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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Life Cycle Analysis of Greenhouse Gas Emissions for the Niagaran Reef Complex CO 2 -EOR Operations

A greenhouse gas emissions life cycle analysis (LCA) was completed for 22 years of carbon dioxide-enhanced oil recovery (CO 2 -EOR) operations at the Niagaran reef complex in the northern Michigan Basin based on research by the Midwest Regional Carbon Sequestration Partnership (MRCSP). The objective of the greenhouse gas LCA was to account for the total greenhouse gas emissions generated through the CO 2 -EOR process..

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Life Cycle Analysis of Greenhouse Gas Emissions for the Niagaran Reef Complex CO 2 -EOR Operations

The Midwest Regional Carbon Sequestration Partnership (MRCSP) was founded in 2003 as part of the U.S. Department of Energy’s (DOE’s) Regional Carbon Sequestration Partnership initiative. Since its founding, MRCSP has made significant strides toward making CCUS a viable option for states in the region. The public/private consortium, funded through the DOE Regional Carbon Sequestration Initiative, brings together nearly 40 industry partners and 10 states. Battelle, as the project lead, oversees research, development and operations and coordinates activities among the partners. The incremental, phased approach has built a valuable knowledge base for the industry and paved the way for commercial-scale adoption of CCUS technologies. From 2008 to 2020, MRCSP Phase III focused on the development of large-scale injection projects. This report is part of a series of reports prepared under the Midwestern Regional Carbon Sequestration Partnership (MRCSP) Phase III (Development Phase). These reports summarize and detail the findings of the work conducted under the Phase III project. A greenhouse gas emissions life cycle analysis (LCA) was completed for 22 years of carbon dioxide-enhanced oil recovery (CO 2 -EOR) operations at the Niagaran reef complex in the northern Michigan Basin based on research by the MRCSP. The objective of the greenhouse gas LCA was to account for the total greenhouse gas emissions generated through the CO 2 -EOR process. Methane produced from the 300 to 500 meter-deep Antrim shale has CO 2 content ranging from 5 percent to 30 percent. Therefore, the produced gas stream is gathered outside of Gaylord, Michigan, at the Chester 10 central gas processing plant, and run through an amine-based CO 2 separation process to remove the CO 2 so the methane can be sold to the market. The CO 2 is periodically dehydrated, compressed, and transported via pipeline for CO 2 -EOR in nearby 5,000- to 6,500-foot (1500-2000 m) deep carbonate Niagaran reefs. The produced mixture of oil, water, and CO 2 is separated at the Dover 36 oil processing facility. The processed CO 2 is recycled to the reefs for CO 2 -EOR in a closed loop system. During this process, a portion of the CO2 remains permanently stored in the reefs.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Optimization of WAAM Process to Produce AUSC Components with Increased Service Life

Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Wire Arc Additive manufacturing (WAAM) process is a class of directed energy deposition process enabling higher build rate and allowing custom wire feedstock allowing spatial variation of microstructure. The larger spot size and lower speed creates a larger melt pool, reducing residual stress and often time creating directional/columnar microstructure for Ni-superalloy. The use of flexible platform provides freedom in deposition strategy, which can accommodate complex substrates, including feature addition onto existing structures, non-flat layers, and repair methods. However, the certification of the final component needs to match the strength requirement. Hence, the quality of the component produced is stringently monitored to avoid buildup of residual stress, cracks, porosity and to reduce detrimental segregated phases commonly observed during alloy solidification. Simulation plays a huge role in predicting the melt pool dimension and can be used to optimize the process parameter. Similar development is also required to perform physics-based modeling of microstructural development in WAAM that can predict the microstructural features during solidification and can be used to optimize the process more effectively. To move toward this goal, Raytheon Technologies Research Center together with Siemens worked to create a set of computational tools to control the process parameters, enable on-line measurements and acquisition with feedback to the optimized process parameters, and eventually track material evolution through each step of the additive process. Computational fluid dynamics is used for accurate prediction and calibration of the thermal field during WAAM process. and phase field models for microstructure evolution as a function of processing parameters to establish a connection between additive parameters and the final microstructure. Here we report cellular automata (CA) model development to predict the dendritic microstructure evolution with surface and bulk nuclei for single track and multiple layers. The CA model was developed to account for secondary element addition and predict segregation, local melting, and latent heat release as well as prediction of Euler angles from orientation information and validated against experiments. This framework was utilized to tailor spatially-varying composition in a part by appropriately controlling the microstructure evolution during the additive process. Functionally graded Haynes 282 alloy with high Cr content at the surface was tested for oxidation and mechanical properties. An advanced physics-based reaction-diffusion model predicting the simultaneous creation of chromium oxide and alumina is developed and validated to extend life expectancy of the WAAM manufactured high temperature part. A machine-learning data-driven framework establishing the process-structure relationship from a dataset of real microstructure images and corresponding process history data has been developed and implemented in the NX Siemens design system. The digital twin configuration along with the tool path generation enabled prediction of WAAM component buildup time and the techno-economic analysis provided a favorable option for all 4 cases with 15-40% cost reduction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Remaining Life Determination

