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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Risk-averse optimization for resilience enhancement of complex engineering systems under uncertainties

With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable to external disturbances. Hence, managing potential disruptive events during the design, operating, and recovery phase of an engineered system and therefore improving the system’s resilience is an important yet challenging task. Here, to ensure system resilience after the occurrence of failure events, this study proposes a mixed-integer linear programming (MILP) based restoration framework using heterogeneous dispatchable agents. The scenario-based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from nature. Moreover, different from conventional SO using deterministic equivalent formulations, the CVaR risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves a large-scale MILP problem and thus an adequate decomposition technique i.e. modified Lagrangian dual decomposition, is also employed to achieve tractable computational complexity. Case study results based on the IEEE 37-bus test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.

42 ENGINEERING↗
DOE OSTI2017

Contaminant source identification using semi-supervised machine learning

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Security-Constrained Unit Commitment for Electricity Market: Modeling, Solution Methods, and Future Challenges

This paper summarizes the technical activities of the IEEE Task Force on Solving Large Scale Optimization Problems in Electricity Market and Power System Applications. This Task Force was established by the IEEE Technology and Innovation Subcommittee to first review the state-of-the-art of the security-constrained unit commitment (SCUC) business model, its mathematical formulation, and solution techniques in solving electricity market clearing problems. The Task Force then investigated the emerging challenges of future market clearing problems and presented efforts in building benchmark mathematical and business models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Data-driven Mori–Zwanzig modeling of Lagrangian particle dynamics in turbulent flows

The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly nontrivial statistical behavior, motivating the development of surrogate models that can reproduce these trajectories without incurring the high computational cost of direct numerical simulations of the full Eulerian field. This task is particularly challenging because reduced-order models typically lack access to the full set of interactions with the underlying turbulent field. Novel data-driven machine learning techniques can be powerful in capturing and reproducing complex statistics of the reduced-order/surrogate dynamics. In this work, we show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions (with respect to Kolmogorov time) and stable and statistically accurate at long times. This approach is based on the Mori–Zwanzig formalism, which prescribes a mathematical decomposition of the full dynamical system into resolved dynamics that depend on the current state and the past history of a reduced set of observables, and the unresolved orthogonal dynamics due to unresolved degrees of freedom of the initial state. We show how by training this reduced order model on a point-wise error metric on short time-prediction, we are able to correctly learn the dynamics of Lagrangian turbulence, such that also the long-time statistical behavior is stably recovered at test time. This opens up a range of applications, for example, for the control of active Lagrangian agents in turbulence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Phase Transformation of Thermally Aged and Neutron Irradiated Duplex Stainless Steel Used in LWRs. Final report

The lifetime of reactor components made of duplex stainless steels can be limited by the embrittlement from thermal aging, neutron irradiation or a synergistic effect between thermal aging and neutron irradiation. There is still a large scientific knowledge gap in understanding the phase evolution in duplex stainless steels upon thermal aging with or without neutron irradiation, the synergistic effect between thermal aging and neutron irradiation, and the impacts of structural evolution on material mechanical responses. In this project, totally, five major tasks were completed through this project: a) the in-situ WAXS tensile tests of thermally aged cast duplex stainless steels, b) the EXAFS investigation of thermally aged cast austenitic stainless steel and welds of austenite sainless steel, c) the characterization of irradiation effect on neutron irradiated CASS using APT, d) characterize the ferrite hardening of neutron irradiated CASS using nanoindentation, and e) the FEM modelling to validate the in-situ WAXS tensile tests and to simulate the stress/strain distribution during deformation for thermally aged duplex stainless steel. In summary, the thermal aging without neutron irradiation can significantly increase the strength of ferrite phase through the spinodal decomposition and G-phase precipitations. The in-situ X-ray experiments show that the hardening effect in ferrite phase can significantly alter the load partition between the ferrite and austenite phases. Atom probe tomography characterizations on samples irradiated to 0.08, 3, 5, 10, 20, and 40 dpa shows that neutron irradiation has dominated the microstructural evolution after 3 dpa, and the spinodal decomposition has saturated yet even at 40 dpa based on the quantified wavelength and amplitude.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

