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H.A.P. Blom
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1
In MJLS literature the separation principle between filtering and control has been established in case the Markov mode switching process {θt} is fully observed, and the Euclidean state process {xt}is partially observed. In case the exact {θt}remains hidden, the separation principle has only been established under a linear filtering restriction. Since nonlinear filters can provide significant better estimates, the desire to extend the separation principle to MJLS with hidden {θt}is a long-standing challenge. The objective of this paper is to resolve this long-standing challenge in three steps. The first step is to transform the MJLS stochastic control problem into control under a quadratic performance criterion of a linear system driven by a martingale which is influenced by the control. The certainty equivalence (CE) condition known in literature applies to stochastic control of a linear system that is driven by a control independent martingale. Therefore, the second step is to relax this known CE condition such that it allows this control influence on the martingale. The third step is to prove that the relaxed CE condition is satisfied for the general MJLS control problem considered. The overall achievement is a CE control law for a partially observed MJLS, which assures the Separation Principle between filtering and control. The paper also shows that for the case that {xt}is fully observed and the exact{θt}remains hidden, that the novel CE control law differs significantly from the in literature well-developed Averaging MJLS control policy.
...
In MJLS literature the separation principle between filtering and control has been established in case the Markov mode switching process {θt} is fully observed, and the Euclidean state process {xt}is partially observed. In case the exact {θt}remains hidden, the separation principle has only been established under a linear filtering restriction. Since nonlinear filters can provide significant better estimates, the desire to extend the separation principle to MJLS with hidden {θt}is a long-standing challenge. The objective of this paper is to resolve this long-standing challenge in three steps. The first step is to transform the MJLS stochastic control problem into control under a quadratic performance criterion of a linear system driven by a martingale which is influenced by the control. The certainty equivalence (CE) condition known in literature applies to stochastic control of a linear system that is driven by a control independent martingale. Therefore, the second step is to relax this known CE condition such that it allows this control influence on the martingale. The third step is to prove that the relaxed CE condition is satisfied for the general MJLS control problem considered. The overall achievement is a CE control law for a partially observed MJLS, which assures the Separation Principle between filtering and control. The paper also shows that for the case that {xt}is fully observed and the exact{θt}remains hidden, that the novel CE control law differs significantly from the in literature well-developed Averaging MJLS control policy.
To reduce the safety risk posed by small Unmanned Aircraft System (UAS) to persons on the ground, one of the mitigating measures is to equip the UAS with an airbag in combination with a parachute, both of which are deployed in case of an uncontrolled descent. In literature, methods for the evaluation of the effect of a parachute alone have been developed. This paper develops a method to assess the safety risk for persons on the ground posed by a UAS that is both equipped with an airbag and a parachute. For the descent phase of the UAS to the ground, existing models are used. The novel part is the dynamical simulation of the effect on a human body of impact and interaction of a UAS with airbag. For the human impact simulation, use is made of Multi Body System (MBS) model for the UAS and the human; in combination with Finite Element (FE) model of the airbag. This method is applied for a specific parcel delivery UAS, of 15 kg weigh, for cases with and without airbag. The results obtained show that the combination of parachute and airbag can reduce the safety risk posed to people on the ground by more than one order in magnitude. Comparison with existing models for parachute alone, show that the novel method is much better in taking UAS design and material properties into account. The paper also shows that the dynamical simulation results obtained provide effective feedback to the further improvement of the airbag design.
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To reduce the safety risk posed by small Unmanned Aircraft System (UAS) to persons on the ground, one of the mitigating measures is to equip the UAS with an airbag in combination with a parachute, both of which are deployed in case of an uncontrolled descent. In literature, methods for the evaluation of the effect of a parachute alone have been developed. This paper develops a method to assess the safety risk for persons on the ground posed by a UAS that is both equipped with an airbag and a parachute. For the descent phase of the UAS to the ground, existing models are used. The novel part is the dynamical simulation of the effect on a human body of impact and interaction of a UAS with airbag. For the human impact simulation, use is made of Multi Body System (MBS) model for the UAS and the human; in combination with Finite Element (FE) model of the airbag. This method is applied for a specific parcel delivery UAS, of 15 kg weigh, for cases with and without airbag. The results obtained show that the combination of parachute and airbag can reduce the safety risk posed to people on the ground by more than one order in magnitude. Comparison with existing models for parachute alone, show that the novel method is much better in taking UAS design and material properties into account. The paper also shows that the dynamical simulation results obtained provide effective feedback to the further improvement of the airbag design.
Conference paper
(2025)
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Alessandro Abate, Omid Akbarzadeh, H.A.P. Blom, Sofie Haesaert, Sina Hassani, Abolfazl Lavaei, Frederik Baymler Mathiesen, Rahul Misra, Amy Nejati, More authors...
