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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

BETO 2021 Peer Review - WBS 4.1.2.32: Bioeconomy Scenario Analysis

The Bioeconomy Scenario Analysis project uses systems thinking and analysis to assess current and/or prospective techno-economics, research and development, deployment strategies, policy, and market conditions and their impact on the potential development trajectories of the bioenergy industry over time. Results from this project include identification of opportunities and constraints to industrial development, quantification of multiple metrics (energy, economic, environmental) and informing researchers, decision makers, and industry of the steps needed for a sustainable, nationwide biofuels industry. Analyses from this project enable the creation of a bioenergy industry by (1) inciting policy-makers to explore scenarios for nationwide biofuels production, identifying policy actions consistent with pathways for growth; (2) improving industry’s understanding of industry growth potential under different technology and investment conditions, better targeting their development efforts; and (3) providing universities and other interested stakeholders with tools and analyses that can be adapted to meet research and teaching objectives, connecting students with careers that build the industry. One of the many modeling tools used in this project, the Biomass Scenario Model (BSM) is a publicly-available, unique, validated, state-of-the-art, award-winning, fourth-generation model of the domestic biofuels supply chain which explicitly focuses on how and under what conditions biofuel technologies might be deployed to contribute to the U.S. transportation energy sector. We use models like the BSM to examine the implications of policies and incentives as well as their potential side-effects. The BSM uses a system-dynamics simulation to model dynamic interactions and transitions across the supply chain; it tracks the deployment of biofuels given industrial learning and the reaction of the investment community in the context of land availability, projected oil markets, consumer demand for biofuels, and government policies over time. Under expected market conditions, analyses using the BSM suggest that the biofuels industry may require significant external actions in the early years to thrive. Interventions that accelerate the industrial learning process (e.g. operation of pre-commercial and commercial facilities) have been identified as having strong influence in starting the growth of a commercial biofuel industry. Policies which are coordinated across the whole supply chain in BSM foster the growth of the biofuels industry and production of tens of billions of gallons of biofuels may occur under sufficiently favorable conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Bioeconomy Scenario Analysis

The Bioeconomy Scenario Analysis (BSA) project uses systems thinking and analysis to assess how techno economics, research and development, deployment strategies, policy, and market conditions affect the potential development trajectories of the developing bioenergy industry. This project informs researchers, decision makers, and industry by identifying opportunities for and constraints to industrial development and quantifying important industry metrics (e.g., energy, economic, environmental) towards a sustainable domestic bioenergy system. One of the tools used in this project, the Bioenergy Scenario Model (BSM) is a publicly-available, unique, validated, state-of-the-art, award-winning, fourth-generation model of the domestic biofuels supply chain which explicitly focuses on how and under what conditions biofuel technologies might be deployed to contribute to the U.S. transportation energy sector. Analysis products from this effort enable the development of the bioenergy industry by (1) encouraging policy-makers to explore multiple levers simulating outside impacts on biofuels production, identifying policy actions; (2) improving industry's understanding of growth potential under different market conditions, better targeting their development efforts; and (3) providing universities and other interested stakeholders with analysis tools and analyses that can be adapted to meet research and teaching objectives, thus connecting students with careers that build the industry.

bioenergy↗
DOE OSTI2021

Bioeconomy Scenario Analysis

The Bioeconomy Scenario Analysis project has shed light on system interactions within the biomass-to-biofuel supply chain for the past 15 years. This presentation highlights recent analyses on investment and policy scenarios for sustainable aviation fuel, the economic and policy considerations for the historical buildout of the starch ethanol industry, and the potential impact of U.S. Department of Energy investment in bioenergy R&D.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario

Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work here focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed, propensity to repeat work, filtering of data, identification of relevant information, and typical investigation strategies. We see that data coverage (i.e., what was interacted with) provides provenance information without limiting individual investigation freedom. On the other hand, while interaction history (i.e., when something was interacted with) does not significantly encourage more mimicry, it does take more time to comfortably understand, as represented by less confident conclusions and less relevant information-gathering behaviors. In conclusion, our results contribute empirical data towards understanding how provenance summarizations can influence analysis behaviors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

ProFAST (Production Financial Analysis Scenario Tool) [SWR-23-88]

The Production Financial Analysis Scenario Tool (ProFAST) provides a quick and convenient in-depth financial analysis for production facilities. The model uses a generally accepted accounting principles analysis framework and provides annual projections of income statements, cash flow statements, and balance sheets. ProFAST allows users to calculate the levelized cost of the produced commodity by providing an expected financial performance, or on the other hand, calculate the financial performance based on an input price. Model inputs generally capital expenditures, operating expenditures, and financing structure. The programmatic approach of ProFAST allows users to easily perform large sensitivity analyses and integrates easily with other python tools.

