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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Supporting ARPA-E Power Grid Optimization (Final Report)

Pacific Northwest National Laboratory (PNNL), Arizona State University (ASU), Georgia Institute of Technology (Georgia Tech), Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), The University of Texas at Austin (UT), and the University of Wisconsin-Madison (UW-M) supported the ARPA-E Grid Optimization (GO) Competition by providing a common problem formulation, data format, datasets, evaluation mechanism, scoring, rules, and results that resulted in the awarding of $\$9.24$ million dollars to teams from academia, industry, and national labs for solving three sets of increasingly difficult non-linear, security- constrained AC Optimal Powerflow (AC-OPF) optimization problems in order to increase the efficiency of the US Electric Grid. It is estimated that a 1% increase in efficiency can save $\$1$ billion. Current industry practices typically use a linear DC model (DC-OPF) in order solve the OPF problem within the time constraints of the operation schedule. The GO Competition challenges the best power engineers, mathematicians, and computer scientists to make possible operational decisions based on accurate physical models. To accomplish this, the GO Competition created a series of Challenges and funded teams to produce the best solver. Challenge 1 was to solve the security constrained Alternating Current Optimal Power Flow (ACOPF) problem. Challenge 2 extended that to by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment (UC). Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. While Challenge 3 was being developed, the entrants were invited to find better solutions to the Challenge 2 synthetic datasets with no restrictions on time, hardware, or algorithms. The Challenge 2 solutions turned out to be very good. Challenge 3 expanded the Challenge 2 problem further by using multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. These problems included active bid-in demand and topology optimization. Together the Challenges used nearly 30 million CPU hours. Since each team was working on the same problem, using the same data, and running on the same hardware, fair comparisons could be drawn as to the best solver. The datasets were varied enough, however, that the best solver for one dataset was not necessarily the best at another, so cumulative scores were used. The process was managed by the PNNL maintained website https://GOCompetition.energy.gov, where Entrants could find information about the problem, the data, the rules, submit their solver for evaluation, and see the scores of all the competing teams on a Leaderboard. Interest was world-wide but only American teams were eligible for prizes. The Competition has produced 34 journal articles 115 papers and been cited over 500 times in the literature, including 12 dissertations (4 from foreign countries; Columbia (2), Germany, and Italy) and 3 from the DOE ExaScale project. Software developed by Pearl Street Technologies for Challenges 1 and 2 is now deployed by Southwest Power Pool (SPP) and Midcontinent Independent Service Operator (MISO). Other teams have received inquiries from venture capitalists. Google DeepMind has thanked the Competition for making the datasets developed for the Competition public. They are using it to train machine learning models. The larger datasets have billions of unknowns to be solved for, but only a small percent matter in the final solution. Knowing what unknowns are important can dramatically speedup the solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Quantifying Value and Representing Competitiveness of Electricity System Technologies in Economic Models

Evaluating competition between electricity technologies is challenging because it depends on both their costs and their values. While technology costs can typically be estimated from projections of the cost components - capital, fuel, and O&M - estimating a technology's value is more complex due to its dependence on its contributions to multiple different grid services, each with prices that can vary substantially over space and time. In this work, using an electricity model of the contiguous United States, we develop relationships between relative value and share of total generation for major electricity generation technologies which, when paired with projections of technology costs, can be used to estimate technology competitiveness. We identify significant differences in the relationship between relative value and generation share for variable renewable energy (VRE) and non-VRE sources, but we demonstrate that all technologies require consideration of their dynamic values (in addition to cost) when evaluating competitiveness. In addition, we demonstrate that relative value of a technology is substantially impacted by not only its own generation share but also other aspects of the system state, in particular the mix of other technologies present in the system. Finally, we use the developed relative value relationships in combination with projections of future technology costs in a coarse resolution model that competes technologies based on a comprehensive competitiveness metric: profitability-adjusted LCOE (PLCOE). We show that this simple representation of technology competition approximately recovers the generation mix from a detailed model, which is not possible using LCOE alone. Such an approach can be used to improve the representation of technology competition in coarse-resolution models such as integrated assessment models, for which simplified metrics are often needed.

electricity model↗
DOE OSTI2026

Forest residue harvest optimization: spanning the bridge between plant biology and biorefinery performance

