Design, Fabrication, and Testing of a High Temperature Foam Core Recuperator for Small Modular Reactors
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Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic’s evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (M pro ), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. Here, this work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic “warhead” to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of M pro with micromolar affinities (K I of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.
The integration of distributed energy resources (DERs) in distribution networks has become a pivotal strategy for achieving decarbonization, enhancing grid resilience, and optimizing grid efficiency. Remote monitoring and control op- erations of such resources rely on a network of sensors and communication infrastructure, exposing the system to potential cyber threats. Therefore, as the deployment of DERs increases, ensuring secure monitoring and control becomes an imperative challenge. This paper utilizes real-time feeder models, which are instrumental in developing cybersecurity testbeds tailored for hardware-in-loop (HIL) systems. These models enable users to simulate cyber attacks in a real-world environment and analyze the power distribution operations during vulnerabilities. Furthermore, we discuss several practical sets of grid parameters to identify critical levels of DERs and evaluate various scenarios that simulate cyber threats on sensitive DERs. The modified IEEE 123-bus model is used as the test case for demonstrating the proposed scenarios. The findings from this study provide valuable insights into the vulnerabilities and potential consequences of cyber attacks on DERs, allowing for better mitigation strategies and improved cyber resilience in future distribution networks.
The increasing demand for lithium-ion batteries (LIBs) is driving development of advanced recycling and refining methods. In this project, new cathode active materials are prepared from recycled lithium battery materials and subsequently electrochemically evaluated in coin cells. In detail, scrap LIBs were mechanically processed into a metal rich black mass, then reductive acid leached into an aqueous metal solution, and finally high-purity metal hydroxide precursor materials were prepared by selective electrochemical flow precipitation. Closed-loop LIB recycling into active electrode materials will enable US manufacturers to break their reliance on foreign sources of critical materials. For example, electroextraction process used here has potential to significantly reduce chemical & water use as well as waste production as compared to traditional metallurgic techniques. To this end, electroextracted materials refined from used batteries were collected and processed to be tested as precursor cathode active material (pCAM). For this project, two samples of high purity recycled NMC hydroxide (~1 kg each) were received from N th Cycle’s Ohio demonstration facility. The NMC compositions of the two materials are similar, and the levels of impurities have been confirmed. Indeed, impurities like copper, boron and sodium are present in quantities that could negatively impact battery performance. However, some studies have demonstrated that the control of the quantity of elements like copper or boron may improve the electrochemistry properties of Lithium-NMC batteries. The principal objective is to determine the electrochemical performance of these 2 NMC hydroxide batches and clearly determine the effect of the impurities on the performance.
Current Circular Economy (CE) frameworks applied to product chains exhibit notable shortcomings. These include neglecting the resilience and robustness of design, requiring detailed economic and environmental data for impact assessment, and relying on qualitative rather than quantitative metrics capturing certain CE design aspects. In the current contribution, we addressed these shortcomings by developing an Ecologically inspired (Eco-inspired) Framework using the mathematical foundations of Ecological Network Analysis (ENA). While ENA metrics have previously found application in designing circular economies, particularly in Industrial Symbiosis (IS) networks, our adaptation tailors these metrics for use in product-level CE, recognizing the inherent distinctions between product-level CE and IS. Our Eco-inspired Framework comprises three key categories to provide holistic and granular-level metrics for designing product-level CE. The first set of metrics assesses circularity and resource efficiency. The second set gauges network intensity and robustness as complementary indicators ensuring a CE is both sustainable and resilient. The third group of metrics evaluates enhancement potential of CE strategies through introducing quantitative metrics for measuring the degree of closed-loop strategies and average circularity level of a CE design. The three comprehensive set of indicators within the Eco-inspired Framework uniquely captures various facets of circular design, whether originating from technological innovations and recovery improvement at the end of life (EoL), shifts in human consumption patterns, alterations in product design, or changes in business models. The framework’s application is tested in designing a CE for multilayer Polyethylene-Polyamide (PE-PA) films. Using the Eco-inspired Framework, we identified the best strategy for designing a resilient and sustainable CE for PE-PA films. A diverse set of EoL strategies along with a reduction in product consumption can improve circularity and resilience by 650% and 255%, respectively, and mitigate greenhouse gas emissions by 90%. The framework minimizes trade-offs between sustainability and circularity goals and offers insights on how to enhance each strategy for achieving a resilient and sustainable CE for products.
