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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Managing Solar Photovoltaic Integration in the Western United States: Power System Flexibility Requirements and Supply

As penetrations of variable renewable energy generation technologies such as wind and solar photovoltaics (PV) continue to increase across the United States, greater uncertainty and variability in the net load often lead to a concern about how power systems may adapt. Managing the system net load (i.e., load minus contribution from variable generation technologies) may become more challenging with increasing variable generation, as the magnitude and frequency of net ramps increase. However, there is inherent flexibility in power systems through the conventional generator fleet (under least-cost unit commitment and economic dispatch), less-conventional generation sources (e.g., storage, demand response, concentrating solar power with thermal energy storage), and imports and exports with neighbors. In this analysis, we create an open-source tool to analyze the flexibility of the results of a specific commercial unit commitment and economic dispatch tool (PLEXOS), but the code can be applied generically as well. The tool assesses the flexibility requirements (or demand) of a system through a net load analysis. The constraints and limitations of each generator are then considered to determine the availability (or supply) of flexibility. Then, the supply and demand of flexibility are compared to gain a more complete picture of potential flexibility concerns. We apply this open-source tool to high-penetration PV scenarios constructed for three focus regions in the western United States defined using the Resource Planning Model (RPM) capacity expansion modeling tool: RPM-OR, RPM-CO, and RPM-AZ. Generally, we find few flexibility concerns, as the western United States represents a large and interconnected power system with significant inherent flexibility. In addition, the PV scenarios we analyzed are overbuilt on capacity, leaving plenty of ramping ability on the system. We do find that for each focus region, the impact of imports on meeting ramping needs is essential. This means the PV integration in each focus region impacts the entire rest of the system. Each system has different dominant sources of flexibility. The conventional generator fleet (especially coal and gas combined-cycle technologies) as well as less-conventional sources such as storage are all shown to be important sources of flexibility. The scenarios evaluated here were designed to study the planning and operations impact of high solar penetration in each of three focus regions. However, none of the three focus regions likely will deploy PV in isolation, meaning the ability of imports and exports to provide flexibility may be considerably different in scenarios with strong PV deployment in every region. Overall, we intend that the framework we present here will be useful in future analysis of other system evolutions to identify whether and how flexibility may constrain the successful deployment of variable generation technologies.

14 SOLAR ENERGY↗
DOE OSTI2023

Hydropower Value Drivers

Conventional hydro resources generate the majority of their value by providing energy under most conditions, but the relative fraction of value generated by providing ancillary services and capacity increases with increasing penetration of resources with zero fuel costs. Pumped storage hydropower resources generate the majority of their value by providing capacity under most conditions, but the relative fraction of value generated by providing energy increases with increasing penetration of resources with zero fuel costs. The total value of conventional hydropower generally decreases in systems with increasing penetration of resources with zero fuel costs; this is largely due to the associated decrease in average energy prices. The total value of pumped storage generally increases in systems with increasing penetration of resources with zero fuel costs, largely due to opportunities to operate in pumping mode when energy prices are low or even negative. Energy storage representation must be enhanced to ensure that models accurately capture system value streams for these resources. Current power system models have a limited ability to capture the price dynamics of ancillary services, and it is still challenging to assess the role and magnitude of ancillary service value streams in future systems. Power systems are currently in a state of rapid and dramatic evolution due to a number of different factors, including the increasing penetration of variable renewable energy (VRE) sources, such as wind and solar, and battery energy storage systems (BESS). This evolution will change the way power systems are fundamentally planned and operated. Some of these changes may be incremental, while others may be more significant, but the result will likely be parallel evolution in the definition and requirement of different grid services and therefore a subsequent shift in their relative values. This report presents a framework developed to identify such system value drivers and quantify their relative impact on several different value streams, with a specific focus on implications for conventional hydropower and pumped storage hydropower (PSH) resources. This value drivers framework (VDF) encompasses five core analytical steps: 1. Identify potential drivers and develop scenarios, 2. Execute production cost models, 3. Calibrate prices, 4. Optimize hydropower operations, and 5. Quantify value drivers.

