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DOE OSTI2022

Development, construction and qualification tests of the mechanical structures of the electromagnetic calorimeter of the Mu2e experiment at Fermilab

The “muon-to-electron conversion” (Mu2e) experiment at Fermilab will search for the Charged Lepton Flavour Violating neutrino-less coherent conversion of a muon into an electron in the field of an aluminum nucleus. The observation of this process would be the unambiguous evidence of physics beyond the Standard Model. Mu2e detectors comprise a straw-tracker, an electromagnetic calorimeter and an external veto for cosmic rays. The calorimeter provides excellent electron identification, complementary information to aid pattern recognition and track reconstruction, and a fast calorimetric online trigger. The detector has been designed as a state-of-the-art crystal calorimeter and employs 1340 pure Cesium Iodide (CsI) crystals readout by UV-extended silicon photosensors and fast front-end and digitization electronics. A design consisting of two identical annular matrices (named “disks”) positioned at the relative distance of 70 cm downstream the aluminum target along the muon beamline satisfies the Mu2e physics requirements.The hostile Mu2e operational conditions, in terms of radiation levels (total ionizing dose of 12 krad and a neutron fluence of 5x1010 n/cm2 @ 1 MeVeq (Si)/y), magnetic field intensity (1 T) and vacuum level (10$^{-4}$ Torr) have posed tight constraints on the design of the detector mechanical structures and materials choice. The support structure of the two 670 crystal matrices employs two aluminum hollow rings and parts made of open-cell vacuum-compatible carbon fiber. The photosensors and service front-end electronics for each crystal are assembled in a unique mechanical unit inserted in a machined copper holder. The 670 units are supported by a machined plate made of vacuum-compatible plastic material. The plate also integrates the cooling system made of a network of copper lines flowing a low temperature radiation-hard fluid and placed in thermal contact with the copper holders to constitute a low resistance thermal bridge. The data acquisition electronics is hosted in aluminum custom crates positioned on the external lateral surface of the two disks. The crates also integrate the electronics cooling system as lines running in parallel to the front-end system.The constraints on the calorimeter mechanical structures design, the development from the conceptual design to the specifications of all the structural components, the status of components production and the components quality assurance tests are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Discrete-Time Causal Control of a Wave Energy Converter With Finite Stroke in Stochastic Waves

We consider feedback control design for a wave energy converter (WEC) for which the power takeoff (PTO) system has a finite stroke limit. Stationary stochastic wave loading is assumed, with a known spectrum, and the plant dynamics are assumed to be linear. We develop a technique for the design of a discrete-time controller, which has three design stages. In the first stage, a linear controller is optimized while imposing a relaxation of the maximum stroke constraint on the design. In the second stage, a tandem nonlinear feedback loop is designed for the purpose of stroke protection. In the third stage, the two designs (linear and nonlinear) are fused in a manner that preserves the stability of the overall system. The technique is demonstrated in a simulation of a simple cylindrical buoy. We show that the controller may be tuned through the adjustment of scalar design parameters, which adjust the tradeoff between the mean generated power and the force levels required to protect the stroke.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Design of a supervisory control system for autonomous operation of advanced reactors

Advanced reactors to be deployed in the coming decades will face deregulated energy markets, and may adopt flexible operation to boost profitability. To aid in the transition from baseload to flexible operation paradigm, autonomous operation is sought. This work focuses on the control aspect of autonomous operation. Specifically, a hierarchical control system is designed to support constraint enforcement during routine operational transients. Within the system, data-driven modeling, physics-based state observation, and classical control algorithms are integrated to provide an adaptable and robust solution. A 320 MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design basis for demonstrating the proposed control system. The hierarchical control system consists of a supervisory layer and low-level layer. The supervisory layer receives requests to change the system's operating conditions (e.g., the current reactor power to meet a load -follow), and accepts or rejects them based on constraints that have been assigned. Constraints are issued to keep the plant within an optimal operating region. The low-level layer interfaces with the actuators of the system to fulfill requested changes, while maintaining tracking and regulation duties. Further, to accept requests at the supervisory layer, the Reference Governor algorithm was adopted. To model the dynamics of the reactor, a system identification algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution of process variables that cannot be directly measured (e.g., the propagation of delayed neutron precursors), the Unscented Kalman Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The composition of these algorithms led to a numerical demonstration of constraint enforcement during a 40% power drop transient (at a rate of 5 %/min). Uncontrolled secondary-side temperatures were successfully constrained. Adaptability of the proposed system was demonstrated by modifying the constraint values, and enforcing them during the transient. Robustness was also demonstrated by enforcing constraints under noisy environments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models

Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Snyder, Abigail C.↗

DOE OSTI2022

Catalytic DNA Polymerization Can Be Expedited by Active Product Release**

Abstract The sequence‐specific hybridization of DNA facilitates its use as a building block for designer nanoscale structures and reaction networks that perform computations. However, the strong binding energy of Watson–Crick base pairing that underlies this specificity also causes the DNA dehybridization rate to depend sensitively on sequence length and temperature. This strong dependency imposes stringent constraints on the design of multi‐step DNA reactions. Here we show how an ATP‐dependent helicase, Rep‐X, can drive specific dehybridization reactions at rates independent of sequence length, removing the constraints of equilibrium on DNA hybridization and dehybridization. To illustrate how this new capacity can speed up designed DNA reaction networks, we show that Rep‐X extends the range of conditions where the primer exchange reaction, which catalytically adds a domain provided by a hairpin template to a DNA substrate, proceeds rapidly.

Moerman, Pepijn G.↗

DOE OSTI2022

Catalytic DNA Polymerization Can Be Expedited by Active Product Release**

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

wa-hls4ml and lui-gnn: A benchmark and GNN-based surrogate model for hls4ml resource and latency estimation

As machine learning (ML) increasingly serves as a tool for addressing real-time challenges in scientific applications, the development of advanced tooling has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as model synthesis, are now becoming limiting factors in the rapid iteration of designs. To reduce these emerging constraints, multiple efforts are being launched toward designing an ML-based surrogate model that estimates resource usage of synthesized accelerator architectures. This model would reduce the design iteration time, especially when designing within a set of given hardware constraints. This approach shows considerable potential, but as it stands, the effort is early and would benefit from coordination and standardization to assist future work as it emerges. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of more than 100,000 fully connected neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. In addition to the resource utilization and latency data provided, the dataset includes generated artifacts and log files for many of the synthesized neural networks, in order to support future research in ML-based code generation. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, as well as the average performance across a subset of the dataset. We measure the performance of a given predictor model through multiple metrics, including $R^2$ score and SMAPE on regression tasks, as well as inference time to further characterize the estimator under test. Additionally, we introduce the latency/utilization inference graph neural network (lui-gnn), a surrogate model that uses a graph neural network to represent input architectures in the form of a directed graph. This graph representation allows for a diverse set of model architectures to all be effectively handled by a surrogate model. We present the architecture and performance of the model, as evaluated by the new proposed benchmark, including SMAPE, $R^2$ score, and inference times, and find that lui-gnn generally predicts latency and utilization for the 75\% quantile within several percent of the synthesized resources on the synthetic test dataset, indicating that this approach of estimating resource and latency via a surrogate models has promise and warrants further research.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Fully Distributed Multi-Material Magnetic Sensing Structures for Multiparameter DAS Applications

This dissertation demonstrates the first of its kind distributed magnetic field sensor based on a fiber optic distributed acoustic sensing (DAS) scheme. Ferromagnetic nickel and Metglas® were dispersed internally within a fiber optic preform and then drawn on an in-house fiber optic draw tower to lengths in the kilometers. Due to the close proximity of the ferromagnetic metals and fiber optic core, the magnetostrictive strain response of the ferromagnetic materials when exposed to a magnetic field would perturbate within the fiber cladding and transfer that strain, internally, to the fiber optic core. Strain resulting from the magnetostrictive effect allows the DAS based sensor to accurately translate strain into readable magnetic field data. Due to the high sensitivity seen in this sensor design, multiparameter sources, acoustic and magnetic fields, were tested and validated and a three dimensional magnetic-field vector sensor was proposed. Numerical analysis of the novel sensor design was first implemented using COMSOL Multiphysics, where inputs such as magnetostrictive element shape, size, distance, and number were first investigated. Upon optimizing system constraints, the sensor design was further modified such that single mode operation was consistent across multiple fiber draws while retaining high strain transfer from the ferromagnetic elements to the fiber optic core. Ferromagnetic material selection was evaluated as a function of the saturation magnetostriction constants and a total of 4 modules were used to fully characterize the complex physics involved in this sensor design. All fabrication and testing were performed in-house using a full scale 3-story fiber draw tower and custom environmental testing stations to imitate naturally occurring events such as magnetic or acoustic point sources. A unique stacking method was used to embed ferromagnetic nickel and Metglas® into a fiber optic preform which when combined with a custom fiber draw process resulted in consistent multi-material fibers drawn to lengths of 1-km. In-house testing facilities included different types of electromagnetic generators, in addition to a soil test bed, and an outdoor test bed which allowed 100 meters of fiber to be tested simultaneously. All tested sensors demonstrated high strain transfer capabilities on the order of 0.01-10 μϵ depending on the materials used, ferromagnetic rod number, and core to metal spacing. Due to the sensitivity of the system the difference between AC and DC was distinct, and directional magnetostriction was studied. Transverse and longitudinal magnetic wave propagation was controlled through a solenoid and rectangular Helmholtz coil, both built in-house. A three-dimensional magnetic field vector sensor was proposed due to the success of the magnetic field sensor, and a design was proposed and initially tested to validate direction as a function of field strength and distance. To summarize, this dissertation explores the first fully distributed magnetic field sensor using DAS based techniques and one of the first multi-material fiber draw processes which can produce consistent single mode fiber up to 1-km. Due to extensive FEA modeling, multiple iterations of the magnetic sensor were fully characterized and an equation describing the relationship between sensor design and strain transfer has been created and validated experimentally. Multi-parameter tests including acoustic and magnetic fields were implemented and an algorithm was developed to separate the mixed signals. Finally, a test was performed to demonstrate the feasibility of sensing magnetic fields directionally. Cumulative results demonstrate a high-quality sensor alternative to current designs which may surpass other magnetic sensors due to innate multi-parameter capabilities, in addition to the inexpensive production cost and extremely long operating lengths.

