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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Assessing the life-cycle environmental impacts of the wood pallet sector in the United States

Wood pallets play a critical role in the movement and storage of goods worldwide. They are an important component in the complex global supply chain and used by almost every industry. It is therefore important to assess the environmental implications of the wood pallet supply chain and identify optimization strategies that can be implemented. In this paper, primary 2018 annual production data collected from U.S. pallet manufacturers were used to develop the first industry-average life-cycle inventory (LCI). A new functional unit was proposed to perform a more refined and accurate environmental life-cycle assessment of the wood pallet supply chain. Using the LCI data developed, a cradle-to-grave industry-average life-cycle impact assessment was performed. This novel approach quantifies environmental impacts of a generic multi-use pallet, including repair and remanufacturing. The total global warming impact was 10.4 kg CO 2 e per 45.4 t of pallet loads of product delivered using wood pallets. The manufacturing stage contributed the most, about 35%, followed by the raw material supply stage. About 41% of total primary energy consumption was from renewable sources, with most sourced from biomass. Fossil fuels comprised about 52% of the total (225 MJ per functional unit) primary energy consumption. Total environmental impact was significantly affected by two main parameters: reference service life and load-bearing capacity. Pallet repair was also found to be an important component of the wood pallet supply chain, which has a low environmental footprint compared with the overall impact of a pallet and enables mitigation of overall impact by extending the reference service life. Finally, at end-of-life, common industry practices demonstrated substantial potential environmental benefits that can minimize overall environmental impact.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

An Intermediate-Scale Version of the Volturnus + Floating Offshore Wind Turbine Platform Concept in a Real Ocean Environment with an Operating Turbine off the Coast of Maine (Final Scientific/Technical Report)

This project was undertaken to advance the technical readiness and commercial viability of a next-generation, industrialized concrete floating foundation for offshore wind turbines called VolturnUS +. The design objective is to deliver a platform that is lower cost, faster to manufacture, simpler to deploy, and optimized for domestic supply chains and local workforce participation. To enable project financing and commercial adoption, an at-sea demonstration under representative operating conditions was required and therefore this project aimed to deploy a ¼-scale VolturnUS+ prototype offshore the Coast of Maine.

17 WIND ENERGY↗
DOE OSTI2020

Optimization of harvest and logistics for multiple lignocellulosic biomass feedstocks in the northeastern United States

A mixed-integer linear programming model was developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $\$67.90$ to $\$86.97$ per dry Mg with an average of $\$79.58$ /dry Mg. The biomass delivered costs by county were from $\$67.90$ to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $\$85.30$ /dry Mg for forest residues, $\$84.47$ /dry Mg for hybrid willow, $\$99.68$ for switchgrass and $\$97.87$ per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Lastly, our results showed that procurement radius, facility capacity, and forest residue availability were the most sensitive factors affecting the biomass delivered costs.

09 BIOMASS FUELS↗
DOE OSTI2025

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗
DOE OSTI2025

Game theoretic modeling and optimization of competition and collaboration in dual channel electronic waste supply chains

The rapid growth of electronic waste (e-waste) presents critical challenges for sustainable resource recovery and environmental protection. This study develops a dual-channel closed-loop supply chain (CLSC) model formulated as a hierarchical Stackelberg game, that integrates dynamic pricing and cost-sharing mechanisms to optimize both economic and environmental outcomes. The model explicitly captures strategic interactions between manufacturer-led and third-party recycling channels, accounting for consumer behavior, regulatory incentives, and market competition. Numerical simulations conducted (implemented over a four-iteration horizon using a commercial optimization solver) show that, relative to the baseline equilibrium, manufacturer profit increases from 11.6 thousand USD to 37.9 thousand USD (+226.8%), total recycled volume rises from 7,848 to 7,942 units (+1.2%), and collector profit nearly doubles under cost-sharing, enabling more equitable profit distribution. Furthermore, scenario-based simulations across Sub-Saharan Africa, high-income economies, and emerging Asian industrial countries reveal that infrastructure quality, policy intensity, and labor costs critically shape recycling efficiency and profit allocation. These findings demonstrate that subsidies alone are insufficient to ensure system efficiency. Instead, coordinated strategies that integrate internal incentive alignment with context-sensitive policy support are required. Overall, this study offers a robust framework for designing resilient, efficient, and regionally adaptable e-waste management systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Next-Generation Reverse Logistics Networks of Photovoltaic Recycling: Perspectives and Challenges