This report is concerned with a friendly competition for formal verification and policy synthesis of stochastic models. The main goal of the report is to introduce new benchmarks and their properties within this category and recommend next steps toward next year’s edition of the competition. In particular, this report introduces three recently developed software tools, a new water distribution network benchmark, and a collection of simplified benchmarks intended to facilitate further comparisons among tools that were previously not directly comparable. This friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Summer 2025.
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This report is concerned with a friendly competition for formal verification and policy synthesis of stochastic models. The main goal of the report is to introduce new benchmarks and their properties within this category and recommend next steps toward next year’s edition of the competition. In particular, this report introduces three recently developed software tools, a new water distribution network benchmark, and a collection of simplified benchmarks intended to facilitate further comparisons among tools that were previously not directly comparable. This friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Summer 2025.
DESIGN and be SMART
Eleven engineering challenges to achieve sustainable air transportation under safety assurance in the year 2050
Journal article
(2025)
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Sebastian Wandelt, Henk Blom, Marius Magnus Krömer, Daochun Li, Mihaela Mitici, Tim Ryley, Eike Stumpf, Kun Wang, Xiaoqian Sun, More authors...
The aviation industry faces various challenges in meeting long-term sustainability goals amidst surging demand for air travel and growing environmental concerns of the general public. The year 2050 is set as an ambitious goal for net zero emissions, a substantial reduction in carbon dioxide emissions per passenger kilometer flown, major improvements in aircraft energy efficiency, and a development towards autonomous, intelligent operations. This review explores the pivotal role of advancements in engineering for achieving sustainability in aviation. Through a comprehensive review of existing literature and case studies, our work highlights how innovations in all aspects of aircraft engineering coupled with operations-related technologies, offer promising solutions to mitigate environmental impact, enhance efficiency, and ensure long-term sustainability in aviation operations. To discuss the necessary advances, we promote the so-called ‘DESIGN and be SMART’ framework, consisting of eleven complementary engineering challenges towards reaching sustainability. To address the high safety levels reached in air transportation, our DESIGN and be SMART framework also addresses the safety assurance challenge that is overarching each of the eleven engineering challenges. We believe that through an orchestrated integration of hardware advancements with innovative software solutions, and novel safety assurance methods, the aviation industry can realize synergistic benefits that drive sustainable growth of air transportation. Our review contributes to such an orchestration by describing the status quo and research challenges ahead.
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The aviation industry faces various challenges in meeting long-term sustainability goals amidst surging demand for air travel and growing environmental concerns of the general public. The year 2050 is set as an ambitious goal for net zero emissions, a substantial reduction in carbon dioxide emissions per passenger kilometer flown, major improvements in aircraft energy efficiency, and a development towards autonomous, intelligent operations. This review explores the pivotal role of advancements in engineering for achieving sustainability in aviation. Through a comprehensive review of existing literature and case studies, our work highlights how innovations in all aspects of aircraft engineering coupled with operations-related technologies, offer promising solutions to mitigate environmental impact, enhance efficiency, and ensure long-term sustainability in aviation operations. To discuss the necessary advances, we promote the so-called ‘DESIGN and be SMART’ framework, consisting of eleven complementary engineering challenges towards reaching sustainability. To address the high safety levels reached in air transportation, our DESIGN and be SMART framework also addresses the safety assurance challenge that is overarching each of the eleven engineering challenges. We believe that through an orchestrated integration of hardware advancements with innovative software solutions, and novel safety assurance methods, the aviation industry can realize synergistic benefits that drive sustainable growth of air transportation. Our review contributes to such an orchestration by describing the status quo and research challenges ahead.
Journal article
(2025)
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Borrdephong Rattanagraikanakorn, H.A.P. Blom, Derek I. Gransden, M.J. Schuurman, C. de Wagter, Alexei Sharpanskykh, R. Happee
Although Unmanned Aircraft Systems (UASs) offer valuable services, they also introduce certain risks—particularly to individuals on the ground—referred to as third-party risk (TPR). In general, ground-level TPR tends to rise alongside the density of people who might use these services, leading current regulations to heavily restrict UAS operations in populated regions. These operational constraints hinder the ability to gather safety insights through the conventional method of learning from real-world incidents. To address this, a promising alternative is to use dynamic simulations that model UAS collisions with humans, providing critical data to inform safer UAS design. In the automotive industry, the modelling and simulation of car crashes has been well developed. For small UAS, this dynamical modelling and simulation approach has focused on the effect of the varying weight and kinetic energy of the UAS, as well as the geometry and location of the impact on a human body. The objective of this research is to quantify the effects of UAS material and shape on-ground TPR through dynamical modelling and simulation. To accomplish this objective, five camera–drone types are selected that have similar weights, although they differ in terms of airframe structure and materials. For each of these camera–drones, a dynamical model is developed to simulate impact, with a biomechanical human body model validated for impact. The injury levels and probability of fatality (PoF) results, obtained through conducting simulations with these integrated dynamical models, are significantly different for the camera–drone types. For the uncontrolled vertical impact of a 1.2 kg UAS at 18 m/s on a model of a human head, differences in UAS designs even yield an order in magnitude difference in PoF values. Moreover, the highest PoF value is a factor of 2 lower than the parametric PoF models used in standing regulation. In the same scenario for UAS types with a weight of 0.4 kg, differences in UAS designs even considered yield an order when regarding the magnitude difference in PoF values. These findings confirm that the material and shape design of a UAS plays an important role in reducing ground TPR, and that these effects can be addressed by using dynamical modelling and simulation during UAS design.
