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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Small-Scale Irrigation: Improving Food Security under Changing Climate and Water Resource Conditions in Ethiopia

We develop a new systems modeling tool that integrates knowledge from hydrology, agriculture, and economics to understand the effect of small-scale irrigation on food security and groundwater sustainability in Ethiopia. Irrigation is an effective tool to mitigate climate impacts and improve agricultural yields. Small-scale irrigation, such as decentralized groundwater irrigation, is well suited for developing countries where smallholder farming communities are widely dispersed and can only afford small infrastructure investment. We study the underlying interdependencies between food and water systems in Ethiopia, where small-holder agriculture is the foundation of the nation’s economy and climate variability has led to great challenges to its food security. Our coupled market and crop model with groundwater module captures the interdependencies of climate, water availability (including irrigation), crop yield, farmland allocation, crop production, transport and consumption based on a system approach across multiple spatial scales. We study the implication of small-scale irrigation to Ethiopia’s food security and water resource conditions as a “what-if” question by comparing an irrigation scenario to the calibrated baseline in 2015, a year of significant drought and crop failure over a large portion of Ethiopia. Our model offers fresh insights into geographic disparities in outcomes that are driven by baseline climate variability, soil fertility, and market conditions. In general, we find that small-scale irrigation can potentially improve food security through increases in food consumption, but it requires policy support to direct the increases of production to domestic consumption while maintaining a sustainable groundwater condition. By using Ethiopia as an example, we show the strength of our model to study how water infrastructure resources support critical functions and service in water and food systems.

Zhang, Ying↗
DOE OSTI2025

Development and preliminary validation of a mechanistic multiscale model for fuel-cladding chemical interaction in metallic nuclear fuels

Despite decades of fuel rod material and design improvements, fuel-cladding chemical interaction (FCCI) remains the single-most lifetime-limiting behavior for modern metallic fuel rods. Constraining fuel lifetime increases operating costs, limiting the economic viability of commercializing metallic nuclear fuel technology. A mechanistic multiscale model utilizing the finite element method-based MARMOT and BISON codes was developed to more confidently predict cladding-side FCCI and its impact on fuel performance. The new BISON model incorporates mesoscale models for the effects of fuel microstructure evolution on the transport of wastage-inducing lanthanides through the fuel and for the kinetics of cladding wastage layer growth. The mesoscale models, in turn, build on lanthanide transport property data obtained from the atomistic scale. Preliminary validation studies using wastage thickness and cladding profilometry data from four fuel rods irradiated in Experimental Breeder Reactor II experiment X447 and one fuel rod from Fast Flux Test Facility experiment IFR1 show that the new model predicts cladding wastage and its effects on cladding deformation as well as existing empirical FCCI correlations. The new model is expected to aid in the design of new metallic fuel concepts, including fuel additives, cladding liners, and sodium-free annular fuel geometries. In conclusion, future work will focus on broader validation and refinement of the model’s treatment of different fuel alloys and cladding materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Combined Land Use of Solar Infrastructure and Agriculture for Socioeconomic and Environmental Co-Benefits in the Tropics

Solar photovoltaics (PV) are on the rise even in areas of low solar insolation. However, in developing countries with limited capital, land scarcity, or with geographically isolated agrarian communities, large solar infrastructures are often impractical. In these cases, implementation of low-density PV over existing crops may be required to integrate renewable energy services into rural communities. Here, using Indonesia as a model system, we investigated the land use, energy, greenhouse gas emissions, economic feasibility, and the environmental co-benefits associated with off-grid solar PV when combined with high value crop cultivation. The life cycle analyses indicate that small-scale dual land-use systems are economically viable in certain configurations and have the potential to provide several co-benefits including rural electrification, retrofitting diesel electricity generation, and electricity for processing agricultural products locally. A hypothetical full-density off-grid solar PV for a model village in Indonesia shows that electricity output (1907.5 GJ yr-1) is much higher than the total residential consumption (678 GJ yr-1), highlighting the opportunity to downscale the PV infrastructure by half to lower capital cost, to co-locate crops, and to support secondary income generating activities. Economic analysis shows that the 30-year net present cost of electricity from the half-density co-located PV system (12,257 million IDR) is significantly lower than that of the flat cost of diesel required to generate equivalent electricity (14,702 million IDR). Our analysis provides insights for smarter energy planning by optimizing the efficiency of land use and limiting conversion of agricultural and forested areas for energy production.

