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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

System Analysis Modeling and Intermodal Transportation for Commercial Spent Nuclear Fuel

The United States Department of Energy (DOE) Office of Nuclear Energy is applying knowledge and understanding in the areas of systems level engineering, analysis, and decision making to better inform the waste management pathways for U.S. spent nuclear fuel (SNF). Currently there are 93 operating and 23 shutdown commercial nuclear reactors in the United States. SNF at most of these locations is being stored in spent fuel pools, dry storage, or both [1, 2]. This paper initially goes over the basics of the agent-based simulation tool known as the Next Generation System Analysis Model (NGSAM) that has the capability to model interaction and movements of individual components or groups like casks, railcars etc. The next section covers some of the methods implemented in the Java Transportation Operations Model (JTOM) to facilitate the movement of assemblies, casks, railcars etc. and the various intermodal transfer options. This section also covers the current cask loading and intermodal transfer times implemented in NGSAM. The last section goes over the proposed values for intermodal transportation, and cask transfer/loading operations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Decarbonizing Building Thermal Systems: A How-to Guide for Heat Pump Systems and Beyond

Buildings account for a substantial portion of carbon emissions, primarily due to the widespread use of fossil fuels in heating systems. Decarbonization of heating is essential to meet climate targets and reduce the environmental impact of buildings. Heat pumps are capable of leveraging renewable energy sources and can provide heating and cooling in an energy-efficient manner. By leveraging heat pump technology, buildings can significantly reduce their carbon footprint, minimize energy consumption, and decrease their reliance on fossil fuels. The design and construction community plays a pivotal role in facilitating the transition to heat pump systems for heating and cooling. However, this transition requires specialized knowledge and expertise. This resource was developed for architects, engineers, and contractors in response to an industry need for a comprehensive technical resource that guides them through the intricacies of heat pump system design, installation, and maintenance. This resource provides detailed information on system sizing, selection of appropriate heat systems, heat sources, and integration with existing building systems. Moreover, it emphasizes best practices for ensuring operational efficiency, system longevity, and reliability. The development of this recourse was a collaborative effort between NREL/DOE Better Buildings Design and Construction Allies and ASHRAE Task Force for Building Decarbonization. This resource is composed of two complimentary portions that will be completed and released on separate time frames. The first portion will be completed and released in 2023, while the second portion will be released in 2024.

building thermal systems↗
DOE OSTI2026

Plant Design for a Developing Bioeconomy Workshop Report: Frontier Science for the Bioeconomy Workshop Series

Recent advances in fundamental plant biology research, synthetic biology, and artificial intelligence (AI) are unlocking powerful new capabilities in plant biodesign, offering unprecedented potential to reimagine plants as programmable platforms for resource-efficient production of bioenergy, biomaterials, chemicals, and more. The U.S. Department of Energy (DOE) convened the Plant Design for a Developing Bioeconomy virtual workshop on March 12 through 14, 2025, to bring together leaders across plant science, engineering, and computation to assess the current landscape and define a bold vision for future research. Discussions during the workshop built upon findings included in DOE’s Biological and Environmental Research (BER) workshop report Overcoming Barriers in Plant Transformation: A Focus on Bioenergy Crops (U.S. DOE 2024; genomicscience. energy.gov/plant-transformation). Participants identified critical knowledge gaps, technical barriers, and emerging opportunities in the design and engineering of plant systems to support a robust, resilient domestic bioeconomy aligned with DOE’s mission.

