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DOE OSTI2025

Designing robust energy policy packages under deep uncertainty: A multi-metric decision support framework

The complexity of transitioning to sustainable energy systems requires policy frameworks capable of balancing multiple objectives while addressing deep uncertainty. However, existing approaches often lack systematic methods to identify combinations of policy levers that remain effective across a wide range of uncertain futures. This paper presents a novel decision support framework that guides the selection of robust policy packages based on their performance across multiple objectives under uncertainty. Our method leverages a large ensemble of scenarios and applies scenario discovery techniques to identify influential policy levers. Here, we introduce new indicators to assess the robustness of policies by evaluating their ability to mitigate adverse outcomes across metrics. These indicators support an iterative process to build a robust policy package. Finally, we map the technological and energy pathways associated with the robust policy package by leveraging an energy system optimization model. We illustrate the application of this framework to the Spanish energy system, providing insights into how specific combinations of policy levers shape decarbonization pathways under uncertainty.

Decision-support method↗

DOE OSTI2024

Uncertainty characterization in a coupled human-natural system: Modeling agricultural adaptation in the Great Lakes Region

The Great Lakes Region's water quality and ecological health are threatened by the export of nutrients from agricultural lands, which causes eutrophication, hypoxia, and destructive algal blooms. The intensification of hydrologic cycles brought about by climate change is expected to exacerbate nutrient loading in the region, and, at the same time, agricultural adaptation to changing conditions is also expected to affect loading through shifting amounts and timing of fertilization. Quantifying these future effects and their interactions necessitates modeling both the human and natural processes as a coupled system, by pairing land use and agricultural management with hydrologic modeling. At the same time, compounding uncertainties arising from the complex interactions in both systems significantly limit our predictive understanding of the region's impacts. This study utilizes the Soil and Water Assessment Tool (SWAT), developed for simulating the impact of various farmer decisions on watershed functions in Western Lake Erie watersheds, and an under-development agent-based model (ABM) for agricultural management decisions. The aim of this study is to use global sensitivity analysis on the coupled ABM and SWAT models to quantify how uncertainty in both models interactively affects nutrient loading. To do so, we will conduct Sobol sensitivity analysis experiments at different levels of coupling assumptions to quantify how various uncertain factors (e.g., soil moisture and crop choice) and their interactions affect our estimates of nutrient loading. The results of this analysis will allow us to quantify how complex interactions and dependencies between both systems amplify the effect of uncertainties. Insights gained from this study will have broader implications for modeling the adaptive co-evolution of human and natural systems under climate change and can inform effective management of nutrient loading in the Great Lakes Region.

Climate Change↗

DOE OSTI2021

Processing Aleatory and Epistemic Uncertainties in Experimental Data From Sparse Replicate Tests of Stochastic Systems for Real-Space Model Validation

This paper presents a practical methodology for propagating and processing uncertainties associated with random measurement and estimation errors (that vary from test-to-test) and systematic measurement and estimation errors (uncertain but similar from test-to-test) in inputs and outputs of replicate tests to characterize response variability of stochastically varying test units. Also treated are test condition control variability from test-to-test and sampling uncertainty due to limited numbers of replicate tests. These aleatory variabilities and epistemic uncertainties result in uncertainty on computed statistics of output response quantities. The methodology was developed in the context of processing experimental data for “real-space” (RS) model validation comparisons against model-predicted statistics and uncertainty thereof. The methodology is flexible and sufficient for many types of experimental and data uncertainty, offering the most extensive data uncertainty quantification (UQ) treatment of any model validation method the authors are aware of. It handles both interval and probabilistic uncertainty descriptions and can be performed with relatively little computational cost through use of simple and effective dimension- and order-adaptive polynomial response surfaces in a Monte Carlo (MC) uncertainty propagation approach. A key feature of the progressively upgraded response surfaces is that they enable estimation of propagation error contributed by the surrogate model. Sensitivity analysis of the relative contributions of the various uncertainty sources to the total uncertainty of statistical estimates is also presented. Finally, the methodologies are demonstrated on real experimental validation data involving all the mentioned sources and types of error and uncertainty in five replicate tests of pressure vessels heated and pressurized to failure. Simple spreadsheet procedures are used for all processing operations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Systematic Source Determination and X-Ray Radiography Detection of Nonuniformities on High-Density Carbon Ablators