This project developed and demonstrated the capability of nondestructive evaluation methods for detecting early stage fatigue damage in grey cast iron metal. Non-collinear ultrasonic wave mixing and laser speckle differentiation were the primary technologies evaluated on custom fatigue specimens having properties specified by Caterpillar, Inc. The ultrasonic method showed the greatest potential for estimating the remaining fatigue life in grey cast iron, which may provide remanufacturers with a level of confidence when fielding used component cores in high-stress applications. While successes were realized during this project, there are still follow-on research tasks required to prove the commercial applicability of the methods. The following report sections document the steps completed during the project and an assessment of the technologies applied.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Task 12 PV Sustainability - Environmental Life Cycle Assessment of Residential PV and Battery Storage Systems

Using a life cycle assessment (LCA), the environmental impacts from generating 1 kWh of electricity for self-consumption via a photovoltaic-battery system are determined. The system includes a 10 kWp multicrystalline-silicon photovoltaic (PV) system (solar irradiation about 1350 kWh/m2/year and annual yield 1000 kWh/kWp), an iron phosphate lithium-ion (LiFePO4) battery, and other components such as the control system, battery housing, and two inverters (one for the PV system and one for the battery system). Three options for the AC-coupled system with changing battery capacities (5, 10, or 20 kWh nominal capacity) are investigated. The environmental impacts are assessed using the indicators greenhouse gas emissions and cumulative energy demand (separated into total and non-renewable cumulative energy demand). In addition, the four most important impact categories for PV electricity - respiratory inorganics (particulate matter), acidification, energy carrier resource use, and minerals and metals resource use - are assessed according to the environmental footprint (EF) method. Data are drawn from the DETEC data DQRv2:2018, recent literature, and product details provided by manufacturers. The results show larger environmental impacts of PV-battery systems with increasing battery capacity; for capacities of 5, 10, and 20 kWh, the cumulative greenhouse gas emissions from 1 kWh of electricity generation for self-consumption via a PV-battery system are 80, 84, and 88 g CO 2- eq/kWh, respectively. The cumulative greenhouse gas emissions of PV electricity consumed directly or fed into the grid are 54 g CO 2 -eq/kWh. The corresponding total cumulative energy demands are 5.27, 5.40, and 5.50 MJ oil-eq/kWh, with non-renewable energy carriers contributing 1.16, 1.22, and 1.29 MJ oil-eq/kWh. In the investigated EF impact categories, we similarly observe a larger environmental burden with increasing battery capacity, except in the use of minerals and metals. Our sensitivity analyses show that using a nickel cobalt manganese oxide (NCM) lithium-ion battery, instead of an LiFePO 4 battery, leads to a comparable environmental impact in terms of greenhouse gas emissions and cumulative energy demand. However, the NCM battery increases the impact in the EF categories of acidification and respiratory inorganics by 7 and 10%, respectively, whereas energy carrier resource use decreases by 4% and minerals and metals resource use decrease by 1%. Using a copper indium selenium (CIS) PV panel instead of a multicrystalline-silicon decreases greenhouse gas emissions by 24%, non-renewable cumulative energy demand by 13%, and particulate matter emissions by 60% (the largest decrease). Furthermore, the calculated environmental impacts are sensitive to the assumed battery lifetime. A decrease from 5000 to 3000 charge cycles increases non-renewable cumulative energy demand by 24% and greenhouse gas emissions by 16%. Increasing from 5000 to 7000 charge cycles decreases the environmental impacts by 6% and 7% in terms of non-renewable cumulative energy demand and greenhouse gas emissions, respectively. A utility-scale battery system case study shows that using batteries to store PV electricity overproduction reduces greenhouse gas emissions compared to using natural gas backup electricity generation.