13 HYDRO ENERGY↗
DOE OSTI2025

Strong Local Passivity in Unconventional Scenarios: A New Protocol for Amplified Quantum Energy Teleportation

Quantum energy teleportation (QET) has been proposed to overcome the restrictions of strong local passivity (SLP) and to facilitate energy transfer in quantum systems. Traditionally, QET has only been considered under strict constraints, including the requirements that the initial state be the ground state of an interacting Hamiltonian, that Alice’s measurement commute with the interaction terms, and that entanglement be present. These constraints have significantly limited the broader applicability of QET protocols. In this work, we demonstrate that SLP can arise beyond these conventional constraints, establishing the necessity of QET in a wider range of scenarios for local energy extraction. This leads to a more flexible and generalized framework for QET. Furthermore, we introduce the concept of a “local effective Hamiltonian,” which eliminates the need for optimization techniques in determining Bob’s optimal energy extraction in QET protocols. As an additional advantage, the amount of energy that can be extracted using our new protocol is amplified to be 7.2 times higher than that of the original protocol. These advancements enhance our understanding of QET and extend its broader applications to quantum technologies. To support our findings, we implement the protocol on quantum hardware, confirming its theoretical validity and experimental feasibility.

amplified energy extraction↗
DOE OSTI2020

Power and Natural Gas Systems Interface Study

Power systems worldwide are becoming more reliant on energy from natural gas, wind, and solar, posing potential reliability and coordination challenges from the tighter coupling of these infrastructure systems. This paper proposes a framework for the market-based coordination of electricity and natural gas system operations. The proposed framework includes a power system model that accounts for flexibility in the commitment of power plants with short start-up and shut-down times, coupled with a dynamic gas model that simulates when gas cannot be delivered to generators. The capabilities of the framework are illustrated using real-world electric power and gas systems, including scenarios around wind and solar penetrations and the analysis of time-variant, “shaped flow” gas nominations. Our results indicate that coordination between power and gas systems improves total gas delivery and reduces out-of-merit order dispatch in the electricity system, and that shaped flows may reduce unserved gas in systems with high penetrations of wind and solar.

coordinated operation↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Game Theory Approaches for System-level Incentive Design

This report presents a generalized Stackelberg game framework for designing and evaluating financial incentives that enhance power system resilience through strategic deployment of distributed energy resources(DERs) under various contingencies. The proposed approach addresses the challenge of coordinating individual community investment decisions to meet system-wide resilience objectives. The framework is demonstrated in a three-community test system subjected to two transmission contingency scenarios: inter-community line failure (Case 1) and complete main grid disconnection (Case 2). In both cases, three incentive levels are compared: a Base case with no financial incentives, and low and high incentive cases. In Case 1, the Base case (no incentives) results in a total installed DER capacity of 217.2 MW, with no load shedding due to alternative routing, but community costs remain high. Increasing incentives raises DER deployment to 286.9 MW, lowers aggregate community costs by $22M annually, and completely avoids the need for costly new transmission line construction. In Case 2, the Base case results in 24.3 MWh of unserved load; introducing incentives eliminates all load shedding and ensures up to 89 MWh of battery storage is available for emergency reserve. These results demonstrate that targeted incentives can dramatically improve grid resilience and cost-effectiveness. The framework thus offers policymakers and system planners a robust tool to quantify and compare the effectiveness of incentive programs for multi-community transmission networks behavior, system resilience, and economic efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Logistics simulation of a remediation effort for a hypothetical radiological contamination scenario

To mitigate the effects following a large-scale nuclear or radiological material release in an urban environment and to expedite recovery, the Integrated Wash-Aid Treatment Emergency Reuse System (IWATERS) was developed. IWATERS consists of three operations: washing contaminated surfaces with an ionic wash solution, collecting, and treating the contaminated wash solution on-site to remove contaminants, and reusing the treated solution throughout operations to preserve the clean water resource. This study develops a framework to simulate the logistics of IWATERS deployment, thereby gaining an understanding of the timeline for decontamination operations. For this purpose, the Analysis of Mobility Platform and GoldSim were leveraged for a hypothetical contamination scenario covering 65,200 m 2 of an urban center. The framework reveals that remediation progress is limited by several resources, notably the availability of vermiculite, a reactive clay that is required to treat the contaminated wash solution. Further, this study also presents how the simulation approach can be used to characterize alternatives to reduce the influence of limited resources on operational progress. Overall, this work lays the foundation for evaluating different decontamination methods through detailed logistics simulation, i.e., by refining simulation assumptions and expanding the range of scenarios the simulation can depict.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2026

Comparison of structurally diverse simulation models for prediction of epidemic outcomes caused by a long-distance dispersed pathogen