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Although Unmanned Aircraft Systems (UASs) offer valuable services, they also introduce certain risks—particularly to individuals on the ground—referred to as third-party risk (TPR). In general, ground-level TPR tends to rise alongside the density of people who might use these services, leading current regulations to heavily restrict UAS operations in populated regions. These operational constraints hinder the ability to gather safety insights through the conventional method of learning from real-world incidents. To address this, a promising alternative is to use dynamic simulations that model UAS collisions with humans, providing critical data to inform safer UAS design. In the automotive industry, the modelling and simulation of car crashes has been well developed. For small UAS, this dynamical modelling and simulation approach has focused on the effect of the varying weight and kinetic energy of the UAS, as well as the geometry and location of the impact on a human body. The objective of this research is to quantify the effects of UAS material and shape on-ground TPR through dynamical modelling and simulation. To accomplish this objective, five camera–drone types are selected that have similar weights, although they differ in terms of airframe structure and materials. For each of these camera–drones, a dynamical model is developed to simulate impact, with a biomechanical human body model validated for impact. The injury levels and probability of fatality (PoF) results, obtained through conducting simulations with these integrated dynamical models, are significantly different for the camera–drone types. For the uncontrolled vertical impact of a 1.2 kg UAS at 18 m/s on a model of a human head, differences in UAS designs even yield an order in magnitude difference in PoF values. Moreover, the highest PoF value is a factor of 2 lower than the parametric PoF models used in standing regulation. In the same scenario for UAS types with a weight of 0.4 kg, differences in UAS designs even considered yield an order when regarding the magnitude difference in PoF values. These findings confirm that the material and shape design of a UAS plays an important role in reducing ground TPR, and that these effects can be addressed by using dynamical modelling and simulation during UAS design.
Integrated air and space management
An agent-based simulation for analysis of space launch impact on air traffic
Conference paper
(2024)
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Zhengyi Wang, Imen Dhief, Wei Zhou, Sameer Alam, H.A.P. Blom, Sven Kaltenhauser, Tobias Rabus
The recent surge in space launch activities, driven by the emergence of commercial space launches, has compelled the aviation and space launch sectors to collaborate for the safe and efficient integration of space launch activities. This paper introduces an agent-based modeling (ABM) and simulation framework designed to assess the impact of spacecraft launches on air traffic within an integrated air and space traffic management system. The proposed framework incorporates various agents involved in the execution phase of space launches and considers the interactions and coordination between air traffic management and space traffic management. The paper firstly provides a comprehensive overview of the current state of space launch operations and their effects. Then, a general agent-based model is developed for space launch execution phase in order to gain an understanding of various entities involved in a space launch activity as well as the interactions among these entities. Using Monte-Carlo simulations based on the ABM, the paper assesses the impact on air traffic operations in the event of a space launch failure. In each simulation, various factors are taken into account, including launch site position, launch slot, failure probability during the execution phase, debris dispersion, and time delay in Air Traffic Management (ATM)/Space Traffic Management (STM) coordination. To demonstrate the practical application of the proposed framework in an operational context, the paper presents a case study of a sea-based space launch in the Singapore FIR. The paper makes a valuable contribution to the field of air and space traffic management by addressing the need for innovative strategies to ensure the safe sharing ofairspace among different stakeholders.
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The recent surge in space launch activities, driven by the emergence of commercial space launches, has compelled the aviation and space launch sectors to collaborate for the safe and efficient integration of space launch activities. This paper introduces an agent-based modeling (ABM) and simulation framework designed to assess the impact of spacecraft launches on air traffic within an integrated air and space traffic management system. The proposed framework incorporates various agents involved in the execution phase of space launches and considers the interactions and coordination between air traffic management and space traffic management. The paper firstly provides a comprehensive overview of the current state of space launch operations and their effects. Then, a general agent-based model is developed for space launch execution phase in order to gain an understanding of various entities involved in a space launch activity as well as the interactions among these entities. Using Monte-Carlo simulations based on the ABM, the paper assesses the impact on air traffic operations in the event of a space launch failure. In each simulation, various factors are taken into account, including launch site position, launch slot, failure probability during the execution phase, debris dispersion, and time delay in Air Traffic Management (ATM)/Space Traffic Management (STM) coordination. To demonstrate the practical application of the proposed framework in an operational context, the paper presents a case study of a sea-based space launch in the Singapore FIR. The paper makes a valuable contribution to the field of air and space traffic management by addressing the need for innovative strategies to ensure the safe sharing ofairspace among different stakeholders.