agrivoltaics↗
DOE OSTI2021

A Review of Lattice-Boltzmann Models Coupled with Geochemical Modeling Applied for Simulation of Advanced Waterflooding and Enhanced Oil Recovery Processes

To maintain economic profit and improve the oil production efficiency after the primary and secondary production phase, advanced waterflooding techniques such as low salinity waterflooding in carbonate reservoirs have been investigated in numerical simulations, laboratory experiments, and field pilot tests. Multiple underlying mechanisms have been proposed based on these studies, and they are still under debate. Various numerical modeling approaches are introduced, but there exists a lack of a pore-scale comprehensive modeling scheme to fully understand the processes. Lattice-Boltzmann method (LBM) is a type of numerical fluid flow modeling technique that shows capabilities and flexibilities in modeling pore-scale fluid flow to integrate physical–chemical processes within complex structures. The intrinsic feature of LBM makes it a promising framework for simulating advanced waterflooding due to its flexibility, accuracy, and parallel efficiency. LBM works either by itself for solving reactive transport problems or by coupling with a third-party reaction solver. This review mainly introduces the LBM fluid flow and reactive transport capabilities and the concept and modeling approaches to simulate advanced waterflooding techniques. Meanwhile, an evaluation of the coupled LBM models for enhanced oil recovery (EOR) simulations is discussed with future research challenges and directions concluded.

02 PETROLEUM↗
DOE OSTI2023

Net-zero CO 2 by 2050 scenarios for the United States in the Energy Modeling Forum 37 study

The Energy Modeling Forum (EMF) 37 study on deep decarbonization and high electrification analyzed a set of scenarios that achieve economy-wide net-zero carbon dioxide (CO 2 ) emissions in North America by mid-century, exploring the implications of different technology evolutions, policies, and behavioral assumptions affecting energy supply and demand. Here, for this paper, 16 modeling teams reported resulting emissions projections, energy system evolution, and economic activity. This paper provides an overview of the study, documents the scenario design, provides a roadmap for complementary forthcoming papers from this study, and offers an initial summary and comparison of results for net-zero CO 2 by 2050 scenarios in the United States. We compare various outcomes across models and scenarios, such as emissions, energy use, fuel mix evolution, and technology adoption. Despite disparate model structure and sources for input assumptions, there is broad agreement in energy system trends across models towards deep decarbonization of the electricity sector coupled with increased end-use electrification of buildings, transportation, and to a lesser extent industry. All models deploy negative emissions technologies (e.g., direct air capture and bioenergy with carbon capture and storage) in addition to land sinks to achieve net-zero CO 2 emissions. Important differences emerged in the results, showing divergent pathways among end-use sectors with deep electrification and grid decarbonization as necessary but not sufficient conditions to achieve net zero. These differences will be explored in the papers complementing this study to inform efforts to reach net-zero emissions and future research needs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Energy Arbitrage with Hydrogen: HTSE + Combustion Turbine

The Light Water Reactor Sustainability (LWRS) program seeks to keep Nuclear Power Plants (NPPs) competitive in the changing energy market. The Flexible Plant Operation and Generation (FPOG) pathway investigates Energy Storage Systems (ESS) to give operators an alternative to curtailment or paying to put power on the grid. This work investigates the concept of diverting heat and electricity to High Temperature Steam Electrolysis (HTSE) to produce hydrogen when grid demand is low, storing that hydrogen, then burning it in a combustion turbine to produce zero-emission electricity when grid demand is high. A process model of the combustion turbine was developed in Aspen HYSYS, which was used to complete a parametric analysis and techno-economic analysis.