09 BIOMASS FUELS↗
DOE OSTI2018

An Open-Access Nuclear Knowledge and Innovation Toolkit to Make Use of Nuclear World and Increase Collaboration

Nuclear engineering began as an urgent weapons development program in the early 1940s, involving work in enrichment, reactor design, shielding design, and isotope production. This work led to investigations of peaceful uses of nuclear technology, including nuclear power production and applications of radiation. Such peaceful technologies, often derived from experience gained through the weapons program, often maintained some level or attitude of secrecy. As the field continued to develop, that secrecy led to different specializations developing with different sets of terminology and methods of computation. For example, radiation safety used buildup and shielding factors while reactor designers use diffusivity and cross sections. Impressive amounts of work went into theory and application development in each sub-field over the next few decades, with much of the work left without adequate documentation or with important documentation made only within the organization. This situation has led to a loss of knowledge and experience, a lack of collaboration across nuclear sub-fields, and immense amounts of time and effort spent on redundant work. This paper proposes an open-access system that combines preserving and passing on the knowledge in all nuclear sub-fields with tools to make that knowledge useful, referred to as the Nuclear Knowledge and Innovation Toolkit (NuKIT). Knowledge would be captured in user-entered and expert-verified databases of reactors, applications of radiation, isotope production methods, and other pertinent nuclear information complete with technical information and tags that can be used to organize and investigate the data. Tools include community tools like forums and the ability to create collaborative projects; visualization and presentation tools; and an online programming system that allows code sharing, collaborative editing, database manipulation, and integration with all other tools. Finally, NuKIT is envisioned to include commercialization tools to allow freelance work and to compensate data entry/moderation work to help encourage participation. Eventually, integration with industry-standard tools like MCNP, SCALE, etc. is expected. This paper will present some proposed work in the context of its use for isotopes and radiation application, as well as possible work to be done in the context of the Combined Materials Experimental Toolkit (CoMET) being developed by the Nuclear Science User Facilities (NSUF) program. It should be noted that any work done towards this system, by any group, will advance the full system. It should also be noted that similar work previously done can be integrated into NuKIT.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Hardware Fuzzing with An Emulator

Bugs in digital logic have led to some significant security vulnerabilities. Hardware bugs are particularly troublesome since they cannot be easily patched. Additionally, if the bug is in the root of trust, all trust built upon it can be vulnerable. Traditional testing either require a deep knowledge of the system, creative attack vectors and lots of human interaction. This is not scalable as there are very few engineers that can wear the hat of a designer, a verification engineer, and a cybersecurity expert. Hardware fuzzing is a relatively new research area in dynamic hardware testing. It has proven to be an effective method for discovering bugs, unexpected behaviors, and security vulnerabilities in software. While hardware fuzzing is new to the hardware domain, it has a strong track record in software testing. Fuzzing is a testing technique that randomly mutates the input data to uncover bugs or vulnerabilities in the design. It is especially good at finding corner cases that test engineers can not envision. Another advantage over other dynamic testing techniques is that, if done well, deep knowledge of the design is not required. Additionally, fuzzing scales well. If the system is set up correctly, it can run unsupervised for weeks if necessary. In this work, we propose using hardware fuzzing to improve the input vector generation for an information flow tracking tool. To get reasonable throughput of test vectors, an emulator is targeted as the execution platform. Efficient emulator execution has some specific requirements.

42 ENGINEERING↗
DOE OSTI2023

Bio-fuel effects and seasonal fuel property variations related to abnormal combustion occurrences observed in the field