This paper offers a formal framework for the rare collision risk estimation of autonomous vehicles (AVs) with multi-agent situation awareness, affected by different sources of noise in a complex dynamic environment. The estimation framework consists of two complementary parts: modeling formalism and a rare event estimation method using sequential Monte Carlo (MC) simulation instead of importance sampling. By defining incremental levels of severity that must be passed before a collision, a sequence of MC simulations can be applied from one level to the next. This particular sequential MC method consists of the simulation of an Interacting Particle System (IPS) in combination with Fixed Assignment Splitting (FAS) of particles that reach the next level. We model AVs equipped with the situation awareness as general stochastic hybrid systems (GSHS), including the IPS-FAS relevant severity levels, and assess the probability of collision in a lane-change scenario where two self-driving vehicles simultaneously intend to switch lanes into a shared one while utilizing the time-tocollision measure for decision-making as required. The IPS-FAS method is subsequently used to estimate collision risk for this GSHS model of the lane-changing scenario. The results show that in contrast to straightforward MC simulation, IPS-FAS is able to quantify the very low collision risk for the scenario of interest.
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This paper offers a formal framework for the rare collision risk estimation of autonomous vehicles (AVs) with multi-agent situation awareness, affected by different sources of noise in a complex dynamic environment. The estimation framework consists of two complementary parts: modeling formalism and a rare event estimation method using sequential Monte Carlo (MC) simulation instead of importance sampling. By defining incremental levels of severity that must be passed before a collision, a sequence of MC simulations can be applied from one level to the next. This particular sequential MC method consists of the simulation of an Interacting Particle System (IPS) in combination with Fixed Assignment Splitting (FAS) of particles that reach the next level. We model AVs equipped with the situation awareness as general stochastic hybrid systems (GSHS), including the IPS-FAS relevant severity levels, and assess the probability of collision in a lane-change scenario where two self-driving vehicles simultaneously intend to switch lanes into a shared one while utilizing the time-tocollision measure for decision-making as required. The IPS-FAS method is subsequently used to estimate collision risk for this GSHS model of the lane-changing scenario. The results show that in contrast to straightforward MC simulation, IPS-FAS is able to quantify the very low collision risk for the scenario of interest.
The transition kernel of an ℝ
n-valued diffusion or jump diffusion process {X
t} is known to satisfy the Feller property if {X
t} is the solution of an SDE whose coefficients are Lipschitz continuous. This Lipschitz route to Feller falls short if {X
t} is the solution of an SDE whose coefficients depend on a state-dependent regime-switching process {θ
t}. In this paper it is shown that pathwise uniqueness and the Feller property are satisfied under mild conditions for a regime-switching jump diffusion process {X
t, θ
t} with hybrid jumps, i.e. jumps in {X
t} that occur simultaneously with {θ
t} switching.
...
The transition kernel of an ℝ n-valued diffusion or jump diffusion process {X t} is known to satisfy the Feller property if {X t} is the solution of an SDE whose coefficients are Lipschitz continuous. This Lipschitz route to Feller falls short if {X t} is the solution of an SDE whose coefficients depend on a state-dependent regime-switching process {θ t}. In this paper it is shown that pathwise uniqueness and the Feller property are satisfied under mild conditions for a regime-switching jump diffusion process {X t, θ t} with hybrid jumps, i.e. jumps in {X t} that occur simultaneously with {θ t} switching.
Advantages of commercial UAS-based services come with the disadvantage of posing third party risk (TPR) to overflown population on the ground. Especially challenging is that the imposed level of ground TPR tends to increase linearly with the density of potential customers of UAS services. This challenge asks for the development of complementary directions in reducing ground TPR. The first direction is to reduce the rate of a UAS crash to the ground. The second direction is to reduce overflying in more densely populated areas by developing risk-aware UAS path planning strategies. The third direction is to develop UAS designs that reduce the product (Formula presented.) in case of a crashing UAS, where (Formula presented.) is the size of the crash impact area on the ground, and (Formula presented.) is the probability of fatality for a person in the crash impact area. Because small UAS accident and incident data are scarce, each of these three developments is in need of predictive models regarding their contribution to ground TPR. Such models have been well developed for UAS crash event rate and risk-aware UAS path planning. The objective of this article is to develop an improved model and assessment method for the product (Formula presented.) In literature, the model development and assessment of the latter two terms is accomplished along separate routes. The objective of this article is to develop an integrated approach. The first step is the development of an integrated model for the product (Formula presented.). The second step is to show that this integrated model can be assessed by conducting dynamical simulations of Finite Element (FE) or Multi-Body System (MBS) models of collision between a UAS and a human body. Application of this novel method is illustrated and compared to existing methods for a DJI Phantom III UAS crashing to the ground.