14 SOLAR ENERGY↗
DOE OSTI2024

Life Cycle Analysis of Natural Gas Extraction and Power Generation: U.S. 2020 Emissions Profile

This analysis expands upon previous life cycle analyses (LCAs) of natural gas systems performed by the National Energy Technology Laboratory (NETL). It provides a complete inventory of emissions to air and water, water consumption, and land use change. These environmental burdens are detailed for all supply chain steps from natural gas production through natural gas distribution. This package includes the report, the NETL Natural Gas Model, and appendices that include several Excel workbooks and a python script to provide transparent access to the calculations and resulting data.<p>To access the NETL Natural Gas Lifecycle model, visit https://doi.org/10.18141/2476250.</p><p>To access the 2020 Report Appendices, visit https://doi.org/10.18141/2438472.</p>

03 NATURAL GAS↗
DOE OSTI2025

Comprehensive Life Cycle Analysis of Methanol Production and Methanol-to-Diesel Conversion

Methanol is a strategic chemical and intermediate in the manufacture of synthetic diesel, due to its versatility, diesel’s compatibility with existing infrastructure, and their role in industrial and transport applications. Conventional production methods for methanol, primarily steam methane reforming (SMR), rely on natural gas and are subject to the price variability due to market conditions and geopolitical events. They are also associated with greenhouse gas (GHG) emissions. Methanol and synthetic diesel production could be integrated with nuclear energy to stabilize fuel prices and insulate pricing from outside geopolitical events due to the relative stability of nuclear fuel as compared to natural gas. This could lead to increased transportation fuel security, reliability and resilience. An added benefit is the abatement of emissions when substituting nuclear energy for conventional energy from natural gas. This report presents a comprehensive life cycle analysis(LCA) framework which was developed to evaluate the GHG emissions reduction potential associated with nuclear integrated methanol production, methanol-to-diesel upgrading, and end-use combustion. Gate-to-gate methanol production and cradle-to-grave emissions were evaluated in, starting with a business-as-usual (BAU) SMR-based methanol plant, and then considering stepwise nuclear integration. Methanol-to-diesel (MTD) conversion was evaluated accounting for nuclear energy integration and hydrogen production via high-temperature steam electrolysis (HTSE) using electricity either from the grid or from a dedicated nuclear power system. This multi-step process diverts stable and reliable nuclear energy into the transportation sector by upgrading low energy dense natural gas into liquid fuels fully compatible with existing infrastructure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Life Cycle Analysis of Natural Gas Extraction and Power Generation: U.S. 2020 Emissions Profile

This analysis expands upon previous life cycle analyses (LCAs) of natural gas systems performed by the National Energy Technology Laboratory (NETL). It provides a complete inventory of emissions to air and water, water consumption, and land use change. These environmental burdens are detailed for all supply chain steps from natural gas production through natural gas distribution. This package includes the report, the NETL Natural Gas Model, and appendices that include several Excel workbooks and a python script to provide transparent access to the calculations and resulting data. This is revision 1 of the 2024 study (published December 17, 2024 and updated on January 24, 2025) and corrects a modeling error in natural gas composition. See the errata on page 2 for more information.

03 NATURAL GAS↗
DOE OSTI2025

2016 U.S. Petroleum Fuels Life Cycle Baseline

The National Energy Technology Laboratory (NETL) has performed a well-to-wheels life cycle assessment (LCA) of petroleum production and refining for the United States (US), both from international and domestic oil sources. This analysis largely follows the same methods and framework established by Cooney et al., which performed a greenhouse gas (GHG) LCA of crude products for the 2014 data year (Cooney et al., 2017). Results are presented for six major petroleum products (gasoline, diesel, jet fuel, fuel oil, coke, bunker/residual fuel oil) across each of the five Petroleum Administration for Defense Districts (PADDs) and at the national US level. However, there are at least seven other refinery outputs (liquified petroleum gas, refinery fuel gas, hydrogen, petrochemical feedstocks, asphalt, sulfur) that are modeled but not shown in this report for brevity. Petroleum product amounts are compared against those reported by US Energy Information Administration (EIA) for each region.