08 HYDROGEN↗
DOE OSTI2025

Investigation of the Performance and Explainability Tradeoffs for Machine-Learning Models for Predictive Maintenance of Circulating Water Systems in Nuclear Power Plants

Predictive maintenance (PdM) has shown great potential for achieving substantial cost savings and enhancing the economic competitiveness of nuclear power plants (NPPs) in today's energy market. Among the different modeling approaches that exist, machine learning (ML) tools in particular have a demonstrated ability to handle high dimensional and multivariate data and to extract hidden relationships within data in industrial environments. While ML methods show great potential, their lack of explainability---especially for black-box models---is a major hurdle to their adoption. Moreover, considering the supposed trade-off between explainability and performance challenges, careful consideration must be made as to which of these quality aspects takes precedence in light of multiple modeling options, resource availability, and domain characteristics. The present work evaluates the performance of six ML models, each with a different degree of explainability, in classifying the conditions of circulating water pumps (CWPs) by utilizing sensor data from nuclear power plants. To determine the drivers behind the trade-offs presented by this array of models, this work also tests different combinations of CWP units as the training and testing data, degrees of data imbalance, and objective functions for hyperparameter tuning. It was found that black-box models tend to afford superior performance in cases where there are far more instances of one type of labeled data than of any other type. It is recommended that a guided procedure be followed for designing and delivering an ML system that is sufficiently explainable to all involved stakeholders.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

NREL Data, Modeling, and Tools Empower Marine Energy Analysis

NREL performs R&D and economic analyses to drive and empower the development of wave energy and tidal, ocean, and river current energy technologies. These technologies deliver renewable electricity to the grid and provide energy solutions that support the evolving "blue economy."

HYDRO ENERGY↗
DOE OSTI2023

Comparative Economic Analysis of Capture, Transport, and Storage from a CO2 Source Perspective

This presentation provides an overview of a study that evaluated multiple carbon capture and storage (CCS) networks within the Central United States using NETL-developed resources and models to assess management options different CO2 source types face from both economic and regional geologic perspectives when capturing, transporting and storing their CO2. It was presented at the Carbon Capture, Utilization, and Storage (CCUS) 2023 conference, organized and presented by the Society of Petroleum Engineers (SPE), American Association of Petroleum Geologists (AAPG), and Society of Exploration Geophysicists (SEG) and held in Houston, Texas, April 25-27, 2023.

Guinan, Allison↗
DOE OSTI2022

Economic feasibility of in-motion wireless power transfer in a high-density traffic corridor

Electricity is expected to become a dominant power source in the transportation sector, but under a stationary recharging model, electric powered vehicles still suffer some disadvantages relative to conventional vehicles. Recent developments in wireless power transfer (WPT) technology can enable an alternative vehicle recharging model wherein power is supplied to vehicles while they are moving, mitigating the need for charging stops and high-capacity on-board energy storage. One of the greatest concerns associated with dynamic WPT is that the required infrastructure costs would be prohibitively high; however, the life-cycle costs and benefits of such a system have been under-researched. Here in this work, we apply a systems-level assessment to a case study of a WPT charging system located on the densely trafficked I-710 corridor in Los Angeles, California. Detailed cost estimates for electronics and implementation, high-fidelity energy consumption modeling, and survey-derived adoption projections are applied to evaluate scenarios of economic feasibility and environmental impact. Results show that a “1st-of-a-kind” system can achieve a payback in 20 years while maintaining operating cost advantages relative to petroleum fueling. System economics are shown to improve substantially with “nth-of-a-kind” capital costs. Discussion focuses on the effect of uptake trajectory on economic viability and detailed economics outcomes for light duty and heavy duty vehicles.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

HERON

HERON is a modeling toolset and plugin for RAVEN to accelerate stochastic technoeconomic assessment of the economic viability of various grid-energy system configurations, especially with application to electrical grids and integrated energy systems (IES). The frameworks enabling this toolset are INL's RAVEN and its TEAL plugin. The toolset includes automated RAVEN workflow generation tools for performing stochastic analysis and optimization driven by technoeconomic factors. The workflows generated employ RAVEN for parametric or optimizing study of grid component characteristics, and again for stochastic sampling of regulated and deregulated markets, load, and weather histories. The toolset further provides methods for economically-optimal dispatch of system components given the stochastic samples of market, load, and weather histories. The toolset allows assembling of existing and new models through the RAVEN interface to the Modelica process modeling language.

