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At least 127 records · Page 7

Future energy infrastructure, energy platform and energy storage

Today fossil energy dominates energy consumption across the world. There has been an increasing momentum to reduce fossil energy consumption and increase renewable energy utilization to more than 70%. Such high penetrations of distributed renewable resources bring large uncertainty and complexity that cannot be easily handled by the current infrastructure. For the last two decades, telecommunication industry has gone through a similar transition from centralized to distributed systems, and more recently to platform approaches, which become the corner stone of today’s vibrant platform economy. Based on telecommunication history, we believe that a platform-based approach, called the energy platform, is a viable solution for addressing the renewable energy challenges. The energy platform consists of an array of computational algorithms, sensing and control technologies for key industry, energy generators and users to jointly manage and control the complex energy infrastructure. It includes the following key components: (1) the hardware and software to generate, store, control and transmit electricity/data (the energy cloud), (2) the digital platforms and tools for key energy producers, digital, service and communication industry to share the assets, participate in and manage the complex infrastructure, and for customers to participate in energy transactions, (3) the transaction platform for new service and business models. Additionally, the energy platform requires breakthroughs in large scale energy storage and many other areas including efficient power electronics, sensors and controls, new mathematical and computational tools, and deep integration of energy technologies and information sciences to control and stabilize such complex chaotic systems.

25 ENERGY STORAGE↗

Analysis of Brackish Water Desalination for Municipal Uses: Case Studies on Challenges and Opportunities

Brackish water is a widely available, nontraditional water resource that can augment limited freshwater supplies. Although brackish water desalination has been continuously implemented in the United States and worldwide, it is necessary to reduce further its energy consumption, costs, and environmental impacts. This study conducted technoeconomic analyses to evaluate the current desalination and brine management technologies, focusing on the key factors and opportunities for sustainable brackish water desalination for municipal uses. Three case studies were selected as baseline representative of different geographic and operational conditions, including water quality, plant size, pretreatment, desalination, and concentrate management. Here, the technoeconomic analyses and model simulations identified challenges, opportunities, and research priorities to achieve specific pathways for enhanced brackish water desalination regarding levelized costs of water, electricity intensity, water recovery, zero liquid discharge, and brine valorization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Data Driven Analysis for Modernization Program Management

NNSA is responsible for managing national nuclear security missions: ensuring a safe, secure, and reliable nuclear deterrent; supplying nuclear fuel to the Navy; and supporting the nation’s nuclear nonproliferation efforts. However, over half of NNSA’s facilities are more than 40 years old, and roughly one-third date back to the Manhattan Project. To execute its critical nuclear security missions, NNSA is making large investments to modernize its nuclear production capabilities. This ramp up represents NNSA’s largest modernization effort since the Cold War. Given the scale of these efforts, NNSA’s Office of Secondary Stage Production Modernization has implemented data driven techniques to prioritize investments and inform strategic decision making. NNSA, with support from its site managing contractors, has developed and implemented an integrated schedule and risk management system to address the issues and limitations with the traditional approach. The multi-year integrated schedules are key to identifying program linkages and managing large portfolios comprised of many different projects and efforts. In conjunction with the integrated schedule, a new program risk management system has also been developed and implemented, which manages program risks and opportunities, along with specific mitigation strategies to reduce or eliminate the risks per timelines that are tracked in the integrated schedule. This paper is supplemented with a follow-on presentation on effective management of program material and throughput modeling.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

2020 High Energy Physics Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the United States and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Throughout 2020,ESnet and the Office of High Energy Physics (HEP) of the DOE SC organized an ESnet requirements review of HEP-supported activities. Preparation for this event included identification of key stakeholders: program and facility management, research groups, technology providers, and a number of external observers. These individuals were asked to prepare formal case study documents about their relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion. ESnet and ASCR use requirements reviews to discuss and analyze current and planned science use cases and anticipated data output of a particular program, user facility, or project to inform ESnet’s strategic planning, including network operations, capacity upgrades, and other service investments. A requirements review comprehensively surveys major science stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Basic Energy Sciences Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Between March and September 2022, ESnet and the Office of Basic Energy Sciences (BES) of the DOE SC organized an ESnet requirements review of BES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the BES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

97 MATHEMATICS AND COMPUTING↗

High Energy Physics Network Requirements Review: One-Year Update

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education (R&E) networking community. In April 2022, ESnet and the Office of High Energy Physics (HEP) of the DOE SC organized an ESnet requirements review of HEP-supported activities. Preparation for the review included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about the group’s relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

97 MATHEMATICS AND COMPUTING↗

Biological and Environmental Research Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education (R&E) networking community. Between August 2022 and April 2023, ESnet and the Office of Biological and Environmental Research (BER) of the DOE SC organized an ESnet requirements review of BER-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the BER ESS program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward. A series of pre-planning meetings better prepared case study authors for this task, along with guidance on how the review would proceed in a virtual fashion.