...
Advantages of commercial UAS-based services come with the disadvantage of posing third party risk (TPR) to overflown population on the ground. Especially challenging is that the imposed level of ground TPR tends to increase linearly with the density of potential customers of UAS services. This challenge asks for the development of complementary directions in reducing ground TPR. The first direction is to reduce the rate of a UAS crash to the ground. The second direction is to reduce overflying in more densely populated areas by developing risk-aware UAS path planning strategies. The third direction is to develop UAS designs that reduce the product (Formula presented.) in case of a crashing UAS, where (Formula presented.) is the size of the crash impact area on the ground, and (Formula presented.) is the probability of fatality for a person in the crash impact area. Because small UAS accident and incident data are scarce, each of these three developments is in need of predictive models regarding their contribution to ground TPR. Such models have been well developed for UAS crash event rate and risk-aware UAS path planning. The objective of this article is to develop an improved model and assessment method for the product (Formula presented.) In literature, the model development and assessment of the latter two terms is accomplished along separate routes. The objective of this article is to develop an integrated approach. The first step is the development of an integrated model for the product (Formula presented.). The second step is to show that this integrated model can be assessed by conducting dynamical simulations of Finite Element (FE) or Multi-Body System (MBS) models of collision between a UAS and a human body. Application of this novel method is illustrated and compared to existing methods for a DJI Phantom III UAS crashing to the ground.
For diffusions, a well-developed approach in rare event estimation is to introduce a suitable factorization of the reach probability and then to estimate these factors through simulation of an Interacting Particle System (IPS). This paper studies IPS based reach probability estimation for General Stochastic Hybrid Systems (GSHS). The continuous-time executions of a GSHS evolve in a hybrid state space under influence of combinations of diffusions, spontaneous jumps and forced jumps. In applying IPS to a GSHS, simulation of the GSHS execution plays a central role. From literature, two basic approaches in simulating GSHS execution are known. One approach is direct simulation of a GSHS execution. An alternative is to first transform the spontaneous jumps of a GSHS to forced transitions, and then to simulate executions of this transformed version. This paper will show that the latter transformation yields an extra Markov state component that should be treated as being unobservable for the IPS process. To formally make this state component unobservable for IPS, this paper also develops an enriched GSHS transformation prior to transforming spontaneous jumps to forced jumps. The expected improvements in IPS reach probability estimation are also illustrated through simulation results for a simple GSHS example.
...
For diffusions, a well-developed approach in rare event estimation is to introduce a suitable factorization of the reach probability and then to estimate these factors through simulation of an Interacting Particle System (IPS). This paper studies IPS based reach probability estimation for General Stochastic Hybrid Systems (GSHS). The continuous-time executions of a GSHS evolve in a hybrid state space under influence of combinations of diffusions, spontaneous jumps and forced jumps. In applying IPS to a GSHS, simulation of the GSHS execution plays a central role. From literature, two basic approaches in simulating GSHS execution are known. One approach is direct simulation of a GSHS execution. An alternative is to first transform the spontaneous jumps of a GSHS to forced transitions, and then to simulate executions of this transformed version. This paper will show that the latter transformation yields an extra Markov state component that should be treated as being unobservable for the IPS process. To formally make this state component unobservable for IPS, this paper also develops an enriched GSHS transformation prior to transforming spontaneous jumps to forced jumps. The expected improvements in IPS reach probability estimation are also illustrated through simulation results for a simple GSHS example.
The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance. We identify new hazards associated with the introduction of data-driven technologies into aircraft maintenance using a structured brainstorming conducted with a panel of maintenance experts. This brainstorming is facilitated by a prior modeling of the aircraft maintenance process as an agent-based model. As a result, we identify 20 hazards associated with data-driven predictive aircraft maintenance. We validate these hazards in the context of maintenance-related aircraft incidents that occurred between 2008 and 2013. Based on our findings, the main challenges identified for data-driven predictive maintenance are: (i) improving the reliability of the condition monitoring systems and diagnostics/prognostics algorithms, (ii) ensuring timely and accurate communication between the agents, and (iii) building the stakeholders’ trust in the new data-driven technologies.
...