Since 2015, the automotive industry has regularly observed sharp increases in customer complaints of poor driveability and increased warranty claims during the months of September and October. These persistent seasonal complaint increases have been tracked to occur from increased abnormal combustion events that coincide with the seasonal transition from summer to winter fuel volatility, beginning on September 15th annually. The Reid Vapor Pressure (RVP) of gasoline during this seasonal transition has been found to be the primary property attributed to this annual issue. It’s hypothesized that an increase in volatile butanes and pentanes from advanced petroleum recovery techniques and hydrofracking production has hastened the transition from summer to winter fuel volatility in the marketplace, rather than the gradual increase of the RVP over time as historically intended. At worst, mid-winter fuel, with an RVP ranging from 12 to 16 psi, depending on location in the U.S., enters the market while ambient temperatures can still be above average for the winter season. This combination of high volatility fuel with unseasonably warm weather will increase fuel system flash boiling, injector spray cavitation, and spray collapse inside the engine’s combustion chamber, ultimately linking fuel property variations to abnormal engine combustion processes. This CRADA program will investigate these known knowledge gaps by coupling with two national laboratories, Oak Ridge National Laboratory, and the National Renewable Energy Laboratory to employ both unique tools and expertise throughout the industry to provide the insight, knowledge, and data critical to understand the observed interactions. Overall findings of this work are that fuel wall retention increased SPI proportionally, and fuels with an increased distillation (i.e. energy fraction) above the wall temperature increased fuel retention. Likewise fuels with increased enthalpy of vaporization (HoV) increased fuel retention proportional to the temperature reduction from the HoV (~13K reduction in this work). Thus, fuel with increased HoV and increased fuel distillation above the linear temperature also exhibited increased SPI rates. Fuel with high RVP did exhibit increased fuel spray collapse form spray imaging measurements, but in SPI testing the high volatility of these fuels at the tested conditions did not necessarily increase SPI rates despite exhibiting increased spray collapse in injector imaging and increased spray-wall impingement measured in fired engine testing. It was also found that spray imaging showed collapse was possible at throttled conditions, but this was not relevant to high boost pressures relevant to SPI conditions, however, under transient load and or speed encountered in real-world operation fuel spray collapse could affect fuel retention and real-world SPI that were not probed in the automated high load test sequence of this work.

09 BIOMASS FUELS↗
DOE OSTI2020

Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development

The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies. Classic machine learning and artificial intelligence techniques cannot provide a full scientific understanding of the inner workings of the underlying models. This raises credibility issues due to the lack of transparency and generalizability. Explainable artificial intelligence is an emerging approach for promoting credibility, accountability, and trust in mission-critical areas such as medicine by combining machine-learning approaches with explanatory techniques that explicitly show what the decision criteria are and why (or how) they have been established. Hence, thinking about how machine learning could benefit from knowledge graphs that combine “common sense” knowledge as well as semantic reasoning and causality models is a potential solution to this problem. In this study, we aimed to leverage explainable artificial intelligence, and propose a proof-of-concept prototype for a knowledge-driven evidence-based recommendation system to improve mental health surveillance. We used concepts from an ontology that we have developed to build and train a question-answering agent using the Google DialogFlow engine. In addition to the question-answering agent, the initial prototype includes knowledge graph generation and recommendation components that leverage third-party graph technology. To showcase the framework functionalities, we here present a prototype design and demonstrate the main features through four use case scenarios motivated by an initiative currently implemented at a children’s hospital in Memphis, Tennessee. Ongoing development of the prototype requires implementing an optimization algorithm of the recommendations, incorporating a privacy layer through a personal health library, and conducting a clinical trial to assess both usability and usefulness of the implementation. This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners’ ability to provide explanations for the decisions they make.

adverse childhood experiences↗
DOE OSTI2015

A Multicomponent Blend as a Diesel Fuel Surrogate for Compression Ignition Engine Applications

A mixture of n-dodecane and m-xylene is investigated as a diesel fuel surrogate for compression ignition engine applications. Compared to neat n-dodecane, this binary mixture is more representative of diesel fuel because it contains an alkyl-benzene which represents an important chemical class present in diesel fuels. A detailed multi-component mechanism for n-dodecane and m-xylene was developed by combining a previously developed n-dodecane mechanism with a recently developed mechanism for xylenes. The xylene mechanism is shown to reproduce experimental ignition data from a rapid compression machine and shock tube, speciation data from the jet stirred reactor and flame speed data. This combined mechanism was validated by comparing predictions from the model with experimental data for ignition in shock tubes and for reactivity in a flow reactor. The combined mechanism, consisting of 2885 species and 11754 reactions, was reduced to a skeletal mechanism consisting 163 species and 887 reactions for 3D diesel engine simulations. The mechanism reduction was performed using directed relation graph (DRG) with expert knowledge (DRG-X) and DRG-aided sensitivity analysis (DRGASA) at a fixed fuel composition of 77% of n-dodecane and 23% m-xylene by volume. The sample space for the reduction covered pressure of 1 – 80 bar, equivalence ratio of 0.5 – 2.0, and initial temperature of 700 – 1600 K for ignition. The skeletal mechanism was compared with the detailed mechanism for ignition and flow reactor predictions. Finally, the skeletal mechanism was validated against a spray flame dataset under diesel engine conditions documented on the Engine Combustion Network (ECN) website. These multidimensional simulations were performed using a Representative Interactive Flame (RIF) turbulent combustion model. In conclusion, encouraging results were obtained compared to the experiments with regards to the predictions of ignition delay and lift-off length at different ambient temperatures.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2017