Talbot, PaulW↗
DOE OSTI2021

Microalgae to biofuels through hydrothermal liquefaction: Open-source techno-economic analysis and life cycle assessment

Hydrothermal liquefaction is a promising conversion technology in algae biofuel research due to its ability to agnostically convert proteins, carbohydrates, and lipids to biocrude. The high-temperature conditions that define this conversion process require the material to maintain a subcritical liquid state, which complicates the assessment of accurate thermochemical properties due to the required pressure. To clarify this issue, this work compares the estimated performance of algal hydrothermal liquefaction between different thermodynamic models. A process model was developed in Aspen Plus from a robust assessment of current literature. Techno-economic assessment and life-cycle assessment metrics are derived from this model and used as key performance indicators. The baseline fuel price contribution of hydrothermal liquefaction is $0.45 per liter gasoline equivalent. Independently decreasing the temperature from 350 °C to 260 °C while maintaining yield reduces the conversion cost by 19%, illustrating the importance of understanding the high-temperature thermodynamics of the system. Different thermodynamic property models can vary fuel conversion cost results by $0.07 per liter gasoline equivalent. The baseline global warming potential is +23 g CO 2 eq MJ -1 and the net energy ratio is 0.30. Environmental metrics beyond global warming potential and net energy ratio are also discussed for the first time. Uncertainties in conversion performance are bounded through a scenario analysis that manipulates parameters such as product yield and nutrient recycle to produce a range of economic and environmental metrics. The report is supplemented with an open source model to support future hydrothermal liquefaction assessments and accelerate the development of commercial-scale systems.

09 BIOMASS FUELS↗
DOE OSTI2021

An investigation of hard-disk drive circularity accounting for socio-technical dynamics and data uncertainty

The installed data storage capacity in the U.S. will reach 2.2 Zettabytes by 2025, generating about 50 million units of end-of-life (EOL) hard-disk drives (HDDs) per year. Due to data security concerns, most EOL HDDs are currently shredded (even when still functioning), representing an economic loss. Moreover, raw material extraction linked to the increased demand for storage causes environmental impacts. Besides mitigating the threat posed by sudden restrictions of raw materials, the circular economy (CE) offers to maximize value retention in the economy and reduce the environmental impacts of human activities. Common CE strategies are reusing and recycling products. However, the reuse of hard disk drives is currently burdened by the lack of trust HDD end-users have toward other non-physical means of data removal than shredding. Here, an agent-based modeling (ABM) approach is proposed to explore how techno-economic and social factors affect end-users' decisions to adopt EOL management practices other than shredding. The proposed method also accounts for data uncertainty by applying a semi-quantitative approach. Results demonstrate how increased green procurement and more robust standards could spur end-users' trust toward data-wiping technologies. Even when accounting for uncertainty, HDDs' reuse brings better environmental and economic benefits than HDD shredding followed by material recovery. The semi-quantitative approach proposed in this study could be more universally applied in future ABM, especially given the often-stochastic nature of such models. The developed ABM is also the first to represent several HDD industry stakeholders and demonstrate how the HDD shredding lock-in situation could be resolved.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

High-Resolution Regional Wave Hindcast for U.S. Pacific Island Territories

This report summarizes modeling efforts for hindcasting of wave climate within the Exclusive Economic Zone around American Samoa, Baker Island and Howland Island, Commonwealth of Northern Mariana Islands, Guam, Jarvis Island, Johnston Atoll, Palmyra Atoll and Kingman Reef, and Wake Island. The report describes the mesh development and data sources used in the process. In addition, it provides the results of a sensitivity analysis performed to determine the optimal model configuration, details the data used for model forcing, shows a detailed skill assessment, and depicts the output.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Virtual Engineering Software Framework for Integrated Biomass Conversion Modeling