Kee, Jamie↗
DOE OSTI2022

A generic scenario analysis of end-of-life plastic management: Chemical additives

Plastic growing demand and the increment in global plastics production have raised the number of spent plastics, out of which over 90% are either landfilled or incinerated. Both methods for handling spent plastics are susceptible to releasing toxic substances, damaging air, water, soil, organisms, and public health. Improvements to the existing infrastructure for plastics management are needed to limit chemical additive release and exposure resulting from the end-of-life (EoL) stage. This article analyzes the current plastic waste management infrastructure and identifies chemical additive releases through a material flow analysis. Additionally, we performed a facility-level generic scenario analysis of the current U.S. EoL stage of plastic additives to track and estimate their potential migration, releases, and occupational exposure. Potential scenarios were analyzed through sensitivity analysis to examine the merit of increasing recycling rates, using chemical recycling, and implementing additive extraction post-recycling. Our analyses identified that the current state of plastic EoL management possesses high mass flow intensity toward incineration and landfilling. Although maximizing the plastic recycling rate is a reasonably straightforward goal for enhancing material circularity, the conventional mechanical recycling method requires improvement because major chemical additive release and contamination routes act as obstacles to achieving high-quality plastics for future reuse and should be mitigated through chemical recycling and additive extraction. In conclusion, the potential hazards and risks identified in this research create an opportunity to design a safer closed-loop plastic recycling infrastructure to handle additives strategically and support sustainable materials management efforts to transform the US plastic economy from linear to circular.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Puerto Rico Energy Efficiency Scenario Analysis Tool (PREESAT)

The National Renewable Energy Laboratory (NREL) developed the Puerto Rico Energy Efficiency Scenario Analysis Tool (PREESAT) to allow stakeholders to estimate the long-term aggregate electricity consumption impacts of various energy efficiency measures in Puerto Rico’s residential, commercial, and industrial sectors. PREESAT is an energy accounting framework that addresses electricity use. This report provides an overview of the tool and serves as a user manual with instructions for users.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Puerto Rico Energy Efficiency Scenario Analysis Tool (PREESAT)

The National Renewable Energy Laboratory (NREL) developed the Puerto Rico Energy Efficiency Scenario Analysis Tool (PREESAT) to allow stakeholders to estimate the long-term aggregate electricity consumption impacts of various energy efficiency measures in Puerto Rico’s residential, commercial, and industrial sectors. PREESAT is an energy accounting framework that addresses electricity use. These presentation slides provide a summary of the electrical sector in Puerto Rico and an overview of how the PREESAT tool can be used to project energy savings from various energy efficiency measures in the residential, commercial, and industrial sectors.

commercial↗
DOE OSTI2020

Why does cyber deterrence fail and when might it succeed? A framework for cyber scenario analysis

Through cyberattacks on information technology and digital communications systems, antagonists have increasingly been able to alter the strategic balance in their favor without provoking serious consequences. Conflict within and through the cyber domain is inherently different from conflict in other domains that house our critical systems. These differences result in new challenges for defending and creating resilient systems, and for deterring those who would wish to disrupt or destroy them. The purpose of this paper is to further examine the question of whether or not deterrence can be an effective strategy in cyber conflict, given our broad and varied interests in cyberspace. We define deterrence broadly as the creation of conditions that dissuade antagonists from taking unwanted actions because they believe that they will incur unacceptably high costs and/or receive insufficient benefits from taking that action. Deterrence may or may not be the most credible or effective strategy for achieving our desired end states in cybersecurity. Regardless of the answer here, however, it is important to consider why deterrence strategies might succeed under certain conditions, and to understand why deterrence is not effective within the myriad contexts that it appears fail. Deterrence remains a key component of U.S. cyber strategy, but there is little detail on how to operationalize or implement this policy, how to bring a whole-of-government and whole-of- private-sector approach to cyber deterrence, which types of antagonists can or should be deterred, and in which contexts. Moreover, discussion about how nations can and should respond to significant cyber incidents largely centers around whether or not the incident constitutes a "use of force," which would justify certain types of responses according to international law. However, we believe the "use of force" threshold is inadequate to describe the myriad interests and objectives of actors in cyberspace, both attackers and defenders. In this paper, we propose an approach to further examine if deterrence is an effective strategy and under which conditions. Our approach includes systematic analysis of cyber incident scenarios using a framework to evaluate the effectiveness of various activities in influencing antagonist behavior. While we only examine a single scenario for this paper, we propose that additional work is needed to more fully understand how various alternative thresholds constrain or unleash options for actors to influence one another's behavior in the cyber domain.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Impact of methane and black carbon mitigation on forcing and temperature: a multi-model scenario analysis

