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DOE OSTI2026

The need for standardization and improved open (meta)data practices in metaproteomics

Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatics stages hinder reproducibility and comparability, limiting integration with other omics data. We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices.

Armengaud, Jean [Universite Paris-Saclay, France]↗

DOE OSTI2023

CEBAF 22 GeV FFA energy upgrade

Extending the energy reach of CEBAF by increasing the number of recirculations, while using the existing linacs is explored. This energy upgrade is based on the multi-pass acceleration of electrons in a single non-scaling Fixed Field Alternating Gradient (FFA) beam line, using Halbach-style permanent magnets. Encouraged by the recent successful demonstration of CBETA, a proposal was formulated to nearly double the energy of CEBAF from 12 to 22~GeV by replacing the highest energy arcs with FFA transport. The new FFA arcs would support simultaneous transport of an additional 6 passes spanning roughly a factor of two in energy. One of the challenges of the multi-pass (11) linac optics is to assure uniform focusing over a wide range of energies. Here, we propose a triplet lattice that provides a stable periodic solution covering an energy ratio of 1:33. The current CEBAF injection at 123 MeV, makes optical matching in the first linac impossible due to the extremely high energy ratio (1:175). Replacement of the current injector with a 650 MeV recirculating injector will alleviate this issue. Orbital and optical matching from the FFA arcs to the linacs is implemented as a compact non-adiabatic insert. The design presented here is anticipated to deliver a 22 GeV beam with normalized emittance of 76 mm·mrad and a relative energy spread of 1×10\textsuperscript{-3}. Further recirculation beyond 22 GeV is limited by the large (974 MeV per electron) energy loss due to synchrotron radiation.

Benesch, J.↗

DOE OSTI2023

CEBAF 22 GeV FFA energy upgrade

Benesch, J.↗

DOE OSTI2026

Hydrogenolysis Versus Hydrocracking for Polyolefin Upcycling

Global plastic production has reached 413.8 million metric tons in 2024 and is forecasted to surpass 1.2 billion metric tons by 2050. Polyolefins, mainly polyethylene (PE) and polypropylene (PP), dominate single-use packaging and account for approximately 55% of global plastic waste. The chemical inertness that makes these materials desirable for commercial applications also renders them persistent in the environment. Current recycling technologies have proven to be insufficient to divert plastic waste from landfills or environmental loss due to technical limitations and poor economic incentives. Conventional mechanical recycling is a form of downcycling, in which the polymer remelting process results in products with inferior material properties and reduced market value. Pyrolysis, a thermochemical route used for the chemical recycling of plastic wastes into refinery feedstock, requires severe conditions, typically 400–600 °C in an oxygen-free environment. The high temperature drives up energy costs and produces a wide range of poorly defined products, including undesirable light gases and heavy tars that pose challenges for downstream processing. As an alternative, catalytic chemical recycling offers a promising route for converting waste polyolefins back into value-added hydrocarbons—such as fuels, lubricant base oils, and other chemical feedstocks—at temperatures typically below 300 °C. This opinion article focuses on two dominant pathways in heterogeneous catalysis that are used to cleave the C–C bonds of polyolefins: hydrogenolysis and hydrocracking. Both catalytic pathways have the potential to selectively convert polyolefin waste into valuable fuels and chemical feedstocks under mild conditions. Notably, while hydrocracking primarily yields branched hydrocarbon products, hydrogenolysis predominantly produces linear hydrocarbons. Here, we compare the mechanisms and catalyst designs for hydrocracking and hydrogenolysis, analyze critical technical challenges from catalyst stability to process engineering, and provide an outlook on how these complementary pathways can be used to repurpose plastic waste into valuable products.