Coupled-cluster methods provide highly accurate models of molecular structure through explicit numerical calculation of tensors representing the correlation between electrons. These calculations are dominated by a sequence of tensor contractions, motivating the development of numerical libraries for such operations. While based on matrix–matrix multiplication, these libraries are specialized to exploit symmetries in the molecular structure and in electronic interactions, and thus reduce the size of the tensor representation and the complexity of contractions. The resulting algorithms are irregular and their parallelization has been previously achieved via the use of dynamic scheduling or specialized data decompositions. We introduce our efforts to extend the Libtensor framework to work in the distributed memory environment in a scalable and energy-efficient manner. We achieve up to 240× speedup compared with the optimized shared memory implementation of Libtensor. We attain scalability to hundreds of thousands of compute cores on three distributed-memory architectures (Cray XC30 and XC40, and IBM Blue Gene/Q), and on a heterogeneous GPU-CPU system (Cray XK7). As the bottlenecks shift from being compute-bound DGEMM's to communication-bound collectives as the size of the molecular system scales, we adopt two radically different parallelization approaches for handling load-imbalance, tasking and bulk synchronous models. Nevertheless, we preserve a unified interface to both programming models to maintain the productivity of computational quantum chemists.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Metatranscriptomic analysis of lignocellulolytic microbial communities involved in high-solids decomposition of rice straw

New lignocellulolytic enzymes are needed that maintain optimal activity under the harsh conditions present during industrial enzymatic deconstruction of biomass, including high temperatures, the absence of free water, and the presence of inhibitors from the biomass. Enriching lignocellulolytic microbial communities under these conditions provides a source of microorganisms that may yield robust lignocellulolytic enzymes tolerant to the extreme conditions needed to improve the throughput and efficiency of biomass enzymatic deconstruction. Identification of promising enzymes from these systems is challenging due to complex substrate-enzyme interactions and requirements to assay for activity. In this study, metatranscriptomes from compost-derived microbial communities enriched on rice straw under thermophilic and mesophilic conditions were sequenced and analyzed to identify lignocellulolytic enzymes overexpressed under thermophilic conditions. To determine differential gene expression across mesophilic and thermophilic treatments, a method was developed which pooled gene expression by functional category, as indicated by Pfam annotations, since microbial communities performing similar tasks are likely to have overlapping functions even if they share no specific genes. Differential expression analysis identified enzymes from glycoside hydrolase family 48, carbohydrate binding module family 2, and carbohydrate binding module family 33 domains as significantly overexpressed in the thermophilic community. Overexpression of these protein families in the thermophilic community resulted from expression of a small number of genes not currently represented in any protein database. Genes in overexpressed protein families were predominantly expressed by a single Actinobacteria genus, Micromonospora. In conclusion, coupling measurements of deconstructive activity with comparative analyses to identify overexpressed enzymes in lignocellulolytic communities provides a targeted approach for discovery of candidate enzymes for more efficient biomass deconstruction. Furthermore, glycoside hydrolase family 48 cellulases and carbohydrate binding module family 33 polysaccharide monooxygenases with carbohydrate binding module family 2 domains may improve saccharification of lignocellulosic biomass under high-temperature and low moisture conditions relevant to industrial biofuel production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

On the equivalence of dynamically orthogonal and bi-orthogonal methods: Theory and numerical simulations

The Karhunen–Lòeve (KL) decomposition provides a low-dimensional representation for random fields as it is optimal in the mean square sense. Although for many stochastic systems of practical interest, described by stochastic partial differential equations (SPDEs), solutions possess this low-dimensional character, they also have a strongly time-dependent form and to this end a fixed-in-time basis may not describe the solution in an efficient way. Motivated by this limitation of standard KL expansion, Sapsis and Lermusiaux (2009) [26] developed the dynamically orthogonal (DO) field equations which allow for the simultaneous evolution of both the spatial basis where uncertainty ‘lives’ but also the stochastic characteristics of uncertainty. Recently, Cheng et al. (2013) [28] introduced an alternative approach, the bi-orthogonal (BO) method, which performs the exact same tasks, i.e. it evolves the spatial basis and the stochastic characteristics of uncertainty. In the current work we examine the relation of the two approaches and we prove theoretically and illustrate numerically their equivalence, in the sense that one method is an exact reformulation of the other. We show this by deriving a linear and invertible transformation matrix described by a matrix differential equation that connects the BO and the DO solutions. We also examine a pathology of the BO equations that occurs when two eigenvalues of the solution cross, resulting in an instantaneous, infinite-speed, internal rotation of the computed spatial basis. We demonstrate that despite the instantaneous duration of the singularity this has important implications on the numerical performance of the BO approach. On the other hand, it is observed that the BO is more stable in nonlinear problems involving a relatively large number of modes. Several examples, linear and nonlinear, are presented to illustrate the DO and BO methods as well as their equivalence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