02 PETROLEUM↗

DOE OSTI2022

Cryogenic energy storage: Standalone design, rigorous optimization and techno-economic analysis

Energy storage allows flexible use and management of excess electricity and intermittently available renewable energy. Cryogenic energy storage (CES) is a promising storage alternative with a high technology readiness level and maturity, but the round-trip efficiency is often moderate and the Levelized Cost of Storage (LCOS) remains high. The complex flowsheets with intricate thermodynamics at cryogenic temperatures as well as the presence of multiple loops and refrigeration cycles pose considerable challenges for rigorous model-based design and optimization of CES systems. We present an optimization strategy that couples rigorous process simulation and Bayesian optimization with flowsheet decomposition and identification of hidden coupling constraints to optimally design standalone CES systems. Further refinement is done via a local search using the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm. Here our results indicate that it is possible to achieve more than 52% round-trip efficiency and an LCOS of $153/MWh for a standalone 100 MW/400 MWh CES system limited to short-term storage with daily charging–discharging. However, a detailed techno-economic assessment reveals that the LCOS considering total capital investment may exceed $267/MWh when all direct and indirect costs of installation and operation are considered.

25 ENERGY STORAGE↗

DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗

DOE OSTI2023

High-throughput exploration of the WMoVTaNbAl refractory multi-principal-element alloys under multiple-property constraints