Zhang, Ruoxi [Iowa State Univ., Ames, IA (United S↗

DOE OSTI2025

Chemistry of Materials Underpinning Photoelectrochemical Solar Fuel Production

Since its inception, photoelectrochemistry has sought to power the generation of fuels, particularly hydrogen, using energy from sunlight. Efficient and durable photoelectrodes, however, remain elusive. Here we review the current state of the art, focusing our discussion on advances in photoelectrodes made in the past decade. We open by briefly discussing fundamental photoelectrochemical concepts and implications for photoelectrode function. We next review a broad range of semiconductor photoelectrodes broken down by material class (oxides, nitrides, chalcogenides, and mature photovoltaic semiconductors), identifying intrinsic properties and discussing their influence on performance. We then identify innovative in situ and operando techniques to directly probe the photoelectrode|electrolyte interface, enabling direct assessment of structure–property relationships for catalytic surfaces in active reaction environments. We close by considering more complex photoelectrochemical fuel-forming reactions (carbon dioxide and nitrogen reduction, as well as alternative oxidation reactions), where product selectivity imposes additional criteria on electrochemical driving force and photoelectrode architecture. By contextualizing recent literature within a fundamental framework, we seek to provide direction for continued progress toward achieving efficient and stable fuel-forming photoelectrodes.

08 HYDROGEN↗

DOE OSTI2024

Upland Yedoma taliks are an unpredicted source of atmospheric methane

Landscape drying associated with permafrost thaw is expected to enhance microbial methane oxidation in arctic soils. Here we show that ice-rich, Yedoma permafrost deposits, comprising a disproportionately large fraction of pan-arctic soil carbon, present an alternate trajectory. Field and laboratory observations indicate that talik (perennially thawed soils in permafrost) development in unsaturated Yedoma uplands leads to unexpectedly large methane emissions (35–78 mg m -2 d -1 summer, 150–180 mg m -2 d -1 winter). Upland Yedoma talik emissions were nearly three times higher annually than northern-wetland emissions on an areal basis. Approximately 70% emissions occurred in winter, when surface-soil freezing abated methanotrophy, enhancing methane escape from the talik. Remote sensing and numerical modeling indicate the potential for widespread upland talik formation across the pan-arctic Yedoma domain during the 21 st and 22 nd centuries. Contrary to current climate model predictions, these findings imply a positive and much larger permafrost-methane-climate feedback for upland Yedoma.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

AN INITIAL ASSESSMENT OF THE CREEP-RUPTURE STRENGTHS FOR WELDMENTS WITH ALLOY 800H BASE METAL AND ALLOY 617 FILLER METAL

In Section III, Division 5 of the American Society of Mechanical Engineers Boiler and Pressure Vessel Code, Alloy 800H is qualified for elevated-temperature nuclear construction for temperatures up to 760°C (1,400°F) and a maximum service life of 300,000 hours. There are two permissible filler metals for Alloy 800H weldments specified in Division 5: ENiCrFe-2 (Alloy A) and ERNiCr-3 (Alloy 82). Low creep-rupture strengths of these weldments at the upper limits of the qualified temperatures and service lives may restrict the design envelope for elevated-temperature nuclear construction with Alloy 800H. As a result, an alternative filler metal is desired to improve the creep-rupture strengths of Alloy 800H weldments for the qualified temperatures and service lives. This work investigates an overmatched filler metal. Specifically, a weldment with Alloy 800H base metal and Alloy 617 filler metal fabricated by semiautomated gas tungsten arc welding is investigated. A scoping creep-rupture test program was conducted of cross-weld specimens at temperatures ranging from 750 to 1,000°C (1,292 to 1,832°F). Preliminary results on the creep-rupture strengths of the Alloy 800H weldment with Alloy 617 filler metal do not show significant improvement compared to the filler metals currently qualified in Division 5 for Alloy 800H weldments. Consequently, work is in progress to investigate a matching filler metal.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Status of CEBAF Upgrades