02 PETROLEUM↗
DOE OSTI2026

Integrated Life Cycle and Techno-Economic Assessments of Central Appalachian Legacy Mine Sites for Biomass Development and Waste Coal Utilization

This project, funded by the U.S. Department of Energy – National Energy Technology Laboratory (DOE-NETL) under award DE-FE0032212, evaluated how legacy coal mine lands and coal refuse piles in Central Appalachia (West Virginia and Pennsylvania) can be reclaimed and repurposed to support biomass development and beneficial utilization of waste coal, with the long-term goal of supporting net-zero or net-negative greenhouse gas (GHG) pathways. The project had two primary objectives: 1. Characterize legacy mine sites (including site conditions, waste coal/refuse resources, and soil/ecosystem indicators) and develop reclamation and best management practices (BMPs) for biomass cultivation; and 2. Conduct integrated machine learning (ML)-assisted life cycle assessment (LCA) and techno-economic analysis (TEA) to quantify environmental and economic outcomes for multiple biomass and waste-coal utilization pathways. Across West Virginia, the team identified ~625 coal refuse sites covering ~19,705 acres, and developed methods to estimate refuse pile volume using digital elevation models (DEMs) and geospatial workflows. A large subset of sites received volume estimates totaling ~1.6 billion m³.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Agent-based Modeling for the Multi-objective Optimization of Energy Production Pathways: Integrated Techno-Economics and Life Cycle Assessment

This project developed an integrated modeling framework to evaluate how purpose-grown bioenergy feedstocks could support large-scale sustainable aviation fuel production in the United States. Led by Colorado State University with project partners, the work combined geographically resolved analysis, techno-economic assessment, life cycle assessment, multi-objective optimization, and agent-based modeling to examine feedstock performance, deployment potential, land-use implications, and adoption dynamics. The overall goal was to identify practical and sustainable pathways for producing aviation fuel from feedstocks such as energy crops, woody crops, oilseeds, and algae while helping inform policy, investment, and research needs tied to national SAF goals. In essence, the project provides a systems-level assessment of the opportunities and constraints associated with scaling bioenergy-based aviation fuels in the U.S.

09 BIOMASS FUELS↗
DOE OSTI2022

AssessCCUS: An Integrated Approach for Aggregating Resources to Enable Techno-Economic and Life Cycle Assessment of Carbon Management Technologies

Carbon capture, utilization, and storage (CCUS) - also sometimes known as carbon management - technologies are becoming an increasingly important part of the portfolio of technologies necessary to mitigate climate change and defossilize industrial production systems (Sick, 2021). These technologies capture carbon dioxide from industrial point sources or from the atmosphere directly and then either sequester it or use it as a carbon source in valuable products. Potential utilization pathways include, but are not limited to, concrete, fuels, and certain commodity chemicals, and sequestration pathways can include permanent geological storage or temporary storage in natural sinks ranging from forests to agricultural soil. Regardless of the pathway, assessment of the economic and environmental performance of the technologies is important for understanding their potential scalability and impact as well as developing plans to minimize life cycle costs and potential environmental trade-offs. A full discussion of potential trade-offs associated with CCUS is outside the scope of this article, but promoting assessment broadly helps to stimulate important conversations about the benefits and drawbacks of any particular technological choice.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways

Comparisons of emerging carbon capture and utilization (CCU) technologies with equivalent incumbent technologies are necessary to support technology developers and to help policy-makers design appropriate long-term incentives to mitigate climate change through the deployment of CCU. In particular, early-stage CCU technologies must prove their economic viability and environmental reduction potential compared to already-deployed technologies. These comparisons can be misleading, as emerging technologies typically experience a drastic increase in performance and decrease in cost and greenhouse gas emissions as they develop from research to mass-market deployment due to various forms of learning. These changes complicate the interpretation of early techno-economic assessments (TEAs) and life cycle assessments (LCAs) of emerging CCU technologies. The effects of learning over time or cumulative production themselves can be quantitatively described using technology learning curves (TLCs). While learning curve approaches have been developed for various technologies, a harmonized methodology for using TLCs in TEA and LCA for CCU in particular is required. To address this, we describe a methodology that incorporates TLCs into TEA and LCA to forecast the environmental and economic performance of emerging CCU technologies. This methodology is based on both an evaluation of the state of the art of learning curve assessment and a literature review of TLC approaches developed in various manufacturing and energy generation sectors. Additionally, we demonstrate how to implement this methodology using a case study on a CO 2 mineralization pathway. Finally, commentary is provided on how researchers, technology developers, and LCA and TEA practitioners can advance the use of TLCs to allow for consistent, high-resolution modeling of technological learning for CCU going forward and enable holistic assessments and fairer comparisons with other climate technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Life-Cycle and Techno-Economic Assessment of Early-Stage Carbon Capture and Utilization Technologies—A Discussion of Current Challenges and Best Practices