MP1 Intermediate Characterization Report Summary

Great effort worldwide has been invested in replacing high enriched uranium (HEU) fuel with low enriched uranium (LEU) fuel in nuclear plants. This effort aims in safeguarding nuclear material and promoting the proliferation resistance of current reactors. The United States Department of Energy (DOE), National Nuclear Security Administration's (NNSA) Materials Management and Minimization Reactor Conversion program is actively working to convert civilian research and test reactors from the use of HEU to LEU fuel. To date, DOE NNSA has converted or verified the shutdown of various domestic reactors and also reactors worldwide. The task to develop and test a new LEU fuel while maintaining current performances has been assigned to the High-Performance Research Reactor (USHPRR) program. U alloyed with Mo achieves the goal of high uranium density necessary for the conversion of the high performance research reactors, and has also demonstrated good performance in pile [1]. The alloying process of U with Mo stabilizes the body-centered cubic (bcc) gamma phase (?-U) at room temperature. Without this alloying property enhancement, this phase is only stable at temperatures only above 770 °C. Retaining this isotropic gamma phase within the U-Mo alloy provides swelling and oxidation resistance and desired mechanical properties [2]. Generally 10% w. has been considered sufficient to achieve stabilization. However, local phenomena within the alloy can affect the local Mo content and lead to gamma phase decomposition. Because of this, it has been suggested that controlling gamma phase decomposition is most critical during processing [2-3]. The work detailed in this document is part of the HPPR program and focuses on the characterization of fresh fuel (a.k.a. as fabricated, before reactor testing) in collaboration with other laboratories including PNNL (Pacific Northwest National Laboratory), LANL (Los Alamos National Laboratory) and BWXT (Babcock and Wilcox Technologies). This testing is part of a 4 way validation for which similar samples from master plates were taken and distributed to each laboratory to independently analyze utilizing a standardized set of measurement and analysis procedures. When complete this work will supply a “toolbox” of information for alloying and fabrication parameters with its direct effects on microstructure quantified. Various parameters have been analyzed in this characterization effort (including grain size, quality of the coating layer, composition of the samples, carbide content etc.), based on the direction of the working group in ref.[4]. The final aim of this work is to increase the understanding of the impact of processing conditions on the final fuel microstructure, using fabrication processes that meet “commercial viability” requirements [4]. The strength of this campaign rests on the extent of the samples studied and the variation of the manufacture parameters which will permit to achieve the required understanding. Thus permitting to use these experiments to down-select test samples used to evaluate irradiation performance [4] to be tested in various locations of the Idaho National Laboratory (INL) Advanced Test Reactor (ATR).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows

Quantum algorithms exploiting real-time evolution under a target Hamiltonian have demonstrated remarkable efficiency in extracting key spectral information. However, the broader potential of these methods, particularly beyond ground-state calculations, is underexplored. In this work, we introduce the framework of multiobservable dynamic mode decomposition (MODMD), which combines the observable dynamic mode decomposition (DMD), a measurement-driven eigensolver tailored for near-term implementation, with classical shadow tomography. MODMD leverages random scrambling in the classical shadow technique to construct, with exponentially reduced resource requirements, a signal subspace that encodes rich spectral information. Notably, we replace typical Hadamard-test circuits with a protocol designed to predict low-rank observables, thereby broadening the use of classical shadow tomography for predicting many low-rank observables. We establish theoretical guarantees on the spectral approximation from MODMD, taking into account distinct sources of error. In the ideal case, we prove that the spectral error scales as exp (−Δ⁢𝐸⁢𝑡 max ), where Δ⁢𝐸 is the Hamiltonian spectral gap and 𝑡 max is the maximal simulation time. This analysis provides a rigorous justification of the rapid convergence observed across simulations. To demonstrate the utility of our framework, we consider its application to fundamental tasks, such as determining the low-lying, i.e., ground or excited, energies of representative many-body systems. Our work paves the path for efficient designs of measurement-driven algorithms on near-term and early fault-tolerant quantum devices.