With the growing adoption of solar energy as a key component of the global energy transition and its new industrial policy (the Inflation Reduction Act and others), the United States is witnessing a significant increase in solar investments. This surge in installations and domestic and reshored manufacturing of solar photovoltaic (PV) panels brings with it a pressing issue: the proper management of end-of-life (EoL) PV panels. As these panels are decommissioned, either due to reaching the end of their lifespans or due to breakage across the various stages of the forward supply chain, it becomes crucial to establish efficient reverse supply chain logistics systems to address the challenges associated with their disposal, while also unlocking the value of the inherent materials that are of critical value for other forward supply chains. This perspective article examines the challenges regarding EoL PV panels and relevant reverse supply chain and logistics networks, and proposes future research directions based on the gaps observed among academic research, industry, and policy-making challenges. We identify the main bottlenecks and hurdles including, among others, the lack of supportive regulations and absence of structured, optimized recycling infrastructure. To this end, it is proposed that the key to achieving a sustainable reverse supply chain network for solar PV panels lies in relentless end-to-end supply chain cost optimization efforts supported by enabling policies. Moreover, it is proposed that designing systematic decision-making modeling frameworks is vital for examining different possibilities and scenarios for state, region or nation-wide optimization of solar PV reverse supply chain networks. Indicative to this effect, we discuss the development of a Resource-Task-Network (RTN)-based model and demonstrate its application and benefits through a case study. We wrap up with conclusions and future research directions.

circular economy↗
DOE OSTI2026

A framework and tool for designing cost-effective, resilient, and circular net-zero supply chains under uncertainty with an application to multilayer plastic films

While 55% of Fortune 500 companies have committed to achieving net-zero emissions and/or zero-waste operations by 2035, only 2% are currently on track, revealing a critical gap between ambition and action. Designing supply chains that reduce both emissions and waste is a complex non-intuitive, multi-objective challenge, compounded by the high costs of new technologies and the need for resilient, profitable solutions. This paper aims to address this challenge by presenting a generic framework and multi-objective optimization formulation for designing cost-effective, circular, and resilient supply chains under uncertainty, implemented through a user-friendly decision-support tool with intuitive data visualization capabilities, enabling communication of results to both technical and non-technical stakeholders. We demonstrate the application of this framework in the context of multilayer plastic films (barrier films), which are widely used in food packaging and composite materials. The model quantifies trade-offs across three objectives: minimizing global warming potential, maximizing circularity, and minimizing cost. A key contribution of this work is the explicit modeling of technological resilience, the ability of supply chains to maintain function under disruption. In the cost-minimization case, the resilience constraint makes the design approximately three times more expensive in the short-term metric, but shifts the system from relying on a single recovery pathway to a portfolio of four recovery pathways, improving the robustness of the optimization solution under uncertainty. Lastly, we introduce TranZero, a decision-support tool that integrates material flow analysis, hotspot identification, and optimization-based scenario planning to support net-zero and circularity decisions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Analysis of Infrastructures for Processing Plastic Waste using Pyrolysis-Based Chemical Upcycling Pathways

Modern mechanical recycling infrastructure for plastic is capable of processing only a small subset of waste plastics, reinforcing the need for parallel disposal methods such as landfilling and incineration. Emerging pyrolysis-based chemical technologies can "upcycle" plastic waste into high-value polymer and chemical products and process a broader range of waste plastics. In this work, we study the economic and environmental benefits of deploying an upcycling infrastructure in the continental United States for producing low-density polyethylene (LDPE) and polypropylene (PP) from post-consumer mixed plastic waste. Our analysis aims to determine the market size that the infrastructure can create, the degree of circularity that it can achieve, the prices for waste and derived products it can propagate, and the environmental benefits of diverting plastic waste from landfill and incineration facilities it can produce. We apply a computational framework that integrates techno-economic analysis, life cycle assessment, and value chain optimization. Our results demonstrate that the infrastructure generates an economy of nearly 20 billion USD and positive prices for plastic waste, opening opportunities for compensation to residents who provide plastic waste. Our analysis also indicates that the infrastructure can achieve a plastic-to-plastic degree of circularity of 34% and remains viable under various external factors (including technology efficiencies, capital investment budgets, and polymer market values). Finally, we present significant environmental benefits of upcycling over alternative landfill and incineration waste disposal methods, and comment on ongoing work expanding our modeling methodology to other chemical upcycling pathway case studies, including hydroformylation of specific plastics to chemicals.