In the standard model of particle physics, the masses of the W and Z bosons, the carriers of the weak interaction, are uniquely related. A precise determination of their masses is important because quantum loops of heavy, undiscovered particles could modify this relationship. Although the Z mass is known to the remarkable precision of 22 parts per million (2.0 MeV), the W mass is known much less precisely. A global fit to measured electroweak observables predicts the W mass with 6 MeV uncertainty [1$-$3]. Reaching a comparable experimental precision would be a sensitive and fundamental test of the standard model, made even more urgent by a recent challenge to the global fit prediction by a measurement from the CDF Collaboration at the Fermilab Tevatron collider [4]. Here we report the measurement of the W mass by the CMS Collaboration at the CERN LHC, based on a large data sample of $W \to \mu \nu$ events collected in 2016 at the proton-proton collision energy of 13 TeV. The measurement exploits a high-granularity maximum likelihood fit to the kinematic properties of muons produced in W decays. By combining an accurate determination of experimental effects with marked in situ constraints of theoretical inputs, we reach a precise measurement of the W mass, of 80 360.2 $\pm$ 9.9 MeV, in agreement with the standard model prediction.
The realization of quantum networks requires the development of devices and methods unprecedented in conventional networks, and yet they critically depend on the latter for implementing foundational blocks and essential operations. We describe a testbed to support the development and testing of their functionality and performance by providing quantum and conventional data planes and devices, together with a secure conventional control plane. It incorporates a variety of entangled photon sources, qubit technologies, detector technologies, photonic components, and supporting conventional switches and workstations. It implements a novel fiber telescoping scheme that provides suites of connections using fiber spools and inground-aerial fiber loops. We briefly summarize a variety of experiments conducted over this testbed including: (i) flex-grid quantum connection experiments, (ii) quantum state and channel tomography, (iii) utilization of quantum key distribution keys to secure conventional encryption and firewall devices, (iv) comparative study of analytical capacity estimates and entanglement throughput, (v) deployed squeezing coexisting with conventional communications, and (iv) measurement of polarization time variation.
This paper presents a two-stage current limiting control strategy with fault ride-through capability for the direct-droop-controlled grid-forming (GFM) inverters. The proposed approach comprises Stage 1 and Stage 2 current limiting controls. During faults, Stage 1 current limiting control instantly clamps the inverter output current magnitude to its maximum transient limit by blocking the switching pulses to the insulated-gate bipolar transistors (IGBTs) with the help of a hysteresis loop control, and Stage 2 current limiting control limits the inverter output current magnitude to its steady-state limit by regulating the amplitude and frequency of the modulating waveform utilizing the active and reactive current limiting control loops. By implementing the current limiting actions in two stages, this approach can effectively limit the over-current within a few cycles after the fault occurrence and ensure synchronism to the grid after long-term fault events. The efficacy of the proposed control approach is validated through electromagnetic transient (EMT) simulation results conducted on a single GFM inverter-based test system and the IEEE 39-bus test system with multiple GFM inverters in the PSCAD platform.