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The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance. We identify new hazards associated with the introduction of data-driven technologies into aircraft maintenance using a structured brainstorming conducted with a panel of maintenance experts. This brainstorming is facilitated by a prior modeling of the aircraft maintenance process as an agent-based model. As a result, we identify 20 hazards associated with data-driven predictive aircraft maintenance. We validate these hazards in the context of maintenance-related aircraft incidents that occurred between 2008 and 2013. Based on our findings, the main challenges identified for data-driven predictive maintenance are: (i) improving the reliability of the condition monitoring systems and diagnostics/prognostics algorithms, (ii) ensuring timely and accurate communication between the agents, and (iii) building the stakeholders’ trust in the new data-driven technologies.
Conference paper
(2023)
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Alessandro Abate, H.A.P. Blom, Nathalie Cauchi, Joanna Delicaris, Sofie Haesaert, Birgit van Huijgevoort, Abolfazl Lavaei, Anne Remke, Oliver Schon, More authors...
Abstract
This report is concerned with a friendly competition for formal verification and policy synthesis of stochastic models. The main goal of the report is to introduce new benchmarks and their properties within this category and recommend next steps toward next year’s edition of the competition. Given that the tools for stochastic models are at their early stages of development compared to those of non-probabilistic models, the main focus is to report on an initiative to collect a set of minimal benchmarks that all such tools can run, thus facilitating the comparison between the efficiency of the implemented techniques. This friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Summer 2023. ...
This report is concerned with a friendly competition for formal verification and policy synthesis of stochastic models. The main goal of the report is to introduce new benchmarks and their properties within this category and recommend next steps toward next year’s edition of the competition. Given that the tools for stochastic models are at their early stages of development compared to those of non-probabilistic models, the main focus is to report on an initiative to collect a set of minimal benchmarks that all such tools can run, thus facilitating the comparison between the efficiency of the implemented techniques. This friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Summer 2023. ...
Abstract
This report is concerned with a friendly competition for formal verification and policy synthesis of stochastic models. The main goal of the report is to introduce new benchmarks and their properties within this category and recommend next steps toward next year’s edition of the competition. Given that the tools for stochastic models are at their early stages of development compared to those of non-probabilistic models, the main focus is to report on an initiative to collect a set of minimal benchmarks that all such tools can run, thus facilitating the comparison between the efficiency of the implemented techniques. This friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Summer 2023.
This report is concerned with a friendly competition for formal verification and policy synthesis of stochastic models. The main goal of the report is to introduce new benchmarks and their properties within this category and recommend next steps toward next year’s edition of the competition. Given that the tools for stochastic models are at their early stages of development compared to those of non-probabilistic models, the main focus is to report on an initiative to collect a set of minimal benchmarks that all such tools can run, thus facilitating the comparison between the efficiency of the implemented techniques. This friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Summer 2023.
This paper focuses on estimating reach probability of a closed unsafe set by a stochastic process. A well-developed approach is to make use of multi-level MC simulation, which consists of encapsulating the unsafe set by a sequence of increasing closed sets and conducting a sequence of MC simulations to estimate the reach probability of each inner set from the previous set. An essential step is to copy (split) particles that have reached the next level (inner set) prior to conducting a MC simulation to the next level. The aim of this paper is to prove that the variance of the multi-level MC estimated reach probability under fixed assignment splitting is smaller or equal than under random assignment splitting methods. The approaches are illustrated for a geometric Brownian motion example.
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This paper focuses on estimating reach probability of a closed unsafe set by a stochastic process. A well-developed approach is to make use of multi-level MC simulation, which consists of encapsulating the unsafe set by a sequence of increasing closed sets and conducting a sequence of MC simulations to estimate the reach probability of each inner set from the previous set. An essential step is to copy (split) particles that have reached the next level (inner set) prior to conducting a MC simulation to the next level. The aim of this paper is to prove that the variance of the multi-level MC estimated reach probability under fixed assignment splitting is smaller or equal than under random assignment splitting methods. The approaches are illustrated for a geometric Brownian motion example.
Conference paper
(2022)
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Alessandro Abate, H.A.P. Blom, More Authors..., Joanna Delicaris, Sofie Haesaert, Arnd Hartmanns, Birgit van Huijgevoort, Abolfazl Lavaei, H. Ma, Mathis Niehage, Anne Remke
This report presents the results of a friendly competition for formal verification and policy synthesis of stochastic models. It also introduces new benchmarks and their properties within this category and recommends next steps for this category towards next year’s edition of the competition. In comparison with tools on non-probabilistic models, the tools for stochastic models are at the early stages of development that do not allow full competition on a standard set of benchmarks. We report on an initiative to collect a set of minimal benchmarks that all such tools can run, thus facilitating the comparison between efficiency of the implemented techniques. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Summer 2022.
...