quantum algorithms & computation↗
DOE OSTI2017

Ceramic Proppant Design for In-situ Microbially Enhanced Methane Recovery

This project designed a new type of multi-functional lightweight proppant. The proppant is utilized as the conventional lightweight proppant but also transports microorganisms to coalbed reservoirs. The proppant is coated with a polymer which protects the methanogenic microorganisms and serves as a time-release delivery for methane generation. To produce the multifunctional proppant, we assigned five tasks: 1) culturing methanogenic microbes from natural carbon sources; 2) identifying optimized growth and methanogenesis conditions for the microbial consortia; 3) synthesizing the lightweight ceramic proppant; 4) encapsulating the consortia and proppant; and 5) demonstrating lab scale simulated performance by monitoring in-situ methane generation and hydraulic conductivity. Task 1) To evaluate the feasibility of ex-situ cultivation, natural microbial populations were collected from various hydrocarbon-rich environments and locations characterized by natural methanogenesis. Different rank coals, complex hydrocarbon sources, hydrocarbon seeps, and natural biogenic environments were incorporated in the sampling. Three levels of screening allowed selection of microbial populations, favorable nutrient amendments, sources of the microbial community, and quantification of methane produced from various coal types. Incubation periods of up to 24 weeks were evaluated at 23°C. Headspace concentrations of CH 4 and CO 2 were analyzed by gas chromatography. After a two-week incubation period of the most promising microbes, generated headspace gas concentrations reached 873,400 ppm for methane and 176,370 ppm for carbon dioxide. Task 2) A central composite design (CCD) was used to explore a broad range of operational conditions, examine the effects of the important environmental factors, such as temperature, pH and salt concentration, and query a feasible region of operation to maximize methane production from coal. Coal biogasification was optimal for this consortium at an initial pH value of 5.5, at 30 °C, and at a NaCl concentration 3.7 mg/cm 3 (i.e., 145,165 ppm). Task 3) To create the lightweight ceramic proppant, the kaolinite and iron oxide (Fe 2 O 3 ) mixture were sintered under reducing atmosphere varying partial pressures of oxygen from 1.7×10 -13 atm to 1.8×10-11 atm. The Fe 2 O 3 reduces to FeO and reacts with kaolinite decomposed to mullite to form Fe 2 SiO 4 , FeSiO 3 , and FeAl 2 O 4 . As a result the proppant develops large pores (~100 µm), giving it a low bulk density (1.43 g/cm 3 ), and high porosity (45.2 vol%) at P_(O_2 )of 1.8×10 -11 atm. The proppant sintered at P_(O_2 )of 1.7×10 -13 atm is characterized by smaller pores (26 µm), higher density (1.72 g/cm3) and lower porosity (37.5 vol%). Crush resistance testing at 9,000 psi yields 6.8 wt% fine particles increasing to 17.7 wt% in porous samples. Acid solubility varies from 5.5 wt% loss increasing to 12.9 wt% in porous samples. Task 4 and 5) The microbial consortia obtained from the optimized condition and lightweight proppant were encapsulated by calcium-alginate polymer. The encapsulation system, including microbes and a proppant, was stored at 20°C and 36°C. The sample stored at 36°C generated 7.1×[10] 3 ppm of methane at 670 h, whereas the sample incubated at 20°C generated 1.1×[10] 3 ppm of methane. Benchtop lab-scale bioreactors were constructed and showed with real-time measurements that, as the microbes produced methane, the pH decreased, and hydrogen and carbon dioxide gases were consumed. A calcium ion-selective sensor revealed the release mechanism of the microbes from encapsulation. The calcium ions interconnecting the alginate monomers were disassociated by counter cations such as protons and potassium ions. The counter cations bound to the carboxyl group on the alginate monomer, which thus lost its ionic interactions with the calcium ions, resulting in decomposition of the hydrogel structure. Furthermore, the conductivity was increased to ~700 mD-ft from ~530 mD-ft once the encapsulated sample was applied to the coal pack. The particle size of coal was reduced when the encapsulated sample was added to the coal. The reduced coal size implied that the microbes would consume the hydrocarbon in coal.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2019