97 MATHEMATICS AND COMPUTING↗

Bridging paradigms: Designing for HPC-Quantum convergence

Here, this paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum–classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum–classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources.

97 MATHEMATICS AND COMPUTING↗

Advanced architectures for high-performance quantum networking

As practical quantum networks prepare to serve an ever-expanding number of nodes, there has grown a need for advanced auxiliary classical systems that support the quantum protocols and maintain compatibility with the existing fiber-optic infrastructure. We propose and demonstrate a quantum local area network design that addresses current deployment limitations in timing and security in a scalable fashion using commercial off-the-shelf components. First, we employ White Rabbit switches to synchronize three remote nodes with ultra-low timing jitter, significantly increasing the fidelities of the distributed entangled states over previous work with Global Positioning System clocks. Second, using a parallel quantum key distribution channel, we secure the classical communications needed for instrument control and data management. Therefore, the conventional network that manages our entanglement network is secured using keys generated via an underlying quantum key distribution layer, preserving the integrity of the supporting systems and the relevant data in a future-proof fashion.

97 MATHEMATICS AND COMPUTING↗

Nuclear Physics Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet interconnects DOE national laboratories, user facilities, and major experiments so that scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large datasets, and access distributed data repositories. ESnet is specifically built to provide Between July 2023 and October 2023, ESnet and the Nuclear Physics program (NP) of the DOE SC organized an ESnet requirements review of NP-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the NP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

97 MATHEMATICS AND COMPUTING↗

Accelerating Scientific Workflows on HPC Platforms with In Situ Processing

Scientific workflows drive most modern large-scale science breakthroughs by allowing scientists to define their computations as a set of jobs executed in a given order based on their data dependencies. Workflow management systems (WMSs) have become key to automating scientific workflows-executing computational jobs and orchestrating data transfers between those jobs running on complex high-performance computing (HPC) platforms. Traditionally, WMSs use files to communicate between jobs: a job writes out files that are read by other jobs. However, HPC machines face a growing gap between their storage and compute capabilities. To address that concern, the scientific community has adopted a new approach called in situ, which bypasses costly parallel filesystem I/O operations with faster in-memory or in-network communications. When using in situ approaches, communication and computations can be interleaved. In this work, we leverage the Decaf in situ dataflow framework to accelerate task-based scientific workflows managed by the Pegasus WMS, by replacing file communications with faster MPI messaging. We propose a new execution engine that uses Decaf to manage communications within a sub-workflow (i.e., set of jobs) to optimize inter-job communications. We consider two workflows in this study: (i) a synthetic workflow that benchmarks and compares file- and MPI-based communication; and (ii) a realistic bioinformatics workflow that computes mu-tational overlaps in the human genome. Experiments show that in situ communication can improve the bioinformatics workflow execution time by 22% to 30% compared with file communication. Our results motivate further opportunities and challenges for bridging traditional WMSs with in situ frameworks.

Decaf↗

Flue-Gas Desulfurization Effluent Management using an Innovative Low-Energy Biosorpotion Treatment System to Remove Key Contaminants

Among the most critical water contaminants of concern affecting wide geographical regions and a number of industries and natural systems is selenium. Selenium found in surface, ground and wastewater in originates from natural sources, as well as industrial sources such as petroleum refineries, electronics manufacturing, pesticides, and coal power plants and mining also contribute to selenium contamination in water in the US. At high concentrations, selenium is toxic to human and wildlife. There are a number of technologies that have been used to treat selenium and other similar contaminants in water. Biological treatment of selenium has been used in the past to reduce soluble SeVI and/or SeIV to insoluble Se0, which is then filtered in the same vessel. The insoluble selenium (Se0) is then backwashed from the system and solids are separated for subsequent disposal, if they meet the leaching and water content criteria. In order to promote biological reduction to insoluble Se0, heating of bioreactor is needed in some applications, and excess food source (electron donor) is added so that all selenium can be filtered. An additional disadvantage of these systems is the significant amount of water lost due to extensive and frequent backwash and rinse cycles. When comparing the advantages and energy requirements of the various treatment technologies, RO membrane filtration immediately stands out due to the excessive energy expenditure needed to pump water across the membrane although RO is an effective way to remove selenium. In addition, RO requires extensive pretreatment, such as MF membrane, and frequent maintenance, rendering it an expensive option that may be out of reach for certain applications. In fact, although the performance was good during the pilot testing by the NSMP Working Group for treatment naturally occurring selenium in the surface water, the high electricity requirements and significant reject water stream made it an infeasible alternative. While conventional ion exchange maybe an effective treatment option, it requires frequent regeneration of the resin when applied to highly contaminated water, which leads to several tons of contaminant-laden, high-salinity brine that needs to be disposed off-site each day. One of the water systems in the west coast currently uses ion exchange for selenium treatment and has been trucking selenium laden hazardous brine waste weekly in the last several years. Pneumatic pumping and rinse water pumping required for ion exchange also increase the energy usage. In comparison, adsorption process is a passive treatment system where contaminated water comes in contact with an adsorption media in a vessel. Typically, there is no mixing, backwash, or recycle pumping required, thus significantly reducing the energy usage. A passive single-use adsorption system does not require backwash, thereby generating small amount of process waste, and producing the highest water yield among the alternatives. The energy and water efficiencies, and applicability for SeVI and SeIV are summarized in Table 1. Despite these benefits though, adsorption typically does not work for the most oxidized form of selenium (SeVI). The innovative biosorption process integrates both process to increase the treatment efficiency while minimizing energy, chemical, and time required to treat both SeVI and SeIV. Additional advantages include simple partial biological reduction with reduced on-site waste generation, which lead to water and electricity savings, and less operational need compared to biological treatment alone. This makes biosorption especially suitable for remote areas, where liquid backwash and brine disposal may be cost prohibitive or infeasible.