The beneficial human gut bacterium Akkermansia muciniphila provides metabolites to other members of the gut microbiota by breaking down host mucin, but most of its other metabolic functions have not been investigated. A. muciniphila strain Muc T is known to use cobamides, the vitamin B 12 family of cofactors with structural diversity in the lower ligand. However, A. muciniphila Muc T is unable to synthesize cobamides de novo, and the specific forms that can be used by A. muciniphila have not been examined. We found that the levels of growth of A. muciniphila Muc T were nearly identical with each of seven cobamides tested, in contrast to nearly all bacteria that had been studied previously. Unexpectedly, this promiscuity is due to cobamide remodeling—the removal and replacement of the lower ligand—despite the absence of the canonical remodeling enzyme CbiZ in A. muciniphila. We identified a novel enzyme, CbiR, that is capable of initiating the remodeling process by hydrolyzing the phosphoribosyl bond in the nucleotide loop of cobamides. CbiR does not share similarity with other cobamide remodeling enzymes or B12-binding domains and is instead a member of the apurinic/apyrimidinic (AP) endonuclease 2 enzyme superfamily. We speculate that CbiR enables bacteria to repurpose cobamides that they cannot otherwise use in order to grow under cobamide-requiring conditions; this function was confirmed by heterologous expression of cbiR in Escherichia coli. Homologs of CbiR are found in over 200 microbial taxa across 22 phyla, suggesting that many bacteria may use CbiR to gain access to the diverse cobamides present in their environment.
We have made significant progress toward the development of an integrated nucleic acid amplification system for Autonomous Medical Devices Incorporated (AMDIs) Optikus handheld diagnostic device. In this effort, we developed a set of loop-mediated isothermal amplification (LAMP) primers for SARS-CoV-2 and then demonstrate amplification directly on a surface acoustic wave (SAW) sensor. We built associated hardware and developed a C-code to control the amplification process. The goal of this project was to develop a nucleic amplification assay that is compatible with SAW sensors to enable both nucleic and serological testing in a single handheld diagnostic device. Toward this goal, AMDI is collaborating Sandia National Laboratories to develop a rapid, portable diagnostic screening device that utilizes Sandias unique surface acoustic wave biosensor (SAW) for COVID-19 detection. Previously, the SANDIA- AMDI SAW sensor has successfully detected multiple high-profile bacteria viruses, including Ebola, HIV, Sin Nombre, and Anthrax. Over the last two years, AMDI and SANDIA have significantly improved the sensitivity and detection capability of the SAW biosensor and have also developed a modular hand-held, portable platform called the Optikus, which uses CD microfluidics and handheld instrumentation to automate all sample preparation, reagent introduction, sample delivery, and measurement for a number of different assay targets. We propose to use this platform for the development of a rapid (%3C30 minutes), point-of-care diagnostic test for detection of COVID-19 from nasal swab samples.
The Microreactor Applications Research Validation and Evaluation (MARVEL) reactor will offer experimental capabilities that are not currently available at DOE’s national laboratories. The MARVEL reactor is a microreactor which uses eutectic sodium-potassium alloy (NaK) as a primary coolant. The primary coolant is circulated through four primary loops by natural convection of the coolant. In each loop is a closed well which will accommodate an intermediate heat exchanger (IHX) for extracting heat from the loop. These wells will be referred to in this specification as the “IHX wells”. It is intended for the IHX containment to also be filled with NaK. The MARVEL design team is currently looking for alternative means of extracting heat from these wells to be utilized in downstream applications. This Heat Utilization System (HUS) will enable MARVEL operations including the ability to test, demonstrate, and address issues related to installation, startup, and operations. In addition, it will allow down-stream utilization of process heat for various uses. Depending on the methodology used, the interface between the secondary coolant and the HUS is the Process Heat Extraction System (PHES) and is the subject of this specification.
This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.
