Simulation-based Framework Research Articles

Modeling the Nonlinear Power Spectrum in Low-redshift H i Intensity Mapping

Abstract Neutral Hydrogen (H i) serves as a competitive tracer of the large scale structures, especially with the advent of more Intensity Mapping H i surveys. In this work, we present a simulation-based framework to forecast the H i power spectrum on non-linear scales (k ≳ 1 Mpc−1), as measured by interferometer arrays like MeerKAT in the low-redshift (z ≤ 1.0) universe. Building on a galaxy-based H i mock catalog, we meticulously consider various factors, including the emission line profiles of H i discs and some observational settings, and explore their impacts on the H i power spectrum. We find that the H i power spectrum is relatively insensitive to the profile shape of H i emission line at these scales, while showing a strong correlation with the profile width. We propose an empirical model to simulate the emission line profile width for each H i source. The resulting H i power spectrum is consistent with the results from the IllustrisTNG hydrodynamical simulation and follows the trend of the measurements obtained by MeerKAT at z ≈ 0.44, though with a significantly lower amplitude. We demonstrate how the HI abundance ΩHI and the amplitude parameter in our width model can be constrained with the MeerKAT measurements, though a strong degeneracy is uncovered. Our work shows the potential to constrain statistical properties of HI emission line profiles with future HI Intensity Mapping experiments.

Power calculation for mosquito bioassays: Quantifying variability in the WHO tube bioassay and developing sample size guidance for the PBO synergism assay using a Shiny application

Background The WHO tube bioassay is a method for exposing mosquitos to determine susceptibility to insecticides, with mortality to discriminating doses <98% indicating possible resistance and <90% confirming resistance. This bioassay is also used for synergism testing to assess if susceptibility is restored by pre-exposure to the synergist piperonyl butoxide. Methods Here we perform testing with pyrethroid-susceptible and pyrethroid-resistant An. gambiae to quantify the variability of the WHO tube bioassay and identify its sources. These estimates of within and between day variability are then used to evaluate the power of the bioassay to detect a mortality difference between pyrethroid-only and pyrethroid-PBO. Results We show that approximately two-thirds of variation occurs between days, with the pyrethroid-susceptible strain twice as variable as the pyrethroid-resistant strain. The total number of mosquitoes in the tube and their bodyweight contributes to approximately 10% of this variability. Changes in temperature and humidity, within a climate-controlled insectary, didn’t impact mortality. Using a simulation-based framework, we show that the current synergism guidelines, using a 4x4 design, can reliably detect a difference between 90% and 100% mortality (>90% power). However, as the mortality of either group gets closer to 50%, a 10% difference between groups is more difficult to reliably detect. In the worst-case scenario where the mortality of either group is 50%, the mortality difference must be >22.5% to be detected with 80% power. We provide an R shiny application to assess power for other comparisons. Conclusions Our findings indicate that detecting synergism with the WHO tube assay is more difficult than assumed by the current WHO guidelines. Additionally, we demonstrate the value of using a Shiny application to make the outputs of simulation-based power analysis readily available to end-users, allowing them to determine the number of tubes needed to detect a given mortality difference.

A simulation framework for automotive cybersecurity risk assessment

Human-initiated disruptions such as cyberattacks on connected vehicles have the potential to cause cascading failures in transport systems, leading to systemic risks. ‘ISO/SAE 21434:2021 Road vehicles - Cybersecurity engineering’ is the current standard for risk management of road vehicles. However, the threat analysis and risk assessment framework given in the standard focuses on asset-level analysis and assessment. Hence, this study develops a novel simulation-based framework to perform threat analysis and risk assessment on connected vehicles from a transport network perspective. The proposed framework is developed based on the ISO/SAE 21434 threat analysis and risk assessment methodology. We demonstrate the applicability and usefulness of the framework through a remote attack via the cellular network on the in-vehicle communication bus system of a connected vehicle to show the potential impacts on the transport network. Based on the findings of our case studies, we exemplify how cyberattacks on individual system components of a connected vehicle have the potential to cause systemic failures.