Iodine Distribution During Evaporation of Hanford Waste Treatment Plant Direct Feed Low Activity Waste Effluent Management Facility Simulant

The Hanford Waste Treatment and Immobilization Plant (WTP) Low Activity Waste (LAW) vitrification facility will generate an aqueous condensate recycle stream from the off-gas system. The baseline plan for disposition of this stream during full WTP operations is to send it to the WTP Pretreatment Facility, where it will be blended with LAW, concentrated by evaporation, and recycled to the LAW vitrification facility. However, during the Direct Feed LAW (DFLAW) scenario, planned disposition of this stream involves concentrating the condensate in a new evaporator at the Effluent Management Facility (EMF) and returning it to the LAW melter. The LAW melter condensate stream will contain components, e.g. halides and sulfates, that are volatile at melter temperatures, have limited solubility in glass waste forms, and present a material corrosion concern. Further, some minor constituents in the melter condensate, such as volatile organic compounds and iodine, have been found to be at least partially volatile in the EMF. The condensate from the EMF will be sent to the Hanford Effluent Treatment Facility (ETF). The presence of some recalcitrant organics and iodine, as 129I, are expected to be problematic in the ETF. In order to better predict and prepare for the distribution of organics and 129I in the flowsheet, it is key to understand their chemistry and experimentally determine their behavior during evaporation. To do this, modeling and testing were performed to examine the speciation of iodine under relevant conditions, as well as determine partitioning of iodine and the organics in the EMF evaporator. This overall program examines the potential treatment and immobilization of the LAW melter condensate stream to enable alternative disposal. The objective of this task was to (1) perform modeling to determine the speciation and fate of iodine species, (2) prepare a simulant of the LAW Melter Off-gas Condensate expected during DFLAW operations, (3) spike in the key volatile organics and non-radioactive iodine, and (4) demonstrate evaporation in order to predict the final composition of the effluents from the EMF evaporator to aid in planning for their disposition. The results of the modeling of iodine speciation indicate that the iodine is expected to remain in the evaporator pot as iodide and possibly iodate ion if the pH is raised to 11.5-12, which is the planned condition for the EMF. Experimental testing confirmed that the iodide remained in the evaporator concentrate and was below detection limits in all condensate and knock-out pot samples, indicating it was not volatile. The organic nitrile compounds that were added to the simulant were absent from the evaporator feed solution, either lost through evaporation, or more likely, decomposition. The acetone and methylene chloride were below the target values in the feed and condensate samples, and were presumably lost to evaporation or decomposition.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Fully Homomorphic Encryption

This code implements a Fully Homomorphic Encryption (FHE) system, enabling secure computation on encrypted data without requiring decryption. It supports encryption, decryption, and homomorphic operations like matrix multiplication and addition. This code is adaptable for integrating FHE into linear-time invariant (LTI) systems, including digital control and filtering. With proper configuration from subject matter expertise, encrypted system parameters and signals can be manipulated to perform tasks like state updates, output calculations, and convolution in the encrypted domain. By preserving the structure of LTI systems while ensuring privacy, the framework facilitates secure applications in areas such as autonomous systems, signal processing, and industrial automation. The code initializes the encryption system using parameters provided in the env dictionary. These parameters include the ciphertext modulus, key dimension, plaintext fixed-point scaling factor, and noise bound. During initialization, a secret key is generated, which is essential for encrypting and decrypting data securely. The modular design allows users to tailor these parameters to specific use cases or security requirements. The code implements multiple cryptographic schemes. The learning with errors (LWE) encryption method encodes cleartext message to their plaintext fixed-point representation then encrypted into ciphertext space with additive noise. This noise ensures the security of the scheme, relying on the computational hardness of the LWE problem. The code also includes the Gentry-Sahai-Waters (GSW) scheme based off the LWE problem. Homomorphic matrix multiplication is performed between the LWE and GSW to encrypted data. This is achieved using a decomposition function on the LWE ciphertext during the multiplication operation. For higher-dimensional data, the code includes a method to encrypt entire matrices (GSWMat) using GSW encryption. These encrypted matrices can then be used for homomorphic matrix multiplications (MatMult). The decryption function uses the secret key to recover the original plaintext, removing the added noise and scaling that was originally applied during encryption.