Electrochemical pH-swing strategies offer a promising avenue for cost-effective and energy-efficient carbon dioxide (CO 2 ) capture, surpassing the traditional thermally activated processes and humidity-sensitive techniques. The concept of elevating seawater’s alkalinity for scalable CO 2 capture without introducing additional chemical as reactant is particularly intriguing due to its minimal environmental impact. However, current commercial plants like chlor-alkali process or water electrolysis demand high thermodynamic voltages of 2.2 V and 1.23 V, respectively, for the production of sodium hydroxide (NaOH) from seawater. These high voltages are attributed to the asymmetric electrochemical reactions, where two completely different reactions take place at the anode and cathode. Here, we developed a symmetric electrochemical system for seawater alkalization based on a highly reversible and identical reaction taking place at the anode and cathode. We utilize hydrogen evolution reaction at the cathode, where the generated hydrogen is looped to the anode for hydrogen oxidation reaction. Theoretical calculations indicate an impressively low energy requirement ranging from 0.07 to 0.53 kWh/kg NaOH for established pH differences of 1.7 to 13.4. Experimentally, we achieved the alkalization with an energy consumption of 0.63 kWh/kg NaOH, which is only 38% of the theoretical energy requirements of the chlor-alkali process (1.64 kWh/kg NaOH). Further tests demonstrated the system’s potential of enduring high current densities (~20 mA/cm 2 ) and operating stability over an extended period (>110 h), showing its potential for future applications. Notably, the CO 2 adsorption tests performed with alkalized seawater exhibited remarkably improved CO 2 capture dictated by the production of hydroxide compared to the pristine seawater.
Background: Accelerated global control of cervical cancer would require primary prevention with human papillomavirus (HPV) vaccination in addition to novel screening program strategies that are simple, inexpensive, and effective. We present the feasibility and outcome of a community-based HPV self-sampled screening program. Methods: In Ile Ife, Nigeria, 9406 women aged 30–49 years collected vaginal self-samples, which were tested for HPV in the local study laboratory using Hybrid Capture-2 (HC2) (Qiagen). HPV-positive women were referred to the colposcopy clinic. Gynecologist colposcopic impression dictated immediate management; biopsies were taken when definite acetowhitening was present to produce a histopathologic reference standard of precancer (and to determine final clinical management). Retrospective linkage to the medical records identified 442 of 9406 women living with HIV (WLWH). Results: With self-sampling, it was possible to screen more than 100 women per day per clinic. Following an audio-visual presentation and in-person instructions, overall acceptability of self-sampling was very high (81.2% women preferring self-sampling over clinician collection). HPV positivity was found in 17.3% of women. Intensive follow-up contributed to 85.9% attendance at the colposcopy clinic. Of those referred, 8.2% were initially treated with thermal ablation and 5.6% with large loop excision of transformation zone (LLETZ). Full visibility of the squamocolumnar junction, necessary for optimal visual triage and ablation, declined from 68.5% at age 30 to 35.4% at age 49. CIN2+ and CIN3+ (CIN- Cervical intraepithelial neoplasia), including five cancers, were identified by histology in 5.9 and 3.2% of the HPV-positive women, respectively (0.9 and 0.5% of the total screening population), leading to additional treatment as indicated. The prevalences of HPV infection and CIN2+ were substantially higher (40.5 and 2.5%, respectively) among WLWH. Colposcopic impression led to over- and under-treatment compared to the histopathology reference standard. Conclusion: A cervical cancer screening program using self-sampled HPV testing, with colposcopic immediate management of women positive for HPV, proved feasible in Nigeria. Based on the collected specimens and images, we are now evaluating the use of a combination of partial HPV typing and automated visual evaluation (AVE) of cervical images to improve the accuracy of the screening program.