Development of next-generation gas turbines requires the design and fabrication of novel high-temperature structural materials capable of operating beyond 1300°C. Here, we propose a high-throughput alloy design framework under multiple-property constraints to discover new refractory multi-principal element alloys (MPEAs) for high-temperature applications. The framework treats the development of MPEAs as a composition-agnostic constraint satisfaction problem, i.e., no prescriptions are made concerning the design space before performing investigatory calculations. We target alloys in the WMoVTaNbAl chemistry space that are predicted to meet constraints on the following properties simultaneously: single-phase stability, density, solidus temperature, yield strength at 1300°C, and ductile-to-brittle-transition temperature. These properties are relevant to both applications in gas turbines and manufacturability. A set of 214 MoNbV-rich alloys meet these relevant constraints. These feasible alloys are investigated with density functional theory (DFT) to provide a fundamental electronic basis for their superior properties. Three compositionally representative alloys from the feasible design space (Mo 45 Nb 35 Ta 5 V 15 , Mo 25 Nb 50 V 20 W 5 , and Mo 30 Nb 35 Ta 5 V 25 W 5 ) are selected with a k-medoids-based design scheme for detailed DFT analysis and experimental characterization. The DFT analysis predicted a single-phase BCC at high temperatures with a high yield strength for all three MPEAs, in agreement with CALPHAD (CALculation of PHAse Diagrams) and experiments, respectively. These three alloys are benchmarked against a public database of 1546 MPEAs. Concerning the aforementioned constraints, the Mo 30 Nb 35 Ta 5 V 25 W 5 alloy outperforms these 1546 MPEAs. The present work demonstrates the ability of the proposed design methodology to identify candidate alloys for a given application under multiple property constraints in a combinatorically vast design space.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale (V.2.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore, the resilience challenge for extreme-scale HPC systems requires coordination between various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in future HPC systems, they are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. Therefore, the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance & power efficiency characteristics. Additionally, few implementations of current resilience solutions are portable to newer architectures and software environments that will be deployed on future systems. We developed a new structured approach to the management of HPC resilience using the concept of resilience-based design patterns. In general, a design pattern is a repeatable solution to a commonly occurring problem. We identified the well-known solutions that are commonly used to deal with faults, errors and failures in HPC systems. In the initial design patterns specification (version 1.0), we described the various solutions, which address specific problems in the design of resilient HPC environments, in the form of patterns. Each pattern describes a problem caused by a fault, error or failure event in an HPC environment, and then describes the core of the solution of the problem in such a way that this solution may be adapted to different systems and implemented at different layers of the system stack. The catalog of these resilience design patterns provides designers with a collection of design elements. To construct complete resilience solutions using combinations of various patterns, we defined a framework that enhances HPC designers' understanding of the important constraints and the opportunities for the design patterns to be implemented and deployed at various layers of the system stack. The design framework is also useful for establishing interfaces and mechanisms to coordinate flexible fault management across hardware and software components, as well as to consider the trade-off between performance, resilience, and power consumption when constructing a solution. The resilience design patterns specification version 1.1 included more detailed explanations of the pattern solutions, the context in which the patterns are applicable, and the implications for hardware or software design. It also provided several additional examples and detailed case studies to demonstrate the use of patterns to build realistic solutions. In version 1.2 of the specification document, we have improved the pattern descriptions, including graphical representations of the pattern components. These improvements are largely based on critical comments, feedback and suggestions received from pattern experts and readers of the previous versions of the specification. The pattern classification has been modified to further clarify the relationships between pattern categories. This version of the specification also introduces a pattern language for resilience design patterns. The pattern language presents the patterns in the catalog as a network, revealing the relations among the resilience patterns. The language provides designers with the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language also enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack. The overall goal of this work is to provide hardware and software designers, as well as the users and operators of HPC systems, a systematic methodology for the design and evaluation of resilience technologies in HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types. Version 2.0 expands the resilience design pattern classification and catalog to include self-stabilization patterns and reliability, availability and performance models for each structural pattern.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Transformational challenge reactor analysis to inform preconceptual core design decisions: Sensitivity study of transient analysis in a hydride-moderated microreactor

The Transformational Challenge Reactor (TCR) program aims to demonstrate a revolutionary design approach enabled by advanced manufacturing and data analytics in the design of nuclear reactors. This article discusses scoping analyses of preconceptual designs to inform TCR design decisions and the evaluation of sensitivities and uncertainties on postulated transient scenarios. The applicability of the systems codes TRACE and RELAP5-3D to TCR transient analysis are examined, and RELAP5-3D models are used to examine the transient response of two candidate core designs at multiple power levels. Then, the uncertainty quantification code RAVEN is used to quantify the effect of several design parameters on reactivity-initiated accident (RIA) progression at hot zero power (HZP) and hot full power (HFP) as well as to assess the impact of uncertainties in transient parameters for the pressurized loss of forced cooling (PLOFC).When results were compared, TRACE and RELAP5-3D showed good agreement in their ability to predict system behavior, but RELAP5-3D calculations were closer to analytical predictions for the RIA. Furthermore, models for a PLOFC accident in two designs (a UO2 and TRISO core) at multiple power levels showed greater temperature margins for the TRISO core at all power levels. Using this information, along with other scoping analyses and constraints, the TCR design team selected a power level of 3 MWth and a TRISO-based core design. For this design RAVEN was applied to vary RIA parameters in RELAP5-3D models at HZP and HFP to understand the effect on figures of merit such as peak power, fuel and coolant temperature, and energy deposition. This sensitivity study found that the inserted reactivity worth was the most important parameter controlling all figures of merit, but for insertion up to 1.5$ no failure of TCR fuel is anticipated. For constant reactivity insertions, the magnitude of the fuel temperature coefficient was found to have the greatest effect on all figures of merit under most circumstances. These results not only demonstrate the anticipated robust safety of the proposed TCR fuel form but also provide a reference for future metal-hydride moderated systems to understand RIA behavior. In the PLOFC, the impact of heat transfer enhancement due to wavy flow channel effects dominated the variance in peak temperatures, and variations in heat exchanger elevation provided the greatest control on natural circulation flow rate. Additionally, no fuel particle failure is anticipated in the PLOFC.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

A Unified Grid-Forming and Grid-Following Primary Control Design With Optimized Enforcement of Grid Operational Constraints

Not Available

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Bayesian Entropy Neural Networks for physics-aware prediction

This article addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring flexible model behavior to incorporate non-data sample information. We introduce Bayesian Entropy Neural Networks (BENN), a framework grounded in Maximum Entropy (MaxEnt) principles, designed to impose constraints on Bayesian Neural Network (BNN) predictions. BENN is capable of constraining not only the predicted values but also their derivatives and variances, ensuring a more robust and reliable model output. To achieve simultaneous uncertainty quantification and constraint satisfaction, we employ the method of multipliers approach. This allows for the concurrent estimation of neural network parameters and the Lagrangian multipliers associated with the constraints. Our experiments, spanning diverse applications such as beam deflection modeling and microstructure generation, demonstrate the effectiveness of BENN. The results highlight significant improvements over traditional BNNs and showcase competitive performance relative to contemporary constrained deep learning methods.