Extending CEBAF energy reach by increasing the number of recirculations, while using the existing SRF system is being explored. Proposed energy upgrade is based on a new approach to multi-pass acceleration of electrons in a single Fixed Field Alternating Gradient (FFA) beam line, configured with Halbach-style permanent magnets. Encouraged by recent success of CBETA, a proposal was formulated to nearly ?double? CEBAF energy by replacing the highest energy arcs with a pair of FFA arcs. The new non-scaling FFA arcs would support simultaneous transport of additional 6 passes with energies spanning a factor of two. One of the challenges of the multi-pass (11) linac optics is to assure uniform focusing in a vast range of energies, in a fixed field lattice. Here, we propose a triplet lattice that would provide a stable periodic solution covering energy ratio of 1:33. The current CEBAF injection at 123 MeV, makes optical matching in the first linac virtually impossible due to extremely high energy span ratio (1:175). Replacement of the current injector with a 650 MeV recirculating injector will alleviate that problem. Orbital and optical matching from the FFA arcs to the linacs is implemented as a compact non-adiabatic insert. Presented scheme would promise to deliver a 22 GeV beam with normalized emittance of 76 mm·mrad and with a relative energy spread of 1×10-3. Further recirculation beyond 22 GeV is limited by large, 974 MeV per electron, energy loss due to synchrotron radiation

Bogacz, Alex↗

DOE OSTI2023

22 GeV CEBAF with Novel Fixed Field Alternating Gradient Design

Extending the energy reach of CEBAF up to 22 GeV within the existing tunnel is being explored. Proposed energy upgrade can be achieved by increasing the number of recirculations, while using the existing CEBAF SRF cavity system. Presented scheme is based on an exciting new approach to acceleratie electrons efficiently with multiple passes in a single FFA beam line. Encouraged by recent success of the CBETA Test Accelerator, a proposal was formulated raise CEBAF energy by replacing the highest-energy arcs with Fixed Field Alternating Gradient (FFA) arcs. The new pair of arcs configured with FFA lattice would support simultaneous transport of additional 6 passes with energies spanning a factor of two, using the non-scaling FFA principle implemented with Halbach-derived permanent magnets - a novel magnet technology that significantly saves energy and lowers operating costs. One of the challenges of the multi-pass (11) linac optics is to provide uniform focusing in a vast range of energies, using fixed field lattice. Here, we propose a triplet lattice scaled up with increasing momentum along the linac. This would provide a stable periodic solution covering energy ratio of 1:33. The current CEBAF configured with a 123 MeV injector, makes optical matching in the first linac virtually impossible due to extremely high energy span ratio (1:175). Therefore, we envision replacement of the current injector with a 650 MeV 3-pass recirculating injector based on the existing LERF facility. Finally, the 22 GeV CEBAF would promise to deliver in 10-passes a beam with normalized emittance of 80 mm·mrad and with a relative energy spread of 1.5 × 10-3. Further recirculation beyond 22 GeV is limited by large, 974 MeV per electron, energy loss due to synchrotron radiation.

Bogacz, Slawomir↗

DOE OSTI2025

Securing the Modern Grid: Federal Investments, Digitization, and Supply Chain Strategy