Analyzing the thermal performance of walling systems in low-income housing through computational fluid dynamics approach

The thermal properties of wall materials have a direct impact on the thermal performance of the indoor environment, by also influencing the occupant’s well-being. Coupled with airflow dynamics, the thermal performance of wall materials influences the indoor air temperature distribution thereby impacting thermal comfort, particularly in naturally ventilated (NV) spaces. However, defining the relationships between the wall characteristics, airflow dynamics, and thermal comfort for selecting the appropriate wall material for low-income NV buildings is still not evident in the current literature. Therefore, this study proposes a simulation-based framework that considers the relationships between wall materials, airflow dynamics, and thermal comfort using a computational fluid dynamics (CFD) approach by spatially plotting the thermal comfort within the NV spaces.The study analyses locally accessible wall materials, subjecting them to different operating conditions within NV dwelling units of low-income housing in India. Out of the analyzed wall materials, aerated autoclaved blocks exhibited a significant indoor temperature reduction of 20.7 % compared to the compressed stabilized earth blocks, with constrained airflow exacerbating temperature fluctuations. The global implications of these findings are substantial for urban low-income dwellers experiencing excessive heat. Mass housing efforts worldwide can benefit from using thermally efficient materials in construction thereby improving living conditions, and mitigating the impact of climate change on thermal comfort and health.

Improving Effectiveness of the Safety Service Patrol Programs: A Discrete Event-Based Simulation Approach

Safety service patrols (SSPs) play an important role in incident management on highways. It is critical to respond to incidents in a timely manner as this can significantly reduce nonrecurrent congestion and improve safety. Therefore, it is essential to allocate available SSP vehicles to highway segments such that their effectiveness is maximized. This study aimed to develop a simulation-based framework to assist with SSP service optimization. More specifically, a discrete event-based simulation tool (i.e., SSP-OPT) with customizable parameters was developed to help plan the optimum patrol routes based on available SSP resources and predicted incidents. The developed tool was tested with roadway traffic and incident data from the Virginia highway network. After model calibration, the simulation results showed that the developed SSP-OPT tool could replicate the patrol routes with similar performance to the field observations, validating the tool. Further, adopting the tool for corridor-level optimization could help to identify the best patrol plan to minimize SSP response time and maximize SSP response rates for a given number of SSP vehicles. The SSP-OPT tool requires minimal user input (e.g., segment lengths, annual average daily traffic) and has the flexibility to be easily applied to any highway corridor once calibrated. The tool generates various performance metrics to enable more informed decision making in SSP route planning.

A Simulation-Based Framework to Determine the Kinematic Compatibility of an Augmentative Exoskeleton during Walking

Augmentative exoskeletons (AEs) are wearable orthotic devices that, when coupled with a healthy individual, can significantly enhance endurance, speed, and strength. Exoskeletons are function-specific and individual-specific, with a multitude of possible configurations and joint mechanisms. This complexity presents a challenging scenario to quantitatively determine the optimal choice of the kinematic configuration of the exoskeleton for the intended activity. A comprehensive simulation-based framework for obtaining an optimal configuration of a passive augmentative exoskeleton for backpack load carriage during walking is the theme of this research paper. A musculoskeletal-based simulation approach on 16 possible kinematic configurations with different Degrees of Freedom (DoF) at the exoskeleton structure’s hip, knee, and ankle joints was performed, and a configuration with three DoF at the hip, one DoF at the knee, three DoF at the ankle was quantitatively chosen. The Root Mean Square of Deviations (RMSD) and Maximum Deviations (MaxDev) between the kinematically coupled human–exoskeleton system were used as criteria along with the Cumulative Weight Score (CWS). The chosen configuration from the simulation was designed, realised, and experimentally validated. The error of the joint angles between the simulation and experiments with the chosen configuration was less than 3° at the hip and ankle joints and less than 6° at the knee joints.

The new bond on the block — Designing a carbon-linked bond for sustainable investment projects

Over the last decade, the green bond market experienced strong growth rates fueled by the need to combat climate change. However, the discourse on enhancing the effectiveness of green bonds primarily revolves around regulatory measures, often overlooking the possibility of designing inherent incentives. We show that a green bond with a coupon structure positively related to the carbon price development stimulates (early) investment in an emission-reducing project and creates higher net present values (NPVs) when applied in project financing. In our simulation-based framework, we model carbon prices using a geometric Brownian motion, and create a general optimal stopping time problem regarding the start of the project. The green bond in our setting carries the risk of default, also mitigated by its carbon price-linked coupon structure.

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

Simulated Guidance in Interpreting Nano-Patterned Co70Fe30 Film Imaging with Differential Phase Contrast.

Our paper introduces a simulation-based framework designed to interpret differential phase contrast (DPC) magnetic imaging within the transmission electron microscope (TEM). We investigate patterned magnetic membranes, particularly focusing on nano-patterned Co70Fe30 thin-film membranes fabricated via focused ion beam (FIB) milling. Our direct magnetic imaging reveals regular magnetic domain patterns in these carefully prepared systems. Notably, the observed magnetic structure aligns precisely with micromagnetic simulations based on the dimensions of the underlying nanostructures. This agreement emphasizes the usefulness of micromagnetic simulations, not only for the interpretation of DPC data, but also for the prediction of possible microstructures in magnetic sensor systems with nano-patterns.