A kinetic modeling study on octane rating and fuel sensitivity under HCCI conditions

With the development of advanced compression ignition engines such as the homogeneous charge compression ignition (HCCI) and the reactivity controlled compression ignition (RCCI), the long existing research and motor octane rating for fuels, i.e., RON and MON, might be insufficient to describe the fuel reactivity and performance with these new combustion strategies due to their distinctive engine operation conditions. It is also noted that with the stringent regulation for CO2 emission, a detailed knowledge of fuel reactivity is urgently needed for the selection of novel alternative bio-fuels for transportation, and optimization of their engine performance in the future. It is therefore very important to understand the reactivity and sensitivity for general classes of fuels as a function of the operating conditions in these advanced combustion engines. In this modeling study, using validated kinetic mechanism for ignition, we have simulated the performance of different classes of fuels including hydrogen, alkanes (straight and branched), alcohols and potential bio-fuels under typical HCCI engine conditions using detailed chemical kinetics. The crank angle corresponding to 50% total heat release (CA50) is utilized as an indicator for the fuel reactivity. Iso-contours of CA50 are then plotted in the engine operating parameter coordinate, such as the intake temperature, and pressure. It is shown that CA50 value can vary significantly with different engine operating conditions, and that fuels with the same RON/MON even exhibit qualitatively different CA50 iso-contours in specific operating regimes, implying differences in the intrinsic reactivity of different classes of fuels, as well as the insufficiency of RON/MON as comprehensive metrics of fuel reactivity in advanced compression ignition (ACI) strategies. In addition, the operating regime with high and low sensitivity, as well as potential non-monotonic change in fuel reactivity, has been identified by the CA50 iso-contour map for all studied fuels.

Wu, Tong↗
DOE OSTI2019

Two-stage ignition behavior and octane sensitivity of toluene reference fuels as gasoline surrogate

Current approaches to improve the efficiency of Spark-Ignition (SI) gasoline engines have been focusing on turbocharging, increasing the compression ratio, and pursuing advanced low-temperature combustion concepts. In order to maximize these strategies, it is important to optimize the knock resistance of the fuel, and therefore knowledge of the sensitivity of the ignition process under a wide range of engine operating conditions is required. Octane sensitivity (OS), which is defined as the difference between Research Octane Number (RON) and Motored Octane Number (MON), has been introduced to represent how fuel's ignition reactivity changes relative to the primary reference fuels (n-heptane/iso-octane) within RON/MON conditions. Previous works have indicated that OS is intimately related to low temperature reactivity of the fuel, which can be revealed as two-stage heat release characteristics during an ignition event. Prompted by these findings, in this paper, we investigate the relationship between two-stage ignition behavior and OS, using chemical kinetic simulations of 24 Toluene Reference Fuels (TRFs)/ethanol blends. TRFs are ternary mixtures of n-heptane/iso-octane/toluene, which is capable of capturing aromatic content and positive values of OS of real gasoline fuels. Simulation results show that fuels with weak or no two-stage ignition behavior tend to have high OS, due to their lack of Negative Temperature Coefficient (NTC) effect and high sensitivity in ignition delay time. Leveraging such observations, we develop a correlation between two-stage behavior and OS as an OS prediction method. Two metrics that represent the strength of the two-stage ignition behavior are proposed and used as OS predictors, which are Low Temperature Heat Release percentage (LTHR%) and Heat Release Rate at the end of first stage (HRR inf ) calculated from a simple kinetic simulation. Regression analysis shows a clear trend between decreases in the proposed two-stage behavior metrics and increases in the value of OS of the fuel. We also test the new metric (LTHR%) using simulation results of 0-D reactors with imposed pressure time histories obtained from engine experiments, as well as using different TRF kinetic mechanisms. Here, the results demonstrate the effectiveness of the metric as a representation of the two-stage ignition behavior in practical combustion systems, highlighting the importance of the proposed relationship, and its potential as a simple and effective OS predictor.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Multicycle large-eddy simulations of a direct-injection hydrogen-fueled optical engine