Interdisciplinary↗
DOE OSTI2020

Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A framework and calculator for evaluating the impacts of shelf life extension and other food loss and waste reduction technologies

Optimization of the food supply chain (FSC) depends on reducing food waste, especially at the consumer stage, where a substantial portion of food is not eaten, but instead disposed of via landfill, incineration, or in-sink disposals. One key strategy is to increase the time that consumers have before food goes bad or expires. This study developed a framework to assess the efficacy of shelf-life extension (SLE) technologies for mitigating food loss and waste (FLW), such as packaging improvements. The impact flows through the entire FSC, reducing FLW, energy use, and other inputs at each stage. The framework and resulting calculator can be used to evaluate the impact of FLW reduction at any stage for any food commodity. As shown by two SLE cases, the calculator is valuable for policy-makers, government entities, and professionals, specifically those in marketing, business development, and capital projects teams, to comprehensively evaluate the impacts of FLW reduction technologies and practices. The framework and calculator are sensitive to the shape of the consumption curve, the fraction of inedible waste, and the current shelf life. The calculator was used to assess the impacts of the United States goal of reducing food waste by consumers through various SLE lengths. It was found that uptake of several near-ready-to-deploy SLE technologies would reduce annual food production demand by about 10–19 MMT and supply chain energy consumption by 240–410 PJ in the United States.

Food loss and waste (FLW)↗
DOE OSTI2022

Integrating tactical planning, operational planning and scheduling using data-driven feasibility analysis

Supply chain operations and scheduling are well-studied problems in the literature. Although these problems are related, they are often solved sequentially. This uncoordinated approach usually leads to sub-optimal solutions. Here this paper proposes a methodology for integrating scheduling operations into the supply chain network, motivated by the available enterprise data, and feasibility analysis. Existing literature on integrated models for addressing the supply chain is reviewed and classified. Then the mathematical model formulation for each level is discussed, after which the proposed data-driven integrated framework is described. The methodology is tested on two different case studies of varying dimensions. For each case study, the solution solves a multi-objective problem with the overall aim of an optimal solution that is robust towards achieving optimal cost while keeping customer satisfaction in mind.

42 ENGINEERING↗
DOE OSTI2026

Opportunities and challenges for the expansion of LFP battery supply chains

Global markets for energy storage are growing rapidly, with some applications transitioning from traditional LiNi x Mn y Co 1−x−y O 2 (NMC) toward LiFePO 4 (LFP) due to cost, safety, and performance advantages. Battery growth has seeded interest in critical material supplies such as high-purity lithium precursors. In contrast, challenges in securing high-purity iron and phosphorus, historically not considered critical materials, are often overlooked. Precursors must remain inexpensive to maintain LFP's current cost advantage, which leverages the low-cost (∼$\$100$ per t) FeSO 4 byproduct from titanium dioxide manufacturing and will not be available as LFP manufacturing expands. As an alternative, iron is mined primarily for steel manufacturing, for which the existing supply chain and beneficiation process is optimized. LFP batteries require small iron volumes compared to steel (0.11%), but profit margins associated with existing low-cost iron ores and high costs (∼$27 000 per t) associated with low volume high-purity iron oxides may limit interest in manufacturing small volumes of high-purity, specialized iron battery precursors. The complementary LFP precursor, phosphoric acid (H 3 PO 4 ), is primarily utilized in fertilizers. Of the current phosphate ore demand for fertilizers, 4–22.8% would be required to meet projected 2045 LFP H 3 PO 4 demand, suggesting significant supply chain planning is needed to achieve projected demand. If only higher-grade material is considered, demand jumps to 15–77% of current world production. While lithium precursor purity requirements have been evaluated (Li 2 CO 3 is commonly defined as ≥99.5%), “battery grade” iron and phosphorus precursors remain poorly defined, with no internationally accessible and widely adopted standard, further challenging expanding industry by creating manufacturing uncertainty and increasing potential costs.