The mitigation of climate change requires research, development, and deployment of new technologies that are not only economically viable but also environmentally benign. Systematic and continuous technology assessment from early technology maturity onwards allows assessment practitioners to identify economic and environmental characteristics. With this information, decision-makers can focus time and resources on the most promising technologies. A broad toolset for technology assessment exists—stretching from the well-established life cycle assessment (LCA) methodology to more loosely defined techno-economic analysis (TEA) methods and the increasingly popular principles of technology maturity assessment such as the concept of technology readiness levels (TRL). However, current technology assessment practice faces various challenges at early stages, resulting in a potential mismatch of study results and stakeholders' needs and an escalation of assessment effort. In this practice review, we outline current challenges in the interplay of LCA, TEA, and TRL and present best practices for assessing early-stage climate change mitigation technologies in the field of carbon capture and utilization (CCU). The findings help practitioners systematically identify the TRL of a technology and adapt technology assessment methodologies accordingly. We highlight the methodological challenges for practitioners when adapting the goal and scope, identifying benchmark technologies, creating a comprehensive inventory, comparing early stage to commercial stage, ensuring clarity of recommendations for decision-making under high uncertainty, and streamlining conventional LCA and TEA assessment approaches and provide actionable recommendations. Overall, this work contributes to identifying promising technologies faster and more systematically, accelerating the development of new technologies for climate change mitigation and beyond.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Gate-to-Gate Life Cycle Assessment for the CO 2 -EOR Operations at Farnsworth Unit (FWU)

Greenhouse gas (GHG) emissions related to the Farnsworth Unit’s (FWU) carbon dioxide enhanced oil recovery (CO 2 -EOR) operations were accounted for through a gate-to-gate life cycle assessment (LCA) for a period of about 10 years, since start of injection to 2020, and predictions of 18 additional years of the CO 2 -EOR operation were made. The CO 2 source for the FWU project has been 100% anthropogenically derived from the exhaust of an ethanol plant and a fertilizer plant. A cumulative amount of 5.25 × 106 tonnes of oil has been recovered through the injection of 1.64 × 106 tonnes of purchased CO 2 , of which 92% was stored during the 10-year period. An LCA analysis conducted on the various unit emissions of the FWU process yielded a net negative (positive storage) of 1.31 × 106 tonnes of CO 2 equivalent, representing 79% of purchased CO 2 . An optimized 18-year forecasted analysis estimated 86% storage of the forecasted 3.21 × 106 tonnes of purchased CO 2 with an equivalent 2.90 × 106 tonnes of crude oil produced by 2038. Major contributors to emissions were flaring/venting and energy usage for equipment. Improvements on the energy efficiency of equipment would reduce emissions further but this could be challenging. Improvement of injection capacity and elimination of venting/flaring or fugitive gas are methods more likely to be utilized for reducing net emissions and are the cases used for the optimized scenario in this work. This LCA illustrated the potential for the CO 2 -EOR operations in the FWU to store more CO 2 with minimal emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types

To improve the viability of nuclear power plants, there is a need to reduce their operational costs. Operational costs account for a significant portion of a plant’s yearly budget, due to their scheduled-based maintenance approach. In order to reduce these costs, proactive methods are required that estimate and forecast the state of a machine in real time to optimize maintenance schedules. In this research, we use Bayesian networks to develop a framework that can forecast the remaining useful life of a centrifugal pump. To do so, we integrate survival analysis with Bayesian statistics to forecast the health of the pump conditional to its current state. We complete our research by successfully using the Bayesian network on a case study. This solution provides an informed probabilistic viewpoint of the pumping system for the purpose of predictive maintenance.

42 ENGINEERING↗