The relatively short atmospheric lifetimes of methane (CH 4 ) and black carbon (BC) have focused attention on the potential for reducing anthropogenic climate change by reducing Short-Lived Climate Forcer (SLCF) emissions. This paper examines radiative forcing and global mean temperature results from the Energy Modeling Forum (EMF)-30 multi-model suite of scenarios addressing CH 4 and BC mitigation, the two major short-lived climate forcers. Central estimates of temperature reductions in 2040 from an idealized scenario focused on reductions in methane and black carbon emissions ranged from 0.18–0.26 °C across the nine participating models. Reductions in methane emissions drive 60% or more of these temperature reductions by 2040, although the methane impact also depends on auxiliary reductions that depend on the economic structure of the model. Climate model parameter uncertainty has a large impact on results, with SLCF reductions resulting in as much as 0.3–0.7 °C by 2040. We find that the substantial overlap between a SLCF-focused policy and a stringent and comprehensive climate policy that reduces greenhouse gas emissions means that additional SLCF emission reductions result in, at most, a small additional benefit of ~ 0.1 °C in the 2030–2040 time frame.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ectomycorrhizal effects on decomposition are highly dependent on fungal traits, climate, and litter properties: A model-based assessment. Dataset.

To simulate the effects of mycorrhizal fungi on soil organic matter cycling, we incorporated mycorrhizal processes into the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model to develop a new soil model Myco-CORPSE. The new model was calibrated and evaluated against soil measurements taken at temperate forests in New Hampshire (NH) and Georgia (GA). A series of scenario analysis were also conducted to explore the conditions under which ectomycorrhizal (ECM) N acquisition processes can induce different soil C accumulation in ECM systems compared to arbuscular (AM) systems.In this data package, we included:-The Python codes of the standard Myco-CORPSE model we developed: "Standard Myco_CORPSE python codes.zip". The main program is the "gradient_sim.py" which calculates the bulk soil microbes and CN content along a user defined gradient of clay, soil temperature, soil moisture and mycorrhizal dominance, and relies on two subprograms "CORPSE_deriv.py" and "CORPSE_integrate.py". "CORPSE_deriv.py" calculated the changes in all simulated soil stock within every time step and "CORPSE_integrate.py" integrate the changes in all simulated soil stock within simulated time period. The program "Plot.py" is used to plot the major outputs produced by the main program "gradient_sim.py".-The modified Python codes of Myco-CORPSE models with site-level environmental inputs (in NH and GA) used to conduct simulations in NH and GA sites: "NH_GA model simulations.zip". -The Python codes used to evaluate the Myco-CORPSE simulation outputs in NH and GA sites against site-level measurements: "Plot NH_GA simulation against measurements.zip". It includes both the evaluation Python code, the model outputs on NH and GA sites, and the measured soil properties in both sites.-The modified Python codes of Myco-CORPSE models "Scenario analysis_model simulations.zip" that is used to conduct scenario analysis of how different litter properties, mycorrhizal fungal traits, climate, and seasonal variation in temperature and vegetation phenology impact the mycorrhizal effects on soil CN properties. The sub file folder "Scenario analysis_litter traits" contains the codes for scenario analysis of different litter properties; The sub file folder "Scenario analysis_ECM types" contains the codes for scenario analysis of different ECM fungal traits; The sub file folder "Scenario analysis_climate&seasonality" contains the codes for scenario analysis of different climate and seasonalities;-"Scenario analysis_model results and plotting codes.zip" contains all the output files from the the scenario analysis of Myco-CORPSE model as described above and the plotting codes used to the generate the heatmaps and scatterplots shown in the manuscript "Ectomycorrhizal effects on decomposition are highly dependent on fungal traits, climate, and litter properties: A model-based assessment"The majority of the model outputs did not have specific geographic information or temporal coverage because the analysis we conducted are mainly hypothetical model simulations. We only provided geographic description, coordinates and temporal coverage for those soil measurements which we used for model evaluations (included in the "Plot NH_GA simulation against measurements.zip").