Hydrogen (H 2 ) is a carbon-free chemical energy carrier and one promising solution for achieving effective decarbonization of the transportation sector, particularly for internal combustion engines (ICEs). With a focus on ICEs, and compared to port-fuel injection, direct injection (DI) of gaseous H 2 during the compression stroke offers potential advantages, which include backfire avoidance and reduction of preignition occurrence. In these last two decades, much research, experimental and numerical, has been devoted to understanding H 2 's mixing and combustion processes in ICEs. Computational fluid dynamics modeling efforts commonly rely on unsteady Reynolds-averaged Navier Stokes (URANS) turbulence frameworks, mostly due to their computational affordability. However, many authors have pointed out the opportunity to perform large-eddy simulations (LESs) to investigate the cyclic variability of H 2 engines and assess potential advantages of using LES in place of URANS, especially for lean operation. This study addresses this knowledge gap and presents a computational fluid dynamics (CFD) study of the H 2 DI process in an optical engine operating at relatively low tumble conditions, using multicycle LESs. In conclusion, the manuscript presents a thorough validation of the results against experimental data available from the literature as well as direct comparison with URANS, demonstrating the feasibility of multicycle LESs for CFD modeling of DI H 2 -fueled ICEs.

Direct injection↗
DOE OSTI2024

Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production

Identifying gene targets for enhancing metabolite production in metabolic engineering is challenging due to the vast research literature and the approximation in genome-scale metabolic model (GEM) simulations. Here, to address this, we propose the Gene-Metabolite Association Prediction task, which automates gene discovery for given metabolite-gene pairs, accompanied by a benchmark dataset of 2474 metabolites and 1947 genes for Saccharomyces cerevisiae (SC) and Issatchenkia orientalis (IO). This task is complicated by incomplete metabolic graphs and metabolic heterogeneity. We introduce an Interactive Knowledge Transfer mechanism based on Metabolism Graphs (IKT4Meta) to enhance prediction accuracy by integrating cross-metabolism knowledge. Using Pretrained Language Models (PLMs) to generate inter-graph links mitigates heterogeneity issues, while intra-graph links are propagated via these anchors. Gene-metabolite predictions are then performed on the enriched graphs integrating multiple microorganisms’ knowledge. Experiments show that IKT4Meta outperforms baselines by up to 12.3% in link prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Aluminum Clad Spent Nuclear Fuel Long Term Dry Storage Technical Issues Action Plan - Technical and Engineering Activities

The DOE Spent Nuclear Fuel Working Group (SNFWG)’s report on technical considerations and challenges for extended dry storage of aluminum-clad spent nuclear fuel (ASNF) entitled, Aluminum-Clad Spent Nuclear Fuel: Technical Consideration and Challenges for Extended (>50 Years) Dry Storage, DOE/ID RPT-1575, June 2017 identified five knowledge gaps and technical data needs. This DOE SNFWG report also made several recommendations including one to develop an action plan to identify needed technical and engineering activities and analyses to address the identified knowledge gaps and technical data needs. A team composed of twenty SNF management experts and appropriate subject matter experts from the Idaho National Laboratory and Savannah River National Laboratory was assembled to develop this action plan. The action plan identifies and recommends six technical and engineering tasks and estimated cost and schedule for task completion. It is estimated that $5M and 18 months will be required to execute the recommended tasks.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