Recent deuterium-tritium (D-T)–layered implosion experiments at the National Ignition Facility have achieved a burning plasma and >1-MJ neutron yield. A series of repeat experiments have shown that the degree of performance is very likely dependent on capsule quality, including the quantity of what are collectively termed “high- Z particles.” These particles are detected on a custom-built radiography system, known as the Sagometer, during the final target qualification process. The term particles is misleading, as the source of these nonuniformities in the capsule images is uncertain; the term detection will be used instead. An increased number of D-T targets have been rejected at the final stages of production due to Sagometer detections. Late detections are deleterious in terms of loss of production parts, effort, and overall operating efficiency. In response, we undertook an effort to determine the origin of these detections and to ultimately mitigate target losses caused by them. Through careful testing and analysis, we have determined neither insufficient production cleanliness nor hohlraum shedding is responsible for the detections on the capsule. Here, we determined that the detections are inherent to the capsule and have made efforts to use the Zeiss Xradia to identify them earlier in the production process. While testing revealed the Xradia is not currently sufficient for identifying such particles using radiography images, we continue to look to other forms of metrology to down select the capsules early in the process.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Linear and Nonlinear Solvers for Simulating Multiphase Flow within Large-Scale Engineered Subsurface Systems

Simulation of multiphase flow in the subsurface is well-known to be computationally challenging. While there have been many studies that have explored approaches to overcoming these challenges, they often utilize relatively simple case studies. In this paper, we focus on the unique numerical challenges posed by modeling large-scale engineered subsurface systems, characterized by discrete features embedded in a heterogeneous natural subsurface setting. The man-made features such as shafts, tunnels, and barriers often cause multiple challenges in modeling the domain for multiphase porous media flow. This flow scenario can have a wide range of applications such as nuclear waste repositories, enhanced recovery of a petroleum reservoir, geothermal engineering, and carbon sequestration. An example of these severe numerical challenges is the case of performance assessment (PA) for Waste Isolation Pilot Plant (WIPP), the only operating deep geological repository in the US, which simulates extreme material properties of bedded salt rock formation and extreme contrast due to open excavation next to the formation. The models have extremes not only of permeability and porosity but also of the constitutive models needed for multiphase flow; additionally, they have process models like salt creep closure reducing porosity over time, fracturing in clay and anhydrite interbeds of the bedded salt, gas generation from the waste materials, and unintentional human borehole intrusions in some scenarios. Numerical simulations require the solution of coupled systems of nonlinear PDEs; in our work, we use the open-source simulator PFLOTRAN which is based on Finite Volume discretization. The solution of the nonlinear equations requires use of the Newton-Raphson iteration at each time step, which entails the solution of the linearized Jacobian system at each iteration. The effects of all the processes (i.e., large number of unknowns, highly nonlinear constitutive relations, large contrasts in material properties in short distances) lead to an ill-conditioned Jacobian matrix that severely challenges traditional linear solver, i.e., stabilized biconjugate gradient with block Jacobi incomplete LU preconditioner (BCGS-ILU) leading to non-convergence for traditional Newton-Raphson nonlinear solver causing unacceptably long computation time for each model. This paper presents linear solvers such as constrained pressure residual (CPR) two-stage preconditioner with alternate-block-factorization (ABF) and quasi- implicit pressure and explicit saturation (QIMPES) decouplers and flexible generalized residual solver (FGMRES). The new general-purpose nonlinear solver, Newton trust-region dogleg Cauchy (NTRDC), is also introduced to resolve extreme nonlinearities in the models. We demonstrate the effectiveness of each method relative to the default BCGS-Newton solver. The two best cases had nearly 50 times speed-up and achieved completion of a simulation in 14 hours that never completed due to non-convergence with the default solver. We also investigate the strong scalability of each method and discuss some of the deficiencies found for Block Jacobi preconditioner using parallel domain decomposition, and node packing effects of modern processor architecture.