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Biological Valorization of Natural Gas for the Production of Lactic Acid: Techno-Economic Analysis and Life Cycle Assessment

Due to the availability of natural gas, shale gas, and flared gas, there is increased interest in converting methane in these gases into commodity chemicals via cost-effective and sustainable processes. Recently, we have demonstrated an efficient process concept converting methane to lactic acid (LA) by methanotrophic bacteria with high carbon flux, pivoting potentials for commercialization. Herein, we present a first-of-kind conceptual design and a techno-economic analysis (TEA) of a biorefinery process with calculated minimum selling prices. This study includes both sensitivity analysis and scenario analysis for key cost drivers and for projecting path forwards in research and development. The analyses reveal that the conversion efficiency, LA product titer, LA productivity, and gas flow rate have the most significant impacts on cost. The MSP varies from $5.83-$2.17/kg LA depending on these key process attributes. Further, a life cycle assessment of this process concept is evaluated and compared to understand the sustainable aspects. In this TEA study, the bio-upgrading process from natural gas to LA presents a potentially promising approach in terms of the feedstock versatility, economic viability, and environmental impact.

BIOMASS FUELS,NATURAL GAS↗
DOE OSTI2026

Uncertain quantum computing futures and potential energy and physical resource impacts at scale

Considerable attention has recently focused on the vast energy and water demands of supercomputing, namely large-scale data centers that underpin artificial intelligence (AI), one of the great disruptors of contemporary society. Looking ahead some years from now, quantum computing is poised to disrupt established computing paradigms once again. Scientists and engineers are now working intensely to bring this century-old dream of physicists to fruition. Yet, as quantum computers begin to be integrated with classical supercomputing architectures, the implications for energy and physical resource use also need to be understood, especially how they compare to today’s AI data centers. These impacts have not yet been quantified by the research community – a notable gap in the literature, even if commercial-scale deployment of Quantum-Accelerated Computing Infrastructure (QuACI) is not expected for a few more years. This study is the first to conduct such an assessment. Using publicly available information from academic sources and private industry, we characterize multiple configurations of superconducting qubit-based, fault-tolerant quantum computers (FTQC) that could plausibly be deployed at scale in the 2030s and into the 2040s. By parameterizing these FTQC systems at a process level, we conduct a prospective scenario analysis to quantify their energy and physical resource needs. While these estimates are uncertain, given the current state of quantum technologies and their unknown future trajectories, important insights can already be drawn. One key finding is that while the electricity needs for a fleet of FTQCs are within the bounds of previous modeling studies that have explored high electricity demand futures, the needs for certain physical resources, namely water and helium-3, could pose bottlenecks to QuACI scale-up.

Computing↗
DOE OSTI2026

NASA analysis of space mission options for the 2025 planetary defense conference hypothetical asteroid impact threat scenario

The 2025 Planetary Defense Conference (PDC) hypothetical asteroid impact threat exercise is being conducted in coordination with the United Nations-endorsed Space Mission Planning Advisory Group (SMPAG), exercising SMPAG's processes for assessing space mission options and communicating to decision makers. Here, in this paper, we describe the work performed by our NASA-led team and present the results we contributed to the SMPAG effort for the exercise. Our NASA-led team assessed mission options for asteroid reconnaissance (flyby and rendezvous), deflection, and robust disruption using several viable techniques: kinetic impactors, ion beam deflection, and nuclear explosive devices. Our simulations and analyses considered how much change-in-velocity an asteroid can tolerate before fragmentation onset, and we worked towards establishing requirements for robustly disrupting an asteroid. Heuristics informed by the simulation results are incorporated into optimization of deflection and disruption mission campaign options. Finally, we make some observations about useful generalizations from these results with potential applicability to any planetary defense scenario.