This report presents the results of a friendly competition for formal verification and policy synthesis of stochastic models. It also introduces new benchmarks and their properties within this category and recommends next steps for this category towards next year’s edition of the competition. In comparison with tools on non-probabilistic models, the tools for stochastic models are at the early stages of development that do not allow full competition on a standard set of benchmarks. We report on an initiative to collect a set of minimal benchmarks that all such tools can run, thus facilitating the comparison between efficiency of the implemented techniques. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Summer 2022.
UAS-based commercial services such as urban parcel delivery are expected to grow in the upcoming years and may lead to a large volume of UAS operations in urban areas. These flights may pose safety risks to persons and property on the ground, which are referred to as third-party risks. Path-planning methods have been developed to generate a nominal flight path for each UAS flight that poses relative low third-party risks by passing over less risky areas, e.g., areas with low-density unsheltered populations. However, it is not clear if risk minimization per flight works well in a commercial UAS operation that involves a large number of annual flights in an urban area. Recently, it has been shown that when using shortest flight path planning, a UAS-based parcel delivery service in an urban area can lead to society-critical third-party risk levels. The aim of this paper is to evaluate the mitigating effect of state-of-the-art risk-aware path planning on these society-critical third-party risk levels. To accomplish this, a third-party risk simulation using the shortest paths is extended with a state-of-the-art risk-aware path-planning method, and the societal effects on third-party risk levels have been assessed and compared to those obtained using shortest paths. The results show that state-of-the-art risk-aware path planning can reduce the total number of fatalities in an area, but at the cost of a critical increase in safety risks for persons living in areas that are favored by a state-of-the-art risk-aware path-planning method.
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UAS-based commercial services such as urban parcel delivery are expected to grow in the upcoming years and may lead to a large volume of UAS operations in urban areas. These flights may pose safety risks to persons and property on the ground, which are referred to as third-party risks. Path-planning methods have been developed to generate a nominal flight path for each UAS flight that poses relative low third-party risks by passing over less risky areas, e.g., areas with low-density unsheltered populations. However, it is not clear if risk minimization per flight works well in a commercial UAS operation that involves a large number of annual flights in an urban area. Recently, it has been shown that when using shortest flight path planning, a UAS-based parcel delivery service in an urban area can lead to society-critical third-party risk levels. The aim of this paper is to evaluate the mitigating effect of state-of-the-art risk-aware path planning on these society-critical third-party risk levels. To accomplish this, a third-party risk simulation using the shortest paths is extended with a state-of-the-art risk-aware path-planning method, and the societal effects on third-party risk levels have been assessed and compared to those obtained using shortest paths. The results show that state-of-the-art risk-aware path planning can reduce the total number of fatalities in an area, but at the cost of a critical increase in safety risks for persons living in areas that are favored by a state-of-the-art risk-aware path-planning method.
Conference paper
(2021)
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Alessandro Abate, H.A.P. Blom, Marc Bouissou, Nathalie Cauchi, Hassane Chraibi, Joanna Delicaris, Sofie Haesaert, Arnd Hartmanns, H. Ma, More authors...
This report presents the results of a friendly competition for formal verification and
policy synthesis of stochastic models. It also introduces new benchmarks within this category, and recommends next steps for this category towards next year's edition of the competition. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Spring/Summer 2021. ...
policy synthesis of stochastic models. It also introduces new benchmarks within this category, and recommends next steps for this category towards next year's edition of the competition. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Spring/Summer 2021. ...
This report presents the results of a friendly competition for formal verification and
policy synthesis of stochastic models. It also introduces new benchmarks within this category, and recommends next steps for this category towards next year's edition of the competition. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Spring/Summer 2021.
policy synthesis of stochastic models. It also introduces new benchmarks within this category, and recommends next steps for this category towards next year's edition of the competition. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Spring/Summer 2021.
Motivated by the need to understand and further optimize AOC decision making processes under uncertainty, this paper implements and evaluates the effects of operational uncertainties using Agent-Based Modelling and Simulation. The specific application concerns a challenging scenario composed of two consecutive disruptions. To evaluate the effects of uncertainties, an agent-based model of AOC processes has been developed using a logic-based ontology. Subsequently, this agent-based model is used to analyze the sensitivities of different model parameters. The simulation results provide novel insights into the effects of operational uncertainties on AOC decision-making and consequently airline performance. For the aircraft breakdown scenario considered, it is shown that adding buffers into the schedule promote a degree of self-recovery. The sensitivity analysis also reveals that transit buffer time and crew duty slack time act as tipping points for the airline operating costs. This demonstrates that ABMS allows to analyze and bring into light various sensitivities, which can be used in the early design phase to increase airline resilience, and train airline controllers for different environment states. The paper concludes that ABMS is a valuable approach that can enable a paradigm shift from reactive recovery to proactive recovery.