Preconditioner, Nonlinear, Porous media, Multiphas↗

DOE OSTI2020

Data-driven analysis of relight variability of jet fuels induced by turbulence

For safety purposes, reliable reignition of aircraft engines in the event of flame blow-out is a critical requirement. Typically, an external ignition source in the form of a spark is used to achieve a stable flame in the combustor. However, such forced turbulent ignition may not always successfully relight the combustor, mainly because the state of the combustor cannot be precisely determined. Uncertainty in the turbulent flow inside the combustor, inflow conditions, and spark discharge characteristics can lead to variability in sparking outcomes even for nominally identical operating conditions. Prior studies have shown that of all the uncertain parameters, turbulence is often dominant and can drastically alter ignition behavior. For instance, even when different fuels have similar ignition delay times, their ignition behavior in practical systems can be completely different. In practical operating conditions, it is challenging to understand why ignition fails and how much variation in outcomes can be expected. The focus of this work is to understand relight variability induced by turbulence for two different aircraft fuels, namely Jet-A and a variant named C1. A detailed, previously developed simulation approach is used to generate a large number of successful and failed ignition events. Using this data, the cause of misfire is evaluated based on a discriminant analysis that delineates the difference between turbulent initial conditions that lead to ignition or failure. From the discriminant analysis, a compressed sensing algorithm is then applied to help pinpoint the locations of relevant turbulent features. Findings from the discriminant analysis are confirmed with the time history of near kernel properties. Next, a clustering strategy is used to identify ignition and misfire modes. With this approach, it was determined that the cause of ignition failure is different for the two fuels. While it was found that Jet-A is influenced by fuel entrainment, C1 was found to be more sensitive to small scale turbulence features. Finally, a larger variability is found in the ignition modes of C1, which can be subject to extreme events induced by kernel breakdown.

42 ENGINEERING↗

DOE OSTI2020

Glacial fluctuations in tropical Africa during the last glacial termination and implications for tropical climate following the Last Glacial Maximum

The tropics are a dynamic component of the global climate system and exert a profound influence on modern climate variability. Yet the role of the tropics in past climate changes, for example during the last glacial termination, is uncertain. This uncertainty is due in part to the relative paucity of terrestrial temperature records from the tropics, which hinders efforts to understand the mechanisms that influenced deglacial warming and abrupt climate events. Tropical glaciers are sensitive to changes in climate, and mapping and dating past tropical glacial fluctuations provides a valuable record of terrestrial climate changes in the low latitudes. We report a chronology of past glacial extents in the Rwenzori Mountains of Uganda (0.3°N, 29.9°E) based on 51 beryllium-10 ( 10 Be) surface-exposure ages from three separate glacial catchments. Results indicate that Rwenzori glaciers retreated considerably during Heinrich Stadial 1. The rate of net glacial recession slowed between ~15 and 11 ka, although glaciers continued to retreat during this period. Rwenzori glaciers then retreated more rapidly during the early Holocene. The Rwenzori glacial chronology is broadly similar to glacial chronologies reconstructed elsewhere in East Africa and in tropical South America. We suggest that this similarity may reflect coherent, tropics-wide temperature fluctuations during the last deglaciation. We report that the Rwenzori glacial chronology provides crucial information on the global footprint of deglacial warming and abrupt climate events and, thus, the potential mechanisms that influenced tropical climate during the last glacial termination.

58 GEOSCIENCES↗

DOE OSTI2025

Investigating Np(VI) Nitrate Speciation Control through Temperature and Optical Spectroscopy