Asteroid deflection↗
DOE OSTI2023

Scenario Discovery Analysis of Drivers of Solar and Wind Energy Transitions Through 2050

Deep human-Earth system uncertainties and strong multi-sector dynamics make it difficult to anticipate which conditions are most likely to lead to higher or lower adoption of renewable energy, and models project a broad range of future solar and wind energy shares across future scenarios. To elucidate these dynamics, we explore a large data set of scenarios simulated from the Global Change Analysis Model (GCAM) and use scenario discovery to identify the most significant factors affecting solar and wind adoption by mid-century. We generated a data set of over 4,000 scenarios from GCAM by varying 12 different socioeconomic factors at high and low levels, including assumptions about future energy demand, resource costs, and fossil fuel emissions paths, as well as specific technology assumptions including wind and solar backup requirements and storage costs. Using scenario discovery, we assess the most important factors globally and regionally in creating high fractions of solar and wind energy and explore interconnected effects on other systems including water and non-CO 2 emissions. Globally and regionally, we found that solar and wind-related technology costs were the primary drivers of high wind and solar energy adoption, though a few regions depend heavily on other parameters like carbon capture and storage costs, population and gross domestic product trajectories, and fossil fuel costs. We also identify four key paths to high solar and wind energy by mid-century and discuss their tradeoffs in terms of other outcomes.

14 SOLAR ENERGY↗
DOE OSTI2020

A Comprehensive Approach to Measure the Efficiency of Freight Transport: Freight Mobility Energy Productivity (F-MEP) Metric

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement is critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregated in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance of freight systems. Addressing this research need, this paper presents the 'Freight Mobility Energy Productivity' metric to quantify freight performance of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight performance. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight efficiency as a result of vehicle powertrain technology improvement. Results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the efficiency of existing as well as future freight trends and technologies.

25 ENERGY STORAGE↗
DOE OSTI2020

Electrification Futures Study: Methodological Approaches for Assessing Long-Term Power System Impacts of End-Use Electrification

By its nature, electrification enhances the coupling between the electric sector and end-use sectors. Assessing the impacts of electrification requires both an examination of the complex interactions between sectors and a broader assessment of multiple parts of the energy system. The Electrification Futures Study (EFS) uses several complementary modeling and analysis tools to analyze the impacts of electrification on the U.S. energy system. In particular, the EFS relies on an overarching scenario analysis approach, but through the use of separate modeling approaches designed to assess various electricity demand- and supply-side futures. The primary model employed to generate the supply-side scenarios is the Regional Energy Deployment System (ReEDS) model, which is a capacity expansion model for the U.S. electricity system through 2050. Traditionally, the model has been primarily exercised in scenario analysis that implicitly assumed limited electrification. Because of this assumption, resulting future load profiles are approximated by historical ones, load growth is driven primarily by population and economic growth only, and changes in direct end-use natural gas consumption do not effect natural gas costs for electricity generation. In this report, we (1) reflect the potential for resource sharing between regions given these changes in demand, (2) represent how changes in natural gas consumption in end-use sectors could impact the economics of natural gas-fired generation, and (3) document a new model representation of demand-side flexibility used for the EFS. These improvements to ReEDS are employed for the EFS supply-wide analysis, which is summarized in a companion EFS report titled Electrification Futures Study: Scenarios of Power System Evolution and Infrastructure Development for the United States (Murphy et al. 2019). The data and methods documented in this report could also be adapted for other models with similar scope and limitations as ReEDS, and these data and methods could be used to assess future electric system scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Comprehensive Approach to Measure the Mobility Energy Productivity of Freight Transport

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement are critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting has been gaining pace in recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregate in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance or productivity of freight systems. Addressing the research need, this paper presents the “Freight Mobility Energy Productivity” metric to quantify freight productivity of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, and shows intuitive results in quantifying freight productivity. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight productivity as a result of vehicle electrification. The relative differences in Freight Mobility Energy Productivity scores help identify which zones benefit from the vehicle powertrain technology improvement. The results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the productivity of existing as well as future freight trends and technologies.

47 OTHER INSTRUMENTATION↗