Forestry residues have immense potential as alternative feedstocks to petroleum, yet their inherent complexity remains a major challenge to widespread use. Pairing the temporal rhythms of plant biology with biorefinery performance is critical to industrial-scale biorefinery development. Here, we provide the first report of a techno-economic analysis (TEA) and life cycle assessment (LCA) for a model integrated reductive catalytic fractionation (RCF)–molten salt hydrolysis process for forestry residues varying in tree part, species, and phenophase. All forestry residues resulted in net-negative greenhouse gas (GHG) emissions vs. comparable petroleum feedstocks, with GHG emissions potentially reduced >4.0× through composition-based feedstock selection (e.g., harvesting American beech bark in spring vs. summer). Moreover, American beech twigs/branchlets and bark in leafed and emergence phenophases, respectively, had 7.9× lower predicted phenolic minimum selling prices (MSPs) vs. other feedstocks and MSPs within the current global phenolic market range. Hemicellulose content and RCF yield emerged as key parameters impacting GHG emissions and biorefinery revenue, identifying hardwood twigs/branchlets in the leafed phenophase as optimal biofeedstocks. Biorefinery expenses were dominated by purchased equipment, raw materials, and utility costs, highlighting essential areas for future study. Notably, RCF reactor pressures drove 85–90% of equipment costs, but sensitivity analysis revealed that decreasing the pressure 20% could reduce the phenol MSP 4-fold. Structural carbohydrate dynamics were also investigated using a two-step acid hydrolysis method to resolve tissue- and species-level patterns in biomass composition throughout the year to enable harvest optimization based on TEA/LCA findings. Ultimately, elucidating the impact of biofeedstock dynamics on biorefinery performance enables harvest optimization, informed engineering design, and progress towards an expanded bioeconomy.

Shapiro, Alison J. [University of Delaware, Newark↗
DOE OSTI2021

Deep Reinforcement Learning for Automatic Generation Control of Wind Farms

This paper provides a model-free framework for real-time control of wind farms to accurately track a power reference signal. This problem requires tractable dynamical models for capturing the aerodynamic interaction between wind turbines and controllers that can make decisions in realtime given varying atmospheric conditions. In this paper, we propose a deep reinforcement learning framework to provide real-time yaw control of a wind farm. Modifications have been made to FLOw Redirection and Induction in Steady State (FLORIS), a modeling tool that incorporates transient wake behavior. The control problem is formulated to track a synthetic power reference signal based on historical atmospheric (wind speed and direction) information, price signals, and regulation deployment data from U.S. regional transmission operators. Results indicate that a wind farm, with this control paradigm, can achieve good tracking performance when tested with real atmospheric data.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2020

Global Electrification of Vehicles and Intertwined Material Supply Chains of Cobalt, Copper and Nickel

Electric vehicles (EVs) will be an important part of a low-carbon economy. Future EV adoption depends on overcoming multiple barriers, including charging infrastructure, sufficient electricity generation, and the availability of battery raw materials at acceptable costs, including cobalt, copper and nickel – which typically are co-produced. Various EV projections have been made, along with associated requirements for battery raw materials (demand). There have been separate assessments of the availability of raw materials (supply). However, integrated assessments of supply and demand and their interaction over time are lacking to support decision-making. Using a dynamic market model that reflects the co-produced nature of cobalt, copper and nickel production as well as recycling, we estimate global output of these three metals under different EV growth scenarios from 2020 to 2040. In this work, results suggest that starting in 2025, replacement demand for battery raw materials becomes important. Thus, estimates for future material demand that consider only demands from new EVs are incomplete and misleading. EV deployment and associated increased demand for raw materials will have a greater impact on prices for cobalt than nickel and, in turn, for nickel than copper. High EV deployment scenarios will be difficult to achieve without larger supplies of copper, nickel and cobalt than forthcoming under the conditions modeled here, which anticipates a six-year time lag between a demand increase and the expansion of metal-production capacity. Shortening battery lifetimes and improving collection and recycling rates increase secondary supply, but the extent to which these factors support additional EV deployment is mixed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Status of conceptual horizontal splitter design for FFA@CEBAF energy upgrade

Jefferson Lab’s Continuous Electron Beam Accelerator Facility (CEBAF) is currently investigating the feasibility of upgrading its maximum operating energy using Fixed-Field Alternating-gradient (FFA) recirculating arcs to increase the total number of recirculations the beam through the pair of LINACs. These FFA arcs will be composed of permanent magnets, with small Panofsky-style multipole correctors. Horizontal splitters are needed to control the beam parameters through these FFA arcs. The geometrical and physical constraints, as well as the beam matching requirements are very restrictive, complicating the design. This work will show the current status of the most mature design, which includes matching solutions, as well as options for extraction of the beam.