Technology readiness levels (TRLs) of electrolysis systems have dramatically increased in recent years as the interest in clean hydrogen production and decarbonization of transportation, industrial and other sectors increases across the globe. This is especially true of high temperature steam electrolysis (HTSE) / solid oxide electrolysis cell (SOEC) systems which show promise of much higher system efficiencies than other more developed electrolysis technologies. This possibility of higher efficiencies of HTSE / SOEC systems has been previously assumed to be theoretically possible but in recent years it has become less theoretical and more realistic as an increasing amount of suppliers complete lab and pilot tests showing very promising results. Research in the areas of manufacturing techniques, material selection, electrode and electrolyte compositions, and balance of plant size and integration continues at a fast pace as an increasing number of suppliers both internationally and domestically become involved. The advantages of HTSE become more pronounced when HTSE is coupled with nuclear power plants (NPPs). This is because thermal energy produced by the nuclear reactor can be used in a series of heat transfer loops and heat exchangers to vaporize HTSE feedwater, which drastically improves the economics of the process. Idaho National Laboratory (INL) has been very involved in the research and modeling of HTSE systems for a number of years, in collaboration with other national laboratories, academia, and industry stakeholders both on the hydrogen production as well as the hydrogen demand side. The modeling completed over the years on a large variety of projects has led to a wealth of knowledge at INL including in the area of the technoeconomic assessment (TEA) of HTSE systems. TEAs include process modeling of the HTSE systems to calculate system energy requirements and equipment sizing, followed by estimation of capital and operating costs to enable calculation of the levelized cost of hydrogen (LCOH). The TEA work performed has produced incremental improvements and tuning of the methods, assumptions, models, and results of the analyses as well as providing some opportunities for validating these results. The purpose of this document is to record the current baseline HTSE analyses led by INL to show the current status of assumptions and costs of these systems. Given the rapid development of this technology, the variety of suppliers entering the space, and the increasing attention government and industry are giving to such systems, this document may be updated on a periodic basis with updated analysis and assumptions. This document compiles various analyses results and approaches completed over a period of years into a single document to be used as a baseline going forward. It represents what the INL HTSE analysis group assumes to be the internal best estimate of the current operation, costs, and landscape of the HTSE industry state of the art capability for current SOEC technology in an Nth-of-a-Kind (NOAK) plant, which in this study is defined as existence of the manufacturing capacity to support previous deployment of N = 100 count of 25 MWe modular HTSE blocks (with modular equipment component cost reductions specified as following a 95% learning curve). That said, This is a public document and as such so no proprietary data was used or included in this report. There may be HTSE suppliers that have performance specifications, and cost estimates, and test data that differ from the analysis presented in this document. This document is meant to be a best conservative estimate of the technology and not an absolute reference.
Technology readiness levels (TRLs) of electrolysis systems have dramatically increased in recent years as the interest in clean hydrogen production and decarbonization of transportation, industrial and other sectors increases across the globe. This is especially true of high temperature steam electrolysis (HTSE) / solid oxide electrolysis cell (SOEC) systems which show promise of much higher system efficiencies than other more developed electrolysis technologies. This possibility of higher efficiencies of HTSE / SOEC systems has been previously assumed to be theoretically possible but in recent years it has become less theoretical and more realistic as an increasing amount of suppliers complete lab and pilot tests showing very promising results. Research in the areas of manufacturing techniques, material selection, electrode and electrolyte compositions, and balance of plant size and integration continues at a fast pace as an increasing number of suppliers both internationally and domestically become involved. The advantages of HTSE become more pronounced when HTSE is coupled with nuclear power plants (NPPs). This is because thermal energy produced by the nuclear reactor can be used in a series of heat transfer loops and heat exchangers to vaporize HTSE feedwater, which drastically improves the economics of the process. Idaho National Laboratory (INL) has been very involved in the research and modeling of HTSE systems for a number of years, in collaboration with other national laboratories, academia, and industry stakeholders both on the hydrogen production as well as the hydrogen demand side. The modeling completed over the years on a large variety of projects has led to a wealth of knowledge at INL including in the area of the technoeconomic assessment (TEA) of HTSE systems. TEAs include process modeling of the HTSE systems to calculate system energy requirements and equipment sizing, followed by estimation of capital and operating costs to enable calculation of the levelized cost of hydrogen (LCOH). The TEA work performed has produced incremental improvements and tuning of the methods, assumptions, models, and results of the analyses as well as providing some opportunities for validating these results. The purpose of this document is to record the current baseline HTSE analyses led by INL to show the current status of assumptions and costs of these systems. Given the rapid development of this technology, the variety of suppliers entering the space, and the increasing attention government and industry are giving to such systems, this document may be updated on a periodic basis with updated analysis and assumptions. This document compiles various analyses results and approaches completed over a period of years into a single document to be used as a baseline going forward. It represents what the INL HTSE analysis group assumes to be the internal best estimate of the current operation, costs, and landscape of the HTSE industry state of the art capability for current SOEC technology in an Nth-of-a-Kind (NOAK) plant, which in this study is defined as existence of the manufacturing capacity to support previous deployment of N = 100 count of 25 MWe modular HTSE blocks (with modular equipment component cost reductions specified as following a 95% learning curve). That said, this is a public document and as such so no proprietary data was used or included in this report. There may be HTSE suppliers that have performance specifications, and cost estimates, and test data that differ from the analysis presented in this document. This document is meant to be a best conservative estimate of the technology and not an absolute reference.