The Brachypodium ENCODE Project – from sequence to function: Predicting physiological responses in grasses to facilitate engineering of biofuel crops

The overarching goal of this proposal was to gain deep knowledge of chromatin dynamics and gene networks related to stress responses in grasses. To accomplish this goal our Brachypodium ENCODE project (1) coupled drought-focused physiological analysis with multiple layers of ENCODE ‘omics’ profiling; (2) obtained fundamental information about the Brachypodium transcriptome, epigenome, chromatin dynamics, and gene regulatory networks; and (3) elucidated the regulatory dynamics of gene networks that can be manipulated to increase energy grass crop productivity.

09 BIOMASS FUELS↗
DOE OSTI2023

Convoluted filtering for process cycle modeling

Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process-structure–property-performance relationships hidden in materials data. The process-structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process-structure relations. In this work, a novel convoluted model-filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe-based alloy heat treatment cycles. The artifacts were pre-trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mechanism of Lithium Ion Exchange Into Spinel Manganese Oxide

Lithium manganese oxide (LMO) is an effective absorbent for lithium recovery from brines, characterized by its high adsorption capacity, excellent regeneration performance, and selectivity. Here, this study investigates the mechanisms of material degradation during lithium loading and unloading in LMO, employing a combination of time‐resolved Raman spectroscopy, X‐ray diffraction, and X‐ray photoelectron spectroscopy. Key findings reveal that the A 1g vibrational mode wavenumber shifts from 635 cm −1 at the onset of lithium ion exchange to 656 cm −1 after 12 min, indicating local symmetry loss and manganese (Mn) loss during lithium intercalation. The proposed lithium exchange mechanism involves initial occupation of tetrahedral interstitial voids, followed by migration into interstitial sites between Mn octahedral sites. Computational modeling suggests that the loss of symmetry in MnO cubane‐like groups occurs as lithium migrates, significantly affecting the Raman spectra. Importantly, the study demonstrates that after 100 cycles of lithium/hydrogen loading, a 24% loss in Mn is observed, which correlates with decreased structural stability; however, the structural integrity of LMO is enhanced when subjected to multiple cycles without complete loading. Here, these insights contribute to the understanding of LMO's performance in lithium recovery applications and highlight potential strategies to optimize its use in future technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Towards generic memory forensic framework for programmable logic controllers

A Programmable Logic Controller (PLC) is a microprocessor-based controller that is used to automate physical processes in critical infrastructure and various other industries and manufacturing sectors. Initially, PLCs were completely isolated from the Internet, and cyber security was not incorporated at the time of development. The introduction of industry 4.0 and the evolution of ICS systems to communicate over public IP addresses from the Internet enhanced productivity and efficiency, but Internet connectivity exposed the systems and their vulnerabilities, which led to an increase in cyber attacks. When a system is sabotaged/compromised, security analysts need to get to the root cause of the attack as quickly as possible to recover the system. To do so, memory forensic analysis is critical to provide a unique insight into the run-time memory activities and extract a reliable source of evidence. In this paper, we analyze the memory structure of the Schneider Electric Modicon M221 PLC. To build a memory profile, we reverse engineer the communication protocol and conduct differential analysis to gain knowledge about the structure of the memory and the low-level representation of control logic instructions. We then identify dynamic and static memory regions by modifying different project fields and conducting differential analysis, which allows us to identify boundaries of critical memory structures and extract important forensic artifacts that can be found in the memory. The Python implementation of the memory profile can help reduce the time and effort required for manual analysis in case of cyber incident or system failure.

97 MATHEMATICS AND COMPUTING↗