Numerous areas of nuclear processing, such as radioisotope production, nuclear waste remediation, and separations, depend upon the speciation of f-elements in the solution state. However, fundamental knowledge of the actinides─particularly Np─lags behind most of the elements on the periodic table. Despite the importance of Np(VI) chemistry in separations and nuclear processing, its speciation in HNO 3 remains uncertain. This work addresses some of these gaps by investigating the effect of HNO 3 concentration and temperature on the spectra and speciation of the Np(VI) nitrate system. Six samples of Np in 1 to 10 M HNO 3 were prepared and treated with (NH 4 ) 2 [Ce(NO 3 ) 6 ] to stabilize the hexavalent oxidation state. The absorbance spectrum of the 10 M HNO 3 is drastically different from the spectra of the other samples; the 10 M spectrum indicates coordination of nitrate ligands to the Np(VI)O 2 2+ ion to form a complex with high symmetry. Additionally, absorbance spectra were recorded over the temperature range of 15 to 40 °C, and systematic increases and decreases in certain spectral features of the ultraviolet–visible (UV–vis)–near-infrared (NIR) spectra are present in the spectra. The difference in spectral features suggests that Np(VI) nitrate speciation depends on temperature. Dilution studies of this sample were monitored with UV–vis–NIR and Raman spectroscopies. Spectral data indicate the presence of multiple complexes with different symmetries, including a high-symmetry complex with an inversion center. As samples are diluted and aquo ligands replace nitrate ligands bound to the Np(VI) neptunyl cation, changes in spectroscopic signals indicate a decrease in the high-symmetry nitrate complex and an increase in the lower-symmetry aquo complex. Additionally, the Raman band associated with the Np(VI)O 2 2+ symmetric stretch broadens with dilution and is fitted with two peaks. These peaks are assigned to Np(VI) aquo and nitrato complexes, indicating that this vibrational mode is sensitive to the coordination environment of the Np(VI) neptunyl cation and that contributions from these two Np(VI) complexes can be distinguished. This unique experimental data could be used to advance computational models describing the electronic transitions of complex actinyl ions.

Absorption↗

DOE OSTI2022

Evaluating Grid Strength under Uncertain Renewable Generation

The increasing displacement of synchronous generators with renewable resources such as wind and solar via power electronic interfaces causes a reduction in short-circuit strength and weak grid issues. The variation and uncertainty of renewable energy increase challenges for identifying weak grid conditions. This paper proposes an efficient method to analyze the impact of uncertain renewable energy on grid strength. The proposed method uses the probabilistic collocation method (PCM) to approximate the results of grid strength assessment under uncertain renewable generation, in order to reduce computational burden without compromising result accuracy when compared with traditional Monte Carlo simulation (MCS). To improve the accuracy of the approximation results, the proposed method integrates the K-means clustering technique with PCM to select the approximation samples of input variables. The efficacy of the proposed method is demonstrated by comparison with MCS on the modified IEEE 9-bus system and modified IEEE 39-bus system with multiple renewable generators.

grid strength↗

DOE OSTI2023

Uncertain Spatial Pattern of Future Land Use and Land Cover Change and Its Impacts on Terrestrial Carbon Cycle Over the Arctic–Boreal Region of North America

Land use and land cover change (LULCC) represents a key process of human-Earth system interaction and has profound impacts on terrestrial ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regional LULCC projections by integrated assessment models, such as the Global Change Analysis Model (GCAM). The uncertainty associated with the different spatial downscaling methods and its impacts on the subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on their impacts on the carbon cycle over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain, where extensive LULCC is expected. Specifically, we used the Future Land Use Simulation model (FLUS) and the Demeter model to generate 0.25° gridded LULCC data (i.e., LULCC FLUS and LULCC Demeter , respectively) with the same input of regional LULCC projections from GCAM, under both the low (i.e., SSP126) and high (i.e., SSP585) greenhouse gas emission scenarios. The two sets of downscaled LULCC were used to drive the Community Land Model version 5 and prognostically simulate the terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial-temporal differences between LULCC FLUS and LULCC Demeter , and the spatial distributions of the needleleaf evergreen boreal tree, broadleaf deciduous boreal tree, broadleaf deciduous boreal shrub, and C3 arctic grass are particularly different under both SSP126 and SSP585. Additionally, the spatiotemporal differences are larger under SSP126 than SSP585, due to more intensive LULCC under SSP126 than SSP585 from GCAM projection. The differences in LULCC further lead to large discrepancies in the spatial patterns of projected gross primary productivity, ecosystem respiration, and net ecosystem exchange, which represent more than 79% of the contributions of future LULCC in 2100. Additionally, the difference in carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by the spatial downscaling process in future LULCC projections and carbon cycle simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Hysteretic temperature sensitivity of wetland CH4 fluxes explained by substrate availability and microbial activity: Model Archive