25 ENERGY STORAGE↗
DOE OSTI2020

GaAsP/Si Tandem Solar Cells: Pathway to Low-Cost, High-Efficiency Photovoltaics

Si is the dominant PV technology, now and for the foreseeable future, due to its extensive manufacturing infrastructure, supply chain, feedstock availability, and highly optimized degree of fabrication processes, which altogether has produced an economic scenario where PV electricity generation is often cheaper than conventional fossil based generation. In many places, the overarching goal of grid parity has been achieved, but further improvement in performance-cost metrics are still needed to sustain the continued LCOE reductions needed to not only compete with conventional generation, but displace it on a global scale; a matter of critical importance if we stand any hope of slowing climate change. Nevertheless, single-junction Si PV is already nearing its physical limit, both in performance and cost, and is thus cannot meet these long-term goals alone. To this end, we are working on the development of monolithic III-V/Si tandem solar cells, which improve upon the performance of pure Si by providing enhanced utilization (reduced thermalization) of high-energy photons. This architecture nominally combines the substantial existing knowledge base, manufacturing infrastructure, and low cost of Si PV with the high efficiencies afforded by the well-established multijunction approach — the only proven way to break the single-junction limit. Although the metal-halide perovskite/Si tandem architecture has garnered substantial attention in recent years, serious questions regarding reliability and service lifetime remain, whereas III-V PV has a proven track record, including in the harsh concentrator and space environments. Additionally, there are multiple fabrication approaches to producing III-V/Si tandem cells, but we are focused on monolithic epitaxial integration as it is the most likely to yield the lowest ultimate LCOE in a fully mature, scaled technology. In this work we have produced multiple generations of GaAsP/Si tandem solar cells, demonstrating a more than 10% absolute AM1.5G efficiency improvement within the time frame of the project, including two verified world records. We have done this using industry-standard fabrication methods, showing that this platform can ultimately be manufactured at scale using existing or only slightly upgraded Si and III-V tooling. Our scientific and engineering advances across a range of fundamental and applied areas – III-V/Si heteroepitaxial integration, defect control in metamorphic III-V epitaxy, fundamental materials-oriented solar cell design and modeling methodology, and more – have created clear pathways for continued advances toward the goal of >30% AM1.5G cell efficiency (and >25% module) and will serve to inform the broader research community for well beyond this immediate application. Techno-economic modeling indicates that our approach can indeed meet SunShot/SETO LCOE targets, but as with any “post-Si” technology there are difficult, but not insurmountable barriers, requiring continued focused research and development efforts.

14 SOLAR ENERGY↗
DOE OSTI2025

OPTICHEM (OPTimizer for Industrial CHEMical pathways) [SWR-25-70]

Using alternative feedstocks such as biomass and waste could help the chemical sector address growing challenges from supply chain disruptions. This project aims to identify optimal combinations of chemical production pathways that achieve user-defined priorities within set resource constraints. This project contains two main Python modules that work together to perform multi-objective optimization of feedstock usage and post-optimization analysis. The outputs from both modules (e.g., CSV files, PDF diagrams, HTML reports, and pickle files) are automatically saved in dedicated output folders. The folder names include key parameters such as the optimization metric, target year, and whether 2030 results are fixed.

Ghosh, Tapajyoti [National Renewable Energy Labora↗
DOE OSTI2021

Data-driven optimization of mixed-integer bi-level multi-follower integrated planning and scheduling problems under demand uncertainty

The coordination of interconnected elements across the different layers of the supply chain is essential for all industrial processes and the key to optimal decision-making. Yet, the modeling and optimization of such interdependent systems are still burdensome. Here we address the simultaneous modeling and optimization of medium-term planning and short-term scheduling problems under demand uncertainty using mixed-integer bi-level multi-follower programming and data-driven optimization. Bi-level multi-follower programs model the natural hierarchy between different layers of supply chain management holistically, while scenario analysis and data-driven optimization allow us to retrieve the guaranteed feasible solutions of the integrated formulation under various demand considerations. We address the data-driven optimization of this challenging class of problems using the DOMINO framework, which was initially developed to solve single-leader single-follower bi-level optimization problems to guaranteed feasibility. This framework is extended to solve single-leader multi-follower stochastic formulations and its performance is characterized by well-known single and multi-product process scheduling case studies. Through our data-driven algorithmic approach, we present guaranteed feasible solutions to linear and nonlinear mixed-integer bi-level formulations of simultaneous planning and scheduling problems and further characterize the effects of the scheduling level complexity on the solution performance, which spans over several hundred continuous and binary variables, and thousands of constraints.

42 ENGINEERING↗
DOE OSTI2026

Biofuel Infrastructure, Logistics, and Transportation (BILT) Model for Supply Chain Analysis: Documentation for BILT

The Biofuel Infrastructure, Logistics, and Transportation (BILT) model for supply chain analysis developed by Oak Ridge National Laboratory for the US Department of Energy is an accounting and optimization model that tracks the entire biomass-to-end-use supply chain (e.g., biomass production, transportation, and processing) for potential feedstocks, pathways, and end uses. This report summarizes the development, function, and operation of the BILT model.

09 BIOMASS FUELS↗