Lois, Roberts [Idaho National Laboratory (INL), Id↗
DOE OSTI2025

Multiphysics Modeling of Microreactors with NEAMS codes, and Validation Based on KRUSTY Reactivity Insertion

The NEAMS Multiphysics Applications team continues to assess code usability and functionality for microreactor design and safety analyses, while demonstrating that NEAMS tools capture both steady-state and transient behavior across distinct microreactor concepts. In FY2025, the team advanced full-core, high-fidelity, multiphysics models that solve more complex problems and strengthen verification/validation for several microreactor systems: heat-pipe microreactor (HPMR), gas-cooled microreactor (GCMR), and the KRUSTY experiment. These models employ the MOOSE MultiApp/Transfers architecture with Griffin for neutronics, BISON for heat conduction/thermomechanics, Sockeye for heat pipes, SAM/THM for coolant channels and loops, and SWIFT for hydride behavior, with meshes generated via the MOOSE Reactor Module. The graphite models available in the Grizzly code were also investigated for future analyses. For the HPMR, a Na-HPMR variant was constructed to align with recently validated heat-pipe experiments and Sockeye’s LCVF capability, enabling mechanistic heat-pipe transients and startup modeling. The Na-HPMR will serve as the primary model for HPMR investigations in upcoming tasks. The load-following and single heat-pipe failure scenarios (Griffin/BISON/Sockeye), which were previously modeled for the K-HPMR, were replicated for the Na-HPMR, showing strong negative temperature feedback and highly localized thermal effects, respectively, while the startup case captured vapor-front progression and heat-removal activation. Solid mechanics was added to the previously built K-HPMR full-core model in BISON, showing minimal impact on steady-state reactivity yet enabling stress-field predictions that prepare the path for full-core TRISO performance analyses. For the GCMR, automated steady-state and four transient scenarios were executed using Griffin/BISON/SAM/SWIFT. Results confirm robust inherent safety: power collapses promptly in loss-of-cooling events, the inlet-temperature drop settles to a new equilibrium, and a single-channel blockage yields only a ~30 K local fuel-temperature rise with <0.4% power decrease. SWIFT-predicted hydrogen redistribution affects reactivity during both steady-state and transient conditions, underscoring its importance. A Brayton-cycle balance of plant (BOP) model in SAM/THM demonstrated stable startup behavior, and xenon-driven reactivity during load following was analyzed. To improve TRISO-compact temperature fidelity, a fast multiscale Heat Source Decomposition (HSD) treatment was implemented. Against heterogeneous benchmarks, HSD reduces underprediction of kernel temperatures and lowers predicted peak powers in reactivity-insertion transients compared to previous homogenized models. KRUSTY warm-critical validation progressed from FY2024 baselines: the 15Ȼ insertion shows excellent agreement in peak power (~2% high) and temperature trends, and the 30Ȼ case was automated via a feedback controller that maintained power near 3 kW for ~150 s with close agreement to data. The successful modeling of the warm critical tests has laid a strong foundation for simulating more complex nuclear system tests in the years ahead. Throughout FY2025, developer feedback was provided (e.g., MOOSE batch mesh generation, distributed pre-split meshes, Griffin sweeper on displaced meshes), several new models were contributed to the Virtual Test Bed, and an OECD-NEA WPRS multiphysics benchmark based on the HPMR was initiated to enable broader cross-comparison and best-practice development with the nuclear community at large.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Stochastic Programming Approach for Resource Selection under Demand Uncertainty

Cost-efficient selection and scheduling of a subset of geographically distributed resources to meet the demands of a scientific workflow is a challenging problem. The problem is exacerbated by uncertainties in demand and availability of resources. In this paper, we present a stochastic optimization based framework for robust decision making in the selection of distributed resources over a planning horizon under demand uncertainty. We present a novel two-stage stochastic programming model for resource selection, and implement an L-shaped decomposition algorithm to solve this model. A Sample Average Approximation algorithm is integrated to enable stochastic optimization to solve problems with a large number of scenarios. Using the metric of stochastic solution, we demonstrate up to 30% cost reduction relative to solutions without explicit consideration of demand uncertainty for a 24-month problem. We also demonstrate up to 54% cost reduction relative to a previously developed solution for a 36-month problem. We further argue that the composition of resources selected is superior to solutions computed without explicit consideration of uncertainties. Given the importance of resource selection and scheduling of complex scientific workflows, especially in the context of commercial cloud computing, we believe that our novel stochastic programming framework will benefit many researchers as well as users of distributed computing resources.