This Modeling Archive is in support of an NGEE Arctic publication "Hysteretic temperature sensitivity of wetland CH4 fluxes explained by substrate availability and microbial activity" in the Journal Biogeosciences (https://doi.org/10.5194/bg-17-5849-2020), which includes the model data used in the publication. CH4 emissions from terrestrial systems are posited to increase, which can offset mitigation efforts and accelerate climate change. Yet, the accuracy of modeled CH4 emissions is sensitive to the prescribed CH4 production (or emission) temperature dependencies that are currently uncertain. Here, we use a comprehensive biogeochemistry model (ecosys) to investigate factors modulating CH4 production and emission rates across a permafrost thaw gradient encompassing a partly thawed bog and a fully thawed fen. We find that seasonally varying substrate availability drives lower and higher modeled methanogen biomass and activity, and thereby CH4 production, during the earlier and later periods of the thawed season, respectively. Package follows the Model data archiving guidelines with data in a *.zip file with three subfolders containing *.csv files and raw model output files; a data dictionary table (data_dictionary.csv) and two model output description tables (ecosys_plantspecies_output_notes.csv and ecosys_soil_ouput_notes.csv) to explain the format and meaning of individual output variables; and a user guide as a *.pdf. A detailed model description can be found in the supplement of (Grant, 2013). The ecosys source code is available at https://doi:10.5281/zenodo.3906642. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Tri-level hybrid interval-stochastic optimal scheduling for flexible residential loads under GAN-assisted multiple uncertainties

Various building loads, such as heating, ventilation, and air conditioners (HVACs), electric water heaters (EWHs), and electric vehicles (EVs), can introduce opportunities for improving the flexibility of electricity consumption while satisfying the needs of building owners as well as benefiting the resilience of distribution system. To utilize such flexibility, a tri-level distribution market framework is established, including residential consumers, load aggregators (LAs), and the distribution system operator (DSO). In this work, the uncertainties from all three levels are considered. The random consumption behavior at the consumer level is modeled as a Gaussian noise that is also aggregated and transmitted to the LA level. The weather temperature in the LA level is forecasted as an interval, and the photovoltaic (PV) power in the market-clearing level is modeled by a set of power scenarios generated by Generative Adversarial Networks (GANs). Then, a hybrid interval-stochastic programming is proposed to transform the uncertain problems in the first two levels into deterministic ones. For real-time implementations, a rolling horizon optimization (RHO) scheme is employed to continuously optimize the power consumption based on the latest operating information. Finally, case studies on a modified IEEE 69-bus system validate the effectiveness of the proposed uncertainty modeling strategies and the RHO scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Nucleosynthesis of an 11.8 M ⊙ Supernova with 3D Simulation of the Inner Ejecta: Overall Yields and Implications for Short-lived Radionuclides in the Early Solar System

Based on a 3D supernova simulation of an 11.8 M ⊙ progenitor model with an initial solar composition, we study the nucleosynthesis using tracers covering the innermost 0.1 M ⊙ of the ejecta. These ejecta are mostly proton-rich and contribute significant amounts of 45 Sc and 64 Zn. The production of heavier isotopes is sensitive to the electron fraction and hence the neutrino emission from the proto-neutron star. The yields of these isotopes are rather uncertain due to the approximate neutrino transport used in the simulation. In order to obtain the total yields for the whole supernova, we combine the results from the tracers with those for the outer layers from a suitable 1D model. Using the yields of short-lived radionuclides (SLRs), we explore the possibility that an 11.8 M ⊙ supernova might have triggered the formation of the solar system and provided some of the SLRs measured in meteorites. In particular, we discuss two new scenarios that can account for at least the data on 41 Ca, 53 Mn, and 60 Fe without exceeding those on the other SLRs.

