. Models with an upstream flow rate set assumed a pressure drop that was small

cryogenic modeling↗

DOE OSTI2020

Analysis of Space-Conditioning Loads in Commercial Buildings

Space conditioning end-uses, which include heating, cooling, and ventilation, represent a significant fraction of commercial building energy use, with a wide variety of heating and cooling technology options available in the market. In the interest of improving the overall efficiency of heating, ventilation and air-conditioning (HVAC) technologies, governments, utilities and private sector entities have implemented a variety of market transformation policies that aim to influence consumer purchase decisions. To evaluate the costs and benefits of such programs, analysts typically postulate a hypothetical default equipment choice, and compare it to one that provides comparable service with lower energy and/or power use. The corresponding reduced operating cost provides a benefit that offsets the potential higher cost of improved efficiency. Typically, life-cycle cost or cash-flow analyses are used to quantify the net economic benefit. These analyses require the capability to assess how a given equipment design would perform across a broad range of characteristics, both of the building and of the local weather. While these assessments can be performed using customized building simulations, it is generally not practical to develop and validate detailed building simulation code to cover all the potential variations of equipment design and installation. An alternative, and somewhat simpler, approach is to solely use detailed building simulations to generate time series of heating and cooling loads in commercial buildings. These loads can then be used as input to more detailed, stand-alone engineering models that simulate HVAC system performance under different equipment designs. This approach was used to evaluate a range of high-efficiency commercial packaged air conditioner design options for the Department of Energy’s Appliance and Equipment Standards Program (DOE-EERE 2015). While there may be some loss of precision relative to full simulation, the accuracy of this approach is sufficient for practical applications of cost-benefit analysis. This report describes the development of a database of commercial building heating and cooling loads, generated using the EnergyPlus software package, a whole building energy use model supported by the Department of Energy (DOE-EERE 2020a). EnergyPlus takes as input a set of configuration files that describe the building itself (size, zoning, envelope characteristics, etc.) and the various systems within it (HVAC, lighting, water heating, etc.). This analysis uses a publicly available collection of commercial reference buildings (CRB), comprised of sixteen building types and three vintages (DOE-EERE 2020b; Deru et al 2011). Each building is simulated in eighteen different locations, covering a wide range of climatic conditions. The prototype building description files assign the type of HVAC equipment used, and capacities across climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Heat Integration Optimization and Dynamic Modeling Investigation for Advancing the Coal-Direct Chemical Looping Process

The purpose of the project is to address the optimization and startup operation of a modular coal direct chemical looping (CDCL) combustion system integrated with a steam cycle for power generation to reduce the risks involved in further scale-up of the technology. The modular reactor design of the CDCL process provides flexibility in the fabrication of the reactor and in its operating capacity (i.e. turndown ratio) at the cost of a more complex heat exchange network (HEN) design and integration. To address the technology gaps and advance the efficiency and economic feasibility of the CDCL technology, the project will perform a detailed and comprehensive analysis of the integration of a modular CDCL reactor system and a steam cycle system under both static and transient conditions via HEN process performance simulations and system dynamic modeling, respectively. The scope of work consists of 1) Experimental and computational studies of the CDCL combustor reactor 2) Comprehensive static (i.e. steady-state) system HEN design analysis in CDCL 550 MWe commercial unit for power generation and 3) Dynamic modeling of site specific design of 10MWe CDCL large pilot plant. The project team has successfully developed and validated a kinetic model for the oxidation of oxygen carriers in the combustor using the unreacted shrinking core model (UCSM). The model is capable of capturing the oxidation kinetics of fully or partially reduced oxygen carrier particles. A computational fluid dynamics (CFD) model is developed to simulate the hydrodynamics, heat transfer, and chemical reaction occurring in the CDCL combustor. The model is developed in MFIX and ANSYS Fluent. Key aspects of CDCL combustor operation, including heat transfer, oxygen carrier oxidation, and the transport of oxygen carrier particles, are simulated using this CFD model. The HEN for a commercial scale 550 MWe CDCL power plant is simulated and optimized using ASPEN Plus. Practical design considerations are incorporated based on industrial experiences. The performance and cost for the commercial CDCL plant is updated based on these analyses. A dynamic model for the 10 MWe CDCL pilot plant is developed in ProTRAX simulation software. The model is based on the pilot plant design developed in project DE-FE0027654 “10 MWe CDCL Large Pilot Plang – Pre-FEED Study” and the steam cycle data obtained from Dover Light & Power plant. The transient behaviors during pilot plant load variation are simulated using the dynamic model.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

Particle Size Optimization of Thermochemical Salt Hydrates for High Energy Density Thermal Storage

Thermal energy storage (TES) solutions offer opportunities to reduce energy consumption, greenhouse gas emissions, and cost. Specifically, they can help reduce the peak load and address the intermittency of renewable energy sources by time shifting the load, which are critical toward zero energy buildings. Thermochemical materials (TCMs) as a class of TES undergo a solid–gas reversible chemical reaction with water vapor to store and release energy with high storage capacities (600 kWh m −3 ) and negligible self‐discharge that makes them uniquely suited as compact, stand‐alone units for daily or seasonal storage. However, TCMs suffer from instabilities at the material (salt particles) and reactor level (packed beds of salt), resulting in poor multi‐cycle efficiency and high‐levelized cost of storage. In this study, a model is developed to predict the pulverization limit or R crit of various salt hydrates during thermal cycling. This is critical as it provides design rules to make mechanically stable TCM composites as well as enables the use of more energy‐efficient manufacturing process (solid‐state mixing) to make the composites. The model is experimentally validated on multiple TCM salt hydrates with different water content, and effect of R crit on hydration and dehydration kinetics is also investigated.