Reverse causation bias: A simulation study comparing first- and second-line treatments with an overlap of symptoms between treatment indication and studied outcome.

Reverse causation is a challenge in many drug-cancer associations, where the cancer symptoms are potentially mistaken for drug indication symptoms. However, tools to assess the magnitude of this type of bias are currently lacking. We used a simulation-based approach to investigate the impact of reverse causation on the association between the use of topical tacrolimus and cutaneous T-cell lymphoma (CTCL) in a multinational, population-based study using topical corticosteroids (TCS) as comparator. We used a multistate model to simulate patients' use over time of a first- (TCS) and second-line treatment (topical tacrolimus), onset of atopic dermatitis (indication for drugs) and CTCL (the studied outcome). We simulated different scenarios to mimic real-life use of the two treatments. In all scenarios, it was assumed that there was no causal effect of the first- or second-line treatment on the occurrence of CTCL. Simulated data were analysed using Cox proportional hazards models. The simulated hazard ratios (HRs) of CTCL for patients treated with tacrolimus vs. TCS were consistently above 1 in all 9 settings in the main scenario. In our main analysis, we observed a median HR of 3.09 with 95% of the observed values between 2.11 and 4.69. We found substantial reverse causation bias in the simulated CTCL risk estimates for patients treated with tacrolimus vs. TCS. Reverse causation bias may result in a false positive association between the second-line treatment and the studied outcome, and this simulation-based framework can be adapted to quantify the potential reverse causation bias.

COPSRO: An Offline Empirical Game Theoretic Method With Conservative Critic.

This article studies how to learn approximate Nash equilibrium (NE) from static historical datasets by empirical game-theoretic analysis (EGTA), which provides a simulation-based framework to model complex multiagent interactions. Generally, EGTA requires plentiful interactions with the environment or simulator to estimate a cogent and tractable game model approximating the underlying game. However, these exploratory interactions often suffer from low data utilization efficiency and may not be feasible in risk-sensitive applications. To address these problems, this article investigates a new EGTA paradigm for offline settings and introduces a novel algorithm called conservative offline policy space response oracle (COPSRO) to identify NE from fixed datasets without active data collection. COPSRO initiates by extracting a set of strategies from the offline dataset to construct an overcomplete strategy population, achieving an approximation to the policy space of the original game. Then, COPSRO integrates the conservative critic (CC) to tackle the challenge of overestimation inherent in offline learning scenarios. Additionally, it devises the offline NE solver to iteratively compute approximate NE. Consequently, COPSRO can ascertain equilibrium strategies without real-world interaction, markedly enhancing its utility in risk-averse settings. This article provides both theoretical analysis and empirical evaluation to demonstrate the effectiveness and superiority of COPSRO across various real-world tasks in the offline setting. Our method surpasses existing approaches in terms of convergence and exploitability, especially when the coverage ration of dataset is low (20% or 10%).

Dynamic Process Force Simulation Model for Multi-Axis Milling Processes

Process forces are critical in cutting operations, affecting tool integrity and product quality. Milling is characterized by rapid process force fluctuations and unpredictable tool wear, hindering closed-loop control systems in industry, so far. Establishing control strategies require a high developmental effort. A simulation-based framework of the milling process eases the design of novel control strategies. However, this requires a realistic model of the entire system, which is currently lacking. In this work, a dynamic force simulation model for both three- and multi-axis milling is proposed. The results show relative errors of the model between 4.6 % and 10.6 %.

A spatial mapping of thermal comfort and air quality (SMTC-AQ) framework for the built environment using computational fluid dynamics approach

Indoor environmental quality (IEQ), driven by thermal comfort and indoor air quality (IAQ), is a major factor that impacts occupant's well-being and productivity since people spend 80–90 % of their time indoors. The current approaches for assessing and quantifying IEQ presume fixed values and ranges in naturally ventilated (NV) spaces within the simulation environment. However, such an approach cannot capture point-to-point changes that occur in the built environment due to localized variations of environmental variables such as air velocity and temperature. This study proposes a sensing and simulation-based framework, Spatial Mapping of Thermal Comfort and Air Quality (SMTC-AQ) in a computational fluid dynamics (CFD) environment to address this. Salient characteristics of SMTC-AQ include the modification of predicted mean vote (PMV) for NV spaces, specifically for the Indian context, and the implementation of the under-explored concept of domain-decoupling utilizing the ISHRAE weather file. Using SMTC-AQ, the study determines the optimal window-to-wall ratio (WWR) and opening-to-wall ratio (OWR) for a case study room in an Indian academic building, considering both IAQ and thermal comfort. In the study, indoor air velocity and temperature are first validated with field data, followed by a scenario analysis to assess the impact of opening characteristics on IEQ. The findings show that for building spaces in tropical climates with moderate outdoor temperatures and lower relative humidity, thermal comfort, and IAQ can be improved by implementing higher WWR and OWR. Moreover, WWR is a more relevant criterion for improving thermal comfort and a better OWR design is crucial for enhancing IAQ in NV spaces.