Accelerator Physics↗
DOE OSTI2024

NMPC for Mode-Switching Operation of Reversible Solid Oxide Cell Systems

Solid oxide cells (SOCs) are a promising dual-mode technology that generates hydrogen through high-temperature water electrolysis and generates power through a fuel cell reaction that consumes hydrogen. Reversible operation of SOCs requires a transition between these two modes for hydrogen production setpoints as the demand and price of electricity fluctuate. Moreover, a well-functioning control system is important to avoid cell degradation during mode-switching operation. In this work, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional integral (PI) control strategies, while ramping between the modes of hydrogen and power production. While both control methods provide similar performance in many metrics, NMPC significantly reduces cell thermal gradients and curvatures (mixed spatial-temporal partial derivatives) during mode switching. A dynamic process flowsheet of the reversible SOC system was developed in the open-source, equation-based IDAES modeling framework. Our IDAES dynamic simulation results show that NMPC can ramp the SOC system between hydrogen and power production targets within short mode-switching times. Moreover, NMPC can comply with operating limits in the SOC system more effectively than PI, and only NMPC can directly enforce user-specified limits for mixed spatial-temporal partial derivatives of temperature. This allows for management of the trade-off be-tween operating efficiency and cell degradation, which is dependent on these temperature curvatures.

Li, Mingrui↗
DOE OSTI2026

A Physics-Informed Reinforcement Learning Framework for Economic-Thermal Co-Optimization of Crypto Mining Data Centers: Preprint

The rapid expansion of cryptocurrency mining has created a new class of high-density data centers characterized by extreme thermal flux and high sensitivity to volatile economic markets. Traditional thermal management strategies, typically reliant on rule-based control, maintain static setpoints that fail to account for fluctuating electricity prices and cryptocurrency values - factors critical to mining profitability. To address this, we present a physics-informed reinforcement learning (PIRL) framework for economic-thermal co-optimization in crypto mining data centers. This framework consists of a proximal policy optimization (PPO) agent, a virtual testbed powered by high-fidelity physics-based models, and an interactive frontend dashboard. The PPO agent is trained using the virtual testbed and strict hardware safety limits. This physics-informed approach allows the agent to learn a stochastic policy that dynamically balances mining revenue against operational costs by co-optimizing HVAC cooling setpoints and IT computational hashrate. The simulation results demonstrate that the integrated framework achieved an 8.62% increase in net operational profit compared to traditional baseline strategies while strictly adhering to safety-critical temperature constraints (coolant supply temperature < 32 degrees C). This work provides a scalable template for the deployment of reinforcement learning in mission critical facilities where economic volatility and physical safety must be managed simultaneously.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Modeling the Economic and Environmental Impacts of Land Scarcity Under Deep Uncertainty

Abstract Land scarcity is increasing over time, driven by complex multisector dynamics. The impacts of land scarcity on the economy and environment are multi‐faceted and regional, so any action to convert land will contain inherent tradeoffs. These impacts are complicated by the deeply uncertain evolution of the various sectors influencing land scarcity. A need therefore exists to provide multi‐metric and multi‐sector assessments that are robust to myriad uncertainties. Land conservation effectively limits the supply of productive land, while biofuel consumption increases the demand and competition for that land, and how these dynamics individually and jointly propagate to economic and environmental impacts is an important open question. To address this, we adopt the Global Change Analysis Model (GCAM) that has representations of various important systems including the climate, macroeconomic, energy, agriculture and land, and water resources systems. Various scenarios of increased land demand (from biofuels) and decreased land supply (from conservation) under various socioeconomic scenarios drawn from the SSPs were simulated using GCAM. We find that while biofuel consumption and land conservation reduce carbon emissions, this comes at the cost of higher food prices, reduced crop production, and increased water withdrawals. Additionally, some regions experience these tradeoffs more severely than others and are more heavily impacted from the same biofuel mandate or by an additional percent of protected land. These and other findings highlight the importance of multisector modeling frameworks that capture many cross‐sector linkages, and acknowledge the important uncertainties confronting the human‐Earth system when making any analysis of land scarcity impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