Across the United States (U.S.) grid expansion and modernization is underway, paving the way for accelerated load growth and intelligent resource management. Digitization of the grid is supported by several state and federal programs, providing support for utilities installing advanced metering infrastructure (AMI), AI-powered analytics systems, battery energy storage systems (BESS), and distributed energy resource management systems (DERMS) to transform the grid from a one-way power delivery system into an intelligent, responsive network that will enable faster load growth and power expansion of data centers for advanced artificial intelligence (AI) applications. The digital transformation of America's grid presents opportunity for increased efficiency and resiliency but also introduces new digital risks that require careful management. Digital equipment often contains several vulnerabilities such as unencrypted communication protocols, and persistent remote access capabilities that could be exploited to manipulate device settings, coordinate service disruptions, or inject false data into grid operations. These digital risks become particularly important as the grid must rapidly scale to support AI-driven data centers, which the administration has identified as essential for maintaining U.S. technological leadership and economic competitiveness. These vulnerabilities are compounded by supply chain realities: Chinese manufacturers currently produce 70-90% of essential grid components including inverters, batteries, and control systems, with the U.S. lacking domestic manufacturing capacity for critical assets like extra-high voltage transformers. Recent federal legislation has established Foreign Entity of Concern (FEOC) restrictions to address these risks, requiring projects to achieve escalating thresholds of non-FEOC content to receive tax credits while utilities work to expand sourcing channels for their supply chains and strengthen security measures. These restrictions arrive precisely when utilities face unprecedented electricity demand growth driven by the rapid growth in data centers, creating a considerable challenge: rapidly expanding infrastructure while navigating complex compliance requirements while lacking viable alternatives for many critical components. Idaho National Laboratory (INL) and its partners have developed practical approaches to help utilities navigate these intersecting challenges as they leverage federal investment to strengthen and grow the grid. These solutions include Cyber-Informed Engineering (CIE) principles that build resilience directly into systems, the Cirrus tool for secure cloud migration, and enhanced procurement guidance that embeds security requirements throughout equipment lifecycles. Federal initiatives, such as the Technical Assistance for Digital Assurance (TADA) project, provide direct support to utilities implementing these approaches while facilitating knowledge sharing across the industry. While these tools and frameworks cannot eliminate all risks inherent in foreign supply chain dependencies, they offer pragmatic pathways for strengthening security posture without sacrificing the deployment momentum essential to meeting surging electricity demand. Ultimately, securing America's digital energy infrastructure demands dedicated coordination across multiple fronts: building domestic supply chains, implementing robust digital assurance practices, and maintaining the aggressive modernization timeline necessary for reliability, resilience, and energy independence.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Comparative life cycle assessment of various hydrogen pathways for cleaner methanol synthesis

This study evaluates the potential environmental impacts of e-methanol production using hydrogen from green, yellow, and blue sources, including Gas Switching Reforming for high purity hydrogen production with carbon capture (GSR-H 2 ), and compares their performance across current and renewable energy scenarios. Using a cradle-to-gate life cycle assessment (LCA), the study quantifies impacts across seven TRACI categories. Global warming potential (GWP) ranged from 0.28 kg CO 2 eq/kg MeOH for green hydrogen with renewables to 2.55 kg CO 2 eq/kg MeOH for SMR-CC under grid power. Among the scenarios, renewable electrolysis achieves the lowest GWP, while GSR-H 2 under renewable power offered the best balance of emissions reductions and resource efficiency among the fossil-based routes, offering a viable transitional solution in regions dependent on natural gas infrastructure. The study reveals GSR-H 2 's potential as an alternative to conventional steam methane reforming with carbon capture (SMR-CC), showing its advantages in carbon capture efficiency and reduced life cycle emissions as well as significantly lower water consumption. GSR-H 2 , when powered by renewables, consumed only 1.41 L of water per kg MeOH, an 82% reduction compared to grid-powered electrolysis, highlighting its potential in water-scarce regions. This study is the first to evaluate GSR-H 2 as a hydrogen source for e-methanol, providing new evidence for its role as a cleaner, scalable transitional solution aligned with cleaner production principles.

08 HYDROGEN↗

DOE OSTI2021

Physics-informed machine learning

Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning has emerged as a promising alternative, but training deep neural networks requires big data, not always available for scientific problems. Instead, such networks can be trained from additional information obtained by enforcing the physical laws (for example, at random points in the continuous space-time domain). Such physics-informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel-based regression networks. Moreover, it may be possible to design specialized network architectures that automatically satisfy some of the physical invariants for better accuracy, faster training and improved generalization. Furthermore, we review some of the prevailing trends in embedding physics into machine learning, present some of the current capabilities and limitations and discuss diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Examining Perceptual Luminance Uniformity of Simulated Luminaire Patterns