The NexGen burner: Non-Reacting gaseous and spray dynamics

This paper reports the results of a comprehensive investigation to identify turbulent multiphase fluid dynamics through the NexGen burner used for fireworthiness certifications by the Federal Aviation Administration. The burner geometry consists of a draft tube, a stator to swirl the incoming air, a turbulator that is placed immediately downstream of the stator to increase the turbulence intensity of the swirling flow, and a burner cone that exhausts into the atmosphere. A Delavan type-W fuel nozzle, attached downstream of the turbulator injects Jet A into the burner cone, where it mixes with the turbulent swirling air, vaporizes and subsequently burns. The operating conditions consist of air and fuel mass flow rates of 0.0384 kg/s and 2.5 Gph at room temperature and pressure conditions. In this paper, we report on non-reacting fluid dynamics; the reacting flow analysis will be presented in a subsequent manuscript. To systematically analyze the flow configuration, three calculations are conducted. First, to validate our large eddy simulation-based framework, a case without the burner cone is conducted—our results are in reasonable agreement with experimental measurements. In the second configuration, to isolate the effect of liquid injection on turbulent flow physics, simulations are conducted with the burner cone but without fuel spray. Detailed analyses are conducted, including identification of flow structures, swirl generation, and the turbulent kinetic energy budget. Finally, Jet A is injected into the burner, and the spray and gaseous flow fields are quantified and compared with the flow structures developed without spray injection.

Assessing resource utilization, food production and ecosystem service value from spring wheat farmland under reduced irrigation and N fertilization management using a process simulation-based framework

Excessive irrigation and nitrogen (N) fertilization have caused severe reductions in farmland water and N productivity, and functional degradation of agroecosystems in arid to semi-arid regions. Thus, a process simulation-based framework for resource, food, and ecology (RFE) trade-offs was used to optimize the irrigation and N fertilization strategy (IRNF). A scenario study was conducted based on the framework that was developed in our previous companion study in the arid to semi-arid regions of Northwest China. Based on the local IRNF (i.e., 450 mm irrigation and 340 kg ha−1 N fertilization), reduced irrigation and N fertilization management scenarios were proposed considering eleven irrigation depth reduction cases and ten N fertilization reduction cases in three hydrological years (wet, normal, and dry). The integrated responses of spring wheat yield, water and N productivity, and ecosystem service value (ESV) to various IRNFs were quantified and assessed. The results showed that the local IRNF obtained higher yields and positive ESVs, but was also produced higher greenhouse gas (GHG) emissions, N losses, and water consumption, thus resulting in lower water and N productivity and higher negative ESVs. Among the negative ESVs, GHG emission value accounted for the largest proportion of the total negative ESV (75–80 %), followed by water consumption and fertilizer loss values. Compared with the local IRNF, the scenarios with 210 mm irrigation and 240 kg ha–1 N fertilization in wet years, 240 mm irrigation and 200 kg ha–1 N fertilization in normal years, and 300 mm irrigation and 200 kg ha–1 N fertilization in dry years could not only reduce negative ESVs, improve water and N productivity, and save water and fertilizer resources, but also maintain acceptable yields and positive ESVs. These recommendations reduced the total negative ESV by 4.8–7.7 %, increased water and N productivity by 4.0–8.9 % and 44–69 %, and saved irrigation water and N fertilizer resources by 33.3–53.3 % and 29.4–41.2 % in three hydrological years in relation to the local IRNF, respectively. This study indicates that the framework is a reliable tool for achieving farmland RFE trade-offs and generating appropriate IRNFs for different hydrological years in arid to semi-arid regions. Therefore, sustainable development goals in RFE systems may be achieved by optimizing irrigation and fertilization management.