Long-distance dispersal (LDD) pathogens pose substantial challenges for epidemic control due to their ability to generate new infection foci at great distances. While various modeling approaches have been developed to understand and manage such outbreaks, little work has compared how models of different structures behave under shared conditions. Here, in this study, we compare four structurally distinct epidemiological models — EPIMUL, GEMF, PoPS, and Warwick — each adapted to simulate the spread of wheat stripe rust (WSR), a wind-dispersed LDD pathogen, under identical epidemiological parameters and dispersal kernel. Using data from a controlled field experiment, we evaluate the ability of each model to replicate disease prevalence under nine intervention scenarios that vary in timing and culling area. While the models differ substantially in design — ranging from spatial grid-based to network-based and raster-based frameworks — the shared dispersal kernel allowed for close alignment in their predictions. All models accurately captured general epidemic trends, particularly the strong effect of early intervention on disease suppression. We qualitatively compared their behavioral responses across scenarios and also evaluated an ensemble prediction by averaging across model outputs. Our findings highlight how integrating shared epidemiological components into distinct modeling frameworks can improve consistency and accuracy, while reinforcing the importance of early culling in managing LDD pathogen outbreaks.

Dispersal kernel↗
DOE OSTI2026

Knowledge Graph for End-to-End Traceability of an Integrated Human-Earth System Model

Integrated human-Earth system models inform energy-water-land system dynamics and policies, yet their results are difficult to trace through input-data, model structure, scenario configurations, and solved outputs. Because this information is siloed across disconnected artifacts, process-based IAMs have historically lacked a unified, queryable representation. Such lack of traceability prevents researchers from systematically isolating the multi-sector drivers of complex outcomes (such as tracing water-scarcity results back to distant energy-system dynamics) or conducting holistic uncertainty attribution across hundreds of interacting parameters. To address this concern, our work documents the software engineering process of a knowledge graph that unifies these four layers for the Global Change Analysis Model (GCAM-USA_Reference scenario, GCAM v9.1). The graph was built as a relational property graph in DuckDB from the run’s own artifacts: the input-preparation dependency map (gcamdata chunk map), the model’s XML input files, the run configuration, and the results database (BaseX), successfully mapping the model’s declared structure. The resulting graph comprises 204,321 nodes and 1,687,814 edges across 16 node types and 15 edge types, with approximately 16.3 million time-series values stored separately to maintain structural efficiency. To ensure representation fidelity, every edge carries an epistemic-status annotation recording the warrant for the relationship (structural, provenance, dependency, or model-derived), and a machine-readable provenance ledger classifying the origin of every schema element. Evaluation against a fixed five-benchmark suite with locked baselines reports zero structural orphans, zero dangling edge endpoints, and 100% of output-producing technologies traceable to raw input files. Two interactive interfaces present the graph, including a serverless browser application built on DuckDB-Wasm. By establishing the first end-to-end provenance framework for an IAM, this work enables researchers and scientists to systematically audit complex policy scenarios, debug model structures, and trace policy-relevant outputs to their data origins in real time.

Artifical Intelligence↗
DOE OSTI2023

Seasonal drivers of dissolved oxygen across a tidal creek–marsh interface revealed by machine learning

Abstract Dissolved oxygen (DO) is a key biogeochemical control in coastal systems, and its concentration and drivers vary markedly through time and space. This makes it difficult to accurately represent coastal DO and associated biogeochemical processes in models, limiting our ability to predict how these systems will respond to global change. We obtained high‐frequency (5‐min) in situ measurements of DO collected at three locations across the interface of a tidal creek and coastal marsh in the Pacific Northwest, USA. Random Forest machine learning models quantified the importance of three categories of environmental drivers (Aquatic, Climatic, and Terrestrial) of DO variability across the creek–marsh interface. We selected two 4‐month datasets representing Summer and Winter seasonal periods to test two hypotheses on the dominant drivers of DO at the coastal interface. We found that the Terrestrial driver—characterized by long periods of anaerobic conditions and episodic pulses in DO after floods—was most important during the Winter, whereas the Aquatic driver—characterized by variability over tidal, diel, and lunar cycles—was most important during the Summer. We explored how future climate change scenarios could alter the drivers of DO variability using a cumulative sums driver–response framework. Our results suggest that under climate change, Aquatic and Climatic drivers may increase in importance during the Summer, potentially linked to changing metabolic regimes and sea level, with Terrestrial driver importance potentially increasing during the Winter. Our approach highlights useful methods for understanding the spatiotemporal complexity of oxygen across coastal interfaces and quantifying the relative importance of distinct environmental drivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Multi-Model Framework for Assessing Long- and Short-Term Climate Influences on the Electric Grid