Luminaire luminance uniformity is an important aspect that can affect perceived lighting quality, discomfort glare, and efficacy. While several metrics have been proposed to characterize luminance uniformity, previous studies have shown that current metrics such as Max:Min or Avg:Min luminance ratios can be insensitive to important differences in luminance gradient that may affect perceived uniformity. In an attempt to resolve this issue, previous studies incorporated a contrast sensitivity function for the human eye based on spatial frequency, such as in the UHVS metric; however, this metric has not been comprehensively studied in relation to perceived uniformity ratings. The study presented in this paper aimed to examine the relationship between the UHVS metric and perceived uniformity ratings. Specifically, the study used a web-based questionnaire that presented simulated luminance patterns and asked participants to assess uniformity using a two-alternative forced-choice procedure. Responses were collected and analyzed from 94 participants. The results showed a significant correlation and relationship between the UHVS metric and perceived uniformity. However, comparisons between patterns that had similar UHVS and between patterns that had larger differences in UHVS did not yield consistent results. These results suggest that the UHVS metric might be used for general guidance but may warrant further studies to better understand its sensitivity and improve its alignment with perceived uniformity ratings.

Abboushi, Belal K.↗

DOE OSTI2025

Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations

The keyhole phenomenon has been widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced defects, primarily pores, dramatically affect the performance of final products, impeding the broad use of these laser-based technologies. The formation of these pores is typically associated with the dynamic behavior of the keyhole. So far, the accurate characterization and prediction of keyhole features, particularly keyhole depth, as a function of time, has been a challenging task. In situ characterization of keyhole dynamic behavior using the synchrotron X-ray technique is informative but complicated and expensive. Current simulations are generally hindered by their poor accuracy and generalization abilities in predicting keyhole depths due to the lack of accurate laser absorptance data. In this study, we develop a machine learning-aided simulation method that accurately predicts keyhole dynamics, especially in keyhole depth fluctuations, over a wide range of processing parameters. In two case studies involving titanium and aluminum alloys, we achieve keyhole depth prediction with a mean absolute percentage error of 10 %, surpassing those simulated using the ray-tracing method with an error margin of 30 %, while also reducing computational time. This exceptional fidelity and efficiency empower our model to serve as a cost-effective alternative to synchrotron experiments. Our machine learning-aided simulation method is affordable and readily deployable for a large variety of materials, opening new doors to eliminate or reduce defects for a wide range of laser materials processing techniques.

Computational fluid dynamics↗

DOE OSTI2023

Machine learning-based real-time kinetic profile reconstruction in DIII-D

Abstract Kinetic equilibrium reconstruction plays a vital role in the physical analysis of plasma stability and control in fusion tokamaks. However, the traditional approach is subjective and prone to human biases. To address this, the consistent automatic kinetic equilibrium reconstruction (CAKE) method was introduced, providing objective results. Nonetheless, its offline nature limits its application in real-time plasma control systems (PCSs). To address this limitation, we present RTCAKENN, a machine learning model that approximates 7 CAKE-level output profiles, namely pressure, inverse q , toroidal current density, electron temperature and density, carbon ion impurity temperature and rotation profiles, using real-time available inputs. The deep neural network consists of an encoder layer, where the scalars and interdependent inputs such as plasma boundary coordinates and motional Stark effect data are encoded using multi-layer perceptrons (MLPs), while profile inputs are encoded by 1D convolutional layers. The encoded data is passed through a MLP for latent feature extraction, before being decoded in the decoding layers, which consist of upsampling and convolutional layers. RTCAKENN has been implemented in the DIII-D PCS and our model achieves accuracy comparable to CAKE and surpasses existing real-time alternatives. Through clever dropout training, RTCAKENN exhibits robustness and can operate even in the absence of Thomson scattering data or charge exchange recombination data. It executes in under 8 ms in the real-time environment, enabling future application in real-time control and analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Screening a knowledge‐based library of low molecular weight compounds against the proline biosynthetic enzyme 1‐pyrroline‐5‐carboxylate 1 ( PYCR1)