Astronomy & Astrophysics↗

DOE OSTI2020

Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks

One major challenge for on-demand mobility service (OMS) providers is to seamlessly match empty vehicles with trip requests so that the total vacant mileage is minimized. In this work, we develop an innovative data-driven approach for devising efficient vehicle relocation policy for OMS that (1) proactively relocates vehicles before the demand is observed and (2) reduces the inequality among drivers' income so that the proactive relocation policy is fair and is likely to be followed by drivers. Our approach represents the fusion of optimization and machine learning methods, which comprises three steps: First, we formulate the optimal proactive relocation as an optimal/stable matching problems and solve for global optimal solutions based on historical data. Second, the optimal solutions are then grouped and fed to train the deep learning models which consist of fully connected layers and long short-term memory networks. Low rank approximation is introduced to reduce the model complexity and improve the training performances. Finally, we use the trained model to predict the relocation policy which can be implemented in real time. We conduct comprehensive numerical experiments and sensitivity analyses to demonstrate the performances of the proposed method using New York City taxi data. Here, the results suggest that our method will reduce empty mileage per trip by 54-70% under optimal matching strategy, and a 25-32% reduction can also be achieved by following stable matching strategy. We also validate that the predicted relocation policies are robust in the presence of uncertain passenger demand level and passenger trip-requesting behavior.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

The promise of energy-efficient battery-powered urban aircraft

Improvements in rechargeable batteries are enabling several electric urban air mobility (UAM) aircraft designs with up to 300 mi of range with payload equivalents of up to seven passengers. Novel UAM aircraft consume between 130 Wh/passenger-mi and ∼ 1,200 Wh/passenger-mi depending on the design and utilization, compared to an expected consumption of over 220 Wh/passenger-mi and 1,000 Wh/passenger-mi for terrestrial electric vehicles and combustion engine vehicles, respectively. We also find that several UAM aircraft designs are approaching technological viability with current Li-ion batteries, based on the specific power and energy, while rechargeability and lifetime performance remain uncertain. These aspects highlight the technological readiness of a new segment of transportation.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Leveraging Flexible Smart Manufacturing to Accelerate Industrial Supply Chain Recovery

Any major crisis such as the latest coronavirus disease pandemic will have a monumental effect on the worldwide economy and on the international supply chain. Much of the global supply chain relies on China, Germany, and the United States for manufacturing and distribution; this fragile system is subject to failure. Climactic changes in demand and rapid shortages in necessities are the results of the pandemic’s disruption. The recovery forecast for the ongoing pandemic is uncertain and, therefore, any recovery effort will need to be adaptable to build the supply chain resiliency. A wide adoption of flexible smart automated technologies in the manufacturing sector are helping to build robust supply chains and assisting in recovery. Here, a brief discussion of these smart manufacturing technologies is presented, along with a list of potential supply chain issues and corresponding solutions. Such smart manufacturing technologies possess the potential to empower and revolutionize the traditional manufacturing environment by enhancing its resiliency and flexibility. These smart technologies will play a crucial role in accelerating the worldwide supply chain recovery in and after a pandemic.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Enabling a Sustainable Future: Zero-Emission Vehicles Cost Analysis to Inform Decarbonization Pathways

Despite being only 5% of vehicles on the road, medium- and heavy-duty vehicles (MHDVs) are the second largest contributor to transportation emissions (21%) and a major source of local air pollution disproportionally affecting disadvantaged communities. This talk summarizes a cost analysis for zero emissions vehicles (battery electric EVs and hydrogen fuel cell FCEVs) for all medium and heavy duty applications, ranging from large pick-up trucks, to delivery vans, buses, and heavy trucks. Results show that with continued improvements in vehicle and fuel technologies (in line with U.S. Department of Energy targets and vetted with industry), zero-emission vehicles (ZEVs) can reach total-cost-of-driving parity with conventional diesel vehicles by 2035 for all medium- and heavy-duty (MD/HD) vehicle classes without incentives. Assuming economics drives adoption, ZEV sales could reach 42% of all MD/HD trucks by 2030, reflecting lower combined vehicle purchase and operating costs (using real-world payback periods). Two technological solutions - battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs) - are viable in multiple market segments, offering alternative pathways for decarbonization: a) BEVs tend to become cost-competitive for smaller trucks before 2030 and for short-haul (<500-mile) heavy trucks before 2035. b) Hydrogen FCEVs tend to become cost-competitive for long-haul (>500-mile) heavy trucks by 2035. Results are very sensitive to technology improvement trajectories, adoption decision-making, and uncertain assumptions about future freight demand, logistics, and vehicle use explored in multiple scenarios.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