scientific workflows, task scheduling, optimizatio↗
DOE OSTI2014

Enhancements to the SHARP Build System and NEK5000 Coupling

The SHARP project for the Department of Energy's Nuclear Energy Advanced Modeling and Simulation (NEAMS) program provides a multiphysics framework for coupled simulations of advanced nuclear reactor designs. It provides an overall coupling environment that utilizes custom interfaces to couple existing physics codes through a common spatial decomposition and unique solution transfer component. As of this writing, SHARP couples neutronics, thermal hydraulics, and structural mechanics using PROTEUS, Nek5000, and Diablo respectively. This report details two primary SHARP improvements regarding the Nek5000 and Diablo individual physics codes: (1) an improved Nek5000 coupling interface that lets SHARP achieve a vast increase in overall solution accuracy by manipulating the structure of the internal Nek5000 spatial mesh, and (2) the capability to seamlessly couple structural mechanics calculations into the framework through improvements to the SHARP build system. The Nek5000 coupling interface now uses a barycentric Lagrange interpolation method that takes the vertex-based power and density computed from the PROTEUS neutronics solver and maps it to the user-specified, general-order Nek5000 spectral element mesh. Before this work, SHARP handled this vertex-based solution transfer in an averaging-based manner. SHARP users can now achieve higher levels of accuracy by specifying any arbitrary Nek5000 spectral mesh order. This improvement takes the average percentage error between the PROTEUS power solution and the Nek5000 interpolated result down drastically from over 23 % to just above 2 %, and maintains the correct power profile. We have integrated Diablo into the SHARP build system to facilitate the future coupling of structural mechanics calculations into SHARP. Previously, simulations involving Diablo were done in an iterative manner, requiring a large amount manual work, and left only as a task for advanced users. This report will detail a new Diablo build system that was implemented using GNU Autotools, mirroring much of the current SHARP build system, and easing the use of structural mechanics calculations for end-users of the SHARP multiphysics framework. It lets users easily build and use Diablo as a stand-alone simulation, as well as fully couple with the other SHARP physics modules. The top-level SHARP build system was modified to allow Diablo to hook in directly. New dependency handlers were implemented to let SHARP users easily build the framework with these new simulation capabilities. The remainder of this report will describe this work in full, with a detailed discussion of the overall design philosophy of SHARP, the new solution interpolation method introduced, and the Diablo integration work. We will conclude with a discussion of possible future SHARP improvements that will serve to increase solution accuracy and framework capability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Developing Accelerated Test Protocols and Tuning Microstructures of the Common Materials to Improve Robustness, Reliability, and Endurance of SOFC Cells

This work is built on our prior work where we found that phase transformation in praseodymium nickelates, e.g. Pr 2 NiO 4 (PNO) and (Pr 1-x Nd x ) 2 NiO 4 (PNNO), can be electrochemically driven, and is substantially faster when compared to thermal annealing studies. The first task aims at an attempt to further accelerate the phase transformation in the oxygen electrode by alternating the current input in the cells, which lead to the development of accelerated test protocols (ATPs). ATPs showed up to 60x faster phase transformation and up to 10x faster performance degradation in (Pr 0.50 Nd 0.50 ) 2 NiO 4 electrodes, when compared to long-term operation under constant current density. Furthermore, the phase stable Nd 2 NiO 4 and (La 0.6 Sr 0.4 )(Co 0.8 Fe 0.2 )O 3 (LSCF6482) electrodes were tested in full cells under ATPs, and showed up to 10x faster performance degradation within 1,100 hours in a comparison with long-term thermal annealing studies and electrochemical operation under constant current density. The second task aims at the quantification of the contributions of cell components to the total impedance of a solid oxide fuel cell (SOFC) using electrochemical impedance spectroscopy (EIS) and distribution of relaxation times (DRT). Specifically, the role of gas composition at both anode and cathode was systematically studied, aiming at deconvoluting, identifying and quantifying the contributions of different electrode processes. This was achieved by first tuning the partial pressure of H 2 at the anode and subsequently varying the partial pressure of O 2 at the cathode. The results suggest that, while DRT offers a viable way of deconvoluting different times distributions, additional attention is needed before assigning a peak to a specific electrode process due to the significant overlap of the contributions from the cathode and the anode. Density function theory studies show that both Pr-vacancies and O-defects play a key role on the activity and stability for nickelates towards oxygen reduction reaction. The resident O-interstitials and oxygen ions in the PrO layer form peroxide (O 2 2- ) nearby Pr vacancies. The O 2 2- limits oxygen-ion transport due to the required additional energy to break its O-O bond. We further calculated the formation and segregation energies for different Ln ions (La, Pr, Nd, Pm, Sm, Gd, Tb, Dy, and Ho) in PNO and CeO 2 (111) surfaces. In addition to Nd, Pm and La are suggested as potential dopants in PNO to enhance it stability without decomposition due to their more negative formation energies, lower diffusion energies, and positive separation energies.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2017