Climate change influences many aspects of the electric grid, but prior work and industry practices often ignore the potential effects of changing climate, or they only consider a single effect or individual effects in isolation. Challenges vary with each grid and include adapting to long-term trends such as changing temperature and precipitation or shorter-term events such as drought or storms that could increase in frequency or intensity. Here we present a multi-model framework designed to analyze the effects of long and short-term climate impacts in combination. This framework couples capacity expansion and production cost models with hydrologic models and future climate scenario data to analyze alternative climate and energy futures at high spatial, temporal, and process resolutions. Furthermore, we constructed and evaluated the results of a suite of simulated scenarios exploring climate impacts on capacity investment and stress-tested the resulting future infrastructures using hourly dispatch modeling under alternative drought and load conditions. We demonstrate the approach through a case study of the U.S. Western Interconnection, where climate impacts depend on interactions between temperature-induced load, water availability for hydropower, technology competitiveness, and demand flexibility. Changes in 2038 generating capacity range from -8.5-16.6 GW, and changes in 2038 transmission capacity range from -1-2 GW. Capacity increases are driven by higher load from higher temperatures, while capacity reductions can be achieved in scenarios with higher future hydropower availability and increased demand flexibility. Scenarios requiring additional capacity cost an additional $\$5$-$\$17$ billion (discounted) from 2018 to 2038; however, scenarios with capacity reductions cost $\$1$-$\$18$ billion less. Stress tests on four 2038 infrastructures demonstrated that the identified systems were able to serve at least 99.999% of load and 99.96% of reserves. However, drought and unexpected high-load conditions can result in reduced capacity to respond to contingency events we did not model. Although these results are system and scenario specific, they highlight the importance of considering multiple climate change impacts simultaneously in long-term planning efforts and demonstrate a multi-model, multiscale approach that can be flexibly applied to any system and set of climate change concerns.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Modular performance prediction for scientific workflows using Machine Learning

Scientific workflows provide an opportunity for declarative computational experiment design in an intuitive and efficient way. A distributed workflow is typically executed on a variety of resources, and it uses a variety of computational algorithms or tools to achieve the desired outcomes. Such a variety imposes additional complexity in scheduling these workflows on large scale computers. As computation becomes more distributed, insights into expected workload that a workflow presents become critical for effective resource allocation. In this paper, we present a modular framework that leverages Machine Learning for creating precise performance predictions of a workflow. The central idea is to partition a workflow in such a way that makes the task of forecasting each atomic unit manageable and gives us a way to combine the individual predictions efficiently. We recognize a combination of an executable and a specific physical resource as a single module. This gives us a handle to characterize workload and machine power as a single unit of prediction. Overall, our modular technique of creating atomic modules and deployment of longest-path approach to estimate workflow performance, allows the framework to adapt to highly complex nested directed acyclic workflows and scale to new scenarios, since it does not make assumptions of underlying workflow structure. We present performance estimation results of independent workflow modules executed on the XSEDE SDSC Comet cluster using various Machine Learning algorithms. The results provide insights into the behavior and effectiveness of different algorithms in the context of scientific workflow performance prediction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Modeling Framework to Predict Melting Dynamics at Microstructural Defects in TNT-HMX High Explosive Composites

Many high explosive (HE) formulations are composite materials whose microstructure is understood to impact functional characteristics. Interfaces are known to mediate the formation of hot spots that control their safety and initiation. Here, to study such processes at molecular scales, we developed all-atom force fields (FFs) for Octol, a prototypical HE formulation comprised of TNT (2,4,6-trinitrotoluene) and HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine). We extended a FF for TNT and recasted it in a form that can be readily combined with a well-established FF for HMX. The resulting FF was extensively validated against experimental results and density functional theory calculations. We applied the new combined TNT-HMX FF to predict and rank surface and interface energies, which indicate that there is an energetic driver for coarsening of microstructural grains in TNT-HMX composites. Finally, we assess the impact of several microstructural environments on the dynamic melting of TNT crystal under ultrafast thermal loading. We find that both free surfaces and planar material interfaces are effective nucleation points for TNT melting. However, MD simulations show that TNT crystal is prone to superheating by at least 50 K on subnanosecond time scales and that the degree of superheating is inversely correlated with surface and interface energy. The modeling framework presented here will enable future studies on hot spot formation processes in accident scenarios that are governed by strong coupling between microstructural interfaces, material mechanics, momentum and energy transport, phase transitions, and chemistry.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Determining spatially varying profit‐maximizing management practices for miscanthus and switchgrass production in the rainfed United States