..beta..-Ketoadipic Acid Production in P. putida for Performance-Advantaged Nylons

Biomass derived chemicals can possess extended functionality, often in the form of heteroatoms like oxygen and nitrogen, that can enable performance advantaged properties in emergent materials. One illustrative example of this concept is ..beta..-ketoadipic acid (..beta..KA), a C6 dicarboxylic acid with a ketone in its backbone. ..beta..KA can be obtained via biological cultivation from aromatic compounds, sugars, and even waste plastics. When ..beta..KA is implemented into polymers, namely nylons, in place of adipic acid the overall material properties are increased. Specifically, the glass transition temperature (which is the measurement at which temperature a plastic softens) is increased by 69 degrees C and the water permeability is reduced by 20%. Molecular dynamic (MD) simulations reveal that the ..beta..-ketone in ..beta..KA leads to restricted movement across multiple backbone carbons when compared to adipic acid. Furthermore, MD simulations reveal that the ..beta..-ketone, when compared to no-ketone or an a-ketone, can lead to an increase in hydrogen bonding between neighboring chains. Both phenomena may help explain the molecular origin of the increases in polymer performance. Aside from performance advantages in thermomechanical performance, the production of ..beta..KA also is advantaged in its manufacturing. Although estimates for the minimum selling price (MSP) of ..beta..KA exceed adipic acid the manufacture of ..beta..KA would reduce supply chain energy and GHG emissions by >50% and >30% respectively. Further TEA analysis reveals that the MSP of ..beta..KA is primarily driven by the feedstock cost while the capital expenditures are driven largely by fermentation costs. Overall, the work presented within will be illustrative of how biomass derived molecules, namely ..beta..KA, can manifest performance advantages across multiple benchmarks and may enable a path to market for biomass-derived materials.

beta-ketoadipic acid↗
DOE OSTI2023

Nanoscale structure of a hybrid aqueous–nonaqueous electrolyte

A new class of electrolytes have been reported, hybridizing aqueous with non-aqueous solvents, which combines non-flammability and non-toxicity characteristics of aqueous electrolytes with the superior electrochemical stability of non-aqueous systems. Here, we report measurements of the structure of an electrolyte composed of an equal-mass mixture of 21 m LiTFSI-water and 9 m LiTFSI-dimethyl carbonate using high-energy x-ray diffraction and polarized neutron diffraction with isotope substitution. Neutron structure factors from partially and fully deuterated samples exhibit peaks at low scattering vector Q that we ascribe to long-range correlations involving both solvent molecules and TFSI − anions. Here we compare both sets of measurements with results of molecular dynamics simulations based on a polarizable force field. The structures derived from simulations are generally in agreement with those measured, except that neutron structure factors predicted for two partially deuterated samples show very intense scattering increasing up to the low- Q limit of simulation, indicating a partial segregation between the two solvents not observed in experimental measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Advancing Ethanol-to-Jet cost Effectiveness via direct conversion to n -Butene-Rich olefins and Co-Product Valorization

Ethanol is a promising feedstock for sustainable aviation fuel production; however, conventional routes face significant energy and cost challenges, particularly due to the ethanol dehydration step to ethylene. Here, this study leverages breakthrough experimental data to perform comprehensive techno-economic and life-cycle assessments of an innovative ethanol-to-jet process. The process employs a single-step catalytic conversion, enabled by multifunctional Cu-ZrO 2 /SBA-16 catalyst, to directly upgrade ethanol into a mixed olefin stream rich in n-butene. The single-step conversion eliminates the costly ethanol dehydration step in the conventional process. High selectivity toward n-butene offers key advantages: it simplifies downstream oligomerization into jet-range hydrocarbons and enables the co-production of renewable n-butene alongside sustainable aviation fuel. The analysis estimates a minimum fuel selling price as low as $\$$2.50 per gallon, whether using corn ethanol or cellulosic ethanol from corn stover. Life cycle CO 2 equivalent emissions are projected to be as low as 10.6 g CO 2 eq/MJ sustainable aviation fuel, representing over 70% reduction compared to conventional petroleum-based jet fuel. This one-step ethanol upgrading approach not only facilitates SAF and n-butene co-production but also provides operational flexibility. The ability to tailor product outputs allows the ethanol-to-jet process to adapt to varying feedstocks, incentive programs, and market dynamics, ultimately enhancing the economic viability of sustainable aviation fuel production.