25 ENERGY STORAGE↗

DOE OSTI2025

Optimization of direct air capture processes using reactive transport models of adsorption-desorption cycles

In this study, we develop and implement a reactive transport model in COMSOL Multiphysics® to address the challenges of direct air carbon capture. The model is validated against experimental data and used to simulate the cyclic steady state of the adsorption-desorption process. The optimization of this model is achieved through advanced trust-region methods integrated with Gaussian Processes. Key decision variables, including adsorption and desorption times, desorption temperature and pressure, input velocity, bed porosity, column length, and radius were optimized to minimize the capture cost. After optimization, a sensitivity analysis revealed the complex interplay between the decision variables and their effect on the specific energy and cost of removing the CO 2 . We optimized the capture cost while taking into account the trade-off between energy consumption and productivity. The resulting minimum capture cost was determined to be 265.2 $/t-CO 2 , which aligns with expected values reported in the literature. Numerical results suggest the effectiveness of the optimization strategies applied, and underscore the importance of simultaneous decision variable selection in improving the performance in direct air capture processes. We also extend the modeling approach to a 2D axisymmetric model to better visualize CO₂ uptake and temperature profiles, revealing significant radial gradients during the regeneration step. As a main drawback, this enhanced model comes with a computational cost approximately 40 times higher than that of the 1D model.

Adsorption-desorption process↗

DOE OSTI2022

EarthShine: Observing our world as an exoplanet from the surface of the Moon

NASA’s return to the Moon coincides with explosive growth in exoplanet discovery. Missions are being formulated to search for habitable planets orbiting other stars, making this the ideal time to deploy an instrument suite to the lunar surface to help us recognize a habitable exoplanet when we see it. We present EarthShine, a technically mature, three-instrument suite to observe the whole Earth from the Moon as an exoplanet proxy. EarthShine data will validate and improve models critical for designing missions to image and characterize exoplanets, thus informing observing strategies for flagship missions to directly image exoplanets. EarthShine will answer interconnected questions in Earth and lunar science, exoplanets, and astrobiology, related to the credo “follow the water.” EarthShine can take advantage of current NASA programs to conduct science from the Moon with low-cost, mature space hardware to reduce risk and assure success. Like the 1968 Apollo Earthrise image of our home planet, lonely in the black sky, the appeal of EarthShine to a multidisciplinary array of researchers in Earth Science, Planetary Science, and astrophysics will maximize both its scientific impact and its impact on the general public.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Efficient Learning of Accurate Surrogates for Simulations of Complex Systems

Machine learning methods are increasingly deployed to construct surrogate models for complex physical systems at a reduced computational cost. However, the predictive capability of these surrogates degrades in the presence of noisy, sparse, or dynamic data. We introduce an online learning method empowered by optimizer-driven sampling that has two advantages over current approaches: it ensures that all local extrema (including endpoints) of the model response surface are included in the training data and it employs a continuous validation and update process in which surrogates undergo retraining when their performance falls below a validity threshold. We find, using benchmark functions, that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema even when the scoring metric is biased towards assessing overall accuracy. Finally, the application to dense nuclear matter demonstrates that highly accurate surrogates for a nuclear equation of state model can be reliably auto-generated from expensive calculations using few model evaluations.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2008

Practical Application of a Subscale Transport Aircraft for Flight Research in Control Upset and Failure Conditions

Over the past decade, the goal of reducing the fatal accident rate of large transport aircraft has resulted in research aimed at the problem of aircraft loss-of-control. Starting in 1999, the NASA Aviation Safety Program initiated research that included vehicle dynamics modeling, system health monitoring, and reconfigurable control systems focused on flight regimes beyond the normal flight envelope. In recent years, there has been an increased emphasis on adaptive control technologies for recovery from control upsets or failures including damage scenarios. As part of these efforts, NASA has developed the Airborne Subscale Transport Aircraft Research (AirSTAR) flight facility to allow flight research and validation, and system testing for flight regimes that are considered too risky for full-scale manned transport airplane testing. The AirSTAR facility utilizes dynamically-scaled vehicles that enable the application of subscale flight test results to full scale vehicles. This paper describes the modeling and simulation approach used for AirSTAR vehicles that supports the goals of efficient, low-cost and safe flight research in abnormal flight conditions. Modeling of aerodynamics, controls, and propulsion will be discussed as well as the application of simulation to flight control system development, test planning, risk mitigation, and flight research.

Cunningham, Kevin↗