Intrinsic alignment (IA) of galaxies is a challenging source of contamination in the Cosmic shear (GG) signals. The galaxy intrinsic ellipticity-gravitational shear (IG) correlation is generally the most dominant component of such contamination for cross-correlating redshift bins. One of the most effective techniques to mitigate such contamination is the self-calibration (SC) method which extracts the IG correlation and allows for its removal from the GG signal. In a photometric survey, the SC method first extracts the galaxy number density-galaxy intrinsic ellipticity (gI) correlation from the observed galaxy-galaxy lensing correlation using the redshift dependence of lens-source pairs. The IG correlation is computed through a scaling relation using the gI correlation and other lensing observables. The applicability of the SC method has so far been focused on the linear IA scales and the linear galaxy bias. We extend the SC method beyond the linear regime by modifying its scaling relation which can account for the non-linear galaxy bias model and various IA models. In this study, we provide a framework to detect the IG correlation for the redshift bins for source galaxies for the proposed year 1 survey of the Rubin Legacy Survey of Space and Time (LSST Y1). We tested the method for the tidal alignment and tidal torquing (TATT) model of IA and we found that the scaling relation is accurate within 10% and 20% for cross-correlating and auto-correlating redshift bins, respectively. Hence the suppression of IG contamination in observed GG correlation can be accomplished with a factor of 10 and 5, for cross-correlating and auto-correlating redshift bins, respectively. We tested the method's robustness and found that the suppression of IG contamination by a factor of 5 is still achievable for all combinations of cross-correlating bins even with the inclusion of a moderate amount of uncertainties on IA and bias parameters, respectively. We also make available, a branch of the code FAST-PT to provide gI correlations up to 1-loop order term used by the new SC method.
This report was prepared on behalf of three Energy Frontier Topical Groups of the Snowmass 2021 Community Planning Exercise. It summarizes the status and implications of studies of strong interactions in high-energy experiments and QCD theory. We emphasize the rich landscape and broad impact of these studies in the decade ahead. Hadronic interactions play a central role in the high-luminosity Large Hadron Collider (LHC) physics program, and strong synergies exist between the (HL-)LHC and planned or proposed experiments at the U.S. Electron-Ion Collider, CERN forward physics experiments, high-intensity facilities, and future TeV-range lepton and hadron colliders. Prospects for precision determinations of the strong coupling and a variety of nonperturbative distribution and fragmentation functions are examined. We also review the potential of envisioned tests of new dynamical regimes of QCD in high-energy and high-density scattering processes with nucleon, ion, and photon initial states. The important role of the high-energy heavy-ion program in studies of nuclear structure and the nuclear medium, and its connections with QCD involving nucleons are summarized. We address ongoing and future theoretical advancements in multi-loop QCD computations, lattice QCD, jet substructure, and event generators. Cross-cutting connections between experimental measurements, theoretical predictions, large-scale data analysis, and high-performance computing are emphasized.