20 FOSSIL-FUELED POWER PLANTS↗

Panel Session 120: Roundtable: Waste Management - Energy Facilities Contractor Operating Group (EFCOG WM)

This Roundtable focused on the WM EFCOG working group as they met to discuss a variety of issues of importance to US DOE waste management operations. The purpose of the WM EFCOG is to seek out and promote the best management and operating practices, cost effective technologies and disposal options for all waste streams generated at US DOE facilities whether destined for US DOE or commercial facilities. Each site representative provided a radioactive waste management lessons learned briefing. Panelists with presentations: Savannah River Site (SRS) Solid Waste Program Update (Kerri Crawford); Idaho Clean-up Project Status (Frederick Hughes); West Valley Demonstration Project Update - EFCOG Waste Management Working Group (John Rendall); Key Project Challenges and Successes (Kalli Shupe); EFCOG WMWG Challenging Waste Subgroup Update (Tammy Monday); CH TRU Difficult Waste Stream Processing Challenges (Daniel Pancake); Argonne Building 350 Clean-up Success Using Effective Work Control (Daniel Dilday); Problematic Waste Streams on the Oak Ridge Reservation (John Wrapp)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Treatment of uncertainties for security-related design aspects of advanced reactors when using a risk-informed licensing approach

Sabotage of nuclear plants and theft of special nuclear material are different from many other issues potentially affecting public health and safety, and some of those differences drive the content of the present report. A high-level indication of these differences is provided in the US Nuclear Regulatory Commission’s Safety Goal Policy. Promulgated in the mid-1980’s, when it had become reasonably clear that risk analysis had improved to the point where it was possible to understand the risks associated with plant operation, the Safety Goal Policy articulates qualitative safety goals and quantitative health objectives that are meant to guide regulatory and risk management activities, with the following key exceptions noted in the original policy statement: The possible effects of sabotage or diversion of nuclear material are also not presently included in the safety goals. At present there is no basis on which to provide a measure of risk on these matters. It is the Commission’s intention that everything that is needed will be done to keep these types of risks at their present very low level; and it is the Commission’s expectation that efforts on this point will continue to be successful. With these exceptions, it is the Commission’s intent that the risks from all the various initiating mechanisms be considered to the best of the capability of current evaluation techniques. The present report discusses extensions of classical risk management to address some of the special issues that arise in the context of security. Although the present emphasis is on physical security, some attention will be paid to cyber security. A particular focus of the report is on quantitative framework to manage and address uncertainties. This framework is demonstrated via a couple of hypothetical examples.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

The Role of Interfacial Thermal Resistance in Li-Ion Battery Thermal Management: Preprint

Temperature critically affects the performance, life and safety of lithium-ion batteries. Therefore, it is essential to understand heat generation and dissipation within individual battery cells and battery packs to plan a proper thermal management strategy. One of the key challenges is that interfacial heat transfer of a battery unit is difficult to quantify. The steady-state absolute method and the transient laser-flash-diffusivity method were employed to measure heat conductivities of battery layer stacks and individual battery layer separately. Results show flash diffusivity method gives higher thermal conductivity at both cross-plane and in-plane directions. The difference is primarily caused by interfacial thermal resistance so that it can be estimated by steady-state and transient measurements. To investigate the effects of interfacial thermal transport beyond individual cell level, a multiphysics battery model is used. The model is built upon a multi-scale multi-domain modeling framework for battery packs that accounts for the interplay across multiple physical phenomena. Benefits of a battery module using thermal management materials are quantified through numerical experiments. During a thermal runaway event, it is found interfacial thermal resistance can mitigate thermal runaway in a battery module by significantly reducing heat transfer between cells.