14 SOLAR ENERGY↗

DOE OSTI2023

Co-Design of Multijunction Photoelectrochemical Devices for Unassisted CO 2 Reduction to Multicarbon Products

Photoelectrochemical (PEC) CO 2 reduction (PEC CO 2 R) is a prospective approach for utilizing solar energy to synthesize a variety of carbon-containing chemicals and fuels, the most valuable of which are multicarbon (C 2+ ) products, such as ethylene and ethanol. While these products can be produced with high faradaic efficiency using Cu, this occurs over a relatively narrow potential range, which, in turn, imposes constraints on the design of a device for PEC CO 2 R. Herein, we used continuum-scale modeling to simulate the solar-to-C 2+ (STC 2+ ) efficiency of PEC CO 2 R devices fed with CO 2 -saturated, 0.1 M CsHCO 3 . We then explored how cell architecture and the use of single or dual photoelectrode(s) alters the optimal combination of photoelectrode bandgaps for high STC 2+ efficiency. Ultimately, this work provides guidance for the co-design of the device architecture and photoelectrode bandgaps required to achieve high STC 2+ efficiency. The insights gained are then used to identify systems that yield the highest amount of C 2+ products throughout the day and year.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Design and assembling status of the Mu2e electromagnetic calorimeter mechanical structures

The muon-to-electron conversion (Mu2e) experiment at Fermilab will search for the Charged Lepton Flavour Violating neutrino-less coherent conversion of a muon into an electron in the field of an aluminum nucleus. The observation of this process would be the unambiguous evidence of physics beyond the Standard Model. The detector has been designed as a state-of-the-art crystal calorimeter and employs 1348 pure Cesium Iodide (CsI) crystals readout by UV-extended silicon photosensors and fast front-end and digitization electronics. A design consisting of two identical annular matrices (named disks ) positioned at the relative distance of 70 cm, downstream the aluminum target along the muon beamline, satisfies the Mu2e physics requirements. The hostile Mu2e operational conditions, in terms of radiation levels (total ionizing dose of 12 krad and a neutron fluence of 5 10^{10} n/cm2 @ 1 MeVeq (Si)/y), magnetic field intensity (1 T) and vacuum level (10−4 Torr) have posed tight constraints on the design of the detector mechanical structures and materials choice. The support structure of the two 674 crystal matrices employs two aluminum hollow rings and parts made of open- cell vacuum-compatible carbon fiber. The photosensors and service front-end electronics for each crystal are assembled in a unique mechanical unit inserted in a machined copper holder. The 674 units are supported by a machined plate made of vacuum-compatible plastic material. The plate also integrates the cooling system made of a network of copper lines flowing a low temperature radiation-hard fluid and placed in thermal con- tact with the copper holders. The data acquisition electronics is hosted in aluminum custom crates positioned on the external lateral surface of the two disks. The crates also integrate the electronics cooling system. In this poster we will review the constraints on the calorimeter mechanical structures, the technological choices, and the status of assembling at Fermilab.

Pasciuto, D.↗

DOE OSTI2021

Linearized modeling and optimization of shared mooring systems

Shared mooring systems, where adjacent platforms are tethered directly to each other, can reduce anchor quantities and mooring line lengths in a floating array but also introduce new modeling and design challenges. This paper presents and demonstrates a first-order approach to modeling and designing shared mooring systems that simplifies these challenges. We formulate a general, linearized model for the force-displacement response of shared mooring systems, including inter-platform couplings. Using this linearization, we realize significant simplifications to the shared mooring system design problem and propose a corresponding design optimization approach. Finally, we demonstrate the complete approach on a variety of shared-mooring floating wind farm layouts with generic design assumptions and constraints. Some fundamental observations about shared mooring systems can be made from the results. Polygonal array layouts with perpendicular anchor positions appear to maximize shared mooring system efficiency. Also, arrays with greater levels of sharing tend to exhibit larger deviations in offsets between platforms. Overall, the results show how the linearized approach can be applied to efficiently evaluate and systematically optimize preliminary shared mooring system designs.

17 WIND ENERGY↗