Xu, Yiling [Pacific Northwest National Laboratory ↗
DOE OSTI2024

Buyers Collusion in Incentivized Forwarding Networks: A Multi-Agent Reinforcement Learning Study

We present the issue of monetarily incentivized forwarding in a multi-hop mesh network architecture from an economic perspective. It is anticipated that credit-incentivized forwarding and relaying will be a simple method of exchanging transmission power and spectrum for connectivity. However, gateways and forwarding nodes, like any other free market, may create an oligopolistic market for the users they serve. In this study, a coalition scheme between buyers aims to address price control by gateways or nodes closer to gateways. In a Stackelberg competition game, buyer agents (users) and sellers (gateways) make decisions using reinforcement learning (RL), with decentralized Deep Q-Networks to buy and sell forwarding resources. We allow communication links between the buyers with a limited messaging space, without defining a collusion mechanism. The idea is to demonstrate that through messaging, and RL tacit collusion can emerge between agents in a decentralized setup. The multi-agent reinforcement learning (MARL) system is presented and analyzed from a machine-learning perspective. Moreover, MARL dynamics are discussed via mean field analysis to better understand divergence causes and make implementation recommendations for such systems. Finally, the simulation results show the results of coordination among the users.

42 ENGINEERING↗
DOE OSTI2024

A Simulated Evaluation of Powder Flowability Through a Partially Obstructed Consumable in Blown Powder Directed Energy Deposition Systems

Abstract In the interest of continued industrialization of metal additive manufacturing in modern production environments, cost is often referenced as a primary deterrent to new adopters. Conventional economic models for additive systems, processes, and supply chains often focus on specific process applications with little generalizability, or they neglect significant costs associated with production such as machine maintenance and consumable part replacement. Compounding the latter issue are substantial knowledge gaps in consumable part wear characterization for additive and other convergent manufacturing systems. In coaxial blown powder directed energy deposition systems, gas atomized metal powder is wasted during material deposition at a rate that is partly dependent on present wear phenomena in a consumable nozzle housed in the cladding head assembly. The price and lead time required to replace the nozzle incentivizes its reuse even when visibly worn. Often this initiates a process quality decline in the form of underbuilt geometry and internal defects due to losses in powder catchment efficiency. While depositing H13 steel using a hybrid manufacturing machine tool equipped with such a deposition system, a unique partial clog with a bridge-like structure formed at the consumable nozzle exit when supporting argon gas flows failed mid-process. To further understand coaxial multi-phase powder flow in the event of support gas failure, a computational fluid dynamics simulation is tailored to relevant process parameters, H13 powder material profile, and machine operator observations collected after the incident. The resulting differences in powder flow compared to control gas flow parameters is presented and discussed. The powder flowability and performance of the clogged nozzle is then assessed by using an optical profilometer to extract the profile of the clog and recreate the clog geometry within the simulation environment. In past work this simulation has been experimentally validated for a 316L steel powder material profile and used specifically for analyzing powder stream geometry and catchment efficiency. After the initial powder flow characterization, the clog is removed, and the nozzle is reprofiled. After removing the obstructing clog, the newly unobstructed nozzle geometry, the original off the shelf nozzle geometry, and additional nozzle profiles exploring different consumable refurbishment strategies are reevaluated in the simulation. Powder catchment efficiency for all variant nozzle geometries and relevant flow variables are compared and discussed, along with potential mitigation strategies for optimizing powder flowability with worn consumables. This work expands on the known morphology of blown powder obstructions and wear defects present in consumable coaxial nozzles while discussing pragmatic simulation driven responses to unanticipated subsystem failure in hybrid manufacturing machining platforms.

DeWitte, Lisa↗
DOE OSTI2022

A scalable solid-state nanoporous network with atomic-level interaction design for carbon dioxide capture