Roofline Analysis in the Intel® Advisor to Deliver Optimized Performance for applications on Intel® Xeon Phi™ Processor

In this session we show, in two case studies, how the roofline feature of Intel Advisor has been utilized to optimize the performance of kernels of the XGC1 and PICSAR codes in preparation for Intel Knights Landing architecture. The impact of the implemented optimizations and the benefits of using the automatic roofline feature of Intel Advisor to study performance of large applications will be presented. This demonstrates an effective optimization strategy that has enabled these science applications to achieve up to 4.6 times speed-up and prepare for future exascale architectures. # Goal/Relevance of Session The roofline model [1,2] is a powerful tool for analyzing the performance of applications with respect to the theoretical peak achievable on a given computer architecture. It allows one to graphically represent the performance of an application in terms of operational intensity, i.e. the ratio of flops performed and bytes moved from memory in order to guide optimization efforts. Given the scale and complexity of modern science applications, it can often be a tedious task for the user to perform the analysis on the level of functions or loops to identify where performance gains can be made. With new Intel tools, it is now possible to automate this task, as well as base the estimates of peak performance on measurements rather than vendor specifications. The goal of this session is to demonstrate how the roofline feature of Intel Advisor can be used to balance memory vs. computation related optimization efforts and effectively identify performance bottlenecks. A series of typical optimization techniques: cache blocking, structure refactoring, data alignment, and vectorization illustrated by the kernel cases will be addressed. # Description of the codes ## XGC1 The XGC1 code [3] is a magnetic fusion Particle-In-Cell code that uses an unstructured mesh for its Poisson solver that allows it to accurately resolve the edge plasma of a magnetic fusion device. After recent optimizations to its collision kernel [4], most of the computing time is spent in the electron push (pushe) kernel, where these optimization efforts have been focused. The kernel code scaled well with MPI+OpenMP but had almost no automatic compiler vectorization, in part due to indirect memory addresses and in part due to low trip counts of low-level loops that would be candidates for vectorization. Particle blocking and sorting have been implemented to increase trip counts of low-level loops and improve memory locality, and OpenMP directives have been added to vectorize compute-intensive loops that were identified by Advisor. The optimizations have improved the performance of the pushe kernel 2x on Haswell processors and 1.7x on KNL. The KNL node-for-node performance has been brought to within 30% of a NERSC Cori phase I Haswell node and we expect to bridge this gap by reducing the memory footprint of compute intensive routines to improve cache reuse. ## PICSAR is a Fortran/Python high-performance Particle-In-Cell library targeting at MIC architectures first designed to be coupled with the PIC code WARP for the simulation of laser-matter interaction and particle accelerators. PICSAR also contains a FORTRAN stand-alone kernel for performance studies and benchmarks. A MPI domain decomposition is used between NUMA domains and a tile decomposition (cache-blocking) handled by OpenMP has been added for shared-memory parallelism and better cache management. The so-called current deposition and field gathering steps that compose the PIC time loop constitute major hotspots that have been rewritten to enable more efficient vectorization. Particle communications between tiles and MPI domain has been merged and parallelized. All considered, these improvements provide speedups of 3.1 for order 1 and 4.6 for order 3 interpolation shape factors on KNL configured in SNC4 quadrant flat mode. Performance is similar between a node of cori phase 1 and KNL at order 1 and better on KNL by a factor 1.6 at order 3 with the considered test case (homogeneous thermal plasma).

97 MATHEMATICS AND COMPUTING↗