Abstract Determining optimal management practices for the profitable production of perennial energy crops is critical for scaling up production beyond experimental levels. Although many experimental field studies have examined the effects of management practices on the performance of miscanthus and switchgrass, there are no recommendations for economically optimal nitrogen (N) application rates and how they should vary spatially and with the age of the energy crop as well as on optimal rotation age of the energy crop to maximize profits. We develop a modeling framework to determine economically optimal crop management decisions and simulate the variability under various scenarios for miscanthus and switchgrass production across 2287 counties in the rainfed United States. We find that profit‐maximizing N recommendations for these crops vary across maturity stages and regions and can increase the landowner's profits compared with a uniform N rate across ages and regions. We also find that the optimal rotation for these crops is shorter than the productive physical lifespan (15–20 and 10 years for miscanthus and switchgrass, respectively). Specifically, the N rate that maximizes the economic returns is negligible for miscanthus and 111 kg ha −1 for switchgrass production at age 2. The mean profit‐maximizing N rate increases with age for miscanthus, peaking at 151 kg ha −1 at age 11 before declining to 114 kg ha −1 at the optimal rotation age of 13 years while that for switchgrass is 150 kg ha −1 for middle‐aged stands and declines to 114 kg ha −1 at the optimal rotation of 8–9 years. We find that miscanthus is the most profitable energy crop in the northern region of the rainfed United States while switchgrass is most profitable in the south of the rainfed United States. Our findings are useful for improving assessments of the profitability of energy crops and guiding future management decisions by landowners.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Reliability-Informed Life Cycle Warranty Cost and Life Cycle Analysis of Newly Manufactured and Remanufactured Units

Standard life cycle techniques such as life cycle warranty cost (LCWC) analysis and life cycle analysis (LCA) are used to respectively quantify the relative economical and environmental advantages of remanufactured goods while simultaneously identifying avenues for improvement. In this paper, we contribute to the literature on life cycle studies by incorporating reliability into LCWC analysis and LCA with the goal of improving long-term/multiple life cycle decision making. We develop a branched power-law model to incorporate the physical degradation mechanisms leading to reduced reuse rates of system parts over multiple life cycles. We then follow a standard LCA protocol to quantify the difference between a new unit and its remanufactured version in terms of environmental impact items such as abiotic depletion potential, global warming potential, and energy consumption. We then devise four practical warranty policies that vary in the choice of replacement and/or provision for extended warranty. All possible replacement scenarios for multiple life cycles are explored for each policy and a mathematically rigorous framework is provided, where the reliability information is used to calculate probabilistic LCWC and life cycle impact items. This reliability-informed LCWC analysis and LCA framework enables design engineers to compare design options and warranty policies by quantifying both economical and environmental impacts to aid in decision making. Although the framework is presented in a general form applicable to any engineered system, we demonstrate the utility of this framework by using a case study of an infinitely variable transmission used in agricultural equipment.

42 ENGINEERING↗
DOE OSTI2026

Dynamic Disruption Resilience in Intermodal Transport Networks: Integrating Flow Weighting and Centrality Measures

Resilient intermodal freight networks are vital for sustaining supply chains amid increasing threats from natural hazards and cyberattacks. Transportation resilience has been widely studied; understanding how random and targeted disruptions affect structural connectivity and functional performance remains a key challenge. To address this, this study evaluates the robustness of the US intermodal freight network, which consists of rail and water modes, using a simulation-based framework that integrates graph-theoretic metrics with flow-weighted centrality measures. Disruption scenarios are examined, including random failures as well as targeted node and edge removals based on static and dynamically updated degree and betweenness centrality. To reflect more realistic conditions, flow-weighted degree centralities (WDC) and partial node degradation are considered. Two resilience indicators are used: (1) the size of the giant connected component to measure structural connectivity; and (2) flow-weighted network efficiency (NE) to assess freight mobility under disruption. The results show that progressively degrading nodes ranked by WDC to 60% of their original functionality causes a sharper decline in normalized NE, for up to approximately 45 affected nodes, than complete failure (100% loss of functionality) applied to nodes targeted by weighted betweenness centrality or selected at random. This highlights how partial degradation of high-tonnage hubs can produce disproportionately large functional losses. The findings emphasize the need for resilience strategies that go beyond network topology to incorporate freight flow dynamics.

42 ENGINEERING↗
DOE OSTI2023

The Role of Hydropower Flexibility in Integrating Renewables in a Low-Carbon Grid

In this study, we looked at the role of hydropower flexibility in variable renewable energy (VRE) integration. Our focus was on facilities that have at least ten hours of storage, i.e., plants that are "dispatchable" and have capacity values similar to that of fossil-fired generation. For our modeling framework, we used NREL's Mid-Case/95% Carbon Reduction by 2035 standard scenario as our foundation. This zonal model is representative of the buildout of a near-zero carbon policy grid in an otherwise "business-as-usual" policy and economic environment. We chose this scenario because it aligns well with the Administration's net-zero carbon grid by 2035 goal.

13 HYDRO ENERGY↗