This presentation covers the design and implementation of a software tool to systematically connect computational models of unit operations to simulate an integrated process of low-temperature conversion of biomass to fuel. This virtual engineering (VE) software was designed with the overarching goal of connecting unit models written in various programming languages and requiring different computational resources within a single, flexible framework. The models and features currently considered for the VE library include mechanistic models for pretreatment, enzymatic hydrolysis, and aerobic bioreaction; high-fidelity computational fluid dynamics (CFD) simulations for enzymatic hydrolysis and aerobic bioreaction; and the capability to perform techno-economic analyses (TEA) using Aspen Plus, a commercial software package. The CFD models require access to high-performance computing (HPC) resources, so in addition to handling multiple programming languages and interfaces, the VE software must also be capable of interacting with an HPC scheduler to submit, run, and post-process jobs. Using the Python programming language, a new VE software package has been developed that contains functionality to manage the input-output communication between various unit models, schedule simulations to run on NREL's HPC and analyze those results, and interface with existing TEA software workflows. A Jupyter-notebook GUI was also created to solicit user input and provide documentation. In cases where multiple models for a particular unit-operation exist, selection between models is accomplished through a simple checkbox, with the appropriate inputs and outputs being parsed and converted seamlessly in the background. Each operation makes use of a different programming language, but the flow of information from pretreatment to enzymatic hydrolysis to bioreaction is managed with an intuitive, centralized file-communication strategy. In this talk, the programming approach and implementation details of the notebook are presented for multiple possibilities of the conversion process, including a demonstration of the ability to manage HPC resources. Additionally, an example of a sensitivity study of treatment parameters governing the overall conversion outcome is shown which highlights the ease of defining new problems using the VE Notebook workflow and leads into a discussion of ongoing work to enable outer-loop optimization studies.

biofuel↗
NASA NTRS2010

Physics of Life: A Model for Non-Newtonian Properties of Living Systems

This innovation proposes the reconciliation of the evolution of life with the second law of thermodynamics via the introduction of the First Principle for modeling behavior of living systems. The structure of the model is quantum-inspired: it acquires the topology of the Madelung equation in which the quantum potential is replaced with the information potential. As a result, the model captures the most fundamental property of life: the progressive evolution; i.e. the ability to evolve from disorder to order without any external interference. The mathematical structure of the model can be obtained from the Newtonian equations of motion (representing the motor dynamics) coupled with the corresponding Liouville equation (representing the mental dynamics) via information forces. All these specific non-Newtonian properties equip the model with the levels of complexity that matches the complexity of life, and that makes the model applicable for description of behaviors of ecological, social, and economical systems. Rather than addressing the six aspects of life (organization, metabolism, growth, adaptation, response to stimuli, and reproduction), this work focuses only on biosignature ; i.e. the mechanical invariants of life, and in particular, the geometry and kinematics of behavior of living things. Living things obey the First Principles of Newtonian mechanics. One main objective of this model is to extend the First Principles of classical physics to include phenomenological behavior on living systems; to develop a new mathematical formalism within the framework of classical dynamics that would allow one to capture the specific properties of natural or artificial living systems such as formation of the collective mind based upon abstract images of the selves and non-selves; exploitation of this collective mind for communications and predictions of future expected characteristics of evolution; and for making decisions and implementing the corresponding corrections if the expected scenario is different from the originally planned one. This approach postulates that even a primitive living species possesses additional, non-Newtonian properties that are not included in the laws of Newtonian or statistical mechanics. These properties follow from a privileged ability of living systems to possess a self-image (a concept introduced in psychology) and to interact with it. The proposed mathematical system is based on the coupling of the classical dynamical system representing the motor dynamics with the corresponding Liouville equation describing the evolution of initial uncertainties in terms of the probability density and representing the mental dynamics. The coupling is implemented by the information-based supervising forces that can be associated with self-awareness. These forces fundamentally change the pattern of the probability evolution, and therefore, lead to a major departure of the behavior of living systems from the patterns of both Newtonian and statistical mechanics. This innovation is meant to capture the signature of life based only on observable behavior, not on any biochemistry. This will not prevent the use of this model for developing artificial living systems, as well as for studying some general properties of behavior of natural, living systems.

Zak, Michail↗