Abstract Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 1 (PYCR1) is the last enzyme of proline biosynthesis and catalyzes the NAD(P)H‐dependent reduction of Δ 1 ‐pyrroline‐5‐carboxylate toL‐proline. High PYCR1 gene expression is observed in many cancers and linked to poor patient outcomes and tumor aggressiveness. The knockdown of thePYCR1gene or the inhibition of PYCR1 enzyme has been shown to inhibit tumorigenesis in cancer cells and animal models of cancer, motivating inhibitor discovery. We screened a library of 71 low molecular weight compounds (average MW of 131 Da) against PYCR1 using an enzyme activity assay. Hit compounds were validated with X‐ray crystallography and kinetic assays to determine affinity parameters. The library was counter‐screened against human Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 3 and proline dehydrogenase (PRODH) to assess specificity/promiscuity. Twelve PYCR1 and one PRODH inhibitor crystal structures were determined. Three compounds inhibit PYCR1 with competitive inhibition parameter of 100 μM or lower. Among these, (S)‐tetrahydro‐2H‐pyran‐2‐carboxylic acid (70 μM) has higher affinity than the current best tool compoundN‐formyl‐l‐proline, is 30 times more specific for PYCR1 over human Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 3, and negligibly inhibits PRODH. Structure‐affinity relationships suggest that hydrogen bonding of the heteroatom of this compound is important for binding to PYCR1. The structures of PYCR1 and PRODH complexed with 1‐hydroxyethane‐1‐sulfonate demonstrate that the sulfonate group is a suitable replacement for the carboxylate anchor. This result suggests that the exploration of carboxylic acid isosteres may be a promising strategy for discovering new classes of PYCR1 and PRODH inhibitors. The structure of PYCR1 complexed withl‐pipecolate and NADH supports the hypothesis that PYCR1 has an alternative function in lysine metabolism.

Biochemistry & Molecular Biology↗

DOE OSTI2020

Improving air leakage prediction of buildings using the fan pressurization method with the Weighted Line of Organic Correlation

In many countries, the fan pressurization method is the most frequently chosen approach for measuring the air leakage of houses. The measurements are usually performed at pressures that far exceed pressures to which buildings are exposed to under normal conditions. A fit of these tests to the power-law formulation allows an extrapolation to data points outside the measured pressure range. With the Ordinary Least Square (OLS) fitting method, the pressure exponent and flow coefficient can be determined. However, the measurement results are highly sensitive to uncertainties induced by external factors like changing wind conditions during the tests, which is neglected by OLS. This may lead to errors in the prediction of flows at lower pressures. The Weighted Line of Organic Correlation (WLOC) is an alternative approach and takes measurement uncertainty into account. In this paper, a statistical analysis of an extensive data set of pressurization measurements has been performed. Both regression techniques have been compared for almost 7500 fan pressurization measurements of six houses in 109 different house leak configurations. The variability in predicting pressure exponent and flow coefficient for both WLOC and OLS regression was analyzed using probability density functions. It was found that the Weighted Line of Organic Correlation significantly decreases the uncertainty in predicting pressure exponent, flow coefficient, and other low-pressure air leakage metrics compared to the Ordinary Least Square fitting. Here, the authors highly recommend the implementation of WLOC in current measurement standards and test equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Development and verification of the pyIsoDep depletion package for nuclear thermal propulsion reactors

Nuclear thermal propulsion technology is currently being developed by the National Air and Space Administration for future manned Mars missions. There are multiple on-going activities to develop and enhance computational capabilities, single- and multi-physics, to advance the readiness level of this technology. The prediction of short-lived fission products is crucial to understand the reactivity penalty and the decay heat production during the different burnout stages of the vehicle. Generally, the solution entails coupled neutronic-depletion analysis. However, such simulations are somewhat expensive, especially if the duration of each engine burn is to be optimized. Therefore, there is a need to complement such high-fidelity techniques with time-efficient alternatives that do not require the sequential neutronic and depletion analysis. This paper details the methods and capabilities of pyIsoDep, an open-source code used for depletion analysis of nuclear thermal propulsion reactors. The package is general, in principle, and thus can be applied to any fission system, and can potentially be extended for fusion systems. The package was developed to provide a flexible and efficient alternative to pre-existing higher order depletion codes that could integrate well into mission and transient analysis of nuclear thermal propulsion reactors. Finally, this paper presents a validation and verification of pyIsoDep against analytical and Serpent-generated solutions. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