Carbon capture and sequestration reduces carbon dioxide emissions and is critical in accomplishing carbon neutrality targets. Here, we demonstrate new sustainable, solid-state, polyamine-appended, cyanuric acid–stabilized melamine nanoporous networks (MNNs) via dynamic combinatorial chemistry (DCC) at the kilogram scale toward effective and high-capacity carbon dioxide capture. Polyamine-appended MNNs reaction mechanisms with carbon dioxide were elucidated with double-level DCC where two-dimensional heteronuclear chemical shift correlation nuclear magnetic resonance spectroscopy was performed to demonstrate the interatomic interactions. We distinguished ammonium carbamate pairs and a mix of ammonium carbamate and carbamic acid during carbon dioxide chemisorption. The coordination of polyamine and cyanuric acid modification endows MNNs with high adsorption capacity (1.82 millimoles per gram at 1 bar), fast adsorption time (less than 1 minute), low price, and extraordinary stability to cycling by flue gas. This work creates a general industrialization method toward carbon dioxide capture via DCC atomic-level design strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Prospects for quarkonium studies at the high-luminosity LHC

In this work, prospects for quarkonium-production studies accessible during the upcoming high-luminosity phases of the CERN Large Hadron Collider operation after 2021 are reviewed. Current experimental and theoretical open issues in the field are assessed together with the potential for future studies in quarkonium-related physics. This will be possible through the exploitation of the huge data samples to be collected in proton-proton, proton-nucleus and nucleus-nucleus collisions, both in the collider and fixed-target modes. Such investigations include, among others, those of: (i) J/ψ and Υ produced in association with other hard particles; (ii) χ c,b and η c,b down to small transverse momenta; (iii) the constraints brought in by quarkonia on gluon PDFs, nuclear PDFs, TMDs, GPDs and GTMDs, as well as on the low-x parton dynamics; (iv) the gluon Sivers effect in polarised-nucleon collisions; (v) the properties of the quark-gluon plasma produced in ultra-relativistic heavy-ion collisions and of collective partonic effects in general; and (vi) double and triple parton scatterings.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

A reactive flow model for the 3,3'-diamino-4,4'-azoxyfurazan based plastic bonded explosive (PBX 9701)

Here, this paper describes the calibration of the Arrhenius Wescott–Stewart–Davis (AWSD) reactive flow model for the recently developed high explosive PBX 9701, which consists of 97% 3,3'-diamino-4,4'-azoxyfurazan (DAAF) and 3% FK-800 binder by weight. DAAF-based explosive formulations have several desirable qualities as they are relatively insensitive to non-shock insults but have higher performance than triaminotrinitrobenzene based formulations. Equations of state for the explosive reactants and products are calibrated using a combination of existing and new experimental data and theoretical calculations. The AWSD rate law calibration utilizes both one-dimensional shock-to-detonation and multidimensional rate stick experiments to capture the shock initiation and propagating detonation regimes. Validation of the calibrated model is demonstrated through comparison with recent gas-gun experiments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Persistent, extensive channelized drainage modeled beneath Thwaites Glacier, West Antarctica

Abstract. Subglacial hydrology is a leading control on basal friction and the dynamics of glaciers and ice sheets. At low discharge, subglacial water flows through high-pressure, sheet-like systems that lead to low effective pressures. However, at high discharge, subglacial water melts the overlying ice into localized channels that efficiently remove water from the bed, thereby increasing effective pressure and basal friction. Recent observations suggest channelized subglacial flow exists beneath Thwaites Glacier, yet it remains unclear if stable channelization is feasible in West Antarctica, where surface melting is nonexistent and water at the bed is limited. Here, we use the MPAS-Albany Land Ice model to run a suite of over 130 subglacial hydrology simulations of Thwaites Glacier across a wide range of physical parameter choices to assess the likelihood of channelization. We then narrow our range of viable simulations by comparing modeled water thicknesses to previously observed radar specularity content, which indicates flat, spatially extensive water bodies at the bed. In all of our data-compatible simulations, stable channels reliably form within 100–200 km of the grounding line and reach individual discharge rates of 35–110 m3 s−1 at the ice–ocean boundary. While only one to two channels typically form across the 200 km width of the glacier in our simulations, their high efficiency drains water across the entire lateral extent of the glacier. We posit the large catchment size of Thwaites Glacier, its funnel-like geometry, and high basal melt rates together accumulate enough water to form stable channels. No simulations resembled observed specularity content when channelization was disabled. Our results suggest channelized subglacial hydrology has two consequences for Thwaites Glacier dynamics: (i) amplifying submarine melting of the terminus and ice shelf while (ii) simultaneously raising effective pressure within 100 km of the grounding line and increasing basal friction. The distribution of effective pressure implied from our modeling differs from parameterizations typically used in large-scale ice sheet models, suggesting the development of more process-based parameterizations may be necessary.

54 ENVIRONMENTAL SCIENCES↗