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Motivated by the need to understand and further optimize AOC decision making processes under uncertainty, this paper implements and evaluates the effects of operational uncertainties using Agent-Based Modelling and Simulation. The specific application concerns a challenging scenario composed of two consecutive disruptions. To evaluate the effects of uncertainties, an agent-based model of AOC processes has been developed using a logic-based ontology. Subsequently, this agent-based model is used to analyze the sensitivities of different model parameters. The simulation results provide novel insights into the effects of operational uncertainties on AOC decision-making and consequently airline performance. For the aircraft breakdown scenario considered, it is shown that adding buffers into the schedule promote a degree of self-recovery. The sensitivity analysis also reveals that transit buffer time and crew duty slack time act as tipping points for the airline operating costs. This demonstrates that ABMS allows to analyze and bring into light various sensitivities, which can be used in the early design phase to increase airline resilience, and train airline controllers for different environment states. The paper concludes that ABMS is a valuable approach that can enable a paradigm shift from reactive recovery to proactive recovery.
Journal article
(2021)
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Henk A.P. Blom, Chenpeng Jiang, Wouter B.A. Grimme, Mihaela Mitici, Yuk S. Cheung
Commercial aviation distinguishes three indicators for third party risk (TPR): i) Expected number of ground fatalities per aircraft flight hour; ii) Individual risk; and iii) Societal risk. The latter two indicators stem from TPR posed to population by operation of hazardous installations. Literature on TPR of Unmanned Aircraft System (UAS) operations have focused on the development of the first TPR indicator. However the expected increase of commercial UAS operations requires an improved understanding of third party risk (TPR). To support such improvement, this paper extends the existing TPR model for UAS operations with societal and individual risk indicators. The extension is developed both at modelling level and at assessment level. Subsequently the extended approach is applied to a hypothetical UAS based parcel delivery service in the city of Delft. The results obtained for the novel UAS TPR indicators show that this aligns commercial UAS operations with land use policies and standing TPR regulation for airports and hazardous facilities.
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Commercial aviation distinguishes three indicators for third party risk (TPR): i) Expected number of ground fatalities per aircraft flight hour; ii) Individual risk; and iii) Societal risk. The latter two indicators stem from TPR posed to population by operation of hazardous installations. Literature on TPR of Unmanned Aircraft System (UAS) operations have focused on the development of the first TPR indicator. However the expected increase of commercial UAS operations requires an improved understanding of third party risk (TPR). To support such improvement, this paper extends the existing TPR model for UAS operations with societal and individual risk indicators. The extension is developed both at modelling level and at assessment level. Subsequently the extended approach is applied to a hypothetical UAS based parcel delivery service in the city of Delft. The results obtained for the novel UAS TPR indicators show that this aligns commercial UAS operations with land use policies and standing TPR regulation for airports and hazardous facilities.
The unique capabilities of an Unmanned Aircraft System (UAS) creates opportunities for commercial services. The key question is what is an acceptable level of risk posed to third parties on the ground that have no direct benefit from commercial UAS flights. In literature the common view is that an acceptable level of Third Party Risk (TPR) posed by UAS operations follows from an Equivalent Level Of Safety (ELOS) criterion, which means that per flight hour a UAS should not pose more safety risk to persons on the ground than a commercial aircraft does. However in commercial aviation there are also TPR indicators in use that are directed to accident risk posed by all annual commercial flights to the population around an airport. These population directed indicators find their origin in TPR posed by hazardous installations to its environment. The aim of this paper is to improve the understanding of risk posed to the population by annual UAS-based services through learning from TPR knowledge and regulation for airports and hazardous installations. As main result this paper develops an analytical approach to evaluate the annual risk posed by a commercial UAS-based parcel delivery service in urban and metropolitan areas. The obtained results show that the TPR indicators that stem from hazardous installations and airports provide novel insight regarding TPR of commercial UAS-based service.
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The unique capabilities of an Unmanned Aircraft System (UAS) creates opportunities for commercial services. The key question is what is an acceptable level of risk posed to third parties on the ground that have no direct benefit from commercial UAS flights. In literature the common view is that an acceptable level of Third Party Risk (TPR) posed by UAS operations follows from an Equivalent Level Of Safety (ELOS) criterion, which means that per flight hour a UAS should not pose more safety risk to persons on the ground than a commercial aircraft does. However in commercial aviation there are also TPR indicators in use that are directed to accident risk posed by all annual commercial flights to the population around an airport. These population directed indicators find their origin in TPR posed by hazardous installations to its environment. The aim of this paper is to improve the understanding of risk posed to the population by annual UAS-based services through learning from TPR knowledge and regulation for airports and hazardous installations. As main result this paper develops an analytical approach to evaluate the annual risk posed by a commercial UAS-based parcel delivery service in urban and metropolitan areas. The obtained results show that the TPR indicators that stem from hazardous installations and airports provide novel insight regarding TPR of commercial UAS-based service.