An eight-step simulation-based framework to help cities reach building-related emissions reduction goals

With buildings accounting for 40% of global carbon emissions, cities striving to meet sustainability targets aligned with the Paris Agreement must retrofit their existing building stock within 30 years. Previous studies have shown that urban building energy models (UBEMs) can help cities identify technology pathways—combinations of energy efficiency retrofits and renewable energy deployment strategies—to meet emissions reduction goals. UBEMs are currently limited by cost to only the largest cities but must be expanded to all cities if society is going to meet scientifically-identified emissions reduction goals. This manuscript presents an eight-step framework to scale technology pathways analyses using UBEMs to all communities in a repeatable, affordable manner. The roles and responsibilities of three key personas, the sustainability champion, GIS manager, and an energy modeler, for each step are identified. The eight-step process is tested with a case study of 13 100 buildings in Oshkosh, WI, USA. The case study identified a technically-feasible path to nearly net zero emissions for Oshkosh’s buildings. Constraints in the workforce, supply chain, and retrofit adoption to attain this goal were identified to inform policymakers. The case study suggests that the eight-step process is a blueprint for action in communities around the world.

Capacitated vertiport and charging station location-allocation problem for air taxi operations with battery and fleet dispatching considerations: a case study of New York city

Set to begin operations in the coming years, air taxi is an emerging ride-sharing aviation service that plans to commute millions of customers in metropolitan cities every day. However, existing literature has not holistically investigated the impact of the various strategic, tactical, and operations decisions pertaining to air taxi services (ATS). This research adopts a simulation-based framework to address the capacitated infrastructure location and demand allocation problems associated with ATS. Specifically, we determine the following decisions: (i) location and size of operating facilities and charging stations (strategic), (ii) air taxi fleet required to serve the expected demand at a specified service level (strategic), (iii) threshold minimum charge required for efficient air taxi operations (tactical), and (iv) real-time allocation of customer demand (operational). The proposed approach identifies the infrastructure locations using a clustering algorithm, and subsequently addresses the capacitated location-allocation problem for ATS by employing a simulation-based model. To test the effectiveness of the proposed approach, we consider a case study of New York City. We leverage the estimated air taxi demand and evaluate the proposed model by considering the weighted sum of fleet utilization and customer waiting time.

Optimization-informed Rule Extraction for HVAC system: A Case Study of Dedicated Outdoor Air System Control in a Mixed-Humid Climate Zone.

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control.

Detection of outlying patterns from sparse and irregularly sampled electronic health records data

Within the intensive care unit (ICU), vital signs such as arterial blood pressure (ABP) collected from electronic health records (EHRs) are typically recorded at different and uneven sampling frequencies and are often infrequently measured due to the nature of the medical treatment. Furthermore, from a temporal trajectory perspective, EHR data are likely to be corrupted by outlying patterns that deviate from normal samples in terms of the curves’ magnitude and shape. In this work, we propose a two-stage outlier detection approach for sparse and irregularly sampled (SiS) temporal data using functional data analysis (FDA) tools. In the first stage, an outlier identification measure is defined by a max–min statistic and a clean subset that contains nonoutliers. In the second stage, a multiple hypothesis testing problem is formulated based on the asymptotic distribution of the proposed measure. The simulation-based framework shows that the proposed method is robust to different types of shape and magnitude outliers. The detection results are more accurate than the widely used functional depth methods, especially in extremely sparse settings where the proportion of the observed data points over the entire time series is approximately 10%. Extensive experiments are also conducted on the real-world MIMIC-II dataset, which demonstrate that the method effectively detects clinically meaningful outlying patterns.

Simulation-based policy analysis: The case of urban speed limits

Speed limit policies are commonly adopted to manage and control traffic in urban areas due to their effectiveness and ease of implementation. Comprehending the complete effect of a speed limit policy is complicated and requires modeling and quantified investigations. In this paper, we propose a comprehensive simulation-based framework to assess the potential implications of different speed limit policies in urban residential areas. The framework models the policy impacts related to road safety (risk exposure for pedestrians and driving safety), traffic efficiency (travel time) and the environment (fuel consumption, exhaust emissions and noise exposure), using microscopic traffic simulation. The evaluations are conducted at various spatial granularity levels, i.e., link level, route level, origin–destination (OD) level and network level, and can be further utilized to develop relationship models between the key performance indicators (KPIs) and simulation inputs. The framework is implemented in an urban area located in the city center of Munich, Germany, and multiple speed limit scenarios are designed and compared. The results show that speed limit reduction can significantly improve road safety and environmental externalities within the modeled network/area with a relatively small cost to traffic efficiency. Such a framework can be used as an economical evidence collection method for an evidence-based policymaking approach to speed limit policies. The proposed simulation-based framework can also be further extended to adapt the assessment of other traffic-related policies.