33 ADVANCED PROPULSION SYSTEMS↗

High Energy Physics Network Requirements Review: Two-Year Update

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet interconnects DOE national laboratories, user facilities, and major experiments so that scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large datasets, and access distributed data repositories. ESnet is specifically built to provide a range of network services tailored to meet the unique requirements of the DOE’s data-intensive science. In July 2023, the Energy Sciences Network (ESnet) and the High Energy Physics program (HEP) of the DOE SC organized an interim ESnet requirements review of HEP-supported activities, to follow up on the work started during the 2020 HEP Network Requirements Review. Preparation for these events included checking back with the key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare updates to their previously submitted case study documents, so that ESnet could update the understanding of any changes to the current, near-term, and long-term status, expectations, and processes that will support the science activities of the program.

97 MATHEMATICS AND COMPUTING↗

Heat Loss Effects on Emissions in an NH 3 RRQL Combustor

Ammonia (NH 3 ) is a carbon-free energy carrier with an infrastructure for production, storage, and distribution. There is interest in direct NH 3 combustion, but managing pollutant emissions is a key challenge, particularly nitric oxides (NO x ) due to the fuel-bound nitrogen atom, nitrous oxide (N 2 O), which is a potent greenhouse gas, and unburned NH 3 , which is harmful to humans and the environment. Rich staged combustor concepts with extended primary residence times (τ res,primary ), like Rich-Relax- Quick-mix-Lean (RRQL), offer a viable pathway for direct NH 3 combustion with low levels of NO x formation. However, minimizing secondary emissions such as N 2 O and unburned NH 3 and hydrogen (H 2 ) remains a critical challenge. Prior atmospheric-pressure studies have demonstrated that RRQL operation with sufficiently long τ res,primary enables substantial NO x relaxation and promotes NH 3 cracking to H 2 , if heat losses from the relaxation stage are limited. However, the combined influence of elevated pressure and long residence time on RRQL performance has not been explored. The present work examines RRQL operation at pressures up to 5 bar and elevated τ res,primary . Exhaust measurements of NO x , NH 3 , and N 2 O are used to quantify the extent of NO x relaxation and NH 3 cracking under nonadiabatic conditions. To contextualize and quantify the effects of heat losses in the experimental data, chemical reactor networks (CRNs) incorporating prescribed heat loss rates are employed to assess the sensitivity of emissions to thermal losses in the relaxation stage. Collectively, the results demonstrate that management and quantification of heat losses are essential to preserve NO x relaxation and limit NH 3 and N 2 O emissions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Automation of Vulnerability and Patch Management: Information Extraction, Association, and Optimization

Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout the vulnerability management cycle. Security advisories provide key information about mitigation strategies, or actions that can be taken when a patch is unavailable or cannot be installed. Details of these strategies are not shared in public vulnerability databases and must be found manually. We approach this problem by designing a solution to automatically identify that information within vendor security advisories and retrieve it for operator use. We start with an approach that requires domain-specific knowledge of certain frequently-seen reference websites. Next, an approach that can work on an arbitrary website but relies on certain keywords. Finally, an approach that uses Natural Language Processing (NLP) methods and does not require specific knowledge or keywords. Each of these approaches is more general than its predecessor; we demonstrate high accuracy for all approaches Advisories also often contain details of affected products in non-standard or natural language formats. While this information can be easily understood when read by an operator, the non-standard format acts as a barrier to effective automation. We provide an approach for the first step in this process: identifying vendors in security advisories and mapping them to a standard framework for representing digital assets and software products. We evaluate five established string similarity algorithms, plus one of our own design that combines string similarity and information theory, on the task of mapping vendors to their corresponding entries in the Common Platform Enumeration (CPE) repository. Our results show that our proposed metric outperforms all others. Due to the constraints on time, finances, and personnel for organizations, Large Language Models (LLMs) may seem like attractive opportunities for security operators to speed up information gathering; however, it is still not clear whether LLMs can handle vulnerability management tasks well. To answer this question, we perform an empirical study of LLMs’ ability to provide consistent, accurate information about vulnerabilities in order to guide organizations in their adoption of LLMs. We observe poor performance for all models tested, suggesting that these models are not well-suited to the consistent retrieval of accurate vulnerability information. Finally, once vulnerabilities have been identified and any additional information has been obtained, operators must decide which remediation actions to implement based on their available resources. This already-complex problem becomes even more so when we consider that a vulnerability may have multiple avenues for remediation. We formulate this scenario as two knapsack problems and provide solutions, which we then compare against several existing strategies for vulnerability prioritization seen in real operational environments.

McClanahan, Kylie↗